[Federal Register Volume 73, Number 41 (Friday, February 29, 2008)]
[Proposed Rules]
[Pages 11232-11281]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: E8-3643]



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Part III





Department of Health and Human Services





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42 CFR Parts 5 and 51c



 Designation of Medically Underserved Populations and Health 
Professional Shortage Areas; Proposed Rule

  Federal Register / Vol. 73, No. 41 / Friday, February 29, 2008 / 
Proposed Rules  

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DEPARTMENT OF HEALTH AND HUMAN SERVICES

42 CFR Part 5 and 51c

RIN 0906-AA44


Designation of Medically Underserved Populations and Health 
Professional Shortage Areas

AGENCY: Department of Health and Human Services (DHHS).

ACTION: Notice of proposed rulemaking.

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SUMMARY: This proposed rule would revise and consolidate the criteria 
and processes for designating medically underserved populations (MUPs) 
and health professional shortage areas (HPSAs), designations that are 
used in a wide variety of Federal government programs. These revisions 
are intended to improve the way underserved areas and populations are 
designated, by incorporating up-to-date measures of health status and 
access barriers, eliminating inconsistencies and duplication of effort 
between the two existing processes. These revisions are intended to 
reduce the effort and data burden on States and communities by 
simplifying and automating the designation process as much as possible 
while maximizing the use of technology. No changes are proposed at this 
time with respect to the criteria for designating dental and mental 
health HPSAs. Podiatric, vision care, pharmacy, and veterinary care 
HPSAs, which are no longer in use, would be abolished under the rules 
proposed below.
    Additional background information will be available for review on 
the web site of the Health Resources and Services Administration: 
http://bhpr.hrsa.gov/shortage. The methodology is also described in a 
journal article recently published in the Journal of Health Care for 
the Poor and Underserved entitled ``Designating Places and Populations 
as Medically Underserved: A Proposal for a New Approach'' (Ricketts et 
al, 2007).

DATES: Comments on this proposed rule are invited. In particular, 
comments are invited regarding the indicators of need and the weighted 
values of the health care practitioners used in the methodology. To be 
considered, comments must be submitted on or before April 29, 2008.

ADDRESSES: You may submit comments in one of four ways (no duplicates, 
please):
    1. Electronically. You may submit electronic comments on specific 
issues in this regulation to http://www.regulations.gov. Click on the 
link ``Submit electronic comments on HRSA regulations with an open 
comment period.'' (Attachments should be in Microsoft Word, 
WordPerfect, or Excel; however, we prefer Microsoft Word.)
    2. By regular mail. You may mail written comments (one original and 
two copies) to the following address only: Health Resources and Service 
Administration, Department of Health and Human Services, Attention: Ms. 
Andy Jordan, 8C-26 Parklawn Building, 5600 Fishers Lane, Rockville, MD 
20857.
    Please allow sufficient time for mailed comments to be received 
before the close of the comment period.
    3. By express or overnight mail. You may send written comments (one 
original and two copies) to the following address only: Health 
Resources and Service Administration, Department of Health and Human 
Services, Attention: Ms. Andy Jordan, 8C-26 Parklawn Building, 5600 
Fishers Lane, Rockville, MD 20857.
    4. By hand or courier. If you prefer, you may deliver (by hand or 
courier) your written comments (one original and two copies) before the 
close of the comment period to one of the following addresses. If you 
intend to deliver your comments to the Rockville address, please call 
telephone number (301) 594-0816 in advance to schedule your arrival 
with one of our staff members: Room 445-G, Hubert H. Humphrey Building, 
200 Independence Avenue, SW., Washington, DC 20201; or 8C-26 Parklawn 
Building, 5600 Fishers Lane, Rockville, MD 20857. (Because access to 
the interior of the HHH Building is not readily available to persons 
without Federal Government identification, commenters are encouraged to 
leave their comments in the HRSA drop slots located in the main lobby 
of the building. A stamp-in clock is available for persons wishing to 
retain a proof of filing by stamping in and retaining an extra copy of 
the comments being filed.).
    Comments mailed to the addresses indicated as appropriate for hand 
or courier delivery may be delayed and received after the comment 
period.
    Submission of comments on paperwork requirements. You may submit 
comments on this document's paperwork requirements by mailing your 
comments to the addresses provided at the end of the ``Collection of 
Information Requirements'' section in this document.

FOR FURTHER INFORMATION CONTACT: Andy Jordan, 301-594-0197.

SUPPLEMENTARY INFORMATION: The Secretary of Health and Human Services 
proposes below a consolidated, revised process for designation of 
Medically Underserved Populations (MUPs) pursuant to section 330(b)(3) 
of the Public Health Service Act (as amended by the Health Centers 
Consolidation Act of 1996, Public Law 104-299), 42 U.S.C. 254b, and for 
designation of Health Professional Shortage Areas (HPSAs) pursuant to 
section 332 of the Act (as amended by the Health Care Safety Net 
Amendments of 2002, Pub. L.107-251), 42 U.S.C. 254e. Currently, 
regulations at 42 CFR Part 5 govern the procedures and criteria for 
designation of HPSAs, while designation of MUPs has been carried out 
under the Grants for Community Health Services regulations at 42 CFR 
Part 51c.102(e), and implementing Federal Register notices.

Table of Contents

I. Background
    A. Explanation of Provisions
    B. Current Uses of Designations
II. Revising the methodology and designation mechanisms
    A. Relevant Statutes
    B. Purpose of revising the methodology and designation process
III. Development of Methodology to Achieve Goals
    A. 1998 NPRM and summary of comments received
    B. Development of method proposed in this NPRM
IV. Description of Conceptual Framework and Methodology and 
Alternatives Considered
    A. Conceptual Framework
    B. Methodology
    C. Example Calculations
    D. Alternative Approaches Considered
V. Description of Proposed Regulations
    A. Procedures (Subpart A)
    B. General Criteria for Designation of Geographic Areas as MUAs/
Primary Care HPSAs
    C. Rational Service Areas
    D. Applying the Designation Methodology
    E. Data definitions.
    F. Population and clinician counts.
    G. Non-physician primary care clinicians
    H. Contiguous Area Considerations.
    I. Population group designations
    J. ``Facility Designation Method'': Designation of facility 
primary care HPSAs
    K. Dental and mental health HPSAs
    L. Podiatry, vision care, pharmacy and veterinary care HPSAs
    M. Technical and conforming amendments
VI. Impact Analysis
    A. Impact on Number of HPSA Designations
    B. Impact on Number of MUA/P Designations
    C. Impact on number of unduplicated HPSA/MUP designations
    D. Impact on Population of all Designated HPSAs and/or MUPs
    E. Impact on Number of CHCs Covered by Designations

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    F. Impact on Number of NHSC Sites Covered by Designations
    G. Impact on Number of RHCs Covered by Designations
    H. Impact on Distribution of Designations by Metropolitan/Non-
Metropolitan and Frontier Status
    I. Impact on Distribution of Population of Underserved Area and 
Underserved Populations by Metropolitan/Non-Metropolitan and 
Frontier Status
    J. Impact of Practitioner ``Back-outs'' on Number of 
Designations and Safety-Net Providers
VII. Economic Impact
VIII. Information Collection Requirements under Paperwork Reduction 
Act of 1995
IX. Appendix A: References
X. Appendix B: A Proposal for a Method to Designate Communities as 
Underserved: Technical Report on the Derivation of Weights

I. Background

    An earlier version of proposed rules for a consolidated, revised 
MUP/HPSA designation methodology and implementation process was 
published on September 1, 1998 [63 FR 46538-55]. Those proposed rules 
generated nearly 800 public comments, principally concerning the 
perceived high impact in terms the safety-net programs which would have 
lost their existing designations if the rule were finalized. Comments 
were also received on several other important issues related to the 
methodology, types of primary care clinicians included, and data 
collection burden. On June 3, 1999, a Federal Register document was 
published [64 FR 29831] which extended the comment period based on the 
large volume of comments received and the level of concern expressed. 
In light of the volume of comments, it was determined that the impact 
of the proposal as published would be more carefully tested, possible 
revisions and alternative approaches developed as necessary, and a new 
notice of proposed rulemaking (NPRM) would be published.

A. Explanation of Provisions

    This proposed rule describes a revised methodology which combines 
indicators of diminished access to health care services, shortages of 
health professionals, and reduced health status. Developed by a 
research team at the University of North Carolina's Cecil G. Sheps 
Center in consultation with staff from the Health Resources and 
Services Administration (HRSA) and a group of State partners in the 
designation process, this approach was also tested with a comprehensive 
impact analysis (see section VI).
    This proposed rule will replace the existing Part 5 with 
regulations governing both MUP and HPSA designations, and will make 
conforming changes to Part 51c. Together, these changes meet the 
legislative requirements for both MUP designation and HPSA designation, 
while consolidating the two processes to the greatest extent possible 
given the differences in the two authorities. This combined metric, 
which we propose to call ``the Index of Primary Care Underservice,'' 
will replace the existing MUP and HPSA criteria and procedures, while 
maintaining the two separate designations in order to meet the 
legislative requirements of the relevant statutes. Note that the 
abbreviation MUP used here includes not only population group 
designations but also the populations of designated geographic areas, 
also known as medically underserved areas or MUAs. Similarly, the 
abbreviation HPSA includes not only geographic area designations, but 
also population group and facility designations.
    Pursuant to Section 302(b) of the Health Care Safety Net Amendments 
of 2002, a copy of this NPRM will be submitted to the Committee on 
Energy and Commerce of the House of Representatives and to the 
Committee on Health, Education, Labor and Pensions of the Senate upon 
or before the date of its publication, in fulfillment of the statutory 
requirement for a report to those committees describing any regulation 
that revises the definition of a health professional shortage area. 
HRSA has also asked a panel of outside experts to review the proposed 
methodology and provide an assessment of its appropriateness, validity, 
and general approach.
    These regulations will not be finalized until the public comment 
period referenced above is over, and any comments received during that 
time from the public, the panel of outside experts, and from the 
referenced House and Senate Committees have been taken into 
consideration. Moreover, this rule will not be finalized until 180 days 
after delivery of the report to the Congressional committees identified 
above, in accordance with statute.

B. Current Uses of Designations

    The MUP and HPSA designations are currently used in a number of 
Departmental programs. The major use of MUP designations is as a basis 
for eligibility for grant funding of health centers under sections 
330(c) and (e) of the Act, which require that these health centers 
serve medically underserved populations. The major use of HPSA 
designations is by the National Health Service Corps (NHSC); health 
professionals placed through the NHSC can be assigned only to 
designated HPSAs.
    Other health centers not funded by section 330 grants but otherwise 
meeting the definition of a health center in section 330(a)--including 
those which provide services to a MUP--may be certified by the Centers 
for Medicare and Medicaid Services (CMS) upon recommendation by HRSA as 
federally qualified health center (FQHC) look-alikes. FQHC look-alikes, 
like all health centers funded under Section 330, are eligible for 
special Medicare and Medicaid reimbursement methods.
    Clinics in rural areas designated either as an MUA or as a 
geographic or population group HPSA, and whose staff include nurse 
practitioners and/or physician assistants, may be certified by CMS as 
Rural Health Clinics (RHCs). These RHCs are also eligible for special 
methods for determining Medicaid and Medicare reimbursement.
    Physicians delivering services in an area designated as a 
geographic HPSA are eligible for the Medicare Incentive Payments (MIP) 
of an additional 10 percent above the Medicare reimbursement they would 
otherwise receive. The Medicare Modernization Act of 2003 included 
beneficial changes to this incentive program. Payments to providers are 
now automated based on the zip codes of the providers, and the 
information on eligibility is now available on the CMS Web site. The 
MIP, also known as the HPSA Bonus Payment, is distinct from the 
Physician Scarcity Area Program, which does not use HRSA designations 
in determining eligibility.
    Interested Federal Government Agencies and State Health Departments 
can also recommend waiver of the return-home requirements for an 
International Medical Graduate physician who came to the United States 
on a J-1 visa, in return for three years of service by that physician 
in a particular HPSA or MUA.
    In addition, a number of health professions programs funded under 
Title VII of the Public Health Service Act give preference to 
applicants with a high rate of training health professionals in 
medically underserved communities and/or for placing graduates in 
medically underserved communities, defined (in Section 799B of the Act) 
to include both HPSAs and MUPs.
    For most of the programs that use these designations, designation 
of the area or population to be served is a necessary but not 
sufficient condition for allocation of program resources, in that other 
eligibility requirements must also be met and/or there is competition

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among eligible applicants for available resources.

II. Revising the Methodology and Designation Mechanisms

A. Relevant Statutes

Authorizing Statutes
    The current HPSA criteria date back to 1978, when they were issued 
under Section 332 of the Public Heath Service (PHS) Act, as amended in 
1976; their predecessor, the ``Critical Health Manpower Shortage Area'' 
or CHMSA criteria, dates back to the 1971 legislation creating the 
NHSC. Section 332(b) of the Public Health Service Act states that the 
Secretary shall take into consideration the following when establishing 
criteria for the designation of areas, groups, or facilities as HPSAs: 
(1) The ratio of available health manpower to the number of individuals 
in an area or population group, and (2) Indicators of a need for health 
services, notwithstanding the supply of health manpower.
    The current MUA/P criteria date back to 1975, when they were issued 
to implement legislation enacted in 1973 and 1974 creating grants for 
Health Maintenance Organizations (HMOs) and Community Health Centers 
(CHCs), respectively. Section 330(b)(3) of the Public Health Service 
Act defines ``medically underserved population'' as the population of 
an urban or rural area designated by the Secretary of Health and Human 
Services as an area with a shortage of personal health services, or a 
population group designated by the Secretary as having a shortage of 
such services. No specific criteria were included in the statute.
Health Care Safety Net Amendments of 2002
    The Health Care Safety Net Amendments of 2002, Public Law 107-251, 
as amended by Public Law 108-163, included modification of Section 332 
to require the ``automatic'' designation as HPSAs of all FQHCs and RHCs 
meeting the requirements of Section 334 (concerning the provision of 
services without regard to ability-to-pay) for at least six years. 
After six years, such entities must demonstrate that they meet the 
designation criteria for HPSAs, as then in force.
    This legislative provision appears to have had two major goals:
    1. To avoid requiring FQHCs or RHCs from going through two separate 
designation processes. Given that most FQHCs must demonstrate service 
to an MUP in order to be funded (or to be certified as an FQHC look-
alike), it was deemed unnecessary to also require these entities to 
obtain a HPSA designation in order to apply for placement of NHSC 
clinicians. Similarly, every RHC must obtain one of several types of 
designation in order to achieve RHC status (either a HPSA, MUA, or 
Governor Designated and Secretary Certified Shortage Area designation); 
arguably, those for whom this was not a HPSA designation should not be 
required to obtain a second type of designation to apply for NHSC. (It 
is worth noting that this goal will be met once the regulations herein 
are in force, since areas and population groups designated or updated 
under the criteria herein would be both HPSAs and MUPs, eligible for 
the FQHC, RHC and NHSC programs).
    2. To allow a long transition period for phasing in the new 
designation criteria as they might affect existing projects. Existing 
FQHCs and RHCs will have plenty of time to show that the areas where 
they are located, the populations they serve, or the facilities 
involved in fact meet the new criteria, so that their services will not 
be disrupted due to the criteria change.
    Although an extensive impact analysis of the proposed new criteria 
has been conducted to demonstrate that such disruption is unlikely in 
all but a few cases, this legislatively required smooth transition 
should ease concerns about the changes and allow plenty of time to 
adapt to the new designation criteria.

B. Purpose of Revising the Methodology and Designation Process

    As previously stated, the current HPSA and MUA/P criteria date back 
to the 1970s. The original CHMSA criteria required that a simple 
population-to-primary care physician ratio threshold be exceeded to 
demonstrate shortage. The HPSA criteria went further and allowed a 
lower threshold ratio for areas with high needs as indicated by high 
poverty, infant mortality or fertility rates, and for population groups 
with access barriers. The original MUA/P criteria, still in effect, 
employ a four-variable Index of Medical Underservice, including percent 
of the population with incomes below poverty, population-to-primary 
care physician ratio, infant mortality rate and percent elderly.
    Since the time these designation criteria were first developed, 
there has been an evolution both in the types of requests for 
designation received and the application of the HPSA criteria. Instead 
of relatively simple geographic area requests, such as whole counties 
and rural subcounty areas, more requests have been made for urban 
neighborhood and population group designations. The availability of 
census data on poverty, race, and ethnicity at the census tract level 
has enabled the delineation of urban service areas based on their 
economic and race/ethnicity characteristics. Areas with concentrations 
of poor, minority and/or linguistically isolated populations have 
achieved area or population group HPSA designations based on their 
limited access to physicians serving other parts of their metropolitan 
areas. As a result, the differences between HPSA and MUA/P designations 
have become less distinct.
    The methodology for identifying underserved areas, as well as the 
process by which interested State and community parties can obtain 
designation as underserved areas, are being revised to accomplish 
several goals and alleviate problems associated with the existing 
methods of designation.
    In revising the underlying methodology for identifying underserved 
areas, our goals were to create a new system that:
    (a) Is simple to understand for those who seek designation;
    (b) is intuitive and has face validity;
    (c) incorporates better measures or correlates of health status and 
access;
    (d) is based on scientifically recognized methods and is 
replicable;
    (e) minimize unnecessary disruption; and
    (f) constitutes an improvement over current methods in fairly and 
consistently identifying places and people who are in need of primary 
health care and who encounter barriers to meeting those needs.
    In revising the designation process, our goals were to:
    (a) Consolidate the two existing procedures, sets of criteria, and 
lists of designations;
    (b) make the system more proactive and better able to identify new, 
currently undesignated areas of need and areas no longer in need;
    (c) automate the scoring process as much as possible, making 
maximum use of national data and reducing the effort at State and 
community levels associated with information gathering for designation 
and updating;
    (d) expand the State role in the designation process, with special 
attention to the State role in definition of rational service areas;
    (e) reduce the need for time-consuming population group 
designations, by specifically including indicators representing access 
barriers experienced by these groups in the criteria applied to area 
data.

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    These goals are explained more fully below. We believe the proposed 
methodology and designation process address all of these goals and 
therefore offers a significant improvement in the identification of 
communities experiencing limited access to primary care services. In 
turn, we believe these revisions will assist the Department in 
targeting key resources more effectively to areas of greater relative 
need for assistance.
1. Methodological Goals
Simplicity
    The new underservice measure must be understandable and usable by 
those who seek designation. In this vein, we decided the new 
methodology should continue to use the population-to-provider ratio as 
the fundamental metric of underservice because such ratios are well-
recognized and understood by the program participants and would provide 
some continuity between a new proposal and the older methods that 
included the ratios very prominently in the calculations. Discussions 
with the federal agencies and stakeholder groups during the development 
of the revised approach also revealed a preference for using that 
metric as the basis for a revised method.
Face Validity
    The new underservice measure must be intuitive and have face 
validity. For example, factors that reflect progressively worse access 
should result in proportionately increasing scores.
Incorporate Better Measures or Correlates of Health Status and Access
    While both designation statutes speak of the inclusion of health 
status indicators, the only specific measure of health status 
historically mentioned in either statute or included in the existing 
designation criteria is infant mortality rate.
    Low birthweight rate is a more robust indicator of health status 
because there are more events per unit population. Because both infant 
mortality and low birthweight rate are nationally available for all 
counties and for a limited number of sub-county areas (generally, for 
places of population 10,000 or more), these measures were incorporated 
in the proposed methodology. In addition, a new measure of actual/
expected death rate (standardized mortality ratio) is incorporated.
    As described in more detail in section IV, this methodology further 
incorporates other correlates of health status and access, such as 
ethnic minority status and unemployment, based on ready national 
availability of data and the health inequalities literature.
Science-Based
    The new underservice measure must be based on scientifically 
recognized methods and be replicable. For example, the current Index of 
Medical Underservice comprises four variables, each of which 
contributes approximately a quarter to the maximum score. In other 
words, each of the four variables are weighted equally. However, there 
is no empirical justification for why the income variable should have a 
weight equal to the infant mortality rate variable. Rather, in 
designing the new methodology, we believed the contribution of each 
variable to an overall measure should be based on some verifiable 
statistical relationship. As discussed further in section IV, the new 
methodology used an overall conceptual framework to describe access and 
used analytical techniques such as regression and factor analysis to 
arrive at the weighting/scoring system proposed herein.
Minimize Unnecessary Disruption
    Partly due to the Health Care Safety net Amendments of 2002, as 
described earlier, we have attempted to achieve a reasonable transition 
to this new methodology for underserved areas. Though the revised 
designation method will not (and should not) generate the exact same 
designations as the previous method, we have attempted to minimize 
unnecessary disruption where applicable. The new measure will allow us 
to better focus the designations to more needy areas and populations.
Acceptable Performance
    The new system must perform better than the current designation 
criteria using updated data, and it should be seen as an improvement by 
the multiple key stakeholder groups who rely on these designations. We 
used many different evaluating criteria for this guiding principle, but 
the fundamental criterion we used is whether the method fairly and 
consistently identifies places and people who were in need of primary 
health care and who had barriers to meeting those needs.
2. Designation Process Goals
Consolidation and Simplification
    The separate statutes authorizing MUP and HPSA designations address 
the same fundamental policy concern: That is, the identification of 
those areas and populations with unmet health care needs for the 
purpose of determining eligibility for certain Federal health care 
resources. The existence of two similar but quite distinct procedures 
and sets of criteria has been confusing to many and has often led to 
contradictory or inconsistent results.
    The legislative requirements for the two designations are similar 
in many respects, but the designation processes have, until now, been 
largely separate. A major reason for the disparity in the designation 
process is that regular updating of HPSAs is required by statute, 
though such updating is not statutorily required for the MUA/Ps and has 
not regularly been done.
    The rules proposed below attempt to establish uniform procedures 
and criteria, not only to simplify the designation process for the 
agencies, communities, entities, and individuals involved, but also to 
increase the efficient and effective use of Departmental resources. To 
do so, all the legislatively mandated elements of both statutes are 
included in the proposed procedures. The revised criteria for 
geographic HPSAs and MUAs are identical, as are those for most types of 
MUPs and corresponding population group HPSAs, wherever permitted by 
statutory requirements. Since facility designations are only authorized 
for HPSAs, this is one domain for which the two could not be the same.
Proactivity
    The proposed methodology can be applied using national data 
obtained by HRSA and made available to State partners in the 
designation process, thereby enabling more universal application of the 
designation criteria. Applicant familiarity with the designation 
process should also become less of a factor in obtaining designation, 
and the need for independent data collection by applicants will be less 
of a barrier and burden.
    The national databases include updated versions of the data used in 
the development of this methodology: Provider data from appropriate 
professional associations, such as the American Medical Association 
(AMA) physician data; socio-demographic data from the U.S. Census 
Bureau or a vendor which produces intercensal estimates; unemployment 
data from the Department of Labor; and health status data from the 
National Center for Health Statistics. At the same time, States and 
communities will continue to have the opportunity to substitute State 
and local data for the national data if the State and local data are 
more reliable and/or more current. Data from recognized sources such as 
State Data Centers, economic forecasting agencies such as J.D. Powers, 
and similar entities, and

[[Page 11236]]

that are used for other state purposes may be submitted. Provider data 
may be secured from a variety of sources: State licensing boards, state 
or local professional societies, professional directories, etc. Data 
sources, methodologies, and dates must be specified.
Automation
    The proposed methodology will enable a more automated process for 
designation, through the use of a tabular method for scoring areas and 
updating these scores. The new method makes considerable use of census 
variables for which data are available not only at the county level but 
also at subcounty levels (e.g., for census tracts and census 
divisions), so that a wide variety of State- and community-defined 
service areas can be evaluated for possible designation. Also, an 
interactive system for processing designation requests and updates will 
permit State partners in the designation process to work together with 
the federal designation staff using the same databases. The intent is 
to minimize the effort required by States, communities, and other 
entities to designate an area or update its designation.
Increased State Role
    The proposed approach seeks to foster an increased partnership 
between the various levels of government involved in designation, 
including a significantly larger State and local role in defining 
service areas, underserved population groups and unusual local 
conditions. The new criteria are less prescriptive in terms of travel 
time and mileage standards for defining service areas.
    Each State will be encouraged to define, with community input and 
in collaboration with the Secretary, a complete set of rational service 
areas (RSA) covering its territory. Once developed, these service areas 
will be used in underservice/shortage area designations unless and 
until new census data or health system changes require further area 
boundary changes. Currently the agency allows States to provide their 
own provider data through a new interactive system. States with more 
reliable data can substitute them for national data, which will reduce 
the time required for case-by-case review.
Reduce the Need for Population Group Designations
    Designation of population groups is typically more resource-
intensive than designation of geographic areas, both from the 
standpoint of data collection (since obtaining data for a particular 
population is often more difficult than for the area as a whole) and in 
terms of review. As discussed below, specific indicators included in 
the proposed approach represent the access barriers of poverty/low 
income, unemployment, racial minority or Hispanic ethnicity, population 
density and population over 65 years. This approach specifically 
adjusts an area's base population-to-primary care clinician ratio for 
the effects of these variables. Therefore, it is hoped that this method 
will reduce the need for specific population group designations by 
increasing the probability of designation of geographic areas with 
concentrations of these groups.

III. Development of Methodology To Achieve Goals

A. 1998 NPRM and Summary of Comments Received

    Following consultation with two panels of experts and in-house 
impact testing, an NPRM to revise the designation methodology was 
published on September 1, 1998. Those proposed rules (referred to 
hereinafter as ``NPRM1'') would have created one process for 
simultaneous designation of MUPs and HPSAs; set forth revised criteria 
for designation of MUPs using a new Index of Primary Care Services 
(IPCS); and defined HPSAs as a subset of the MUPs, consisting of those 
MUPs with a population-to-practitioner ratio exceeding a certain level. 
The use of RSAs would have been required for application of both the 
MUP and HPSA criteria.
    The IPCS score would have been calculated based on a weighted 
combination of seven variables: Population-to-primary care clinician 
ratio, percent population below 200% poverty, percent population racial 
minorities, percent population Hispanic, percent population 
linguistically isolated, infant mortality rate or percent low 
birthweight births, and low population density. The maximum possible 
IPCS score would have been 100, and RSAs whose IPCS score equaled or 
exceeded 35 would qualify for MUP designation.
    In counts of primary care clinicians, nurse practitioners (NP), 
physician assistants (PA), and certified nurse midwives (CNM) would 
have been included with a weight of 0.5 full time equivalents (FTE) 
relative to primary care physicians. There would have been two tiers of 
designations, with the first tier consisting of those areas which meet 
the criteria when all primary care clinicians practicing in the area 
are counted, and the second tier consisting of those additional areas 
which meet the criteria when certain categories of practitioners (NHSC 
assignees and those practicing in CHCs) are excluded from clinician 
counts.
    HPSA designation would have required a minimum population-to-
primary care physician ratio of 3,000:1, but this threshold could only 
be applied to those RSAs found to have an IPCS score which exceeded the 
MUP designation threshold of 35.
    The period for public comment on the 1998 proposed rule was 
extended to January 4, 1999. Over 800 comments were received, analyzed, 
and categorized. Major issues raised are summarized briefly below:
    1. Impact in Terms of Designations Lost--Many commenters estimated 
that unacceptably high numbers of HPSA designations would be lost in 
their State if the proposed methodology were adopted, particularly in 
rural and frontier areas, as well as significant numbers of MUPs. They 
believed that the impact stated in NPRM1's preamble, in terms of 
percentages of designations lost, was substantially underestimated.
    2. Inclusion of nonphysician primary care providers--A number of 
commenters objected to the inclusion of NPs/PAs/CNMs in primary care 
clinician counts, based on the additional burden on applicants of 
counting them, and cited the lack of adequate State or national 
databases for these clinicians. Others questioned the reasonableness of 
weighting them at 0.5 FTE relative to a primary care physician. 
Typically, responding NPs, PAs, CNMs, professional organizations 
representing them, and certain other health care advocates felt the 0.5 
should be adjusted upward; others felt it should be adjusted downward, 
particularly in States where the scope of practice of these clinicians 
is limited. There were also concerns that NPs, PAs and CNMs who were 
not in clinical, primary care practice would be inadvertently counted 
if available data were used, and that truly underserved areas would 
lose designation as a result.
    3. Threshold for HPSA Designation--The proposed 3,000:1 population-
to-primary care clinician threshold ratio for HPSA designation was 
considered too high by many commenters, especially if NPs/PAs/CNMs were 
to be counted as well as primary care physicians.
    4. Urban/Rural Balance--Many of the indicators selected for 
inclusion in the new IPCS (such as race, Hispanic ethnicity, linguistic 
isolation, and low birthweight births), were viewed as tending to bias 
the new index toward designation of urban areas (as compared with 
indicators like percent elderly,

[[Page 11237]]

which had been included in the previously-used Index of Medical 
Underservice and was seen as favoring rural areas).
    5. HPSAs required to be a subset of MUPs--the proposed requirement 
that an area could receive HPSA designation only if it first qualified 
as an MUP (by having an IPCS score which exceeded the 35 threshold) was 
seen as threatening many legitimate currently-designated HPSAs (i.e., 
HPSAs with population-to-practitioner ratios higher than 3000:1 but 
whose poverty rates and scores on other IPCS variables were not high 
enough to achieve the IPCS threshold).
    6. Two-tiered Designations--The idea of two-tiered designations was 
generally supported, but an issue arose as to which federally-supported 
primary care clinicians should be excluded from counts in tier 2. Most 
agreed that NHSC assignees and physicians in CHCs should be excluded 
(as the proposed rule did). Many felt that those physicians on J-1 
waivers should also be excluded from tier 2 counts, and some suggested 
that primary clinicians in other safety-net settings (such as RHCs or 
State-funded health centers) should also be excluded.
    On June 3, 1999, notice was given in the Federal Register that 
further analysis would be conducted, to include a thorough, updated 
analysis of the impact of the proposed approach as published, as well 
as the testing of alternatives based on analysis of the comments 
received. The Notice indicated that these impact analyses would be 
applied to the most current obtainable national data for all counties 
and currently-defined subcounty MUPs and HPSAs, and that one or more 
outside organizations would verify the impact testing. A new NPRM would 
then be published for public comment.

B. Development of Method Proposed in This NPRM

    During the remainder of 1999, HRSA acquired components of the 
national databases necessary for impact testing, such as practice 
addresses for primary care physicians, PAs, NPs, and CNMs. An extensive 
data cleaning and provider site geocoding process ensued. 
Simultaneously, HRSA began working with researchers at HRSA-funded 
Rural Health Research Centers and Health Professions Workforce Centers 
to develop specifics of the plan for further analysis and testing. 
Ultimately, the Cecil G. Sheps Center of the University of North 
Carolina (UNC) was funded to undertake national testing of the 
previously-proposed methodology in NPRM1 and alternative methodologies, 
and to coordinate efforts by other research groups who would do State 
or regional testing.
    In January 2000, a group of sixteen State Primary Care Office (PCO) 
representatives volunteered to assist by providing recommendations for 
a revised approach to designation from their standpoint, as the ones 
primarily responsible for providing data to HRSA in support of 
designation requests and updates for their States. This led to a series 
of conference calls, a two-day meeting, and eventual preparation of 
draft recommendations for consideration by the appropriate federal 
officials. Meanwhile, researchers at the Sheps Center were considering 
alternative methodologies for simultaneous consideration of various 
indicators of shortage and underservice. The two groups met on several 
occasions to coordinate efforts; the methodology finally developed by 
Sheps researchers and used as the basis for these proposed rules was 
consistent with the recommendations of the group of PCOs.
    Over time, the following specific steps took place:
    (a) A comprehensive database for impact testing was established. 
This entailed: ``cleaning'' and geocoding the various physician 
databases acquired (from professional associations and from federal and 
State agencies approving J-1 visa waivers), and matching them with each 
other and with HRSA's NHSC database; similar activity for data acquired 
on non-physician primary care clinicians (NP/PA/CNM); adding geocoded 
location data for HHS-sponsored safety-net provider sites, including 
CHCs, NHSC sites and RHCs; and the inclusion of appropriate Census data 
(or vendor-supplied intercensal estimates for Census variables) as well 
as data on other health status and access-related variables.
    (b) The group of sixteen PCOs developed their recommended approach 
to a new designation methodology and provided their recommendations to 
HRSA staff. Their original recommendation was essentially to expand the 
number of high need indicators which could be used to adjust the 
population-to-practitioner ratio threshold for designation, to allow 
several different threshold levels depending on the number of high need 
indicators present, and then to compare the area's actual ratio with 
the adjusted threshold appropriate for that area.
    (c) HRSA staff worked with the UNC-Sheps Center team to develop a 
conceptual framework and a methodology responsive to concerns raised in 
public comments and in the PCO recommendations. In response to the 
criticism of the earlier 1998 proposal as using appropriate indicators 
but an arbitrary weighting scheme, this methodology was developed based 
on a general conceptual framework of access and underservice and 
statistical methods. The overall goal was to identify areas and 
communities in need of services to increase access, relative to other 
communities across the country.
    The conceptual framework and methodology will be described further 
in sections IV.A and IV.B. A more technical description is also 
provided in Appendix B. The way the method is applied to determine 
designation status is described in Sections IV.C and V. below. Finally, 
further details are available on HRSA's Web site (http://bhpr.hrsa.gov/shortage) and in a journal article recently published in the Journal of 
Health Care for the Poor and Underserved entitled ``Designating Places 
and Populations as Medically Underserved: A Proposal for a New 
Approach'' (Ricketts et al., 2007).
    (d) The impact of the proposed method on the number and population 
of geographic and low income designations at national and state levels 
was explored and compared with alternatives using updated national data 
allied to: (a) The criteria currently in place; (b) the criteria 
proposed in the September 1, 1998 rule, and (c) the new methodology 
proposed in this rule. In addition, impact analyses with State data 
were performed by Regional Centers for Health Workforce Studies and/or 
PCOs in four States. This analysis, discussed in detail in Section VI 
below, indicated that this proposed method would not have severe 
adverse effects on most safety net providers, and would--at the 
transition from the old method to the new--maintain a similar total 
underserved population.
    (e) However, there remained concerns that some safety net 
facilities--despite serving populations clearly underserved, such as 
the uninsured--might be located in areas that did not meet geographic 
or population group criteria. Consequently, with the help of the group 
of 16 PCOs, a separate method was developed (hereafter referred to as 
the ``facility designation method'') for facility designation of those 
safety-net facilities which could demonstrate high levels of service to 
the uninsured and/or Medicaid-eligibles. This was tested using the 
Uniform Data System for community health centers and found to support 
designation of most Section 330-funded health centers.
    (f) The new methodology's concepts and impact analysis approaches 
have been discussed in a preliminary fashion

[[Page 11238]]

at various meetings of national and State organizations whose members 
are affected by shortage/underservice designations.

IV. Description of Conceptual Framework and Methodology and 
Alternatives Considered

A. Conceptual Framework

    In our model, as in health services research more widely, we 
consider utilization of services an outcome of the demand and supply 
forces within the healthcare system. The conceptual framework for the 
model is based on the idea that barriers to care reduce appropriate 
use, which is reflected in delayed and therefore higher subsequent use 
rates. We call this concept ``thwarted demand.'' For example, 
individuals with diabetes living in remote, rural areas may put off 
seeing their doctors regularly-not because they do not recognize the 
need for regular treatment-but because of the distances involved or 
other potential barriers. These barriers initially reduce utilization. 
When these individuals eventually do seek treatment, it is often 
because their condition worsened to the point where they could no 
longer defer treatment. As the severity of their condition worsens and 
their need for care increases, so too does their utilization of 
services, in terms of treatment volume and/or intensity. They may 
require hospitalization, for instance, or present at an emergency room.
    To estimate the dimensions of both the (a) delayed--and thus 
initially reduced utilization rate--as well as the (b) subsequent 
higher use rates, we created a methodology that centers around the 
level of care experienced by a ``well-served population'' in order to 
establish an initial standard against which an ``under-served 
population'' can be defined. In a ``well-served population,'' where 
there are no barriers to care, healthcare utilization will be an 
expression of healthcare demand (i.e., demand is not thwarted). The 
assumption was made that, for groups without significant barriers to 
care, primary care utilization rates would cluster around the most 
appropriate level of care and, in turn, that their demand for care will 
also reflect their need for care. In an ``under-served population,'' by 
contrast, demand will be initially thwarted and healthcare utilization 
will therefore understate true demand.
    Moreover, healthcare needs tend to be greater in areas with 
disadvantaged populations. The health inequalities literature has 
shown, for example, that conditions like diabetes and cancer are more 
prevalent among minorities. In turn, we can expect that areas with a 
high proportion of minorities will--on average--have greater healthcare 
needs than areas with a lower proportion of minorities. To the extent 
that healthcare needs tend to be greater in underserved populations, 
the level of healthcare utilization observed in underserved populations 
would understate true demand even further. Thus, the model adjusts for 
this increased need and thwarted demand.
    As stated earlier, however, thwarted demand potentially creates a 
paradox since low access often results in subsequent illness that may 
require a higher level of health care use, in terms of either treatment 
volume or intensity. The entry of the patient into a structured care 
system may also induce subsequently higher rates of use of primary care 
services incident to hospitalizations or due to raised familiarity with 
the system. This paradox is likely to affect overall use rates in low-
access areas in such a way as to increase use rates.
    We accepted that these positive and negative factors would be 
simultaneously operating and sought ways to estimate their individual 
effects in terms of both initially reduced and subsequently increased 
visits. The net, overall need for services can be reflected in a 
combination of visits precluded with visits induced.
[GRAPHIC] [TIFF OMITTED] TP29FE08.006

    By adjusting for these bi-directional effects of thwarted demand, 
this methodology effectively allows us to ask, ``What level of care 
would these individuals utilize if they were well-served and barrier 
free?'' This adjusted utilization rate becomes the proxy in our revised 
model for the ``effective need'' in an underserved population. For 
example, an underserved area that contains 100 people may nevertheless 
``effectively need'' the same level of services an area of 1,000 people 
needs. In this underserved area, the ``actual'' population may be 100 
but the ``effective'' population can be thought of as 1,000.
    We then compare this ``effective need'' in an underserved 
population to the available supply of primary care providers in that 
area to create a population-to-provider ratio. The underlying logic is 
that meeting community needs could be expressed in ratios of 
appropriate use to optimal service productivity. The use rate would be 
expressed in population counts and the service productivity in 
practitioner counts. The goal was to reflect the level of a 
population's need for office-based primary care visits in terms of an 
adjusted population count that took into consideration characteristics 
that would affect use of services.
    We considered various other proxies for need besides the 
population-to-provider ratio. We ultimately decided to use an adjusted 
population-to-provider ratio for several reasons. First, the prominence 
of population-to-practitioner ratios in the two existing measurements 
of underservice was recognized. Discussions with the federal agencies 
and stakeholder groups during the development of the revised approach 
also revealed a preference for using that metric as the basis for a 
revised method. Furthermore, practical reasons for the use of this 
ratio as a starting point for the construction of an index included the 
fact that such ratios are well-recognized and understood by the program 
participants and would provide some continuity between a new proposal 
and the older methods that included the ratios in the calculations.
    Such a metric is also sensitive to the two different sources of 
unmet need--provider shortages and barriers to care--that programs 
which rely on the HPSA and MUA/P designations attempt to address. In 
HPSAs, by definition, access is restricted because there are few or no 
primary care health professionals who will take care of certain 
patients. The remedy for this is to supplement the professional supply 
with practitioners who will see all patients, in order to bring the 
numbers of professionals more into line with a level of supply 
generally considered adequate. For MUA/Ps, the primary reasons for 
designation relate to barriers to accessing existing primary care 
services (e.g., financial) or the combination of higher needs and lower

[[Page 11239]]

availability. The central task in combining these two systems was to 
find a common metric that was sensitive to both of these 
characteristics of underservice, which the adjusted population-to-
provider ratio is.

B. Methodology

    The model can be thought of as compromising six basic steps.
    Step 1: Calculate the numerator for the population-to-provider 
ratio: The ``effective barrier free population.''
    The first step is to estimate the effects that differences in the 
structure of the population would have on service utilization based on 
age and gender by assigning weights according to the national use rates 
for people without barriers to care. Accordingly, we call this the 
``effective barrier free population'' because it allows us to estimate 
what the utilization rate would be, after adjusting for age and gender, 
if the population of a community were able to use primary care services 
at the same rate as a population with no constraints due to factors 
like poverty, race, or ethnicity. This step is necessary because 
research shows that age and gender affect utilization rates independent 
of barriers to care. The elderly, for example, use services at higher 
rates than the non-elderly even when barriers to care are controlled 
for.
    To calculate the ``effective barrier free population,'' we adjust 
the area's base population to reflect differential requirements by age 
and gender for primary care services, using utilization rates for 
populations who are effectively ``barrier-free.'' This adjustment uses 
the latest available Medical Expenditure Panel Survey (MEPS) 
utilization data to determine what the expected number of primary care 
office visits for the area's population would be (based on its age/
gender make-up) if usage were at the national average for persons who 
are non-minority, not poor, and employed. This total expected number of 
primary care visits is then divided by the corresponding current 
national mean number of primary care visits per person to obtain the 
``effective barrier free population.'' The effect of this adjustment is 
that a community with more older people or more women of child-bearing 
age than the average national age-gender distribution will appear to be 
a larger population than if the age-gender mix were like the nation's 
as a whole.
    The utilization rates used in developing and testing the 
methodology proposed herein are shown in Table IV-1. These will be 
updated when this regulation is finalized and periodically thereafter 
by notice in the Federal Register that updated data will be posted on 
the HRSA Web site.

Table IV-1.--Barrier Free Population Use Rate, Adjusted for Age and Gender, Expressed as Primary Care Visits Per
                                                 Person Per Year
----------------------------------------------------------------------------------------------------------------
                                                  Average primary care visits ( per year) by age group category
                      Age                      -----------------------------------------------------------------
                                                   0-4        5-17      18-44      45-64      65-74       75+
----------------------------------------------------------------------------------------------------------------
Male..........................................      5.164      2.499      2.867      4.410      6.052      8.056
Standard Error................................       .488       .401       .372       .386       .469       .533
Female........................................      4.046      2.256      5.007      5.480      6.710      8.160
Standard Error................................       .491       .403       .373       .389       .456     .533*
----------------------------------------------------------------------------------------------------------------
The above table is from MEPS, 1996. These data are applied to the actual area age-gender total to derive the
  barrier free total utilization for a population with these age and gender characteristics. The corresponding
  national mean utilization rate is 3.471. *Imputed.

    The calculations for Wichita County, Kansas are shown as an 
illustration of how this step of the model works. The chart below 
provides the population breakout by age and gender, the visit rates for 
each category, and the adjusted population that results from dividing 
by the average visit rate. The steps are detailed below the chart.
    The basic formula is:

Barrier-free use rate = 4.046 * ( of females aged 0-4) + 2.256 
* ( of females aged 5-17) +5.007* ( of females aged 
18-44) + 5.480 * ( of females aged 45-64) + 6.710 * ( 
of females aged 65-74) + 8.160 * ( of females aged 75+) + 
5.164 * ( of males aged 0-4) + 2.499 * ( of males 
aged 5-17) + 2.867 * ( of males aged 18-44) + 4.410 * 
( of males aged 45-64) + 6.052 * ( of males aged 65-
74) + 8.056 * ( of males aged 75+)

                      Table IV-1A.--Applying Table IV-1 Using Wichita, Kansas as an Example
----------------------------------------------------------------------------------------------------------------
                                Ages 0-4        5-17          18-44         45-64         65-74      75 and over
----------------------------------------------------------------------------------------------------------------
Females:                      ............  ............  ............  ............  ............  ............
    Population..............        65           207           363           281           106           113
    Multiplier (from Table           4.046         2.256         5.007         5.48          6.71          8.16
     IV-1)..................
    Visits..................       262.99        466.992      1817.541      1539.88        711.26        922.08
Males:                        ............  ............  ............  ............  ............  ............
    Population..............        93           234           386           108           321            94
    Multiplier (from Table           5.164         2.499         2.867         4.41          6.052         8.056
     IV-1)..................
    Visits..................       480.252       584.766      1106.662       476.28       1942.692       757.264
Female visits...............      5720.743
Male visits.................      5347.916
        Total visits........     11068.659
----------------------------------------------------------------------------------------------------------------

    For Wichita, the calculations are:

Barrier-free use rate
    = 4.046 * (65) + 2.256 * (207) + 5.007 * (363) + 5.480 * (281) + 
6.710 * (1060) + 8.160 * (113) + 5.164 * (93) + 2.499 * (234) + 2.867 * 
(386) + 4.410 * (108) + 6.052 * (321) + 8.056 * (94)
    = 262.99 + 466.992 + 1817.541 + 1539.88 + 711.26 + 922.08 + 480.252 
+ 584.766 + 1106.662 + 476.28 +1942.692 + 757.264

[[Page 11240]]

    = 11068.659 visits.

    Using 1996 MEPS data, individuals who were barrier free had, on 
average, 3.741 visits to their primary care providers. If we then 
divide the barrier-free use rate by this average number of visits, we 
can obtain the ``effective barrier-free population'' estimate. In 
Wichita, the calculation would be: Effective barrier-free population = 
11068.659 / 3.741 = 2958.74338.

    This ``effective barrier-free population'' becomes the numerator--
the ``population'' value--in the population-to-provider ratio. For 
example, the actual population of Wichita, Kansas was 2,436. By going 
through these calculations, however, we see in Table IV-2 that the 
effective barrier-free population is 2,959.

                               Table IV-2
------------------------------------------------------------------------
                                               A                B
------------------------------------------------------------------------
                                                            Effective
              County name                Total pop 1999    barrier-free
                                                            population
------------------------------------------------------------------------
Wichita, KS...........................           2,436             2959
------------------------------------------------------------------------

    Step 2: Calculate the denominator in the population-to-provider 
ratio: The supply of primary care providers.
    The second step is to calculate the actual number of FTE primary 
care clinicians in the target area, including primary care physicians 
(allopathic and osteopathic), NPs, PAs, and CNMs in primary care 
settings.
    Each active physician in the primary care specialties (i.e., 
General Practice, Family Practice, General Internal Medicine, General 
Pediatrics, Ob/Gyn) is included as 1.0 FTE unless there is evidence of 
less than full-time practice, in which case their actual FTE in the 
area is used based on guidance set by the Secretary on the calculation 
of FTEs. As before, physicians in residency training in these 
specialties are counted as 0.1 FTE.
    In this proposed rule, NP/PA/CNMs are also included, but they are 
counted either as 0.5 FTE or, at the applicant's option, 0.8 times a 
State-specific practice scope factor running from 0.5 to 1.0 (in 
recognition that not all NP/PA/CNM practices operate at the same level 
due to state policies). We discuss this issue in further detail in 
section V.G below.
    Data sources are: American Medical Association Masterfile-Dec. 
1998, American Osteopathic Association-May 1999, American College of 
Nurse Midwives-1999, American Association of Nurse Practitioners-1999, 
and American Association of Physician Assistants-July 1999.
    For example, there are 2.5 FTE primary care providers in Wichita, 
Kansas, according to our national data.
    Step 3: Calculate the base population-to-provider ratio.
    The population-to-provider ratio is then calculated using the 
``effective barrier-free population'' (from step 1) as the numerator 
and the number of FTE primary care clinicians (from step 2) as the 
denominator. Using Wichita, Kansas as an example, the base population-
to-provider ratio is 1,183 (table IV-3, column E).

                                                                       Table IV-3
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                               A                   B                   C                   D                   E
                                                     ---------------------------------------------------------------------------------------------------
                     County name                                                                                                      Effective  barrier-
                                                           Total pop      Effective barrier-    Tot FTE primary    Actual population     free  pop/FTE
                                                                            free population          care         to FTE ratio (A/C)      ratio (B/C)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Wichita, KS.........................................               2436                2959                 2.5                 974                1183
--------------------------------------------------------------------------------------------------------------------------------------------------------

    Step 4: Adjust for increases in need for primary care services 
based on community characteristics.
    Because the programs that rely on HPSA and MUA/P designations aim 
to improve access and thereby improve health, this consideration drove 
the design of the analysis to develop weights for need for services in 
areas and for populations. The fourth step of this methodology thus 
computes the effects of community factors that have been demonstrated 
to indicate an even greater need for services but also a lower 
utilization of services than the average well-insured and healthy 
population due to barriers to care.
    The general approach was to take population-level variables that 
correlate with barriers to care and then determine the relationship of 
those variables to the adjusted population-to-practitioner ratio 
described above, using regression analysis. From this analysis, the 
relative influence of those variables on the ratio would be derived 
and, from those parameters, scores could be estimated to adjust or 
``weight'' the overall index.
    Because step 4 can be quite technical, we present only an overview 
here. For a more detailed discussion of step 4 and its place in the 
overall methodology, please refer to Appendix B (please note that what 
we refer to in this rule as ``step 4'' is referred to as ``steps 4-5'' 
and ``step 7'' in Appendix B). The methodology is also described in a 
journal article recently published in the Journal of Health Care for 
the Poor and Underserved entitled ``Designating Places and Populations 
as Medically Underserved: A Proposal for a New Approach'' (Ricketts et 
al., 2007).
    In developing step 4, we followed the conceptual framework of 
access proposed by Andersen and colleagues, who posit that there are 
predisposing and enabling characteristics that can represent need 
(Andersen et al., 1973; Andersen 1995; Aday and Andersen 1975). There 
is no consensus set of community-level indicators that reflect need 
within their framework. Because the programs that rely on HPSA and MUA/
P designations largely address unmet need by placing primary care 
practitioners in areas designated as underserved, we chose to use the 
effective barrier-free population-to-practitioner ratio (calculated in 
steps 1,

[[Page 11241]]

2, and 3) as a proxy indicator of relevant need for this step in the 
methodology.
    We then ran regression analyses to examine how the ratio varied 
with socio-demographic indicators that research has shown to correlate 
with low access and/or poor health status (Mansfield et al., 1999; CDC, 
2000; Krieger et al., 2003; Andersen and Newman 1973; Aday and Andersen 
1975; Robert 1999; Robert and House, 2000; Kawachi and Berkman, 2003).
    We also included factors in the regression model that closely 
parallel the statutory elements of the current HPSA and MUA designation 
processes (health status, ability to pay for services and their 
accessibility), and also directly relate to the programs they initially 
were designed to support: the NHSC and the CHC Programs.
    Three categories of high need indicators were ultimately used, for 
a total of nine indicators, as described in Table IV-4. These factors 
were used because they were shown by the regression to have independent 
effects on access to care as measured by the population-provider ratio.

         Table IV-4.--Variables Used in Creating Proposed Method
------------------------------------------------------------------------
         Demographic                Economic            Health status
------------------------------------------------------------------------
Percent Non-white             Percent population    Actual/expected
 ``NONWHITE'', (src: 1998      <200% FPL             death rate (adj)
 Claritas estimates).          ``POVERTY'', (src:    ``SMR'', (src:
                               1998 Claritas         National Center for
                               estimates).           Health Statistics,
                                                     1998: for previous
                                                     5 year period).
Percent Hispanic              Unemployment rate     Low birth weight
 ``HISPANIC'', (src: 1998      ``UNEMPLOYMENT'',     rate ``LBW'', (src:
 Claritas estimates).          (src: Bureau of       National Center for
                               Labor Statistics,     Health Statistics,
                               1998).                1998: for previous
                                                     5 year period).
Percent population >65 years  ....................  Infant mortality
 ``ELDERLY'', (src: 1998                             rate ``IMR'', (src:
 Claritas estimates).                                National Center for
                                                     Health Statistics,
                                                     1998: for previous
                                                     5 year period).
------------------------------------------------------------------------
    Population density ``DENSITY'' * (src: 1998
               Claritas estimates)
------------------------------------------------------------------------
* Population density is a measure of the market potential for an area as
  well as an indicator of the rural or urban character of a place. As
  places become more densely populated, they tend to attract employment
  and services. Density is also associated with rural and urban settings
  and the behavioral characteristics of populations vary along that
  continuum (Amato and Zuo, 1992).

    A number of other need indicators were considered in the 
development of the methodology. Table IV-5 provides a brief listing and 
an explanation why they were not chosen. In many cases, these elements 
are highly correlated with the ones listed above, so their impact on 
access is already captured by the variables that are included.

     Table IV-5.--Variables Considered for Inclusion But Not Chosen
------------------------------------------------------------------------
          Suggested variables                  Reason for rejection
------------------------------------------------------------------------
Percent low income elderly.............  Used elderly and low income.
Percent children <6....................  Used component in adjusted pop.
Percent children low income............  Used overall low income.
Percent children <4....................  Used component in adjusted pop.
Dependency ratio (%>65+%<18/total        Used combination of factors
 population).                             that capture this.
Racial disparity in low birth weight     Not available for small areas.
 rates.
Disparity in IMR rates.................  Small numbers.\1\
Birth rate.............................  Highly correlated with chosen
                                          measures.
Teen birth rate........................  Not available in sub-county
                                          areas.
Prenatal care (Kessner)................  Unstable in small areas.\1\
Prenatal care index (Kotelchuck).......  Unstable in small areas.\1\
Ambulatory care sensitive admissions     Not available in all states.
 (ACS rates).
Ambulatory care sensitive admissions     Not available in all states.
 for children.
ACS rates restricted to common disease   Not available in all states.
 (diabetes, hypertension, cellulitis.
ACS rates for Medicare population......  Not available in all states.
ACS Rates for common disease for         Not available in all states.
 Medicare population.
Ratio of 100-200% poverty to 100%        High correlation with chosen
 poverty.                                 variables.
Uninsured population...................  Not available in small areas.
Uninsured <18 years....................  Not available in small areas.
Population density threshold (LT 6 p sq  Density used as a continuous
 mile, 7 p sq mile).                      variable instead.
Linguistic isolation...................  Not calculated on a regular
                                          basis. Imputed data.\2\
Migrant impact.........................  Not available.
Farmworker impact......................  Not available.
Seasonal worker impact.................  Not available.
Percent refugees, immigrant............  Not calculated on a regular
                                          basis. Imputed data.\2\
Medicaid eligible population...........  Not readily available in small
                                          areas.
Tuberculosis incidence.................  Not available in small areas.
HIV incidence..........................  Not available in small areas.
STD incidence..........................  Not available in small areas.
Cancer incidence.......................  Not available in small areas.
Cervical cancer incidence..............  Not available in small areas.
Breast cancer incidence................  Not available in small areas.
Hypertension rate......................  Not available in small areas.
COPD rates.............................  Not available in small areas.

[[Page 11242]]

 
Diabetes rates.........................  Not available in small areas.
Diabetes rates for children............  Not available in small areas.
Asthma rates...........................  Not available in small areas.
Asthma rates for children..............  Not available in small areas.
Smoking rates..........................  Not available in small areas.
Smoking rates for children/adolescents.  Not available in small areas.
Obesity................................  Not available in small areas.
Obesity among children.................  Not available in small areas.
Alcohol use rates......................  Not available in small areas.
Alcohol use rates for adolescents......  Not available in small areas.
Binge drinking rates...................  Not available in small areas.
Disparity measures (ratio of rates for   Not available in small areas.
 whites and minorities for disease
 incidence various combinations).
Raw mortality rate.....................  Prefer adjusted mortality
                                          rate.\3\
Disparity in mortality rate............  Small numbers.
Cancer mortality.......................  Small numbers.
Cardiovascular disease mortality.......  Small numbers.
Infectious disease mortality...........  Small numbers.
Suicide rate...........................  Small numbers.
Teen suicide rate......................  Small numbers.
Percent rural population...............  Density captures.
Percent urban population...............  Density captures.
Perceptual measures (other               Varied from state to state.
 designations).
------------------------------------------------------------------------
\1\ Infant mortality remains a relatively rare phenomenon and published
  rates are often compiled from multi-year data. Comparing rates for
  small areas would compound the instability of those rates. The same
  problems are encountered with data that describe the character of
  prenatal care in small and rural areas, although these Indices are
  based on assessments of all births, the degree to which prenatal care
  meets standards of adequacy in smaller and less populated areas may
  vary from year to year due to isolated events or poor care for a
  limited number of newborns due to factors that do not reflect the
  character of the health care in the area (e.g. weather, relocation).
\2\ These data are reported by the Census Bureau and are ``imputed''
  from other variables (reported ethnicity and the likelihood of being a
  refugee or immigrant). The data are not collected directly.
\3\ The mortality rate varies widely according to the age structure of a
  place. A much higher proportion of elderly is often associated with a
  much higher mortality rate. Adjusting for the age structure allows for
  a better comparison of the mortality burden of the community relative
  to its risk.

    To calculate the adjustment factors or ``weights,'' the actual 
value of each high need indicator was converted to a percentile 
relative to the national county distribution, using a conversion table 
(see Table IV-6). For all variables except population density, the 
theoretically worst actual value corresponded to the 99th percentile 
(e.g., the higher the unemployment rate in an area, the higher the 
percentile.) In Wichita, Kansas for example, 3.59% of the population 
were unemployed. Table IV-6 is used to translate this percentage into a 
percentile: In this case, Wichita falls in the 24th percentile.

                      Table IV-6.--High Need Indicators--Breakpoints for Conversion From Community Values to National Percentiles *
--------------------------------------------------------------------------------------------------------------------------------------------------------
                 Percentile                     Poverty      Unemp      Elderly     Density    Hispanic    Non white  Death rate      LBW         IMR
--------------------------------------------------------------------------------------------------------------------------------------------------------
1...........................................       13.31        1.70        6.32        0.66        0.13        0.23       0.674        3.23        0.00
2...........................................       16.15        1.90        7.55        1.01        0.19        0.30       0.729        3.66        0.00
3...........................................       18.29        2.10        8.18        1.49        0.23        0.36       0.766        3.94        0.00
4...........................................       19.74        2.20        8.79        1.79        0.26        0.40       0.788        4.13        0.00
5...........................................       21.15        2.30        9.34        2.16        0.29        0.45       0.805        4.32        3.09
6...........................................       22.27        2.40        9.70        2.54        0.30        0.48       0.816        4.44        3.49
7...........................................       23.25        2.40        9.97        3.01        0.33        0.53       0.826        4.60        3.89
8...........................................       24.24        2.50       10.23        3.38        0.34        0.58       0.837        4.69        4.13
9...........................................       25.01        2.60       10.50        3.80        0.36        0.61       0.846        4.80        4.43
10..........................................       25.68        2.70       10.71        4.24        0.38        0.64       0.853        4.88        4.63
11..........................................       26.25        2.70       10.90        4.73        0.40        0.67       0.861        4.95        4.76
12..........................................       26.83        2.80       11.11        5.32        0.41        0.71       0.867        5.02        4.90
13..........................................       27.36        2.90       11.26        6.23        0.42        0.76       0.873        5.10        4.99
14..........................................       27.83        2.90       11.43        6.82        0.44        0.79       0.878        5.16        5.09
15..........................................       28.42        3.00       11.61        7.82        0.46        0.83       0.883        5.22        5.22
16..........................................       28.93        3.10       11.75        8.41        0.47        0.88       0.889        5.28        5.33
17..........................................       29.39        3.10       11.92        9.36        0.49        0.93       0.894        5.34        5.43
18..........................................       29.91        3.20       12.06        9.97        0.50        0.97       0.899        5.38        5.55
19..........................................       30.29        3.20       12.17       10.98        0.51        1.01       0.903        5.42        5.63
20..........................................       30.66        3.30       12.30       11.96        0.53        1.06       0.908        5.47        5.74
21..........................................       31.12        3.30       12.46       13.02        0.55        1.11       0.913        5.52        5.86
22..........................................       31.57        3.40       12.57       13.90        0.56        1.16       0.917        5.57        5.91
23..........................................       31.90        3.40       12.72       14.60        0.58        1.20       0.920        5.60        6.00
24..........................................       32.24        3.50       12.82       15.78        0.59        1.27       0.925        5.65        6.08
25..........................................       32.62        3.60       12.94       16.66        0.60        1.33       0.928        5.71        6.17

[[Page 11243]]

 
26..........................................       32.98        3.60       13.04       17.63        0.62        1.40       0.932        5.76        6.27
27..........................................       33.43        3.70       13.14       18.40        0.64        1.49       0.937        5.80        6.32
28..........................................       33.71        3.70       13.24       19.03        0.65        1.54       0.938        5.84        6.39
29..........................................       34.07        3.80       13.33       19.94        0.67        1.63       0.941        5.88        6.45
30..........................................       34.45        3.80       13.41       20.92        0.68        1.73       0.945        5.92        6.53
31..........................................       34.83        3.90       13.51       22.15        0.70        1.79       0.948        5.96        6.62
32..........................................       35.15        3.90       13.63       22.85        0.72        1.89       0.952        6.00        6.68
33..........................................       35.57        4.00       13.73       23.76        0.74        1.99       0.956        6.03        6.74
34..........................................       35.85        4.00       13.83       24.61        0.76        2.06       0.958        6.08        6.82
35..........................................       36.22        4.10       13.90       25.83        0.78        2.12       0.961        6.12        6.88
36..........................................       36.53        4.10       14.02       26.76        0.81        2.20       0.965        6.15        6.95
37..........................................       36.82        4.20       14.12       27.67        0.83        2.29       0.968        6.20        7.05
38..........................................       37.07        4.30       14.18       28.48        0.85        2.44       0.971        6.24        7.11
39..........................................       37.34        4.30       14.26       29.56        0.87        2.57       0.974        6.28        7.18
40..........................................       37.62        4.40       14.31       30.35        0.90        2.69       0.978        6.33        7.26
41..........................................       37.83        4.40       14.39       31.51        0.93        2.82       0.981        6.36        7.35
42..........................................       38.16        4.50       14.49       32.46        0.95        3.04       0.985        6.41        7.42
43..........................................       38.35        4.50       14.57       33.33        0.98        3.18       0.989        6.45        7.48
44..........................................       38.63        4.60       14.67       34.49        1.01        3.35       0.992        6.49        7.55
45..........................................       38.85        4.60       14.76       35.63        1.04        3.49       0.996        6.54        7.61
46..........................................       39.14        4.70       14.84       36.72        1.07        3.67       0.999        6.60        7.67
47..........................................       39.44        4.80       14.94       37.69        1.11        3.87       1.002        6.63        7.74
48..........................................       39.74        4.80       15.00       38.72        1.15        4.04       1.005        6.67        7.81
49..........................................       40.06        4.90       15.12       39.88        1.20        4.22       1.009        6.70        7.86
50..........................................       40.31        4.90       15.20       41.38        1.24        4.44       1.013        6.76        7.91
51..........................................       40.61        5.00       15.31       42.64        1.27        4.65       1.018        6.78        7.98
52..........................................       40.93        5.00       15.43       44.24        1.30        4.90       1.021        6.82        8.08
53..........................................       41.21        5.10       15.52       45.78        1.35        5.17       1.024        6.86        8.14
54..........................................       41.49        5.20       15.63       47.24        1.39        5.50       1.027        6.91        8.19
55..........................................       41.72        5.20       15.71       48.65        1.44        5.81       1.030        6.96        8.27
56..........................................       42.04        5.30       15.78       49.94        1.49        6.12       1.034        7.00        8.32
57..........................................       42.35        5.30       15.91       51.61        1.54        6.37       1.039        7.06        8.43
58..........................................       42.62        5.40       15.99       53.18        1.60        6.72       1.042        7.10        8.50
59..........................................       42.98        5.50       16.09       54.53        1.65        7.03       1.045        7.14        8.58
60..........................................       43.38        5.50       16.21       56.26        1.72        7.31       1.049        7.20        8.66
61..........................................       43.67        5.60       16.30       58.03        1.80        7.74       1.052        7.25        8.76
62..........................................       44.01        5.70       16.39       61.20        1.88        8.23       1.055        7.29        8.81
63..........................................       44.25        5.80       16.52       63.54        1.98        8.69       1.060        7.33        8.87
64..........................................       44.65        5.90       16.67       66.32        2.08        9.24       1.064        7.38        8.92
65..........................................       44.90        5.90       16.76       68.59        2.16        9.60       1.067        7.44        9.02
66..........................................       45.15        6.00       16.86       70.91        2.26        9.97       1.071        7.50        9.11
67..........................................       45.38        6.10       16.96       73.19        2.37       10.40       1.074        7.55        9.18
68..........................................       45.77        6.30       17.11       74.78        2.48       10.96       1.079        7.61        9.24
69..........................................       46.13        6.40       17.24       79.13        2.60       11.54       1.083        7.65        9.35
70..........................................       46.52        6.50       17.38       82.37        2.74       12.36       1.087        7.73        9.41
71..........................................       46.90        6.60       17.49       85.72        2.89       13.18       1.093        7.78        9.54
72..........................................       47.19        6.70       17.64       88.76        3.05       14.08       1.097        7.83        9.64
73..........................................       47.48        6.80       17.76       92.97        3.17       14.81       1.102        7.90        9.76
74..........................................       47.85        6.90       17.90       97.05        3.35       15.80       1.108        7.95        9.89
75..........................................       48.14        7.00       17.99      101.55        3.58       16.60       1.112        8.01       10.00
76..........................................       48.49        7.10       18.17      107.04        3.78       17.38       1.117        8.07       10.16
77..........................................       48.83        7.30       18.33      113.07        4.03       18.18       1.122        8.14       10.27
78..........................................       49.15        7.30       18.48      120.40        4.35       19.40       1.127        8.23       10.34
79..........................................       49.66        7.50       18.64      129.38        4.61       20.67       1.132        8.30       10.50
80..........................................       50.03        7.70       18.88      137.50        5.04       22.01       1.137        8.42       10.63
81..........................................       50.39        7.80       19.10      147.51        5.62       23.26       1.143        8.48       10.75
82..........................................       50.88        7.90       19.29      157.66        5.99       24.48       1.146        8.56       10.94
83..........................................       51.22        8.00       19.53      168.72        6.64       25.73       1.153        8.69       11.11
84..........................................       51.70        8.10       19.79      184.45        7.43       26.83       1.160        8.81       11.28
85..........................................       52.21        8.20       20.09      198.45        8.05       28.24       1.167        8.93       11.53
86..........................................       52.63        8.40       20.31      215.14        8.88       30.57       1.173        9.04       11.76
87..........................................       53.05        8.60       20.62      236.02        9.74       31.78       1.181        9.16       11.98
88..........................................       53.51        8.80       20.89      264.75       10.66       33.74       1.190        9.24       12.25
89..........................................       54.01        9.00       21.25      291.58       12.34       35.30       1.200        9.36       12.50
90..........................................       54.75        9.30       21.54      321.29       13.82       37.43       1.210        9.58       12.81
91..........................................       55.46        9.50       21.92      357.86       15.88       39.16       1.218        9.77       13.15
92..........................................       56.23        9.80       22.33      413.68       17.90       41.17       1.230        9.92       13.58
93..........................................       57.26       10.10       22.67      488.71       21.81       43.77       1.238       10.17       13.87
94..........................................       58.23       10.50       23.16      595.16       25.73       46.18       1.252       10.35       14.21
95..........................................       59.13       10.80       23.53      755.53       28.66       48.01       1.268       10.55       14.79
96..........................................       61.07       11.50       24.53      995.22       34.72       52.62       1.289       10.87       15.63

[[Page 11244]]

 
97..........................................       62.59       12.20       25.06     1356.41       42.03       57.51       1.310       11.31       16.56
98..........................................       65.07       13.20       26.22     1759.93       48.46       62.78       1.341       11.72       17.54
99..........................................       68.05       15.20       27.75     3090.35       65.75       69.42       1.407       12.47      19.70
--------------------------------------------------------------------------------------------------------------------------------------------------------
Data Sources: Census Estimates from Claritas 1998; Bureau of Labor Statistics 1998, National Center for Health Statistics 1998.

    The resulting percentile rankings for each of the high need 
indicators in the area are then converted to a score, using a second 
table (see Table IV-7), which expresses the results of the regression 
analysis in terms of partial scores or weights for each indicator. 
Using Table IV-7 and using Wichita as an example, we see that a 
percentile ranking of 24 for unemployment translates into a score of 
32.21.

                                     Table IV-7.--Scores for High Need Indicators, Given Their National Percentiles
--------------------------------------------------------------------------------------------------------------------------------------------------------
                       Percentile                           Poverty      Unemp      Elderly     Density    Hispanic    Non white  Death rate    LBW/IMR
--------------------------------------------------------------------------------------------------------------------------------------------------------
0.......................................................        0.00        0.00        0.00      995.20        0.00        0.00        0.00        0.00
1.......................................................        3.01        1.18        0.54      831.13        0.81        0.00        0.82        0.72
2.......................................................        6.04        2.37        1.09      735.15        1.64        0.00        1.65        1.44
3.......................................................        9.11        3.58        1.65      667.05        2.47        0.00        2.49        2.17
4.......................................................       12.21        4.79        2.21      614.23        3.31        0.00        3.33        2.91
5.......................................................       15.34        6.02        2.77      571.07        4.15        0.00        4.19        3.65
6.......................................................       18.50        7.26        3.34      534.58        5.01        0.00        5.05        4.40
7.......................................................       21.70        8.52        3.92      502.98        5.88        0.00        5.93        5.17
8.......................................................       24.93        9.79        4.51      475.10        6.75        0.00        6.81        5.93
9.......................................................       28.20       11.07        5.10      450.16        7.64        0.00        7.70        6.71
10......................................................       31.50       12.37        5.69      427.59        8.53        0.00        8.60        7.50
11......................................................       34.84       13.68        6.30      407.00        9.44        0.00        9.52        8.29
12......................................................       38.22       15.00        6.91      388.05       10.35        0.00       10.44        9.10
13......................................................       41.64       16.35        7.53      370.51       11.28        0.00       11.37        9.91
14......................................................       45.10       17.70        8.15      354.18       12.21        0.00       12.32       10.73
15......................................................       48.59       19.08        8.78      338.90       13.16        0.00       13.27       11.57
16......................................................       52.13       20.46        9.42      324.55       14.12        0.00       14.24       12.41
17......................................................       55.71       21.87       10.07      311.02       15.09        0.00       15.22       13.26
18......................................................       59.34       23.29       10.72      298.22       16.07        0.00       16.21       14.12
19......................................................       63.00       24.73       11.39      286.08       17.07        0.00       17.21       15.00
20......................................................       66.72       26.19       12.06      274.53       18.07        0.00       18.22       15.88
21......................................................       70.48       27.67       12.74      263.52       19.09        0.00       19.25       16.78
22......................................................       74.29       29.16       13.43      253.00       20.12        0.00       20.29       17.68
23......................................................       78.15       30.68       14.12      242.92       21.17        0.00       21.34       18.60
24......................................................       82.06       32.21       14.83      233.26       22.23        0.00       22.41       19.53
25......................................................       86.02       33.77       15.55      223.98       23.30        0.00       23.49       20.48
26......................................................       90.03       35.34       16.27      215.04       24.39        0.00       24.59       21.43
27......................................................       94.10       36.94       17.01      206.43       25.49        0.00       25.70       22.40
28......................................................       98.22       38.56       17.75      198.13       26.61        0.00       26.83       23.38
29......................................................      102.40       40.20       18.51      190.10       27.74        0.00       27.97       24.38
30......................................................      106.64       41.86       19.28      182.34       28.89        0.00       29.13       25.39
31......................................................      110.95       43.55       20.05      174.83       30.05        0.00       30.30       26.41
32......................................................      115.31       45.27       20.84      167.54       31.23        0.00       31.49       27.45
33......................................................      119.74       47.01       21.64      160.47       32.43        0.00       32.70       28.50
34......................................................      124.24       48.77       22.45      153.61       33.65        0.00       33.93       29.57
35......................................................      128.80       50.56       23.28      146.94       34.89        0.00       35.18       30.66
36......................................................      133.44       52.38       24.12      140.46       36.14        0.00       36.45       31.76
37......................................................      138.15       54.23       24.97      134.15       37.42        0.00       37.73       32.88
38......................................................      142.93       56.11       25.83      128.00       38.72        0.00       39.04       34.02
39......................................................      147.79       58.02       26.71      122.00       40.03        0.00       40.37       35.18
40......................................................      152.74       59.96       27.61      116.16       41.37        0.00       41.72       36.36
41......................................................      157.76       61.93       28.51      110.46       42.73        1.39       43.09       37.55
42......................................................      162.87       63.94       29.44      104.89       44.12        2.81       44.48       38.77
43......................................................      168.07       65.98       30.38       99.44       45.53        4.25       45.90       40.01
44......................................................      173.36       68.06       31.33       94.12       46.96        5.71       47.35       41.27
45......................................................      178.75       70.17       32.31       88.92       48.42        7.20       48.82       42.55
46......................................................      184.24       72.33       33.30       83.83       49.90        8.72       50.32       43.86
47......................................................      189.83       74.52       34.31       78.85       51.42       10.27       51.85       45.19
48......................................................      195.52       76.75       35.34       73.97       52.96       11.85       53.40       46.54
49......................................................      201.33       79.03       36.39       69.18       54.53       13.46       54.99       47.92
50......................................................      207.25       81.36       37.46       64.50       56.14       15.10       56.60       49.33
51......................................................      213.29       83.73       38.55       59.90       57.77       16.77       58.25       50.77
52......................................................      219.45       86.15       39.66       55.39       59.44       18.48       59.94       52.24
53......................................................      225.75       88.62       40.80       50.97       61.15       20.22       61.66       53.74
54......................................................      232.18       91.15       41.96       46.62       62.89       22.00       63.41       55.27

[[Page 11245]]

 
55......................................................      238.75       93.73       43.15       42.36       64.67       23.82       65.21       56.83
56......................................................      245.47       96.36       44.37       38.17       66.49       25.68       67.04       58.43
57......................................................      252.34       99.06       45.61       34.05       68.35       27.58       68.92       60.07
58......................................................      259.38      101.82       46.88       30.01       70.26       29.53       70.84       61.74
59......................................................      266.59      104.65       48.18       26.03       72.21       31.53       72.81       63.46
60......................................................      273.97      107.55       49.52       22.11       74.21       33.57       74.83       65.21
61......................................................      281.54      110.52       50.89       18.27       76.26       35.67       76.89       67.02
62......................................................      289.30      113.57       52.29       14.48       78.36       37.82       79.02       68.87
63......................................................      297.28      116.70       53.73       10.75       80.52       40.03       81.19       70.76
64......................................................      305.47      119.92       55.21        7.08       82.74       42.30       83.43       72.71
65......................................................      313.89      123.22       56.73        3.47       85.02       44.63       85.73       74.72
66......................................................      322.56      126.63       58.30       -0.09       87.37       47.03       88.10       76.78
67......................................................      331.49      130.13       59.91       -3.60       89.79       49.50       90.54       78.91
68......................................................      340.69      133.74       61.58       -7.06       92.28       52.05       93.05       81.10
69......................................................      350.18      137.47       63.29      -10.46       94.85       54.68       95.64       83.36
70......................................................      359.98      141.32       65.06      -13.82       97.51       57.39       98.32       85.69
71......................................................      370.12      145.30       66.90      -17.13      100.25       60.20      101.09       88.10
72......................................................      380.61      149.41       68.79      -20.40      103.10       63.11      103.95       90.60
73......................................................      391.49      153.68       70.76      -23.62      106.04       66.12      106.92       93.19
74......................................................      402.77      158.11       72.80      -26.79      109.10       69.24      110.01       95.87
75......................................................      414.50      162.72       74.92      -29.93      112.27       72.49      113.21       98.67
76......................................................      426.70      167.51       77.12      -33.02      115.58       75.87      116.54      101.57
77......................................................      439.43      172.50       79.42      -36.08      119.03       79.39      120.02      104.60
78......................................................      452.72      177.72       81.83      -39.09      122.63       83.07      123.65      107.76
79......................................................      466.63      183.18       84.34      -42.07      126.39       86.93      127.45      111.08
80......................................................      481.22      188.91       86.98      -45.01      130.35       90.97      131.43      114.55
81......................................................      496.55      194.93       89.75      -47.92      134.50       95.21      135.62      118.20
82......................................................      512.72      201.28       92.67      -50.78      138.88       99.69      140.04      122.05
83......................................................      529.81      207.98       95.76      -53.62      143.51      104.42      144.70      126.11
84......................................................      547.94      215.10       99.03      -56.42      148.42      109.44      149.65      130.43
85......................................................      567.23      222.68      102.52      -59.19      153.65      114.79      154.92      135.02
86......................................................      587.86      230.77      106.25      -61.93      159.23      120.50      160.56      139.93
87......................................................      610.02      239.47      110.26      -64.63      165.23      126.64      166.61      145.21
88......................................................      633.95      248.87      114.58      -67.31      171.72      133.26      173.15      150.90
89......................................................      659.97      259.08      119.28      -69.95      178.76      140.47      180.25      157.10
90......................................................      688.47      270.27      124.43      -72.57      186.48      148.36      188.04      163.88
91......................................................      719.97      282.63      130.13      -75.15      195.02      157.08      196.64      171.38
92......................................................      755.19      296.46      136.49      -77.71      204.56      166.84      206.26      179.76
93......................................................      795.11      312.13      143.71      -80.24      215.37      177.89      217.16      189.27
94......................................................      841.20      330.23      152.04      -82.75      227.85      190.66      229.75      200.24
95......................................................      895.72      351.63      161.89      -85.23      242.62      205.75      244.64      213.21
96......................................................      962.43      377.82      173.95      -87.68      260.69      224.23      262.86      229.10
97......................................................     1048.45      411.58      189.50      -90.11      283.99      248.05      286.36      249.57
98......................................................     1169.68      459.18      211.41      -92.51      316.83      281.62      319.47      278.43
99......................................................     1376.93      540.53      248.87      -94.89      372.97      339.02      376.07      327.76
--------------------------------------------------------------------------------------------------------------------------------------------------------

    This same conversion of percentages to percentiles to scores is 
then done for each of the nine high need indicators. An example is 
included in Table IV-8 to illustrate this step, again using Wichita as 
an example.

                               Table IV-8
------------------------------------------------------------------------
                                                                Wichita
        High need indicators                                  County, KS
------------------------------------------------------------------------
% < 200% Poverty....................  ......................     49.8%
                                      Percentile............     79
                                      Score.................    467
Unemployment Rate...................  ......................      3.59%
                                      Percentile............     24
                                      Score.................     32
% 65+...............................  ......................     15.6%
                                      Percentile............     53
                                      Score.................     41
Population/Sq Mile..................  ......................      3.7%
                                      Percentile............      8
                                      Score.................    475
% Hispanic..........................  ......................     16.4%
                                      Percentile............     91
                                      Score.................    195
% Non-White.........................  ......................      1.2%
                                      Percentile............     22
                                      Score.................      0
Death Rate..........................  ......................       .67%
                                      Percentile............      0
                                      Score.................      0
LBW (Low Birth Weight)..............  ......................      7.78%
                                      Percentile............     71
                                      Score.................     88
IMR (Infant Mortality Rate).........  ......................  N/A *
                                      Percentile............
                                      Score.................
                                     -----------------------------------
Total Score To Be Added.............  ......................  1298
------------------------------------------------------------------------
* The infant mortality rate was not used for Wichita County since it was
  unstable (too few events-births and death in low population county).
  The alternative low birth weight rate was used.

    Because the same metric (i.e. population-to-provider ratio) was 
used to calculate both the effective barrier-free population and the 
scores, the scores can simply be added to the effective barrier-free 
population-to-

[[Page 11246]]

primary care provider ratio to derive the final adjusted population-to-
primary care provider ratio. This adjusted ratio reflects the 
combination of the ``effective barrier free population'' (age-adjusted) 
and the effect of community needs and use factors.
    These ratios can then be used to reflect the relative need of the 
areas, with the highest ratios indicating the areas of greatest need. 
An example is included in Table IV-9, again using Wichita as an example 
and Burlington, New Jersey for comparison. Column G reflects the new 
measure of underservice proposed in these rules and is intended to 
resemble the current MUA/P method in that it creates a score or index 
of underservice.

                                                                       Table IV-9
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                        Actual                                           Final adjusted
                                                       Effective       Total FTE     population to      Effective        Score from         effective
            County name             Total pop 1999   barrier-free    primary care    FTE ratio (A/  barrier-free pop/      weights      barrier-free pop/
                                                      population                          C)         FTE ratio (B/C)                     FTE ratio (E+F)
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                 A               B               C               D               E                 F                 G
--------------------------------------------------------------------------------------------------------------------------------------------------------
Wichita, KS.......................           2,436           2,959             2.5             974            1184              1298              2482
Burlington, NJ....................         416,853         482,594           411.2            1014            1173.6             251.6            1425.3
--------------------------------------------------------------------------------------------------------------------------------------------------------

    Even though there are far fewer people in Wichita than in 
Burlington and the actual population-to-provider ratios are roughly 
equivalent (column D), this methodology shows that the true need in 
Wichita (i.e., the level of care the Wichita population would demand if 
they did not have any barriers to care) is actually much greater than 
in Burlington (column G).
    Though this underlying methodology is conceptually and 
computationally complex, one advantage of this new method is that the 
actual calculations involved have been automated through the use of the 
conversion tables. The new method is, therefore, relatively simple to 
implement by State and local applicants. The system has also been 
developed in a way that allows an applicant to enter their area-
specific or population-specific data into an Internet-based query 
system and have their score returned in real time. This would allow 
applicants to compare their level of underservice with those of other 
designated and undesignated areas and populations in an accessible 
system. Moreover, the use of a tabular method for scoring allows for 
future changes in the scaling of the scores when there are changes in 
the distribution of values. It also allows HRSA to update these values 
without having to change the overall approach to developing scores.
    Step 5: Comparing the final adjusted effective barrier-free 
population-to-provider ratio against a threshold of underservice.
    The fifth step in this method involves comparing the final adjusted 
ratios for various areas against a threshold of underservice. A county 
or other RSA will be designated as undeserved if its final adjusted 
ratio equals or exceeds this threshold. The threshold level proposed is 
3,000 persons for every FTE primary care clinician. A population of 
3,000, distributed according to the national average age-sex 
distribution, is about twice the normal load for a busy primary care 
physician, which is approximately 1500:1. Accordingly, when the 
threshold level of 3000:1 is reached, an area is already one primary 
care clinician short for each primary care clinician it has. The impact 
analysis in Section VI below deals with the effect of this choice on 
the number and population of designated areas.
    While there is no one figure that is a universally accepted 
standard, the 3000:1 threshold is based on an adequacy ratio of 1500:1 
as noted above and is similar to the target ratio used in a number of 
organizations and identified in a variety of studies:
     A study of the Canadian system and its process for 
measuring medical underservice, for example, identified 1500:1 or 
greater as a level of underservice appropriate for a recruitment 
incentive program (Goldsmith 2000).
     A Veterans Administration study recommended a target for a 
primary care panel between 1,000-1,400 patients (Perlin and Miller, 
2003).
     According to the Bureau of Primary Health Care 
(unpublished data), Community Health Centers averaged 1,439 medical 
users per medical FTE in 1999, and this number is very consistent with 
the 1997 and 1998 figures. In addition, the NHSC reports an average of 
1,527 patients per provider.
     A George Washington University (GWU) report on Standards 
for Managed Care related to the Balanced Budget Act of 1997 found that 
State Medicaid programs most frequently required that Medicaid HMOs 
have a panel size of 1500:1
     An article published in the Journal of the American 
Medical Association suggested benchmark ratios to compare relative 
supply that were slightly above and below 1500:1 (Goodman et al, 1996).
     Using data from the National Ambulatory Medical Care 
Survey (NAMCS), which estimates visits per person per year to 
physicians, the national mean ratio of primary care physicians per 
population of 1498:1, very close to 1500:1.
    The 3000:1 threshold is a very conservative estimate of the level 
of need and identifies the worst quartile of the areas analyzed, which 
is a similar standard to that used when the original thresholds were 
set in the existing designation methods. Moreover, this threshold is 
consistent with the level used for HPSA designation of high-need areas 
and population groups in the past.
    Step 6: Determining tiers of shortage.
    An important issue in the preparation of these regulations is 
whether federally-sponsored primary care providers who are present in 
currently-designated areas should be included in computations when 
updating the designations. On the one hand, including these providers 
in the provider count could result in ``yo-yo'' effects, in which an 
area is designated as underserved; a CHC or NHSC intervention occurs as 
a result of the designation; those practitioners are then counted, 
resulting in a loss of the designation; the intervention is removed; 
the area again becomes eligible for designation; and the cycle repeats 
itself. On the other hand, there are concerns about areas remaining on 
the list of designations whose needs have already been met through a 
federally supported program or provider. This has led to situations in 
which additional resources are allocated to an area where providers or 
clinics have previously been placed to help meet the needs of the area.

[[Page 11247]]

    To deal with both sides of this issue, we propose to publish a two-
tiered list of designations. Each designated area or population group 
will be identified as having either a first or second tier of shortage. 
Tier 1 designations will be those areas which continue to exceed the 
threshold even when all federal resources placed in the area are 
counted. Tier 2 designations will be those areas exceed the threshold 
only when certain federal resources placed in those areas are excluded.
    Thus, one final set of calculations is undertaken to identify those 
``Tier 2'' areas which fall below the threshold when certain federally-
sponsored clinicians are counted but would exceed the threshold if they 
were withdrawn. The federally-sponsored clinicians considered here are 
NHSC affiliated clinicians, clinicians obligated under the State Loan 
Repayment Program (SLRP) (a loan repayment program involving joint 
Federal and State funding), physicians with J-1 visa return-home 
waivers, and other clinicians providing services at health centers 
funded under Section 330.
    When determining Tier 2 designations, these federally-sponsored 
clinicians are not counted in the denominator of the area's ratio. 
Finally, steps 3 and 4 are repeated to recalculate the final adjusted 
ratio using this lower clinician count and to compare it with the 
designation threshold. The areas exceeding the threshold when this 
procedure is followed are identified as ``Tier 2'' designations.
    Both types of designations would be eligible for federal programs 
authorized to place resources in MUPs or HPSAs. However, Tier 2 areas 
would typically be eligible only to maintain the approximate levels of 
federal resources already deployed, while Tier 1 areas could apply for 
additional resources.

C. Example Calculations

    Table IV-10 shows calculations for actual population-to-provider 
ratios, the effective barrier-free population-to-provider ratios, the 
scores based on high need indicator percentiles for the area, and the 
resulting population to primary care clinician ratios.

                               Table IV-10.--Example of calculation of Adjusted Population-to-Primary Care Clinician Ratio
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                                                 ``Tier 1''                  ``Tier 2''
                                                                                                                    Final       Ratio w/o       Final
                                                          Effective                   Effective                   adjusted     fed FTE (C-    adjusted
                County name                  Total pop     barrier-     Total FTE   barrier-free   Score from     effective     Federally     effective
                                                1999         free     primary care     pop/FTE       weights    barrier-free    sponsored   barrier-free
                                                          population                 ratio (B/C)                   pop/FTE     clinicians)     pop/FTE
                                                                                                                 ratio (D+E)                 ratio (G+E)
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                      A            B           C             D             E             F             G             H
--------------------------------------------------------------------------------------------------------------------------------------------------------
Wichita, KS...............................        2,436        2,959           2.5        1184          1298          2482        * 5918          7216
Burlington, NJ............................      416,853      482,594         411.2        1173.6         251.6        1425.3        1179.4        1431.0
Coconino AZ...............................      116,977      127,492          91.7        1389.6        1161.4        2551          1444.7        2606.1
St. Lucie, FL.............................      180,937      222,417         105.1        2116.5         918.3        3034.8        2314.7        3233.0
E. Baton Rouge, LA........................      395,635      447,680         379.5        1179.7         640.2        1819.8        1185.9        1826.1
Dunklin, MO...............................       33,006       40,146          22.8        1764.6        1469.4        3234.1        1764.6        3234.1
Bronx, NY.................................    1,185,970    1,366,382        1210.6        1128.7        1665.3        2793.9        1199.6        2864.8
Guernsey, OH..............................       40,854       48,273          20.2        2389.8         751.7        3141.5        2389.8        3141.5
Rusk, WI..................................       15,449       18,501          10.8        1713.0        1070.5        2783.6        8043.7        9114.2 
--------------------------------------------------------------------------------------------------------------------------------------------------------
* Non-federally sponsored FTE = 0.5; 2959/0.5 = 5917/1.

    According to these calculations, Wichita would not qualify for 
designation as a Tier 1 underserved area. However, Wichita would 
qualify for designation as a Tier 2 underserved area when federally 
sponsored FTEs are deleted and high need weights are added.

D. Alternative Approaches Considered

    A variety of other alternative measures and options were considered 
during the development of the method. The research team at the 
University of North Carolina conducted a comprehensive review of 
current and alternative measures of underservice, as noted in a 1995 
report (Ricketts et al., 1995. As part of this effort, two workshops 
were convened in 1999 and 2000 on modeling health professions supply 
and healthcare needs and on measurement of underservice. Several of the 
options considered and the reasons for not pursuing them are described 
below:

--There was consideration of using the simple population to provider 
ratio as the index, but there was no consensus on the ``right'' ratio, 
and there was strong interest in a more multi-factorial approach to 
take other high need factors into account. The PCO Work Group's initial 
recommendations were based primarily on the ratio, with adjustments to 
the ratio for high needs, similar to the current process for HPSAs. 
After continued discussion with HRSA staff and the contractors, the 
Work Group acknowledged that the proposed methodology accomplished much 
the same by incorporating the need variables into the analysis rather 
than adjusting the target ratio, although final agreement was held 
pending review of the impact data. The approach used in the 1998 
proposal, which was an Index of Primary Care Services from 1-100 based 
on a variety of ``need'' factors, was not chosen partly due to the 
history and partly due to the fact that such a scale had no intrinsic 
meaning as a measure of access, while a score related to a ratio of 
population to the providers is more easily understood across the board.
--We considered using hospitalization rates for Ambulatory Care 
Sensitive Conditions (ACSC) as proxies for underservice as they could 
reflect failures in the primary care system to meet the needs of the 
population. However, comprehensive data are not universally available, 
particularly at the sub-county level, where primary care analysis is 
based. In addition, the analysis indicates that these rates are more 
indicative of problems with access to care related to income, 
employment, and race, rather than to lack of providers or services.
--Alternative methodologies used in Canada and the United Kingdom (UK) 
were reviewed for possible use. In Canada, however, each province had a 
different methodology, which did not meet the comprehensive national

[[Page 11248]]

approach. In the UK, the focus was specifically on the location of 
General Practitioners (GPs), whose practice locations are partially 
controlled by the government. In addition, they were partially based on 
interviews with GPs to identify areas of underservice, which is not an 
approach that can be replicated on a national scale and has no 
scientific basis. Both countries did, however, have models that 
incorporated many of the same concepts used in this proposal, including 
distance to care (which has a functional similarity to population 
density in our model), census variables such as ``class,'' 
unemployment, age, and the availability of providers. This reinforces 
the validity of taking into account such variables when measuring 
access to care and underservice.

--Extensive research on the state of the art in health care access led 
to a paper by Dr. Donald Taylor (Taylor et al., 2000) which examined 
the relationship between theoretical need for care and resources to 
provide the care. His conclusion was that there is no one simple 
construct of underservice and no unitary measure, but that there are 
several interlocking components that need to be considered. These 
conceptual components were not actually alternative measures of 
underservice but five components of a comprehensive model. His 
hypothetical model, at the county level, included the following 
components:
    [cir] Momentum: the economic and population dynamics of an area and 
changes over time
    [cir] Demand: based on the age and gender of the population
    [cir] Infrastructure: presence of hospitals and other providers, 
insurance coverage, etc.
    [cir] Need: based on proxies for health status
    [cir] FIT: describes the degree of ``fit'' of the various factors, 
which represents the level of service or underservice
    The conceptual model, the Taylor Indices of Underservice, was 
tested using simultaneous multiple correlations and was found to be 
robust for the prediction of demand, infrastructure and needs but not 
for FIT and momentum. A latent variables testing method was applied and 
the concept of FIT was supported via this analysis. A second order 
confirmatory analysis (CFA) supported this result, which suggested that 
a combination of variables that reflect demand and infrastructure with 
appropriate proxies for need--especially the age structure of the 
community--could generate a useful index, FIT, that summarized 
community underservice. The current proposal builds on this notion of 
FIT as a latent indicator of overall need, as reflected in the score 
that is calculated in the process.
    For several reasons, Dr. Taylor's approach could not have been used 
without modification for purposes of this rulemaking. For example, this 
approach did not appear to correlate well with indicators of 
utilization, which is considered a reliable indicator of access. 
Moreover, counties are not considered an appropriate level of analysis 
in many areas served by HRSA's programs.
    However, the principles and detailed analytical methods used in Dr. 
Taylor's model were incorporated to a large extent in the current 
proposed methodology, which includes age/gender utilization projections 
for expressed need or demand, need (as captured by socio-demographic 
and health status indicators), and infrastructure (as reflected in 
unemployment, poverty, and availability of providers).

--Years of Potential Life Lost (YPLL) was also considered as a 
potential measure. However, similar to the ACSC analysis, there was a 
much stronger correlation between socio-economic factors (race, 
education, etc.) than with the presence or absence of primary care 
providers and services.

V. Description of the Proposed Regulations

A. Procedures (Subpart A)

    The proposed approach to processing MUA, MUP and HPSA designation 
requests, set forth in Subpart A below, is an adaptation of the HPSA 
designation procedures currently in effect, as codified at 42 CFR Part 
5. The previous procedures have been modified to include the particular 
comment and consultation requirements of the MUP legislation, but 
otherwise closely follow the present HPSA designation procedures, 
including those specifically required by statute.
    As before, the proposed procedures involve an interactive process 
between the Secretary, the States, and individual applicants [see Sec.  
5.3(a)-(h)]. Any individual, community group, State or other agency may 
apply for designation of a geographic area or population group MUP and/
or HPSA, or for a facility HPSA; the Secretary may also propose such 
designations. Such requests are reviewed both at State and federal 
levels, including a 30-day comment period for Governors, State health 
agency contacts, State Offices of Rural Health, county or city health 
officials, State primary care associations (non-profit membership 
organizations representing federally qualified health centers and other 
community-based providers of primary care), appropriate medical, dental 
or other health professional societies, and heads of any facilities 
proposed for HPSA designation. Efforts are made to complete action on 
new designation requests within 60 days of receipt.
    Annually, the Secretary will review all designations utilizing the 
proposed methodology, with emphasis on those for which updated data 
have not been submitted during the previous three years; this extends 
to MUA/Ps the review process previously used for HPSAs [see Sec.  
5.3(d)]. As part of such reviews, the latest relevant data from 
national sources described earlier (for those previously-designated 
areas which the Secretary requires be updated) will be made available 
by the Secretary to the appropriate State entities and others for 
review and comment. If no corrections are provided, the national data 
will be used as the Secretary's basis for decisions. (The national data 
for census-collected variables are not typically corrected during the 
designation process with data from State and local sources. On the 
other hand, State and local data regarding provider locations and FTEs 
are often more up-to-date and accurate; use of such data in designation 
will continue to be encouraged where readily available.)
    An expedited review process is also proposed for urgent cases [see 
Sec.  5.3(i)], allowing designations to be obtained within 30 days of 
the date of request when a practitioner dies, retires, or leaves an 
area, thereby causing a sudden and dramatic increase in the area's 
population-to-clinician ratio. The number of requests that will be 
processed per year on this expedited basis is limited.
    Results of designation reviews will be provided in writing or 
electronically to applicants, State partners, and other interested 
parties [see Sec.  5.4]. No less than annually, complete lists of 
designated HPSAs/MUPs will be published by notice in the Federal 
Register that an updated list will be posted on the HRSA Web site; more 
frequent updates will be posted online continuously, reflecting 
designation decisions as they occur. Two tiers will be identified in 
published or posted listings of designated shortage areas. As discussed 
previously, the first tier will include only those areas that meet the

[[Page 11249]]

designation criteria when all relevant (i.e., active primary care) 
clinicians in the area are counted, while the second tier will include 
those additional areas that meet the criteria when certain Federally-
sponsored clinicians are subtracted.
    The regulation also includes a section [Sec.  5.5] describing 
procedures for the transition from the current designation system to 
the new system. These include a process for resolution of any 
overlapping boundaries that may exist between currently-designated 
primary care HPSAs and currently-designated MUA/Ps at the time the new 
regulations go into effect. The new criteria for designation of MUA/Ps 
and/or primary care HPSAs will be phased in over a period of three 
years from the date of publication of the final rule in the Federal 
Register, with State input on the review schedule but with the oldest 
MUA/P and primary care HPSA designations being reviewed first. This 
will relieve States, communities and others from having to provide 
updated data on all designations that are more than three years old 
during the first year the new regulations go into effect.
    In addition, the regulation includes a section [Sec.  5.6] 
describing how the ``automatic designation'' provisions of the Health 
Care Safety Net Amendments of 2002, as amended by Public Law 108-163, 
will be implemented. Briefly, all FQHC and RHC delivery sites that are 
automatically designated will be listed separately as ``automatic'' 
HPSAs until the area or population group they serve or the facility 
achieves designation under the proposed criteria or until 6 years from 
the date of their automatic designation, whichever comes first. Any 
FQHC or RHC sites still being carried on the list of ``automatically'' 
designated sites six years from their date of automatic designation 
will then be required to demonstrate that they meet the criteria in 
order to remain on the list, through the review process outlined in 
section Sec.  5.6.

B. General Criteria for Designation of Geographic Areas as MUAs/Primary 
Care HPSAs

    The criteria and methodology for designation of geographic areas as 
MUAs and primary care HPSAs are set out in Subpart B (Sec.  5.102). In 
brief, areas to be designated must first be RSAs for the delivery of 
primary care services. As described earlier, an adjusted population-to-
primary care clinician ratio is then computed for each such area, by 
combining the area's ``effective barrier-free'' population (based on 
age and gender utilization patterns) to its supply of primary care 
clinicians, with adjustments for access barriers through additive 
scores for a defined group of demographic, economic, and health status 
variables. When this adjusted ratio exceeds the designation threshold 
of 3000:1, the area is eligible for designation. Under certain limited 
conditions, resources in contiguous areas must also be taken into 
consideration.

C. Rational Service Areas

    The proposed rules would continue to require that each area 
proposed for geographic designation be a rational area for the delivery 
of primary care services. A general (or default) definition of the term 
``rational service area'' is included [see Sec.  5.103], in terms of 
geographic size and cohesiveness, which relates its size to the 
accessibility of primary medical services in the area within 30 minutes 
travel time, and its cohesiveness to topography, demographic 
distinctness from contiguous communities, and/or established market 
patterns. Contiguous RSAs would normally be defined so as to have a 
separation of at least 30 minutes travel time from the population 
center(s) of one RSA to the population center(s) of each contiguous 
RSA, with exceptions for RSAs within high-density portions of 
metropolitan areas that demonstrate cohesiveness in other ways.
    RSAs may be defined in terms of U.S. Census Bureau geographic 
units, including counties, census tracts, census divisions, and Zip 
Code Tabulation Areas (ZCTAs), as long as data can be obtained at that 
level. However, States are allowed the flexibility to define their RSAs 
in terms of travel time parameters between 20 and 40 minutes, where the 
final RSA approach to be used is approved by the Secretary.
    States are encouraged to develop a State-wide system that 
subdivides the territory of the State into RSAs, either incrementally 
or all at once, using the general RSA criteria specified in the 
proposed rule or State-specific criteria developed through the 
partnership process just mentioned. Where a State has developed such a 
statewide system of areas, the designation status of a particular RSA 
will be determined through application of the proposed geographic HPSA/
MUA criteria to current data for the RSA, without regard to contiguous 
area resources. Elsewhere, the contiguous area considerations set forth 
in proposed Sec.  5.105 are to be used.
    The proposal allows for State and local input, but is expected to 
greatly reduce the level of effort required at the local and State 
level. At present, no designation takes place without a specific 
request being submitted with the required information, including the 
defined service area, the data on population, physicians, and other 
appropriate information. Upon publication of a final regulation, HRSA 
will first score all existing MUAs and HPSAs using the national 
databases. Areas that qualify using those calculations will be 
designated as underserved with no need for input from the State or 
local level. The submission of additional information will only be 
required for those areas that do not qualify based on national data.
    HRSA expects that a significant number of areas will qualify based 
on national data alone. For example, there were 877 whole county and 
803 geographic service area HPSAs as of March 31, 2007. If the majority 
of these areas meet the criteria using the national calculations, 55 
percent of the current designations (excluding the facility 
designations) would require no action on behalf of the State or local 
agency. In addition, many areas could be qualified with the submission 
of revised data on providers alone, which is a much simpler approach 
than currently required.
    Areas where special population groups would need to be defined 
would continue to require State or local involvement, though we 
anticipate the number of these would decrease as a result of the 
inclusion of some of the need factors directly in the formula itself.

D. Applying the Designation Methodology

    As mentioned above in section IV.B, the proposed rules provide that 
the Secretary of HHS will determine an adjusted effective barrier-free 
population-to-primary care clinician ratio for each RSA considered for 
a primary care underservice designation. The specific methodology for 
this calculation is set forth in proposed Sec.  5.104. Tables IV-1 and 
IV-6 will be updated periodically by notice in the Federal Register 
that updated data will be posted on the HRSA web site as the national 
utilization data and national distributions of the variables used in 
the method change. (Updating these tables will not require proposed 
rulemaking, since the regulations themselves will not be changed.) The 
timeframe for updates will be determined by the availability of updated 
data for the nine high need indicators. Table IV-7, which appears in 
the regulation itself as Appendix A to Part 5, may also be recalibrated 
periodically, but not necessarily on the same timetable, since

[[Page 11250]]

revising it requires repeating the regression analysis.

E. Data Definitions

    The proposed rules identify the data elements needed to determine 
the effective barrier free population, the high need indicator score, 
the final adjusted population-to-primary care clinician ratio, and the 
manner of calculation of these variables. See proposed Sec.  5.104(a) 
to 5.104(c).

F. Population and Clinician Counts

    Although the clinician count requirements are similar to those for 
physicians in the current Part 5, some important changes have been 
made. Foreign (International) medical graduates who are not citizens or 
permanent residents, but entered the U.S. on J-1 visas and have had 
their return-home requirements waived in return for obligated service, 
and/or are here on H visas, are to be counted in ``first tier'' 
designation calculations unless they have restricted licenses; they are 
to be excluded from ``second tier'' designation counts.
    Similarly, clinicians providing medical services for the NHSC, as 
SLRP obligors, or at health facilities funded under section 330 of the 
Act are counted for the first tier and excluded from the second tier. 
It should be noted that, although the proposed rules would allow NHSC 
and section 330 health center practitioners to be excluded from the 
practitioner count for second tier designations, the numbers of these 
practitioners already allocated or funded are included by the 
Department in making decisions as to how to allocate additional NHSC 
and health center grant resources.
    Also, the current HPSA provision allowing the discounting of 
physicians with restricted practices on a case-by-case basis is 
proposed to be eliminated because our experience has been that this 
provision is neither useful nor practical.

G. Non-Physician Primary Care Clinicians

    The significant expansion over the past decade in the numbers of 
NPs, PAs, and CNMs practicing in primary care settings has made their 
inclusion in counts of primary care clinicians essential to the 
validity of any revised designation process, particularly in those 
States and areas where they practice, in effect, as independent 
providers of care and particularly given their role in the RHC program. 
However, there has been controversy as to whether available data permit 
them to be counted accurately and how they should be weighted relative 
to primary care physicians.
    There are several related issues involved. First, significant 
differences exist among the States as to the scope of practice allowed 
for these clinicians, including the extent to which they are allowed to 
work independently, and what medical tasks they are legally allowed to 
perform. Second, the national databases currently available for them 
have some limitations, particularly where practice addresses are 
concerned. While some States have accurate data on the number, location 
and practice characteristics of these clinicians, others do not. 
Finally, for those States in which non-physician clinicians can legally 
provide many of the same services as primary care physicians, exactly 
how they complement physicians and, therefore, how they should be 
weighted relative to physicians has not been well-defined.
    This proposed rule includes these non-physician clinicians by 
requiring that all of them be counted with a weight of 0.5 relative to 
primary care physicians, unless the applicant opts for weighting based 
on the scope of practice in the State involved. (See State option for 
weighting described below.) Please note that the 0.5 relative weighting 
is proposed here only for purposes of estimating primary care clinician 
counts for shortage area designation purposes; it should not be 
construed as representing the relative cost or value of these 
providers' services compared to physician services.
    For non-physician clinicians, there has been a long-standing 
acceptance of counting them as less that a full FTE, for a variety of 
reasons. In the Bureau of Primary Health Care, and its predecessors, 
which oversees the FQHC Program, productivity standards and 
calculations have used the .5 FTE figure. In part, this is a way to 
encourage these programs to hire non-physician providers in areas where 
recruitment is difficult but there may be some resistance otherwise to 
having a mixed practice model. Its use is also consistent with 
productivity standards currently used by CMS for RHCs and FQHCs, which 
are 2100 visits per year for NPs and PAs as compared with 4200 visits 
per year for physicians.
    While there is no absolute standard for estimating the FTE 
contribution of a non-physician provider, there are also a number of 
studies in the literature that support an estimate of 0.5:
     An Integrated Requirements Model (Sekscenski et al., 1999) 
in 1999 used a 0.5 FTE calculation.
     An article in Health Affairs in 1997 (Hart et al., 1997) 
of staffing ratios indicated patient volume levels for NPs from 875-
1,000 per NP.
    Given the lack of data regarding the impact of adding these 
providers to the designation process and the continued need to 
encourage the use of the range of providers who can help meet the needs 
of the underserved, we believe the 0.5 FTE approach is a reasonable 
choice for the proposed method.
    Data on NPs, PAs and CNMs are available from national sources (``A 
Comparison of Changes in the Professional Practice of Nurse 
Practitioners, Physician Assistants, and Certified Nurse Midwives: 1992 
and 2000'' The Center for Health Workforce Studies at the University of 
Albany, available online at http://bhpr.hrsa.gov/healthworkforce/reports/scope/scope1-2.htm.) These data will be made available for use 
as a first approximation, but States will be encouraged to provide more 
accurate State data, where available.
    Some have suggested that different equivalencies be used in 
different States, depending on the degree of independence allowed by 
the different State laws. This option is offered in the proposed rule. 
At the applicant's option, a maximum weighting factor of 0.8 can be 
used together with a State scope of practice factor between 0.5 and 
1.0, using tables from ``Scope of Practice of PAs, NPs, and CNMs in the 
Fifty States,'' (Wing et al., 2003). This document is available at 
http://bhpr.hrsa.gov/healthworkforce/reports/scope/scope1-2.htm
    Those Federally-sponsored NPs, PAs, and CNMs in the NHSC, SLRP, or 
at health facilities funded under Section 330 would be counted for Tier 
1 designations but excluded for Tier 2 designations, just as done for 
physicians.

H. Contiguous Area Considerations

    The previous HPSA criteria required that, when considering any area 
for designation, resources located in all contiguous areas must be 
shown to be excessively distant, overutilized, or otherwise 
inaccessible to the population of the area requested for designation. 
The approach proposed herein would eliminate this requirement wherever 
a set of RSAs has been developed, requiring consideration of contiguous 
area resources only in States where a system of RSAs does not exist, or 
in those portions of a State where RSAs have not yet been defined. See 
Sec.  5.105.

I. Population Group Designations

    The inclusion in the proposed methodology of a number of variables 
representing the access barriers and/or

[[Page 11251]]

negative health status experienced by certain at-risk populations is 
likely to decrease the need for specific population group designations, 
which tend to be more difficult procedurally for both applicants and 
reviewers. However, the proposed rules continue to provide for certain 
types of population group designations within geographic areas which, 
taken as a whole, do not meet the criteria for designation. (See 
Subpart C.) These generally build on the criteria for designating 
geographic areas, with several key differences. First, the proposed 
rules recognize two specific additional types of areas as rational 
areas for the delivery of primary care services for specific population 
groups (i.e. agricultural areas for migratory and seasonal agricultural 
workers; reservations for Native American population groups). Second, 
each variable is to be calculated based on data for the population 
group for which designation is sought, as nearly as possible, rather 
than on the population of the area as a whole.
    The eligible population groups specifically identified for 
designation are: Low income populations (defined to include all those 
with incomes below 200% of the poverty level); Medicaid-eligible 
populations; linguistically isolated populations; migrant and seasonal 
farmworkers and their families; homeless populations; residents of 
public housing; and Native Americans. A new category of MUP is 
recognized, consisting of those uninsured and Medicaid-eligible 
patients who are served by safety net facilities designated as primary 
care HPSAs under Subpart D. Finally, the category ``other population 
groups recommended by state and local officials'' is retained, 
consistent with the MUP statutory authority.
    The proposed provisions also allow for HPSA designation of the 
``special medically underserved'' populations as defined by section 330 
of the PHS Act (as amended by Pub. L. 104-299), which are considered 
already designated as MUPs. These provisions include a ``simplified'' 
designation procedure for migrant, homeless and Native American 
population groups, for use in cases where the area in which the 
requested population group is located has been defined, data on the 
number of individuals in the population group is provided and the total 
is found to exceed 1000, but specific information on the number of FTE 
clinicians accessible to the population group is not available. In 
these cases, a population-to-clinician ratio of 3000:1 may be assumed. 
Requirements for the statutory ``permissible'' designation of ``other 
population groups recommended by state and local officials'' are 
included. ``Local officials'' for this purpose are defined. Such 
requests must document the ``unusual local conditions'' which are the 
basis for the request; these must involve factors not already 
considered by the general criteria for designation of areas and 
population groups as set forth in Subparts A and B.

J. ``Facility Designation Method'': Designation of Facility Primary 
Care HPSAs

    The criteria and procedures for designating facility primary care 
HPSAs are set out in proposed Subpart D. The current criteria for 
designation of ``public or non-profit private medical facilities'' as 
HPSAs are eliminated and replaced by new criteria for the designation 
of ``safety-net facility'' primary care HPSAs (see proposed Sec.  
5.301). These criteria would allow for HPSA designation of facilities 
not in geographic HPSAs designated under Subpart B, if and when these 
facilities qualify as ``safety-net facilities'' by virtue of their 
service to specified minimum percentages of patients that are Medicaid-
eligible and/or low income uninsured, as measured by the number of 
patients treated under a sliding fee scale. Eligibility for this type 
of designation is limited to FQHCs, RHCs, or other public or non-profit 
private clinical sites providing primary medical care services on an 
ambulatory or outpatient basis. The minimum levels of service to 
indigent uninsured and/or Medicaid-eligibles are described in proposed 
Sec.  5.301(b) and shown in Table V-1 below.

   Table V-1.--Minimum Levels of Service to Indigent Uninsured and/or
                           Medicaid-Eligibles
------------------------------------------------------------------------
                                Non-Metropolitan
     Metropolitan areas           areas (except        Frontier areas
                                 frontier areas)
------------------------------------------------------------------------
At least 10% of all patients  At least 10% of all   At least 10% of all
 are served under a posted,    patients are served   patients are served
 sliding fee schedule, or      under a posted,       under a posted,
 for no charge.                sliding fee           sliding fee
                               schedule, or for no   schedule, or for no
                               charge.               charge.
At least 40% of all patients  At least 30% of all   At least 20% of all
 are served either under       patients are served   patients are served
 Medicaid, under a posted      either under          either under
 sliding fee schedule, or      Medicaid, under a     Medicaid, under a
 for no charge.                posted, sliding fee   posted sliding fee
                               schedule, or for no   schedule, or for no
                               charge.               charge.
------------------------------------------------------------------------

    Payment source documentation to establish initial and ongoing 
designation as a facility primary care HPSA will be as required by the 
Secretary. This Safety Net Facility designation would not be recognized 
by CMS for RHC certification.
    The criteria and methodology for designating federal and state 
correctional institutions and youth detention facilities as primary 
care HPSAs in Sec.  5.302 are essentially unchanged from those in the 
current Part 5.

K. Dental and Mental Health HPSAs

    The proposed procedures in Subpart A would apply to the designation 
of dental and mental health HPSAs as well. The criteria currently in 
use for these types of HPSA designations are contained in Appendices B 
and C of the current part 5. No changes to these appendices are 
proposed at this time, but efforts are under way to revise the criteria 
for dental shortage areas (pursuant to Section 302(d)(1) of the Health 
Care Safety Net Amendments of 2002) and those for mental health 
professional shortage areas. When these efforts are complete, 
Appendices B and C will be revised.

L. Podiatry, Vision Care, Pharmacy And Veterinary Care HPSAs

    The existing HPSA regulations at part 5 also contain, in appendices 
D, E, F, and G, criteria for the designation of vision care, podiatric, 
pharmacy, and veterinary care HPSAs. These criteria were originally 
developed for use in connection with student loan repayment programs 
for individuals in those health professions; however, these programs 
are no longer authorized or funded. Consequently, the proposed rule 
would abolish these types of designation by revoking these appendices.

[[Page 11252]]

M. Technical and Conforming Amendments

    Minor technical and conforming amendments to the CHC regulations at 
42 CFR Part 51c are proposed. These amendments refer to Part 5 for 
definition of designated medically underserved populations, and for 
factors to be considered in assessing the needs of populations to be 
served by grantee projects. In addition, they amend the definitions 
section of the CHC regulations to include a definition of ``special 
medically underserved populations,'' which refers to language in the 
statute as amended by Public Law 104-299. This definition states that 
such populations are not required to be designated pursuant to part 5; 
this is consistent with their treatment under prior legislation. 
Finally, the amendments add a provision explicitly stating that a 
grantee which was serving a designated MUA/P at the beginning of a 
project period will be assumed to be serving an MUP for the duration of 
the project period, even if that particular designation is withdrawn 
during the project period.

VI. Impact Analysis

    The agency has conducted an extensive analysis of the national 
impact of the proposed new designation methodology on the designation 
status of whole counties, previously-defined part-county geographic 
HPSAs and MUAs, and low-income population groups, as well as its impact 
on grant-funded CHCs, NHSC sites, and CMS-certified RHCs. This national 
analysis was conducted under a HRSA cooperative agreement with UNC's 
Cecil G. Sheps Center for Health Services Research, using data from 
national sources for all variables. In order to validate this national 
analysis, impact analyses using State data sources were performed by 
Regional Health Workforce Centers and/or PCOs in four states.
    In the actual designation review process, evaluation of areas' 
potential designation status based on application of the criteria to 
national data would represent only the first step in an exchange with 
State and local partners. However, we believe that the aggregate 
results of this impact analysis (in terms of total numbers of areas 
designated or de-designated nationally) represent a reasonable 
approximation to the likely results of the real designation process. 
(If anything, these impact estimates may err on the side of overstating 
negative impacts, since local data in support of designation are more 
likely to be received from areas which the national data would tend to 
de-designate than from areas which they would newly designate or 
continue in designation.)
    The impact is shown below in a series of tables describing 
different types of impact, each of which enables comparison of several 
different scenarios. In general, the first column of each table shows 
baseline numbers corresponding to actual HPSA and MUA designations on 
September 30, 1999; the second column shows the revised numbers that 
would result if these designations were updated by applying the 
criteria now in force to the national database used in this analysis; 
the third column shows the revised numbers that would result if the 
methods proposed in the 1998 NPRM (``NPRM1'') were applied; the fourth 
column shows the results of applying the criteria proposed herein 
(``NPRM2'' criteria) to geographic areas only; the fifth column shows 
the estimated results of applying NPRM2 low-income population group 
criteria to areas not meeting the geographic criteria; and the final 
column shows the estimated combined results of applying the ``NPRM2'' 
criteria first to geographic areas and then to low-income population 
groups in areas not meeting the geographic criteria.
    The first three rows of Tables VI:1-9 provides the breakout of the 
various types of HPSA and/or MUA/P designations, whole county 
geographic, partial county geographic, and low income populations. This 
breakout allows an analysis of the impact of the new method on the 
different types of designations if desired. Row 4 then is total of 
these three rows and includes the aggregate numbers that were used in 
the impact analysis. Row 5 calculates the percentage of the original 
HPSAs/MUA-Ps that was designated under the various methodologies using 
updated data. For example, in Table VI:1, 949 of the original 2282 
HPSAs tested would still be designated using the current method and 
updated data, which is a retention rate of 41.6% (Column 3/Column 2). 
Row 6 is the number of new designations that resulted from the various 
designation methodologies, i.e. areas that had not previously been 
designated that would become designated. Row 7 is the total of Rows 5 
and 6, capturing the total number of areas, old and new, that would be 
designated under the various options. Row 8 calculates the percentage 
of designated areas as a percentage of the original baseline number, in 
order to measure the impact of the various methods in terms of degree 
of change in the number of areas that would be designated. For example, 
under the updated current method with new data, 1055 areas would be 
designated, which is 46.2% of the baseline number of 2282 (Column 3/
Column 2). The same general process is followed for each of the columns 
in the Tables VI:1-V:7. Table VI:8 and VI:9 follow the same process for 
the combined HPSA/MUA-P designations to assess the impact of 
metropolitan/non-metropolitan/frontier areas and populations, with the 
percentages and the actual numbers now in the same row rather than 
separate rows. For example, in Table VI:8, 49% of the total 
designations were retained using the updated current method; Row 2, 
Column 2 divided by Row 2 Column 1 (2188/4447).

A. Impact on Number of HPSA Designations

    As column 1 of table VI-1 shows, in the baseline year of 1999 there 
were 832 whole counties, 858 part-county geographic areas, and 592 low-
income population groups designated as HPSAs in the United States, for 
a total of 2282 designations.
    Since approximately one quarter of the HPSAs are updated each year, 
the 2282 designations considered valid in 1999 represent the results of 
case-by-case review of requests received over the 1996-99 period from 
State and local sources, and were based on a combination of national, 
State and local data as of 1998 or earlier. Column 2 shows the impact 
of simultaneously updating all these designations using the current 
HPSA criteria applied to the Impact Test Data Base assembled by HRSA 
and the UNC Sheps Center. [This data base included 1998 data for 
population, income and other census variables (using Claritas 
intercensus estimates); 1998 national primary care clinician data; and 
county-level vital statistics data for the five-year period 1994-98.] 
The results indicate that only 949 or 42% of the 2282 baseline areas 
would retain their designations if updated under the current criteria. 
However, 106 additional counties would be newly designated, so that the 
new total number of HPSAs would be 46% of the original total.
    Column 3 of Table VI-1 shows the impact of applying the HPSA 
criteria proposed in ``NPRM1'', as published in 1998, to the 2282 
baseline areas, using the same Impact Test Data Base of 1998 national 
data. The results indicate that only 652 or 29% of the baseline areas 
would retain their HPSA designation; 71 counties would be added, for a 
new total of 723 HPSAs, 32% of the baseline total. It is therefore 
quite understandable that the public comments received on NPRM1 
expressed concern about potential loss

[[Page 11253]]

of many HPSA designations. At the same time, it is useful to realize 
(from comparing column 3 with column 2) that 80% of the HPSA 
designations that would be lost if the NPRM1 criteria were adopted 
would also be lost by simply simultaneously updating all areas using 
the HPSA criteria already in force.
    By contrast, Column 4 of Table VI-1 shows that, when the NPRM2 Tier 
1 geographic area criteria are applied, 1660 or 73% of the baseline 
HPSAs retain their HPSA designations. An additional 325 counties are 
newly designated, for a new total of 1985 HPSAs, 87% of the baseline 
total. While this result does not in itself demonstrate the superiority 
of the proposed NPRM2 method, it does indicate that application of the 
proposed method would not result in the loss of many existing HPSA 
designations, a major concern of commenters on the NPRM1 proposal.

                                     Table VI-1.--Impact of NPRM-1 and NPRM-2 Methods on Number of HPSA Designations
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                                                             Number of
                                                                                                                            population     Total number
                                                             Number of       Number of       Number of       Number of        groups       of areas and
                                                               areas           areas           areas           areas       additionally     pop groups
                  Baseline HPSA status                    designated  as  designated  by  designated  by  designated  by    designated      designated
                                                              of 1999         current      NPRM1  (meets      NPRM2-        using NPRM2    using NPRM2-
                                                            (baseline)       criteria/     IPCS & HPSA)     geographic      low income    geographic and
                                                                           updated data         (*)           method         pop group    low income pop
                                                                                                                              method       group method
--------------------------------------------------------------------------------------------------------------------------------------------------------
Whole County Geographic HPSA............................             832             372             243             694             114             808
Part County Geographic HPSA.............................             858             473             332             681             139             820
Low Income Population HPSA..............................             592             104              77             285             190             475
                                                         -----------------------------------------------------------------------------------------------
    Subtotal: Number of Baseline HPSA Designations                 2,282             949             652           1,660             443           2,103
     Retained...........................................
                                                         -----------------------------------------------------------------------------------------------
Percent of Baseline Designations Retained...............  ..............           41.6%           28.6%           72.7%           19.4%           92.2%
--------------------------------------------------------------------------------------------------------------------------------------------------------
New Designations (1,197 Counties had no Baseline HPSA     ..............             106              71             325             452             777
 Designation)...........................................
                                                         -----------------------------------------------------------------------------------------------
    Total Number of HPSA Designations...................           2,282           1,055             723           1,985             895           2,880
                                                         -----------------------------------------------------------------------------------------------
    Total HPSAs as a Percent of Baseline................  ..............           46.2%           31.7%           87.0%           39.2%          126.2%
--------------------------------------------------------------------------------------------------------------------------------------------------------
*For NPRM1, 4 areas are not included because of missing data.

    We also estimated the results of applying the NPRM2 Tier 1 low-
income population group designation criteria to those baseline HPSA 
areas and counties that do not meet the NPRM2 geographic criteria. 
Column 5 shows the number of low-income population group HPSAs that 
would result; they include 253 in areas previously designated as 
geographic HPSAs, 190 previous HPSA population groups retained, and 452 
potential new low-income population group HPSAs in counties not 
previously HPSA-designated.
    Column 6 shows the combined result of applying NPRM2 Tier 1 
geographic and low-income population group criteria: 2103 or 92% of 
areas with baseline HPSA designations would keep either a geographic or 
a low-income population group designation if the NPRM2 criteria were 
applied, while 777 additional geographical areas or low-income 
population groups could potentially be designated. While this last 
number may seem large, this may be related to the fact that all areas 
designated with the NPRM2 approach are both HPSAs and MUAs. Under the 
previous criteria there were considerably more MUAs than HPSAs. 
Therefore, in a new system with combined criteria, even if the total 
number of areas designated (as either MUAs or HPSAs) were to remain 
approximately the same as before, one could expect the number of HPSAs 
to increase.

B. Impact on Number of MUA/P Designations

    As column 1 of table VI-2 shows, in the baseline year of 1999 there 
were 1411 whole counties, 1909 part-county geographic areas, and 138 
low-income population groups designated as MUA/Ps in the United States, 
for a total of 3458 designations.
    Unlike the case with HPSAs, regular reviews and updates to the list 
of MUA/Ps are not legislatively required, and no major review/update 
has occurred since 1982; rather, additions and deletions have been made 
upon request (requested deletions have been infrequent). Therefore, the 
3458 MUA/P designations considered valid in 1999 include many not 
updated since 1982, plus the results of case-by-case review of requests 
received over the 1982-99 period from State and local sources. Column 2 
shows the impact of simultaneously updating all these designations 
using the current MUA criteria applied to the Impact Test Data Base 
discussed above (assembled by HRSA and the UNC Sheps Center from 1998 
data). The results are that only 1312 or 38% of these areas would 
retain their MUA designations. At the same time, 28 additional counties 
would be newly designated, so that the new total number of MUAs would 
be 39% of the baseline total. Thus, using the current methodology to 
update the MUA list would result in more change for MUAs than for 
HPSAs.
    Column 3 of Table VI-2 shows the results of applying the MUA 
criteria proposed in ``NPRM1'', as published in 1998, to the same 3458 
areas, using the same Impact Test Data Base of 1998 national data. Here 
2405, or 70% of the baseline areas, would retain their MUA designation; 
143 counties would be added, for a new total of 2548 MUAs, 74% of the 
baseline total. So the method proposed in NPRM1 would not have 
decreased existing MUA designations, in contrast to the effect it would 
have

[[Page 11254]]

had on HPSAs. And it would have performed significantly better than the 
option of updating using current criteria in terms of retention of MUA 
designations.
    Column 4 of Table VI-2 shows that, when the NPRM2 Tier 1 geographic 
area criteria are applied, 2319 or 67% of the baseline MUAs retain 
their MUA designations. An additional 168 counties are newly 
designated, for a new total of 2487 MUAs, 72% of the original total.

                                     Table VI-2.--Impact of NPRM-1 and NPRM-2 Methods on Number of MUA/P Designations
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                                                                        Total number of
                                                                                                                   Estimated number      areas and pop
                                   Number of areas      Number of areas     Number of areas     Number of areas      of pop groups     groups designated
     Baseline MUA/P status         designated as of      designated by       designated by       deisgnated by     designated using      using NPRM2-
                                   1999 (baseline)     current criteria/  NPRM1 (meets IPCS)   NPRM2-geographic    NPRM2-low income   geographic and low
                                                       updated data (*)          (**)               method         pop group method    income pop group
                                                                                                                                            method
--------------------------------------------------------------------------------------------------------------------------------------------------------
 Whole County Geographic MUA...   1,411.............   499..............   1,067............   1,031............   319..............   1,350
Part County Geographic MUA.....   1,909.............   795..............   1,286............   1,233............   347..............   1,580
Low Income Population MUP......   138...............   18...............   52...............   55...............   33...............   88
                                ------------------------------------------------------------------------------------------------------------------------
    Subtotal: Number of          3,458..............  1,312.............  2,405.............  2,319.............  699...............  3,018
     Baseline MUA/P
     Designations Retained.
                                ------------------------------------------------------------------------------------------------------------------------
Percent of Baseline              ...................  37.9%.............  69.5%.............  67.1%.............  20.2%.............  87.3%
 Designations Retained.
 New Designations (674 Counties  ...................   28...............   143..............   168..............   219..............   387
 had no Baseline MUA/P
 Designation).
                                ------------------------------------------------------------------------------------------------------------------------
    Total Number of MUA/P        3,458..............  1,340.............  2,548.............  2,487.............  918...............  3,405
     Designations.
                                ------------------------------------------------------------------------------------------------------------------------
    Total MUA/Ps as a Percent    ...................  38.8%.............  73.7%.............  71.9%.............  26.5%.............  98.5%
     of Baseline.
--------------------------------------------------------------------------------------------------------------------------------------------------------
* For Current Criteria, Updated Data, 327 areas are not included because of missing data.

    We also estimated the results of applying the NPRM2 Tier 1 low-
income population group designation criteria to those baseline MUAs and 
other counties that do not meet the NPRM2 geographic criteria. Column 5 
of Table VI-2 shows the number of low-income MUPs that would result; 
they include 666 in areas previously designated as geographic MUAs, 33 
previous low-income MUPs retained, and 219 potential new low-income 
MUPs in counties not previously MUA/P-designated.
    Column 6 shows the combined result of applying NPRM2 Tier 1 
geographic and low-income population group criteria: 3018 or 87% of 
areas with baseline MUA/P designations would keep either a geographic 
or a low-income population group designation if the NPRM2 criteria were 
applied, while 387 additional geographical areas or low-income 
population groups could potentially be designated, for a total of 3405 
MUA/P designations, 98% of the baseline number.

C. Impact on Number of Unduplicated HPSA/MUP Designations

    Areas and population groups designated under the criteria proposed 
herein would be considered both MUA/Ps and HPSAs. Therefore, it is 
important to examine not only the impact on HPSA and MUA/P designations 
separately, but also the combined impact on unduplicated HPSA and MUA/P 
designations. This is shown in Table VI-3. As column 1 shows, 1610 
whole counties were designated either as MUAs or HPSAs or both in 1999; 
2350 additional part-county areas were geographically designated as 
MUAs and/or as HPSAs; and 487 low-income population groups in other 
areas were designated as MUPs and/or population group HPSAs, for a 
total of 4447 unduplicated baseline designations (as compared with the 
baseline HPSA total of 2282 and the baseline MUA/P total of 3458). We 
have characterized this combined group of basis areas as the ``any 
designation'' layer of areas.
    Column 2 of Table VI-3 shows the impact on unduplicated number of 
designations of updating using the current HPSA/MUA/P criteria (against 
the 1998 database described above). 2170 or 48.8% of the baseline areas 
would retain designation; 18 additional counties would achieve 
designation, so that the new total of 2188 areas would be 49.2% of the 
baseline total.
    Column 3 shows the impact of applying the previously-published 
NPRM1 criteria to the unduplicated baseline areas. Here 2994 or 67% of 
the baseline areas would retain their designation; with 42 new 
designations, a total of 3036 unduplicated designations would result, 
or 68% of the baseline number. This is compared to the 50% loss 
associated with updating under current criteria, but application of the 
NPRM1 criteria would still have decreased (nearly \1/3\) of 
unduplicated designations.
    Column 4 shows the impact of applying the proposed NPRM2 geographic 
criteria to the unduplicated baseline areas. Here a total of 2962 areas 
are geographically designated, or 67% of the baseline areas, roughly 
the same as the NPRM1 impact. However, when the estimated NPRM2 low-
income population group adjustment is applied and added, we get the 
considerably more favorable combined result shown in Column 5: A total 
of 3882 designations (or 87% of the unduplicated baseline) are retained 
by the NPRM2 method, while 168 new designations are added, for a total 
of 4050 designations or 91% of the baseline.

[[Page 11255]]



                         Table VI-3.--Impact on Number of Combined HPSA/MUA Designations
----------------------------------------------------------------------------------------------------------------
                                                            Number of areas designated
                                 -------------------------------------------------------------------------------
                                                                                                    Total using
                                                                                                       NPRM2
 Baseline HPSA and MUA/P status     As of 1999       By curent       By NPRM1        By NPRM2     geographic and
                                    (baseline)       criteria/      (meets IPCS     geographic      low income
                                                   updated data     threshold)        method       adjustment (2
                                                                                                   step) method
----------------------------------------------------------------------------------------------------------------
Designated as Geog or Low Income
 Population HPSA or MUA/P as of
 1999 (Old):
    Whole County Geog HPSA or              1,610             734           1,177           1,163           1,536
     MUA........................
    Part County Geog HPSA or MUA           2,350           1,351           1,607           1,571           2,003
    Low Income Population HPSA               487              85             210             177             343
     or MUP.....................
                                 -------------------------------------------------------------------------------
        Subtotal: Areas                    4,447           2,170           2,994           2,911           3,882
         Designated (of 1999                               48.8%           67.3%           65.5%           87.3%
         Designated Areas)......
----------------------------------------------------------------------------------------------------------------
New Designations (not Designated  ..............              18              42              51             168
 1999)..........................
                                 -------------------------------------------------------------------------------
        Total: Areas Designated            4,447           2,188           3,036           2,962           4,050
         (of 1999 Designated and                           49.2%           68.3%           66.6%           91.1%
         Undesignated Areas)....
----------------------------------------------------------------------------------------------------------------


    (Note: Tables VI-1 and VI-2 show that 777 new HPSA designations 
and 387 new MUA/P designations result when the proposed NPRM2 
criteria are applied separately to baseline HPSAs plus other 
counties and to baseline MUAs plus other counties. By contrast, when 
the unduplicated set of baseline areas are used in Table VI-3, we 
find only 168 new designations that were not either HPSAs or MUAs 
previously. Also, while Tables 1 and 2 show the total numbers of 
Tier 1 HPSAs and MUA/Ps under NPRM2 to be 126% and 98% of their 
baselines, respectively, Table 3 shows that the total unduplicated 
designations under NPRM2 Tier 1 are only 91% of the unduplicated 
baseline. From here on, impact analysis results are displayed in 
terms of the unduplicated baseline areas.)

D. Impact on Population of all Designated HPSAs and/or MUPs

    While the number and percent of designations retained and the new 
total number of designations under alternative methods are important 
measures of the impact of a change in criteria, these measures can also 
be misleading, since all areas are not equal; different areas have 
different populations, different levels of need, and different numbers 
of safety net providers. Using 1998 Claritas population estimates, the 
total population of all 1999-designated (baseline) HPSAs was 59.1 
million, while the total population of baseline MUA/Ps was 72.1 
million; the unduplicated total population of baseline areas designated 
as HPSAs and/or MUA/Ps was 95.3 million.
    Table VI-4 shows the impact of the various alternatives on this 
unduplicated total designated population. Updating using the current 
criteria against the 1998 Impact Test Database would lower the total 
designated population to 32.7 million, or 34% of the baseline. Use of 
the NPRM2 geographic criteria would result in a total designated 
population of 53.0 million, or 56% of the baseline. Finally, use of the 
NPRM2 method would result in a total designated population of 83.1 
million, or 87% of the baseline. (This is actually quite close to the 
percentage expressed in number of designations, which was 91%.)

                       Table VI-4.--Impact on Unduplicated Population of HPSAs and MUA/Ps
----------------------------------------------------------------------------------------------------------------
                                                                Population in areas
                                 -------------------------------------------------------------------------------
                                                                                                    Total using
                                                                                   By NPRM2 low        NPRM2
 Baseline HPSA and MUA/P Status                     By current       By NPRM2         income      geographic and
                                    As of 1999       criteria/      geographic     adjustment (2    low income
                                    (Baseline)     updated data     method  [A]    step) method    adjustment (2
                                                                                      [B](*)       step) method
                                                                                                       [A+B]
----------------------------------------------------------------------------------------------------------------
Designated as Geog or Low Income
 Population HPSA or MUA/P as of
 1999 (Old):
    Whole County Geog HPSA or         38,400,153      12,044,723      23,080,444      11,501,134      34,581,578
     MUA........................
    Part County Geog HPSA or MUA      37,747,979      17,986,210      24,044,227       8,308,592      32,352,819
    Low Income Population HPSA        19,132,742       2,199,545       4,692,078       6,352,471      11,044,549
     or MUP (*).................
                                 -------------------------------------------------------------------------------
        Subtotal: Population in       95,280,874      32,230,478      51,816,749      26,162,197      77,978,946
         Areas Designated (of
         1999 Designated Areas).
                                 -------------------------------------------------------------------------------
Subtotal: Share of Population in  ..............           33.8%           54.4%           27.5%           81.8%
 Areas Designated in 1999.......
                                 -------------------------------------------------------------------------------
Not Designated as Geog or Low
 Income Population HPSA or MUA/P
 as of 1999 (New):

[[Page 11256]]

 
    New Designations              ..............         481,198       1,111,149       4,057,976       5,169,125
     [28,490,624] Population in
     Areas without Baseline
     Designation)...............
                                 -------------------------------------------------------------------------------
        Total: Population Areas       95,280,874      32,711,676      52,927,898      30,220,173      83,148,071
         Designated (of 1999
         Designated and
         Undesignated Areas)....
                                 -------------------------------------------------------------------------------
Total: Share of Population in     ..............           34.3%           55.5%           31.7%          87.3%
 Areas Designated in 1999.......
----------------------------------------------------------------------------------------------------------------
* Though these designations are associated with Low Income Population, the population counts provided here are
  for all residents of the area [Total Population].

    The results in Table VI-4 suggest that use of the NPRM2 method will 
better target designations--both the number and population of all 
designated areas will decrease by about 10%. At the same time, the 
NPRM2 method should result in a much smoother transition from current 
designation levels than would either updating using current criteria 
(which would significantly decrease MUAs) or updating using NPRM1 
(which would significantly decrease HPSAs).

E. Impact on Number of CHCs Covered by Designations

    Table VI-5 shows, for those CHC sites identified as located in 
areas which were designated in the baseline year, the percentage that 
retain their designations under the various scenarios. Under the 
proposed method, 86% would be in areas that retain designation (either 
as a geographic area or as a low income population group-see fourth 
line of table, last column). By contrast, the NPRM1 method would have 
retained only 76%, while updating the designations under current 
criteria would have retained only 43%.

                          Table VI-5.--Impact on Number of CHCs Covered by Designations
----------------------------------------------------------------------------------------------------------------
                                                              Number of CHCs in areas
                                 -------------------------------------------------------------------------------
                                                                                                    Total using
                                                                                                       NPRM2
 Baseline HPSA and MUA/P Status     As of 1999      By current       By NPRM1        By NPRM2     geographic and
                                    (Baseline)       criteria/      (meets IPCS     geographic      low income
                                                   updated data     threshold)        method       adjustment (2
                                                                                                   step) method
----------------------------------------------------------------------------------------------------------------
Designated as Geog or Low Income
 Population HPSA or MUA/P as of
 1999 (Old):
    Whole County Geog HPSA or                618             252             474             456             583
     MUA........................
    Part County Geog HPSA or MUA             741             354             583             453             629
    Low Income Population HPSA               122              31              61              51              93
     or MUP (*).................
                                 -------------------------------------------------------------------------------
Subtotal: CHCs in Designated               1,481             637           1,118             960           1,305
 Areas (% of 1999 CHCs).........  ..............             43%           75.5%           64.8%           88.1%
Not Designated as Geog or Low     ..............               2               7               4              10
 Income Population HPSA or MUA/P
 as of 1999 (New) New
 Designations (43 CHCs without
 Baseline Designation)..........
                                 -------------------------------------------------------------------------------
    Total: CHCs in Designated              1,481             639           1,125             964           1,315
     Areas (% of 1999 CHCs).....  ..............            43.1           75.9%            62.1           88.8
----------------------------------------------------------------------------------------------------------------
* The number of CHCs is based on the number of FQHC, Community Health Center sites which offer a full range of
  primary care services and where the designation is based on area characteristics or low income. Most part-
  time, special population and satellite clinics are excluded.

F. Impact on Number of NHSC Sites Covered by Designations

    Table VI-6 shows, for those NHSC sites identified as located in 
areas which were designated in the baseline year, the percentage that 
retain their designations under the various scenarios. Under the 
proposed method, 86% would be in areas that retain designation (either 
as a geographic area or as a low income population group--see fifth 
line of table, last column). By contrast, updating the designations 
using current criteria would have retained only 34%.

[[Page 11257]]



                       Table VI-6.--Impact on Number of NHSC Sites Covered by Designations
----------------------------------------------------------------------------------------------------------------
                                                      Number of areas with NHSCs designation
                                 -------------------------------------------------------------------------------
                                                                                                    Total using
                                                                                   By NPRM2 low        NPRM2
 Baseline HPSA and MUA/P status                     By current       By NPRM2         income      geographic and
                                    As of 1999       criteria/      geographic     adjustment (2    low income
                                    (Baseline)     updated data     method [A]     step) method    adjustment (2
                                                                                        [B]        step) method
                                                                                                       [A+B]
----------------------------------------------------------------------------------------------------------------
Designated as Geog or Low
 Income:
    Whole County Geog HPSA or                340             123             218              97             315
     MUA........................
Population HPSA or MUA/P as of
 1999 (Old):
    Part County Geog HPSA or MUA             414             172             245             119             364
    Low Income Population HPSA               178              19              52              72             124
     or MUA/P...................
                                 -------------------------------------------------------------------------------
        Subtotal: NHSC Areas                 932             314             515             288             803
         Designated (of 1999
         Designated Areas)......
                                 -------------------------------------------------------------------------------
Subtotal: Share of NHSC Areas     ..............           33.7%           55.3%           30.9%           86.2%
 Designated in 1999.............
----------------------------------------------------------------------------------------------------------------
Designated as Geog or Low Income
 Population HPSA or MUA/P as of
 1999 (New):
    New Designations (15 Areas    ..............               0               0               4               4
     with NHSCs without Baseline
     Designation)...............
                                 ===============================================================================
        Total: NHSC Areas                    932             314             515             292             807
         Designated (of 1999
         Designated and
         Undesignated Areas)....
                                 -------------------------------------------------------------------------------
Total: Share of NHSC in Areas     ..............           33.7%           55.3%           31.3%           86.6%
 Designated in 1999.............
----------------------------------------------------------------------------------------------------------------

G. Impact on Number of RHCs Covered by Designations

    Table VI-7 shows, for those RHC sites identified as located in 
areas which were designated in the baseline year, the percentage that 
retain their designations under the various scenarios. Under the 
proposed method, 94% of RHCS in currently designated areas would be in 
areas that retain designation (either as a geographic area or as a low 
income population group--see fifth line of table, last column). An 
additional 94 RHCs that were not in designated areas at the time of 
testing would be in areas designated under the new methodology, 
resulting in 97.5% of RHCs being located in designated areas. By 
contrast, updating under current criteria would have retained 46%.

                          Table VI-7.--Impact on Number of RHCs Covered by Designations
----------------------------------------------------------------------------------------------------------------
                                                        Number of RHCs in areas designated
                                 -------------------------------------------------------------------------------
                                                                                                    Total Using
                                                                                   By NPRM2 low        NPRM2
 Baseline HPSA and MUA/P status                     By current       By NPRM2         income      geographic and
                                    As of 1999       criteria/      geographic     adjustment (2    low income
                                    (Baseline)     updated data     method [A]     step) method    adjustment (2
                                                                                        [B]        step) method
                                                                                                       [A+B]
----------------------------------------------------------------------------------------------------------------
Designated as Geog or Low Income
 Population HPSA or MUA/P as of
 1999 (Old):
    Whole County Geog HPSA or              2,173             946           1,503             569           2,072
     MUA........................
    Part County Geog HPSA or MUA             544             336             393             127             520
    Low Income Population HPSA               125              24              43              42              85
     or MUA/P...................
                                 -------------------------------------------------------------------------------
        Subtotal: RHCs                     2,842           1,306           1,939             738           2,677
         Designated (of 1999
         Designated Areas)......
                                 -------------------------------------------------------------------------------
Subtotal: Share of RHCs           ..............           46.0%           68.2%           26.0%           94.2%
 Designated in 1999.............
----------------------------------------------------------------------------------------------------------------
Designated as Geog or Low Income
 Population HPSA or MUA/P as of
 1999 (New):
    New Designations (120 RHCs    ..............              11              28              66              94
     in Areas without Baseline
     Designation)...............
                                 ===============================================================================
        Total: RHCs Designated             2,842           1,317           1,967             804           2,771
         (of 1999 Designated and
         Undesignated Areas)....
                                 -------------------------------------------------------------------------------
Total: Share of RHCs Designated   ..............           46.3%           69.2%           28.3%           97.5%
 in 1999........................
----------------------------------------------------------------------------------------------------------------


[[Page 11258]]

H. Impact on Distribution of Designations by Metropolitan/Non-
Metropolitan and Frontier Status

    Table VI-8 enables comparison of the impact on number of designated 
areas in metropolitan, non-metropolitan, and frontier areas. (Here 
metropolitan areas are those so designated by the Office of Management 
and Budget; non-metropolitan areas are all other areas. Frontier areas 
are generally defined as the subset of non-metropolitan areas with 
population densities less than 7 persons per square mile, but for the 
purpose of these impact tests a file of frontier areas was used that 
was provided by the Frontier Education Center and involved a more 
expansive definition of frontier areas that included a formula based on 
population density and isolation [time and distance from a market area 
as well as other factors]). Table VI-8 (last column) shows that, while 
91% of all baseline designations are retained under the proposed 
method, 82% of those in metropolitan areas, 98% of those in non-
metropolitan areas, and 99% of those in frontier areas are retained. 
Therefore, non-metropolitan and frontier areas are not more negatively 
impacted than metropolitan areas (contrary to the impression many 
commentors seemed to have of the NPRM1 method).

                   Table VI-8.--Impact on Distribution of Designations by Met/Non-Met/Frontier
----------------------------------------------------------------------------------------------------------------
                                                      Current
                                     Baseline        criteria          NPRM1        NPRM2 Geog     NPRM2 Geog +
                                                      updated                                     Low-income pop
----------------------------------------------------------------------------------------------------------------
Total No. of Designations.......           4,447     2,188 (49%)     3,036 (68%)     2,962 (67%)     4,050 (91%)
Metropolitan....................           1,880       861 (46%)     1,223 (65%)     1,112 (59%)     1,532 (82%)
Non-Metro.......................           2,567     1,327 (52%)     1,813 (71%)     1,850 (72%)     2,518 (98%)
Frontier........................           1,026       544 (53%)       800 (78%)       751 (73%)     1,014 (99%)
----------------------------------------------------------------------------------------------------------------

I. Impact on Distribution of Population of Underserved Area and 
Underserved Populations by Metropolitan/Non-Metropolitan and Frontier 
Status

    Table VI-9 enables comparison of the impact on the population of 
underserved areas and underserved populations in metropolitan, non-
metropolitan, and frontier areas. Table VI-9 (last column) shows that, 
while the total designated population under the proposed method would 
be 87% of the baseline designated population, the metropolitan 
component of this NPRM2 designated population is 81% of the baseline 
metropolitan underserved, the non-metropolitan component is 99% of the 
baseline non-metropolitan underserved, and the frontier component is 
102% of the baseline frontier underserved. Therefore, the designated 
population of non-metropolitan and frontier areas would not decrease. 
The metropolitan population identified as underserved would appear to 
decrease, however. We expect this represents better targeting of the 
metropolitan underserved under the proposed method: It may also 
represent the fact that use of a national physician database together 
with gross estimates of the percent of urban practices devoted to low-
income and uninsured populations leads to overestimates of the number 
of FTE clinicians and underestimates of the number of designations and 
the underserved population in metropolitan areas. This suggests that 
case-by-case activity will continue to be necessary in reviewing some 
urban designations, while many non-metropolitan designations will be 
able to be processed using national data together with the new method.

                                      Table VI-9.--Impact on Population of Underserved Areas by Met/Non-Met/Frontier
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                          Current criteria                            NPRM2 Geog + Low-
                                                                        Baseline               updated             NPRM2 Geog            income pop
--------------------------------------------------------------------------------------------------------------------------------------------------------
Total Underserved...............................................            95,280,874      32,711,676 (34%)      52,927,898 (56%)      83,148,071 (87%)
Metropolitan Underserved........................................            63,791,345      21,044,647 (33%)      31,951,255 (50%)      51,804,251 (81%)
Non-Metro Underserved...........................................            31,489,529      11,667,029 (37%)      20,976,643 (67%)      31,343,820 (99%)
Frontier Underserved............................................             8,328,049       3,396,268 (41%)       5,784,509 (70%)      8,528,643 (102%)
--------------------------------------------------------------------------------------------------------------------------------------------------------

J. Impact of Practitioner ``Back-outs'' on Number of Designations and 
Safety-Net Providers

    The tables above represent the impacts when all clinicians are 
counted, i.e. the ``Tier 1'' designations. The tables below describe 
the impact of subtracting federally placed, obligated or funded 
clinicians from the practitioner counts, i.e. the changes that occur 
when ``Tier 2'' designations are included. For example, Table VI-10 
shows the effect on number of designations. Column 1 shows the number 
of baseline designations; column 2 shows the number of Tier 1 
designations under the proposed method. Column 3 shows the new total of 
designations if NHSC and SLRP clinicians are subtracted. Column 4 shows 
the revised total if physicians with J-1 visa return-home waivers who 
are performing obligated service are also subtracted. Finally, column 5 
shows the total number of designations when any other CHC-Based 
clinicians are also subtracted.

[[Page 11259]]



      Table VI-10.--Impact of Practitioner ``Back-outs'' on Total Number of HPSA or MUA/P Areas Designated
----------------------------------------------------------------------------------------------------------------
                                                            Number of areas designated
                                 -------------------------------------------------------------------------------
                                                                                                     By NPRM2
                                                     By NPRM2        By NPRM2        By NPRM2     geographic and
                                                  geographic and  geographic and  geographic and    2 step low
 Baseline HPSA and MUA/P status                     2 step low      2 step low      2 step low     income method
                                    As of 1999     income method   income method   income method  Tier 2-3 (Tier
                                    (baseline)      Tier 1 (all   Tier 2-1 (Tier  Tier 2-2 (Tier   1 less NHSC,
                                                   primary care     1 less NHSC    1 less NHSC,   SLRP, J-1, and
                                                    providers)       and SLRP      SLRP, and J-1        any
                                                                    providers)      providers)     designation)
----------------------------------------------------------------------------------------------------------------
Designated as Geog or Low Income
 Population HPSA or MUA/P as of
 1999 (Old):
    Whole County Geog HPSA or              1,610           1,536           1,546           1,551           1,553
     MUA........................
    Part County Geog HPSA or MUA           2,350           2,003           2,010           2,015           2,038
    Low Income Population HPSA               487             343             346             350             356
     or MUP.....................
                                 -------------------------------------------------------------------------------
        Subtotal: Areas                    4,447           3,882           3,902           3,916           3,947
         Designated (of 1999
         Designated Areas)......
----------------------------------------------------------------------------------------------------------------
Subtotal: Share of Areas          ..............           87.3%           87.7%           88.1%           88.8%
 Designated in 1999.............
----------------------------------------------------------------------------------------------------------------
Designated as Geog or Low Income
 Population HPSA or MUA/P as of
 1999 (New):
    New Designations (376 Areas   ..............             168             168             168             172
     Designated as HPSA or MUA
     without Baseline
     Designation)...............
                                 ===============================================================================
        Total: Areas Designated            4,447           4,050           4,070           4,084           4,119
         (of 1999 Designated and
         Undesignated Areas)....
----------------------------------------------------------------------------------------------------------------
Total: Share of Areas Designated  ..............           91.1%           91.5%           91.8%           92.6%
 in 1999........................
----------------------------------------------------------------------------------------------------------------

As can be seen, the number of additional designations resulting from 
these practitioner back-outs is quite small. However, HRSA considered 
that there could be a significant impact on some particular safety-net 
projects, i.e. certain CHCs, NHSC sites, and RHCs.

    Table VI-11 summarizes the impact on CHCs, NHSC sites, and RHCs. It 
indicates that 49 additional CHCs, 32 additional NHSC sites, and 43 
additional RHCs are in areas which would receive Tier 2 designation 
(change from Column 2 to Column 5). While this is not a large number, 
it clearly would be important for the affected sites. HRSA therefore 
concluded that the Tier 2 designations (with all three types of 
backouts) should be implemented.

 Table VI-11.--Impact of Practitioner Back-Outs on Numbers of CHCs, NHSC Sites, and RHCs Covered by Designations
----------------------------------------------------------------------------------------------------------------
                                                                                                     Number in
                                                                                     Number in        NPRM2-
                                                     Number in       Number in        NPRM2-        designated
                                                      NPRM2-          NPRM2-        designated    tier 1/tier 2-
                                     Number in      designated      designated    tier 1/tier 2-      3 areas
   Type of safety-net provider       baseline      tier 1 areas   tier 1/tier 2-      2 areas     (NHSC, SLRP, J-
                                    designated     (All primary   1 areas  (NHSC    (NHSC, SLRP    1, and other
                                       areas           care          and SLRP         and J-1       section 330
                                                    clinicians      clinicians      clinicians        funded
                                                     counted)       subtracted)     subtracted)     clinicians
                                                                                                    subtracted)
----------------------------------------------------------------------------------------------------------------
CHCs............................           1,481           1,315           1,322           1,328           1,364
(% of baseline CHCs)............  ..............         (88.8%)         (89.3%)         (89.7%)         (92.1%)
NHSC sites......................             932             807             825             828             839
(% of baseline NHSC sites)......  ..............         (86.6%)         (88.5%)         (88.8%)         (90.0%)
RHCs............................           2,842           2,771           2,790           2,794           2,814
(% of baseline RHCs)............  ..............         (97.5%)         (98.2%)         (98.3%)         (99.0%)
----------------------------------------------------------------------------------------------------------------

    In conclusion, it should be stated that it is impossible to predict 
the exact final impact on specific communities and States because of 
the iterative process built into the system. As described above, State 
and local officials will have the opportunity to examine the data used 
to develop these first approximations during the actual designation 
process, and to correct inaccurate provider and other data. In 
addition, they will have the opportunity to reconfigure service areas 
so as to more closely identify the boundaries of areas where shortages 
now exist, which may have changed since some of these service areas 
were constructed (particularly the MUAs). We believe this is a major 
strength of the proposal, since States and communities know best their 
service areas and practitioner supplies. At the same time, it makes it 
difficult to predict precisely the impact of the new method at the 
local level, since the data

[[Page 11260]]

used will be altered by State and local input.

VII. Economic Impact

    Executive Order 12866 requires that all regulations reflect 
consideration of alternatives, costs, benefits, incentives, equity, and 
available information. Regulations must meet certain standards, such as 
avoiding unnecessary burden. Regulations which are found to be 
``significant'' because of their cost, adverse effects on the economy, 
inconsistency with other agency actions, budgetary impact, or raising 
of novel legal or policy issues require special analysis. The 
Department has determined that this rule will not have an annual effect 
on the economy of $100 million or more. However, because this rule 
raises novel policy issues, it does meet the definition of a 
``significant'' rule under Executive Order 12866.
    The Regulatory Flexibility Act requires that agencies analyze 
regulatory proposals to determine whether they create a significant 
economic impact on a substantial number of small entities. ``Small 
entity'' is defined in the Regulatory Flexibility Act as ``having the 
same meaning as the terms `small business,' `small organization,' and 
`small governmental jurisdiction' ``; ``Small organizations'' are 
defined in the Regulatory Flexibility Act as not-for-profit enterprises 
which are independently owned and operated and not dominant in their 
field.
    The small organizations most relevant to this regulation would be 
Health Center grantees. The impact analyses discussed above suggest 
that very few health center service areas would lose MUA/P designation 
under the proposed criteria. In addition, because of the proposed new 
safety net facility type of designation, any negatively affected health 
center will be able to submit a request for this alternate type of 
designation. Moreover, the ``automatic'' designation of all FQHCs as 
HPSAs for six years under the Safety Net Amendments of 2002 will allow 
additional time for any transition to unfunded status that may prove to 
be necessary for some health centers.
    With regard to small businesses, while the designation process may 
negatively affect some small profit-making health care-related 
businesses, it is unlikely that it could have a significant economic 
impact, defined as five percent or more of total revenues on three 
percent or more of all such small businesses. Physician practices can 
obtain a 10 percent Medicare Incentive Payment bonus for those services 
delivered in geographic HPSAs; however, this would be unlikely to 
amount to five percent of the total revenues of a practice operated as 
a small business.
    Private RHCs could be considered small businesses; non-profit RHCs 
could be considered small organizations. RHCs already certified based 
in part on an MUA or HPSA designation have not been adversely affected 
by loss of such designations in the past, since the legislative 
authority for them had a ``grandfather'' clause; once certified, the 
RHC certification could not be withdrawn based only on loss of 
designation. However, the Balanced Budget Act of 1997 provided that, 
effective January 1, 1999, an RHC in an area that has lost designation 
or was designated over 3 years ago is subject to loss of its RHC 
certification, unless the Secretary determines that the RHC is 
essential to the delivery of primary care services in its area. The 
impact analysis shows only 2% of the non-metro designations will be 
lost under the proposed new method, so the likely impact is minimal. 
Therefore, implementation of these regulations will not automatically 
decertify any RHCs.
    ``Small governmental jurisdictions'' are defined by the Regulatory 
Flexibility Act to include governments of those cities, counties, 
towns, townships, villages, or districts with a population of less than 
50,000. Typically, one can expect that such jurisdictions will be found 
in non-metropolitan areas. Our impact analysis indicated that only 2 
percent of all designations in non-metropolitan areas are likely to 
lose a designation (see Table VI-8 above). This suggests that a 
substantial number of small government jurisdictions will not be 
affected. Furthermore, it is unlikely that the economic impact on any 
such affected jurisdictions would be significant, i.e. that they would 
lose more than 5 percent of their federal funding, as discussed in more 
detail below.
    The impact on particular jurisdictions of loss of designation can 
take one or more of three forms: Loss of grant funding for primary care 
services, loss of a source of clinicians to provide primary care 
services, or loss of a more favorable level of Medicaid and/or Medicare 
reimbursement. The first of these types of impact would occur only in 
the case of a Health Center which has lost its area and/or population 
designation, and does not qualify for designation as a safety net site. 
Typically, grant funding forms approximately 25-30 percent of the 
income to a CHC; it is possible that such a health center would be able 
to continue in operation without this revenue. Moreover, dedesignation 
could indicate that not only provider availability but also the income 
of the area's population had increased. As a result, the percentage 
impact on the economy of the area involved would likely be relatively 
low.
    The second of these types of impact corresponds to an area which, 
due to loss of its HPSA designation, is no longer eligible for NHSC 
clinicians, once the tour of duty of any NHSC personnel already placed 
there is completed. If such an area has recently been dedesignated, 
logically there must have been an increase in the number of primary 
care providers in the area and/or a decreased population and/or 
improved demographics, so that loss of NHSC clinicians will be unlikely 
to have a major economic effect on the area. (Furthermore, the 
``automatic'' HPSA designation of FQHCs and RHCs should mitigate any 
adverse effects here during the next several years.)
    The third type of impact applies in the case of FQHCs and/or RHCs 
which lose eligibility for special reimbursement methods, and private 
physicians in former geographic HPSAs which lose the 10 percent 
Medicare bonus. None of these entities would actually cease receiving 
Medicare or Medicaid reimbursement; they simply would receive a lower 
level of reimbursement. In the latter case, it is a loss of 10 percent, 
but it is unlikely that it would amount to 5 percent of the physician's 
total revenue. In the FQHC/RHC case, there could be a 20-30 percent 
decrease in reimbursement to the provider in question, but again this 
would not necessarily be a major economic loss to the county or other 
jurisdiction as a whole.
    It should also be noted that, to the extent that the proposed 
regulation ultimately results in some areas losing designation while 
others gain designation, and some areas therefore losing program 
benefits which go to designated areas while others gain such benefits, 
the total benefits available in a particular fiscal year will not 
decrease but will have been better targeted to the neediest areas, 
because the criteria will have been improved and will have been applied 
to more current data.
    The Department nevertheless requests comments on whether there are 
any aspects of this proposed rule which can be improved to make the 
designation process proposed more effective, more equitable, or less 
costly.

VII. Information Collection Requirements Under Paperwork Reduction Act 
of 1995

    Sections 5.3 and 5.5 of the proposed rule contain information 
collection

[[Page 11261]]

requirements as defined under the Paperwork Reduction Act of 1995 and 
implementing regulations. As required, the Department of Health and 
Human Services is submitting a request for approval of these 
information collection provisions to OMB for review. These collection 
provisions are summarized below, together with a brief description of 
the need for the information and its proposed use, and an estimate of 
the burden that will result.
    Title: Information for use in designation of MUA/Ps and HPSAs.
    Summary of Collection: These regulations revise existing criteria 
and processes used for designation of Medically Underserved Areas/
Populations (MUA/P) and Health Professional Shortage Areas (HPSA). As 
discussed above, service to an area or population group with such a 
designation is one requirement for entities to obtain Federal 
assistance from one or more of a number of programs, including the 
National Health Service Corps and the Community and Migrant Health 
Center Program.
    In order to initially obtain such a designation, a community, 
individual or State agency or organization must request the designation 
in writing. Requests must include data showing that the area, 
population group or facility meets the criteria for designation, 
although these data need not necessarily be collected by the applicant, 
but may be based on data obtained from a State entity or data available 
from the Secretary. If the request is made by a community or 
individual, the State entities identified in the regulation are given 
an opportunity to review it, which implies maintenance by these State 
entities of some record keeping on designation requests previously made 
or commented upon by the State. These requirements apply under both 
current rules and the proposed rule.
    Once a designation based on the proposed criteria has been made, it 
must be updated periodically (at least once every three years) or it 
will be removed from the list of designations. Although in the past 
this requirement applied only to HPSA designations, the proposed rule 
would extend the regular periodic update requirement to MUA/P 
designations (in response to concerns raised by the GAO and 
Congressional committees, among others). The update process involves 
the Secretary each year informing State (and/or community) entities as 
to which of their designations require updates, and providing these 
entities with the most current data available to the Secretary for the 
areas, population groups and facilities involved, with respect to the 
data elements used in designation. The State entities are then asked to 
verify whether the designations are still valid, using the data 
furnished by the Secretary from national sources together with any 
additional, more current or otherwise more accurate data available to 
the State entity (in consultation with the communities involved, as 
necessary). In the past, this has generally meant that the State (or 
community) entities have needed to verify primary care physician counts 
in most of the areas involved, especially subcounty areas, since only 
county-level physician data have typically been available from national 
sources. National population data have been largely limited to 
decennial census data and official Census Bureau intercensus county-
level updates, so that State population estimates were sometimes 
necessary; other relevant data have generally been available from 
national sources.
    Under the proposed new process, the data furnished by the Secretary 
will include provider data and population estimates for subcounty areas 
as well as counties, in an easily accessible database, and these data 
from national sources (including intercensus demographic and population 
projections) may be used without further collection and analysis, if 
acceptable to the State and community involved. This should minimize 
the burden on States and communities, except where the Secretary's data 
suggest withdrawal of a designation, in which cases the State or 
community will need to obtain local data to support continued 
designation. In such cases, the inclusion of non-physician providers 
under the proposed new rules will have a higher burden on those States 
or communities which wish to challenge provider data furnished by the 
Secretary.
    Need for the information. The information involved is needed in 
order to determine whether the areas, populations and facilities 
involved satisfy the criteria for designation and, therefore, are 
eligible for programs for which these designations are a prerequisite. 
While furnishing such information is purely voluntary, failure to 
provide it can prevent some needy communities from becoming eligible 
for certain programs. The Secretary will make a proactive effort to 
identify such communities using national data, but feedback from State 
entities and others with appropriate data is vital to ensuring that the 
designation/need determination process is accurate and current.
    Likely respondents. The entities that generally submit this 
designation-related information to DHHS are the State Primary Care 
Offices (normally within State Health Departments) or the State Primary 
Care Associations (non-profit associations of health centers and other 
organizations rendering primary care). The total burden placed on these 
entities will be determined by the number of applications they submit, 
review or update each year, and, therefore, will vary from State to 
State. Updates of all designated areas will not be required immediately 
when the new method is initiated; State entities will be given the 
opportunity to spread out updates of previously designated areas over a 
3-year period following implementation of the proposed regulation.
    Burden estimate. The overall public reporting and record keeping 
burden for this collection of information is estimated to be minimal 
under the new method. This is primarily because, while the new method 
will require some data collection from the same sources utilized in the 
previous MUA/P and HPSA designation procedures, there is no need to 
submit separate requests for the two types of designation and allows 
the use of national data where acceptable to the State and community. 
We also plan to allow electronic submission of data.
    The burden for compiling a request for new designation (including 
supporting data) or for update of an existing designation, under the 
existing system, was estimated by consulting with State entities who 
prepare such requests/updates about the amount of time required for the 
various aspects of request preparation, varying these estimates for 
requests with several different levels of difficulty, and then 
factoring in the approximate frequency of that type of request. Similar 
estimates for the new system were then made, revising the contributing 
factors to account for those aspects that would require more or less 
effort under the new approach. These estimates also assume that some 
applications are State-prepared, while others involve both an applicant 
and a State consultation or review; the estimates include both parties' 
time where two parties are involved. Under the new method, States and 
communities may use data provided by the Secretary; as mentioned above; 
however, some may wish to provide their own data for primary care 
physicians, while others may wish to provide data for both primary care 
physicians and for the nonphysician primary medical care providers 
which are included in the new designation

[[Page 11262]]

criteria and system (Nurse Practitioners, Physician Assistants, and 
Certified Nurse Midwives). Use of State and/or community data will be 
more likely in those cases where the national data suggest 
dedesignation. The estimates below include consideration of the extent 
to which such local data collection will likely be necessary.

----------------------------------------------------------------------------------------------------------------
                                                                    Number of
                Designation type                    Number of       expected        Hours per       Total hours
                                                   respondents      responses        response
----------------------------------------------------------------------------------------------------------------
MUA/P/HPSA Metro Area..........................              54             391             27.4          10,713
MUP/HPSA Non-Metro Area........................            * 54             909             10.9           9,908
Facility Designations..........................              25              70              2.6             182
                                                ----------------------------------------------------------------
    Total......................................              79           1,370  ...............          20,803
    Mean.......................................  ..............  ..............             15.2  ..............
----------------------------------------------------------------------------------------------------------------
* The Non-Metro applications are completed by the same respondents who complete Metro Area designation requests.
  To prevent double-counting of respondents, these 54 are added only once; therefore, 79 is shown as the total.

    Public comments on information collection requirements: Comments by 
the public on this proposed collection of information are solicited and 
will be considered in (1) evaluating whether the proposed collection of 
information is necessary for the proper performance of the functions of 
the Department, including whether the information will have a practical 
use; (2) evaluating the accuracy of the Department's estimate of the 
burden of the proposed collection of information, including the 
validity of the methodology and assumptions used; (3) enhancing the 
quality, usefulness, and clarity of the information to be collected; 
and (4) minimizing the burden of collection of information on those who 
are to respond, including through the use of appropriate automated 
electronic, mechanical, or other technological collection techniques or 
other forms of information technology; e.g., permitting electronic 
submission of responses.
    Address for comments on information collection requirements: Any 
public comments specifically regarding these information collection 
requirements should be submitted to: Fax Number--202-395-6974, or 
[email protected], Attn: Desk Officer for HRSA. Comments on 
the information collection requirements will be accepted by OMB 
throughout the 60-day public comment period allowed for the proposed 
rules, but will be most useful to OMB if received during the first 30 
days, since OMB must either approve the collection requirement or file 
public comments on it by the end of the 60-day period.

Appendix A.--References

Works Cited

    A description of this revised methodology can be found in:

Ricketts TC, Goldsmith LJ, Holmes GM, Randolph R, Lee R, Taylor DH, 
Osterman J. Designating Places and Populations as Medically 
Underserved: A Proposal for a New Approach. Journal of Health Care 
for the Poor and Underserved. 2007; 18: 567-589.

    These following articles are a sampling of the many source 
documents that provide historical background on measurements of 
underservice and relate to two key factors in the methodology: need 
indicators and benchmarking provider productivity.
    Indicators of Need:

Aday L, Andersen R. Development of Indices of Access to Medical 
care. Ann Arbor, MI: Health Administration Press; 1975.
Amato, Paul R. and Jiping Zuo. 1992. Rural Poverty, Urban Poverty 
and Psychological Well Being. The Sociological Quarterly. Vol 33, 
No. 2 pp 229-40, June 1992.
Andersen RM, Newman JF. Societal and individual determinants of 
medical care utilization in the United States. Milbank Memorial Fund 
Quarterly. 1973; 51(1):95-124.
Andersen RM. Revisiting the behavioral model and access to medical 
care: does it matter? Journal of Health and Social Behavior. 1995; 
36(1):1-10.
CDC. Community Indicators and Health Related Quality of Life. MMWR 
Weekly April 7, 2000.
Kawachi I, Berkman LF. Neighborhoods and Health. New York: Oxford 
University Press; 2003.
Krieger N, Chen JT, Waterman P, Rehkopf D, Subramanian SV. Race/
Ethnicity, gender, and monitoring socioeconomic gradients in health: 
A comparison of area-based socioeconomic measures--the public health 
disparities project. American Journal of Public Health. 2003; 
93(10):1655-1671.
Mansfield CJ, Wilson JL, Kobrinski EJ, Mitchell J. ``Premature 
mortality in the United States: the roles of geographic area, 
socioeconomic status, household type, and availability of medical 
care''; American Journal of Public Health. 1999; 89(6):893-898.
Robert SA. ``Neighborhood socioeconomic context and adult health.'' 
The mediating role of individual health behaviors and psychosocial 
factors''; Annals of the New York Academy of Sciences. 1999; 
896:465-468.
Robert SA, House JS. ``Socioeconomic inequalities in health: 
integrating individual-, community-, and societal-level theory and 
research.'' In: Albrecht GL, Fitzpatrick R, Scrimshaw S, eds. 
Handbook of Social Studies in Health and Medicine. London: Sage 
Publications; 2000:115-135.

    Ratio of Provider/Population and Measures of Underservice:

Bureau of Health Manpower. Report on Development of Criteria for 
Designation of Health Manpower Shortage Areas. Rockville, MD: Health 
Resources Administration; November, 1977. 78-03.
Bureau of Primary Health Care; Uniformed Data System Annual Report 
data; CY 2000-2003; unpublished data
Coyte, P.C., Catz, M., et al. (1997) ``Distribution of physicians in 
Ontario.'' Where are there too few or too many family physicians and 
general practitioners'' Canadian Family Physician 43: 677-83, 733.
Dial, T.H., Palsbo, S.E., et al. (1995) ``Clinical staffing in 
staff- and group-model HMOs.'' Health Affairs, Summer 1995; 14 (2): 
168-80.
Goldsmith, L. J. (2000, March 3). Invitational Workshop of 
Measurement of the Measurement of Medical Underservice. Presented at 
Cecil G. Sheps Center for Health Services Research at The University 
of North Carolina.
Goodman DC, Fisher ES, Bubolz TA, Mohr JE, Poage JF, Wennberg JE. 
``Benchmarking the U.S. physician workforce.''
    An alternative to needs-based or demand-based planning'' 
[published erratum appears in JAMA 1997 Mar 26; 277(12):966]. JAMA. 
1996; 276(22):1811-1817.
Hart, L.G., et al, ``Physician Staffing Ratios in staff-model HMOs: 
a cautionary tale''; Health Affairs, January/February 1997; 16(10; 
55-70
Kehrer B, Wooldridge J. ``An evaluation of criteria to designate 
urban health manpower shortage areas''; Inquiry. 1983; 20:264-275.
Larson, E.H. et al, ``The Contribution of Nurse Practitioners and 
Physician Assistants to Generalist Care in Underserved Areas of 
Washington State''; June 2001; WWAMI Center for Health Workforce 
Studies
Perlin J., MD, Miller, L * * * Report of the Primary Care 
Subcommittee; VHA Physician Productivity and Staffing Advisory 
Group; Veterans Administration; June 30, 2003.

[[Page 11263]]

Ricketts TC, Taylor DH. Examining Alternative Measures of 
Underservice. Proceedings of the 1995 Public Health Conference on 
Records and Statistics. Washington, DC: National Center for Health 
Statistics, 1995 pp. 207-9
Sekscenski, T, Moses, E, (1999) HRSA's Bureau of Health Professions, 
Division of Nursing and Office of Research & Planning Health 
Resources & Services Administration, Integrated Requirements Model
Taylor, DH, Goldsmith, LJ (2000, March 3). Invitational Workshop of 
Measurement of the Measurement of Medical Underservice. Presented at 
Cecil G. Sheps Center for Health Services Research at The University 
of North Carolina.
Woodwell DA. National Ambulatory Medical Care Survey: 1997 Summary. 
Hyattsville, MD: National Center for Health Statistics; May 20, 
1999.
Woodwell DA. National Ambulatory Medical Care Survey: 1998 Summary. 
Advance Data from Vital and Health Statistics of the Centers for 
Disease Control and Prevention. Hyattsville, MD: National Center for 
Health Statistics; July 15, 2000. 315.

Appendix B.--A Proposal for a Method To Designate Communities as 
Underserved

Technical Report on the Derivation of Weights

    This Appendix is intended to provide more technical details 
about the proposed methodology and how it was developed. The 
principal authors of this document are, alphabetically: Laurie 
Goldsmith, Mark Holmes, Jan Ostermann, and Tom Ricketts.

The General Approach

    The overall approach for deriving an empirical, data driven 
system to identify underserved areas and populations is to estimate 
the effect of demographic factors on the population-to-practitioner 
ratio, using a sample of counties as proxies for a health care 
market. These effects are then translated to a score which is added 
to an adjusted ratio for a total ``need'' measure. Thus, the 
implementation is similar to the current IPCS or MUA method in that 
it creates a ``score'' or ``index'' of underservice, however, the 
proposed system's score is based on an adjusted ratio that is meant 
to represent an ``effective'' or ``apparent'' population and its 
primary health care needs.
    There are eight steps to the project, which we divide for 
expository purposes into two distinct ``Tasks''. Please note that 
the specific steps described earlier in the preamble to this rule 
may not match up to the steps described below (for example, ``step 
4'' in the preamble matches up with ``steps 4-5'' and ``step 7'' in 
this appendix).

Task One: Calculate the Weights That Will Be Used To Adjust Ratios 
(``Analysis'')

    This is the analytical portion of the project in which we 
explore the degree to which observable demographic characteristics 
tend to be associated with population to provider ratios. The 
specific steps in this task include:
    1. Create an age-sex adjusted population.
    2. Calculate the base population-provider ratio for regression 
to determine weights for need variables.
    3. Select study sample primary care service area proxies.
    4. Create factor scores to control for interactions of 
variables.
    5. Run regression models to create weights for community 
variables.

Task Two: Calculate the Scores Based on These Factors (``Computation'')

    This is the portion of the process in which scores are assigned 
to geographic areas based on the weights calculated in Task One.
    6. Calculate the base population-practitioner ratio for 
designation determination.
    7. Calculate the scores for each area based on the values for 
each variable for each area and add to the ratio.
    8. Step 8: Compare the ratio to a designation threshold ratio.
    We describe each of these steps in detail in the following 
sections.

Task 1: Analysis Steps

Step 1: Create an Age-Sex Adjusted Population

    Using estimated visit rates from individual-level surveys, we 
weight the population to create a ``base population.'' In this 
manner, populations can be compared across areas. The use of these 
data for this adjustment are discussed in detail in reports and 
background papers for the proposal including the report that 
estimates the national impact of the NPRM-2 proposal, ``National 
Impact Analysis of a Proposed Method to Designate Communities as 
Underserved'' dated September 7, 2001; the background paper, 
``Designating Underserved Populations. A Proposal For An Integrated 
System Of Identifying Communities With Multiple Access Challenges,'' 
which is in draft form; and the ``Executive Summary'' of the 
``Designating * * *'' paper, which has been circulated in draft form 
to the Bureau of Primary Health Care.
    The weights are summarized in Table 1.

                                                     Table 1.--Visit Weights for Age-Sex Adjustment
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                0-4            5-17            18-44           45-64           65-74        75 and over
--------------------------------------------------------------------------------------------------------------------------------------------------------
Female..................................................           4.046           2.256           5.007           5.480           6.710           8.160
Male....................................................           5.164           2.499           2.867           4.410           6.052           8.056
--------------------------------------------------------------------------------------------------------------------------------------------------------
These are the original weights using 1996 data.

    The weighted sum of these populations is calculated as 4.046 * 
( Females 0-4) + 2.256 * ( Females 5-17) +. . .+ 
8.056 *(  Males 75 and over) and equals an age-sex adjusted 
number of visits for a particular population. Dividing this number 
of visits by the mean visit rate (3.741) creates a ``base 
population''. Areas with equal base populations (and equal 
demographics) have an equal need for primary care visits per year. 
This adjustment allows us to compare, say, the population-based 
visit differentials between an area with a high concentration of 
elderly (with a higher need for visits) and an area with a high 
population of middle aged individuals (with a lower need for 
visits). The visit rates were obtained from the Medical Expenditure 
Panel Survey (1996) and were calculated for non-poor, white, non-
Hispanic individuals. Employment status, which was included in the 
MEPS survey and was a significant correlate of use of service, was 
also intercorrelated with the other variables and was not included 
in the final visit calculation.

Step 2: Calculate the Base Population-Provider Ratio for Regression To 
Determine Weights for Need Variables

    With the base population in hand, we calculate the population-
provider ratio to use in the regression to determine factor weights. 
When applying the formula for the initial estimation of weights, the 
number of practitioners is calculated as:

Providers = physicians-(J1--physicians + MHSC--physicians + SLRP--
physicians) + .5* [midlevels-(NHSC--midlevels + SLRP--midlevels)] + 
.1* [residents-(NHSC--residents + SLRP--residents)]

where all practitioners are measured in FTE units and the 
practitioner total includes NPs, PAs and CNMs weighted according to 
agency guidelines. The number of practitioners used in the 
regression to determine weights for the need variables represents 
only those practitioners that are considered to be the ``private'' 
supply. That is, the practitioners who would choose to practice in 
the community without federal support or incentives to practice in 
state- or federally-operated facilities. As such, government 
practitioners (whether federal or state) are not counted here. 
Community Health Center practitioners who are not federal employees, 
however, are counted since many of these are not ``placed'' into 
communities but are practitioners already located in the area that 
are ``reclassified'' as CHC practitioners for later subtraction from 
the practitioner supply at a later step. For the estimation of the 
formula, an area with no practitioners is dropped from use in the 
regression analysis to determine weights for the need variables as a 
ratio is undefined (not calculable).

[[Page 11264]]

Step 3: Select Study Sample

    A sample of counties and county equivalents that serve as 
proxies for a health care market are then selected for analysis to 
derive formula weights. This step was done to identify places which 
functioned as primary care service areas and which reported stable, 
reliable, usable data. According to 2000 Census data, the median 
county land area is 616 square miles, corresponding to an 
approximate radius of 14 miles. The tenth and ninetieth percentiles 
are 288 and 1847 square miles, corresponding to approximate radii of 
10 and 24 miles respectively. The approximate radius of a county 
that is between the tenth and ninetieth percentiles in land area 
reflects a consensus of the extent of distances traveled for primary 
care services. The report describing PCSAs developed by Dartmouth 
and VCU did not identify a median or mean size rather they indicated 
that ``A land area of 1,256 square miles or a radius of 20 miles 
(assuming a circular shape) was used as a crude indicator of 
geographically large PCSAs.'' (Good,man, et al., 2003 p. 297). The 
population threshold we proposed of 125,000 was chosen based on a 
perception that cities and counties with populations greater than 
this level were likely to have many more specialists and tertiary 
care services structure that would substitute for primary care 
alone, thus skewing the relationship between primary care 
practitioners and population. No specific studies were done to 
further support this assumption. The PCSA project reported a median 
population of 17,276 with multiple PCSAs exceeding that threshold. 
Many U.S. counties meet these general qualifications and the process 
selected a range of counties that met three criteria, including:
    i. Populations below 125,000 (410 eliminated\*\)
    ii. Area below 900 square miles (856 eliminated)
    iii. Base population to provider ratio below 4250 (336 
eliminated)
    \*\Some counties had combinations of both values.
    The third criterion effectively eliminated very small counties 
and counties with unusual distributions of health practitioners. The 
goal was to determine the relationship of area characteristics to 
practitioner supply under ``normal'' conditions in order to create 
stable estimates of those relationships in order to apply them to 
all appropriate populations and areas.
    These sample selection criteria were varied; we tested over 2000 
combinations in the estimation process described in the next step to 
test for robustness and sensitivity. The variations included testing 
within the following ranges: Population 80,000-150,000; area 700-
1200 sq. miles; ratio 3000-4250. Overall, the estimations derived 
from the models were not substantially different among the different 
samples. The study sample contained 1643 counties. Counties were 
chosen because they are well-defined and are not endogenous to the 
current system.
    Using currently designated areas would lead to biased 
conclusions due to the fact the subcounty areas are carefully and 
deliberately constructed for purposes of designation. Furthermore, 
dividing a county into a subcounty-designated and subcounty-
undesignated would generate an extremely large number of possible 
observations in the analysis since the county could be divided in 
many different ways and into many subsets of county parts. Finally, 
since some data are calculated and available primarily on a county 
level, measurement error is minimized by using counties. Using other 
units of analysis requires interpolating values for subcounty and 
multicounty areas based on the constituent geographic units.

Step 4: Create Factors

    The proposed designation process, in keeping with the original 
MUA/MUP and HPSA approaches, identified commonly available 
statistics that correlated with a small number of primary care 
practitioners-to-population ratio. The selection of the measures was 
based on reviews of the scientific literature on access to care and 
preliminary work on the development of an alternative measures of 
underservice conducted by Donald H. Taylor, Jr. (Taylor & Ricketts, 
1994). Candidate statistics were also suggested by a working group 
of State Primary Care Associations (PCAs) and Primary Care Offices 
(PCOs) convened by the Division of Shortage Designation (DSD) to 
gather state-level input into the process of revising the method. 
The staff and leadership of the DSD also provided extensive input 
into the design. More than 20 specific variables were suggested 
during this process. Some candidate variables could not be used, 
despite being highly correlated with low access and poor health 
outcomes, due to lack of availability of data for small areas (e.g. 
lack of health insurance). Ultimately, the high intercorrelations 
among candidate variables restricted the calculation to 7-9 
individual indicators (the actual number to be tested depended upon 
the specific combination of variables). The final choice of 
variables and the priority for inclusion in the analysis was based 
on the degree to which the variables best reflected underlying 
components of access as qualitatively assessed by the UNC-CH team, 
the PCA/PCO group, and staff of Bureau of Primary Health Care 
(BPHC). The final measures consist of demographic, economic and 
health status indicators (presented in Table 2).
    Demographic: Population characteristics, especially racial and 
ethnic characteristics, have been consistently shown to affect 
access to primary care (Berk, Bernstein, & Taylor, 1983; Berk, 
Schur, & Cantor, 1995; Schur & Franco, 1999). Measures of the 
percent of population that is non-White and percent of population 
that is Hispanic were used to further adjust the ratio. The 
inclusion of the percentage of population older than 65 years was 
also included because communities with higher percentages of elderly 
have different community characteristics not captured in the initial 
population adjustment. This is likely due to the relative lack of 
younger people to provide supportive care and the fact that 
communities with declining economies, especially rural communities, 
have older age profiles that combine with other factors to create 
overall lower access.
    Economic: Income and employment are very strong indicators of 
ability to access primary health care and to afford health insurance 
(Mansfield, Wilson, Kobrinski, & Mitchell, 1999; Prevention, 2000; 
Robert, 1999). The unemployment rate and the percent of population 
below 200 percent of the poverty level were used to further adjust 
the ratio.
    Health Status: Certain populations and communities have higher 
than average need for health care services based primarily on their 
health status independent of other factors. Therefore, health status 
measures used to adjust the ratio include the standardized mortality 
ratio (General Accounting Office, 1996) and either the infant 
mortality rate or the low birthweight rate (Matteson, Burr, & 
Marshall, 1998; O'Campo, Xue, Wang, & Caughy, 1997). These special 
epidemiological conditions that increase need are not fully 
represented in the age-gender adjustment.

          Table 2.--Variables Used in Creating Proposed Method
------------------------------------------------------------------------
         Demographic                Economic            Health status
------------------------------------------------------------------------
Percent Non-white             Percent population    Actual/expected
 ``NONWHITE''                  <200% FPL             death rate (adj)
                               ``POVERTY''           ``SMR''
Percent Hispanic              Unemployment rate     Low birth weight
 ``HISPANIC''                  ``UNEMPLOYMENT''      rate ``LBW''
Percent population >65 years                        Infant mortality
 ``ELDERLY''                                         rate ``IMR''
------------------------------------------------------------------------
          Population density ``DENSITY''
------------------------------------------------------------------------

    These measures are highly intercorrelated. Table 3 below shows 
the Pearson-product moment correlations. The first column shows that 
poverty and unemployment are positively correlated (+0.64), meaning, 
in counties with high proportions of the

[[Page 11265]]

population living in poverty there is usually a higher unemployment 
rate. Poverty and density are negatively correlated (-0.55), meaning 
that where there is higher density there are lower percentages of 
the population living in poverty. The correlation matrix is 
population-weighted.

                                                         Table 3.--Percentile Correlation Matrix
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                Poverty      Unemp      Density     Elderly    Hispanic    Non-white      SMR         IMR         LBW
--------------------------------------------------------------------------------------------------------------------------------------------------------
Poverty.....................................        1.00  ..........  ..........  ..........  ..........  ..........  ..........  ..........  ..........
Umemp.......................................        0.64        1.00  ..........  ..........  ..........  ..........  ..........  ..........  ..........
Density.....................................       -0.55       -0.21        1.00  ..........  ..........  ..........  ..........  ..........  ..........
Elderly.....................................        0.36        0.28       -0.47        1.00  ..........  ..........  ..........  ..........  ..........
Hispanic....................................       -0.32       -0.23        0.22        0.25        1.00  ..........  ..........  ..........  ..........
Non-White...................................        0.10        0.12        0.22       -0.29        0.25        1.00  ..........  ..........  ..........
SMR.........................................        0.57        0.55       -0.04        0.04       -0.26        0.42        1.00  ..........  ..........
IMR.........................................        0.33        0.25       -0.10        0.08       -0.08        0.41        0.43        1.00  ..........
LBW.........................................        0.40        0.37        0.05       -0.05       -0.14        0.63        0.69        0.54        1.00
--------------------------------------------------------------------------------------------------------------------------------------------------------

Variable Definitions

    Variables were assigned a percentile based on the distribution 
of values of all U.S. counties to all U.S. counties. This allows for 
continuity in the use of the proposed scores if variables are 
defined differently in the future (e.g. the poverty measure is 
changed to 100 percent below poverty instead of 200 percent). It 
also allows policymakers a choice of how often (or whether) to 
update the percentile values without having to change the weights. 
If poverty conditions improve markedly across the nation, scores 
will tend to fall unless the percentile tables are updated. For all 
variables except DENSITY the theoretically worst value corresponded 
to the 99th percentile. At first glance, it might appear that places 
with very low population density would be worse off with regard to 
primary care access and health service needs. Places with extremely 
high density may also have problems caused by overcrowding and the 
population density may reflect problems that are commonly 
encountered in inner-cities. For this variable there is no apparent 
``right'' direction for the weights. We arbitrarily specified the 
functional form such that lower population density corresponds to a 
worse off (higher percentile score) community. Accounting for the 
negative effects of very high density is described below.
    We combined low birth weight and infant mortality into one 
measure (called HEALTH), defined as the maximum percentile of low 
birth weight and the infant mortality rate for a given area. This is 
due to a medium level of correlation between the two and the fact 
that not all areas report both measures. Finally, the use of the 
infant mortality rate in measures of underservice is required by 
existing law and there is precedent for using these measures as 
rough substitutes. The original Index of Primary Care Shortage 
described in NPRM-1 of September 1, 1998 used them interchangeably.
    We defined nonwhite as the maximum of zero or the percentile 
minus 40, so that only the top (most nonwhite) 60 percent of areas 
get ``points'' for the nonwhite variable. In other words, all areas 
less than the 40th percentile are treated equally. There were two 
main reasons for this. The first is that many of the areas have low 
nonwhite percentages (the 40th percentile is about 2.6 percent 
nonwhite). By not making this adjustment, we are differentiating 
areas that have little difference in the underlying measure. The 
second reason is that without this adjustment, the scores were not 
stable; small differences in the definition of this variable 
resulted in wide swings in the magnitude of the nonwhite variable 
when testing multiple randomly chosen samples. We experimented with 
a multitude of cutoff points (0-50 in 10 unit increments). In the 
final specification, small changes in the definition of NONWHITE had 
little substantive effect.
    With the corresponding percentiles in hand, the associated 
scores were transformed to a logarithmic scale so that the highest 
derivative corresponded to the theoretically worst end of the scale. 
For example, the independent variable corresponding to poverty 
(lnpcpov) was defined as Inpcpov = In(100 - pcpov) so that the 
fastest acceleration in the poverty score occurs at high levels of 
poverty rather than at low levels. In other words, we specified the 
model to allow a greater score to accrue to areas ``moving'' from 
the 95th percentile to the 96th percentile than to areas ``moving'' 
from the 5th percentile to the 6th percentile. All variables were 
assumed to have this shape (so that the theoretically worst values 
have the largest derivative). A more detailed description of the 
regression approach is included at the end of this appendix (Notes 
to Appendix B).

Basing the Scores on the Population-Practitioner Ratio

    Although this approach specifies the shape of the function as 
logarithmic and this constrains the rate of change in the scoring as 
variables differ from one percentile to another, it does not 
constrain the sign nor the absolute magnitude of the parameters that 
create the weights. That is, the regression models are indifferent 
to whether a parameter comes out positive or negative or how large 
or small it is when the statistical model is run to create the 
weights. The magnitude is the most important parameter of the three 
and will be used for estimating the scores but the potential effects 
of the size and sign of the weights must fit into our logic of 
additivity of factors. The magnitude of the weights are expressed as 
a synthetic unit which cannot be compared to any other unit--the 
weight for UNEMPLOYMENT, for example, when transformed to the log-
normal form and constrained to a positive value in the course of the 
estimation, is not a ``percent of workforce not working but seeking 
work'' but an abstract number that describes the relative 
contribution of that factor to a total access score at that 
percentile of unemployment given all the value of all the other 
variables and the population structure. The final model creates an 
estimate for the weight for each set of variables using this 
abstract number but that number has to be brought back into a 
logical relationship with the key unit of access we are using--the 
population portion of a practitioner-to-population ratio. The final 
combined sum of these abstract values has to be adjusted back to an 
interpretable relationship with the practitioner-population ratio. 
This requires that some form of restraint on the parameter (weight) 
values be imposed or the solution set may produce a ``best result'' 
that causes one or two variables to dominate the weighting and 
others to vary from positive indicators of barriers to access to 
negative in various combinations.
    In the application of the process this means that the parameter 
is used along with the intercept of the regression models to 
generate the specific weight for each variable. This was done to 
normalize the scores so that the minimum score was zero. This is 
done by adding a fixed number to the log result.
    In an unconstrained solution of the regression models this is, 
indeed, the case. There are possible solution sets that include 
mixes of positive and negative values; in statistical parlance the 
functions are ``two-sided.'' The logic of the scoring system 
anticipated this when we stipulated that factors which restrain use 
of services by creating barriers to access, also create subsequent 
higher levels of need likely to be met by higher levels of use, use 
of services that was preventable but now necessary. In the real 
community, both things are happening, an access program is promoting 
appropriate utilization by overcoming access barriers and all 
practitioners are involved in caring for people who are using the 
system because emergent conditions were not treated appropriately. 
The amount of the increase in use brought about by delayed care must 
be added into the reduction in use to produce a sum of the access 
``problem'' in a community. To account for the ``mirror'' effects of 
these variables, the final value, the sum of the weights are 
doubled, to produce

[[Page 11266]]

a population estimate that is scaled to represent the overall effect 
on the population need.

Factor Analysis

    Because many of these measures are highly correlated, we perform 
factor analysis in order to compute factors for the independent 
variables defined above. Essentially, factor analysis provides a 
method to translate highly correlated variables into orthogonal 
measures to obtain more precise estimates and minimize the impact of 
multicollinearity in the variables of interest. Often used as an end 
product statistical tool, we use it here to improve the precision of 
the estimates.\1\
---------------------------------------------------------------------------

    \1\ Greene (2003) (Greene W. Econometric Analysis, 5th Ed. 
Prentice Hall, New Jersey) acknowledges that the use of principal 
components regression is sometimes used in the presence of 
multicollinearity. One of his criticisms is the inability to 
interpret the underlying regression parameters (p. 59), although 
this criticism is not very applicable here (the underlying 
parameters are never considered by the applicants.) More 
importantly, Greene lays out the tradeoffs: ``If the data suggest 
that a variable is unimportant in the model, the theory 
notwithstanding, the researcher ultimately has to decide how strong 
the commitment is to that theory.'' One of the guiding principles 
was face validity, which essentially says conventionally accepted 
wisdom on important determinants of access should suggest included 
variables.
---------------------------------------------------------------------------

    Our procedure here was to decompose the independent variables 
into factors and then create scores based on these factors. The 
factor scores follow in Table 4. The largest weight in the row is 
the one on which factor the variable weighs most heavily (except for 
SMR, which has two maximum weights of almost equal magnitude). Four 
factors might be interpreted as structuring the data:

I. High health risk, nonwhite
II. Geo-demographics
III. Economic conditions
IV. Hispanic

                                             Table 4.--Factor Scores
----------------------------------------------------------------------------------------------------------------
                                                                                    Factor
                          Variable                           ---------------------------------------------------
                                                                   1            2            3            4
----------------------------------------------------------------------------------------------------------------
Poverty.....................................................       -0.005        0.208       -0.423        0.044
Unemp.......................................................       -0.044       -0.074       -0.338        0.009
Elderly.....................................................       -0.039        0.355        0.021       -0.226
Density.....................................................        0.042        0.440        0.051        0.189
Hispanic....................................................        0.018       -0.002        0.046        0.291
NonWhite....................................................        0.408       -0.012        0.136        0.099
SMR.........................................................        0.206       -0.107       -0.226       -0.124
Health......................................................        0.353        0.066        0.100       -0.046
----------------------------------------------------------------------------------------------------------------

Step 5: Run Regressions

    We regress the base population-to-private supply practitioner 
ratio on the scores obtained from the factor analysis (Ratio = 
Factor I + Factor II . . . + error). By combining the scores from 
the factor analysis with the estimated coefficients from the 
regression, we obtain the effect of our underlying variables on the 
ratio.
    As an example, the factor analysis might yield a result such as:

------------------------------------------------------------------------
                   Variable                       Factor 1     Factor 2
------------------------------------------------------------------------
Poverty.......................................           .2           .4
Unemployment..................................           .3          -.1
------------------------------------------------------------------------

    Which we could translate into a matrix
    [GRAPHIC] [TIFF OMITTED] TP29FE08.002
    
    Suppose regressing the ratio onto these two scores yields 
estimates of

------------------------------------------------------------------------
                          Variable                               Beta
------------------------------------------------------------------------
Factor 1...................................................            1
Factor 2...................................................          -.4
------------------------------------------------------------------------

which would translate to a vector
[GRAPHIC] [TIFF OMITTED] TP29FE08.003

    By multiplying these two matrices, we can obtain the total 
effect of one variable on the ratio:
[GRAPHIC] [TIFF OMITTED] TP29FE08.004

Thus, (in this simple example) the overall effect of Poverty on the 
ratio is calculated as .04 and the overall effect of Unemployment is 
.34. We use the rightmost matrix for computing the scores (see the 
next section) except for one correction (see below).

Weights/Heteroskedasticity

    Because the dependent variable is a ratio with population in the 
denominator, we are concerned about possible heteroskedasticity in 
the dependent variable. This is the property that the sampling 
variability in the dependent variable is not constant across the 
sample. Specifically, we expect the ratio to be estimated more 
precisely as the population grows. See Figure 1 below for support of 
this hypothesis--the ratio tends to become less variable as the 
population increases (population category 1 is the lowest population 
category and population category 10 is the highest population 
category). (The upper and lower bands are the values for the 25th 
and 75th percentiles). The consequence of this violation is that the 
standard errors from the regression are biased and a more efficient 
estimator may exist. As such, we weight the regressions by the total 
population of the county.

[[Page 11267]]

[GRAPHIC] [TIFF OMITTED] TP29FE08.000

    There is a question of whether we are even dealing with a 
``sample'' in the conventional statistical sense. If our analysis is 
composed of the population of interest, then classical statistical 
inference is a bit artificial; there is no uncertainty if we have 
data on all the units of interest. We argue that this is a sample in 
the conventional sense, for reasons including but not limited to the 
following:
    a. Measurement error occurs more often than we expect. County 
population values are estimated in 1997 and the accuracy of provider 
supply is not 100 percent. As the nation observed in the 
presidential vote count in Florida, even simple computations are not 
immune from error. Thus, because the data used here are affected by 
measurement error, we have a sample drawn from the possible data for 
the population of counties.
    b. The units used here are a sample of a much bigger population 
of interest. Not only are we interested in counties other than those 
included in the analysis due to sample criteria, ultimately we are 
using counties as approximations for ``health care markets'' or 
rational primary care service areas, whether they follow the 
boundaries of a county or not. These methods are designed to be 
applied to data for future years and the construction of the areas 
may vary from one based on geography to ZIP code boundaries.

Other considerations, such as errors in model specification or the 
discrete ``lumpiness'' associated with using a dependent variable 
like this one provide support for the use of factor scores.

Sampling Error in the Regression

    We wish to reduce the error in predicting the designation of 
communities. As such, we seek to incorporate the precision with 
which the regression parameters are estimated into the scoring 
procedure. As an example, it is entirely possible, given two 
factors, to have one coefficient be estimated as 100 with a standard 
error of 1 and the other coefficient to be estimated as 400 with a 
standard error of 1000. If asked which factor is more important, 
most people would probably admit that although the 400 is a larger 
point estimate, the 100 is probably more important given its 
statistical significance. As such, the regression estimates are 
adjusted for the statistical significance by the algorithm defined 
below.\2\ 
---------------------------------------------------------------------------

    \2\ An alternative treatment would be to discard any 
statistically insignificant estimates. We have strong conceptual 
biases against employing such stepwise procedures.
---------------------------------------------------------------------------

    1. Obtain the variance-covariance matrix V of the parameter 
estimates from the regression.
    2. Compute the weighting matrix W defined as the inverse of the 
Cholesky transformation of a zero matrix except for the diagonal, 
which consists of the diagonal of V. (This is identical to a zero 
matrix with diagonal elements equal to the reciprocal of the 
standard errors of the parameter estimates).
    3. Transform the vector of parameter estimates (omitting the 
constant) b by b* = b *W* number of factors/trace(W). The trace() 
portion of the expression ensures the weights sum to the number of 
factors.
    4. Compute F = S b* as above.

As an example, return to the hypothetical results for poverty and 
unemployment above. Suppose the (estimated) variance-covariance 
matrix from the regression was

[[Page 11268]]

[GRAPHIC] [TIFF OMITTED] TP29FE08.005

The estimated scores in equation (2) differ from those obtained in 
equation (1) (page 17) due to the weight. Because the regression 
estimate for the first factor is estimated with roughly three times 
the precision as the estimate for the second factor (5/1.42 [ap] 3), 
the estimate for the first factor (1) is weighted more heavily than 
the estimate for the second factor (-.4). In this case, this has the 
end result of increasing the scores from .04 to .24 for poverty and 
.34 to .4844 for unemployment. Vector F is the scoring vector used 
in the next step.

    Although the process for obtaining matrix F is complex and 
multi-stage the process was completed for all possible values of the 
variables. Having done this, data describing a service area can be 
translated readily into percentile scores using a look-up table, a 
simple spreadsheet, or a web-based application. This parallels the 
existing MUA scoring process. Applicants do not need to perform 
Cholesky transformations or any other mathematical calculations. 
Fundamentally, the ``weighting'' step rescales the regression 
parameters, placing more weight on more precisely estimated 
parameters. We are not aware of other published research performing 
this reweighting, but there are at least two reasons this approach 
has intuitive appeal. The reweighted models performed better 
empirically in the sense of minimizing disruption to current 
designation status. We considered dropping statistically 
insignificant principal components from the regression and not 
weighting. Although this would be a more traditional use of 
principal, components regression (with both its advantages and 
disadvantages), in addition to subpar performance, the omission of 
insignificant components drops factors that theory suggests should 
contribute to access barriers. At its core, this unconventional 
approach represented the best tradeoff we could devise between 
health care access theory, statistical theory, and empirical 
performance.

Task 2: Computation

Step 6: Calculate the Base Population-Provider Ratio for Designation 
Determination

    Using the same age-sex adjusted population from Step 1, we 
calculate the population-practitioner ratio. All primary care 
practitioner FTEs in the area are counted to initially determine 
designation, this is termed the ``Tier 1 designation ratio'' and 
follows the FTE allocation of

Providers = active non-federal, primary care physicians + 0.5 * 
primary care NPs, PAs, and CNMs + 0.1 * medical residents in 
training

    For applicants not meeting the threshold criterion, the FTEs for 
practitioners who are supported by safety net programs ( e.g., NHSC 
providers, J-1 visa practitioners, CHC providers) are subtracted 
from the supply total and the applicant ratio is compared to the 
threshold. That step is termed ``Tier 2 designations.'' The formula 
for that calculation follows the same logic as in Step 2, above:

Providers = physicians--(J1--physicians + NHSC--physicians + SLRP--
physicians) + .5* [midlevels--(NHSC--midlevels + SLRP--midlevels)] + 
.1* [residents--(NHSC--residents + SLRP--residents)]

Step 7: Calculate Scores

    With row vector F in hand, we then turn to computing scores for 
geographical units. We compute the ratio of population to providers 
using the algorithm outlined above. We use the percentile scores as 
computed above for the counties. See the document ``Completing the 
NPRM2 Application'' for these percentiles.
    We then calculate the score for the communities and add this 
score, upweighted by 2 to account for the 2-sided properties of the 
regression estimates so the total score for the community equals

ADJUSTED RATIO (or ``INDEX'') = RATIO + 2 * SCORE

This is the total score for the community and determines its 
designation status. The applicants never see the regression 
multiplier; it is embedded in the tables.

    Because the use of the multiplier for the score is applied at 
this stage of the process, it may be seen as an ad-hoc adjustment. 
The statistical logic for this has been described above, the policy 
logic for applying this adjustment is supported by these points:
    1. The multiplier is used to account for the fact that the 
existing measures and processes including: the HPSA formula, the 
IPCS/MUA formulae, and the practical application of the CHC/RHC 
clinic placement process--all recognize the importance of the basic 
population-to-practitioner ratio in determining need. Indeed, some 
simple models run on the study sample provide evidence that the 
multiplier should be closer to 10 rather than 2 if the goal were to 
include every area containing a CHC under the proposed designation 
process (this assumes that the presence of a CHC is an indicator of 
need in and of itself as opposed to the result of the calculation of 
pre-existing unmet need). The IPCS mechanism provided for a maximum 
score from the population-practitioner ratio of 35 points. The 
maximum score available from other factors (poverty 35 points, IMR/
LBW 5 points, minority 5 points, Hispanic 5 points, LI 5 points, 
density 10 points = 65 points) are, collectively, almost twice that 
in terms of potential contribution. Thus, the weighted contribution 
of the factors besides the ratio is roughly twice that of the ratio 
itself. Multiplying the ratio denominator by two intensifies the 
relative effect of the underlying, basic population to practitioner 
ratio in the designation process providing continuity with prior 
policy.
    2. The multiplier functions as a scale/weighting factor. The 
score has a much smaller variance than the ratio. This is not just 
an annoyance--it is used to generate a prediction, and thus will 
have smaller variance than the dependent variable. The dependent 
variable and the score used here have some sort of meaning, a person 
per provider, although the various adjustments make this unit of 
measurement not as meaningful as we might think. One alternative we 
considered is rescaling the ratio and the score into z-scores and 
using these standardized measures rather than the unscaled measures. 
This rescaling would

[[Page 11269]]

involve multiplying the score by a larger factor than the ratio.
    3. The multiplier helps control for the (observed) low ratios 
in, (e.g., metro) areas with high scores. The following example 
illustrates this:

                                                           Table 2.--Example Score and Ratios
--------------------------------------------------------------------------------------------------------------------------------------------------------
                County of HPSA                           State              Ratio         Score         IPCS          IMR          LBW         Poverty
--------------------------------------------------------------------------------------------------------------------------------------------------------
Bronx.........................................  NY.....................       1357.2        1043.5           54          10.1         10.1          77.8
Coconino......................................  AZ.....................       1266.8        1005.6           56           8.1          7.2          65.1
Kings.........................................  NY.....................       1634.7         897.8           52          10.3          9.2          59.2
East Baton Rouge..............................  LA.....................       1660.5         874             46          11.3         10.2          69
St. Lucie.....................................  FL.....................       1138.5         873             44          10            7.3          67
Philadelphia..................................  PA.....................       1055.9         861.2           47          13.3         11.4          61.1
Mahoning......................................  OH.....................       1505.3         839.3           44          10.7          8.9          67.5
--------------------------------------------------------------------------------------------------------------------------------------------------------

The (unmultiplied) maximum score is about 1300. The areas listed 
above are all in the worst 10 percent of scores. Note that these 
areas would not qualify without the ``score x 2'' multiplier rule 
(see below). Perhaps the ratio is a misleading measure in some 
circumstances.

    4. The multiplier fills a statistical role. The score is 
(likely) more stable across years; e.g., if one physician moves out 
of a rural area, the ratio varies dramatically. The score is not 
going to change drastically across years. Thus, it should be given 
more weight.
    5. The multiplier creates a standard which designates roughly 
the same number of people as the IPCS and the current HPSA 
designations.
    6. It performs better than without the doubling. Although this 
particular argument has little theoretical basis, it is still 
compelling.

Why is a portion of the density score function negative?

    The astute reader will note that the constant from the 
regression was dropped and never used. The reason for this is that 
the constant has no clear meaning in this context. We decided to 
norm the scores so that the minimum score--that is, the best area in 
the country--was zero. Thus, although in theory an area could 
receive a negative score if it had very favorable demographics and 
had a high population density, in practice no area had a negative 
score (by definition).

Step 8: Compare to Threshold

    Areas are designated if and only if the ``adjusted ratio'' (or 
ratio+score) is greater than 3000. This threshold was adopted for 
its reflection of the clear need for a single full-time equivalent 
primary care physicians, its consistency with prior threshold 
values, and its familiarity to stakeholders.

Areas With No Practitioners

    The problem of how to treat areas with zero providers emerged 
early in the process of ranking areas as medically underserved. 
There is an informative treatment of the phenomenon in Black and 
Chui (1981).\*\ For areas with zero providers, we have not made any 
firm recommendations and have treated them in one of three ways for 
various parts of the analysis.
    (a) Every area with zero providers automatically gets an 
adjusted ratio of 3000 (which guarantees them designation), to which 
a score for community need indicators are added. This results in all 
areas having a NPRM2 score, including areas with zero providers. 
This method was used in early tabulations and compilations.
---------------------------------------------------------------------------

    \*\ Black, R. A., and Chui, K.-F. (1981). Comparing schemes to 
rank areas according to degree of health manpower shortage. Inquiry, 
18(3), 274-280.
---------------------------------------------------------------------------

    (b) Automatically designate areas with zero providers without 
assigning an adjusted ratio or a score for community need 
indicators. Therefore, areas with zero providers will not have a 
NPRM2 total score. This has occurred when calculations and 
tabulations of the database using the NPRM2 scoring system was 
applied. The places with no score were dropped. This method was used 
in the final impact analysis.
    (c) Assigning an arbitrarily small FTE to the area, such as 0.1 
to create a score that is primarily dependent upon the denominator 
population. This was used only in selected tests of the scoring 
system as an alternative.

    Notes to Appendix B: Regression approach for assignment of 
weights to correlates of ``shortage''

    The basic method for assigning weights to individual variables 
involved the estimation of a county-level linear regression model 
with the adjusted population-to-physician ratio as the left-hand 
side variable, and the variables described in step 4 as right-hand 
side variables. Coefficients on the right-hand side variables can be 
interpreted directly as average differences in the population-to-
physician ratio for counties with specified characteristics relative 
to counties without those characteristics.
    To reduce the effects of extreme outliers (e.g., population 
density in New York City, or per capita income in Silicon Valley), 
all variables were converted into percentages. To allow for non-
linear relationships between each variable and the ratio, the 
variables were further converted from a linear variable, ranging 
from 1 to 100, into twenty five-percentile categorical variables, 
i.e., one each for 1-5th percentile, 6-10th percentile, * * * 96th-
100th percentile. When all but one of these variables are entered on 
the right-hand side of a regression with the population-to-physician 
ratio as the dependent variable, the coefficients on each variable 
represent the average difference in the adjusted population-to-
physician ratio relative to the omitted reference category. In most 
cases, the omitted reference category is the 1-5th percentile, i.e., 
the five percent of counties with the lowest values for a particular 
variable.
    Entering highly collinear variables, such as income and poverty, 
into a single regression model usually results in one coefficient 
being positive, and the other being negative. In order to develop a 
``user-friendly'' scoring system in which all weights are positive, 
variables were added sequentially to the regression model, with the 
effects of previously entered variables constrained to their 
estimated effects. As a result, coefficients on all variable other 
than the first represent the ``marginal differences'' in the ratio, 
after controlling for all previously included variables.
    A decision was made to use a population-to-physician ratio of 
3000:1 as a cutoff criterion for designation. The following analysis 
was restricted to counties with adjusted population-to-physician 
ratios between 500:1 and 3000:1, for which the dependent variables 
was not missing (N=2,493).
    Income was the single most important correlate of the ratio. It 
was entered first, and estimates were obtained for each of 19 
categories; counties in the 95-100th percentile were the excluded 
category. Each of the estimated coefficients represents the average 
difference in the ratio for counties in the respective percentile 
range relative to the omitted group of counties with the highest 
income. Coefficients were graphed and examined visually, and 
differences between the coefficients for ``neighboring'' categories 
were evaluated for statistical significance. Categories with no 
statistically significant differences were combined into single 
variables. As a result of this process, three categories (plus 
reference category) remained, one each for the 1-75th, 76-85th, and 
86-95th percentiles. The regression was run again, suggesting that 
counties in these categories had higher ratios by 628, 344, and 216 
``units'', respectively. (These units are the average differences in 
the population-to-physician ratio).
    Constraining the coefficients on these variables to these 
values, 19 percentile ranges for the next-highest correlate of the 
ratio, population density, were added to the analysis. Visual 
inspection pointed to clear non-linearities in the relationship. 
There appeared to be a statistically significant difference between 
counties in the 95-100th percentile relative to all other counties. 
Furthermore, the effect was increasing up to the 35th percentile of 
counties, and then decreased between the 36th and 95th percentiles. 
Note that these relationships describe the relationship between 
population

[[Page 11270]]

density and the population-to-physician ratio after controlling for 
the effects of income. Consistent with the observed relationship, 
three variables were defined, a categorical variable for the 1-95th 
percentile range, and two splines for the 1-35th and 36-95th 
percentiles, respectively.
    These three variables describing population density were entered 
into the model together with the income variables, and the estimated 
coefficients were used to analyze the marginal effect of 
unemployment according to the same method. Relative to the omitted 
reference group of counties in the 1-5th percentile, counties in the 
6-20th and 21-100th percentile ranges had significantly higher 
population-to-physician ratios, after controlling for income and 
population density. Consequently, two dummy variables for counties 
in these categories were entered into the model. The process was 
repeated for percent of the population under 200% FPL, which 
suggested that--after controlling for income, population density, 
and unemployment--the ratio was lowest for counties with a 
percentage of the population below 200% poverty around the 20th 
percentile of all counties. Below this threshold, the average ratio 
was higher by about 110 ``units'', above that, the ratio gradually 
increased by about 2.5 ``units'' per percentile increment.
    Table 2 shows the results of the final regression model 
containing the four variables described above. After controlling for 
these variables, none of the remaining variables was significantly 
associated with shortage. This finding is consistent with other 
studies of the effects of community characteristics on access to 
health care, in that the economic/barrier variables have been shown 
to have much greater impact than other characteristics. However, 
legislation requires the use of selected morbidity and mortality 
measures such as infant mortality and, even if marginal in their net 
effect, these measures are tied closely to the logic of need for 
primary care and access to primary care.
    To comply with this requirement, the analysis was repeated for 
actual/expected deaths, the maximum of low birth weight/infant 
mortality rate, and the percentage of the population over the age of 
65. Table 3 shows the results of the final regression model and the 
specification of each variable. The coefficient estimates in Tables 
2 and 3 were used to create a single table containing the weights 
associated with each variable, for each percentile increment, 
usually rounding to the nearest increment of 5.

                   Table 2.-Coefficient Estimates for Economic/Barrier Correlates of Shortage
----------------------------------------------------------------------------------------------------------------
                                    Cutoffs
     Correlate of shortage       (percentiles)    Specification     Coefficient         SE               t
----------------------------------------------------------------------------------------------------------------
Income........................            0-74  Dummy Variable..          355.9           59.3             5.997
                                         75-84  Dummy Variable..          186.0           59.6             3.121
                                         85-84  Dummy Variable..           69.7           53.6             1.301
Population Density............            0-95  Dummy Variable..          318.6           51.4             6.197
                                          0-35  Spline..........            4.23           0.95            4.432
                                         35-95  Spline..........           -3.73           0.84           -4.467
Unemployment..................            5-19  Dummy Variable..          167.8           52.0             3.228
                                         20-99  Dummy Variable..          245.4           48.0             5.110
Below 200% FPL................            0-14  Dummy Variable..          109.0           38.8             2.807
                                         15-99  Spline..........            2.36           0.54            4.406
Constant                        ..............  ................          732.0           78.7             9.297
----------------------------------------------------------------------------------------------------------------


                  Table 3.--Coefficient Estimates for Health/Demographic Correlates of Shortage
----------------------------------------------------------------------------------------------------------------
                                   Cutoffs
    Correlate of shortage       (percentiles)    Specification     Coefficient         SE               t
----------------------------------------------------------------------------------------------------------------
Actual/Expected Deaths.......            6-15  Dummy Variable..           66.4            64.0             1.038
                                        16-55  Dummy Variable..          121.6            57.2             2.124
                                        56-75  Dummy Variable..          211.2            59.4             3.554
                                       76-100  Dummy Variable..          278.5            60.2             4.625
Infant Morality..............          81-100  Dummy Variable..           65.73           27.41            2.398
Percent 65+..................           1-100  Continuous......            1.93            0.37            5.161
Constant.....................  ..............  ................         1364.4            57.2            23.872
----------------------------------------------------------------------------------------------------------------

List of Subjects

42 CFR Part 5

    Health care, Health facilities, Health professions, Health 
statistics, Health status indicators, Medical care, Medical facility, 
Dental health, Mental health programs, Physicians, Population census, 
Poverty, Primary care, Shortages, Underserved, Uninsured.

42 CFR Part 51c

    Grant programs--Health, Health care, Health facilities, Reporting 
and recordkeeping requirements.

    For the reasons set out in the preamble the Department of Health 
and Human Services proposes to amend parts 5 and 51c of title 42 of the 
Code of Federal Register as follows:

PART 5--DESIGNATION OF MEDICALLY UNDERSERVED POPULATIONS AND HEALTH 
PROFESSIONAL SHORTAGE AREAS

    1. The heading for part 5 is revised as set forth above.
    2. The authority citation for part 5 is revised to read as follows:

    Authority: 42 U.S.C. 254b, 254e.

    3. The existing text consisting of Sec. Sec.  5.1 through 5.4 is 
designated as subpart A and revised to read as follows:
Subpart A--General Procedures for Designation of Medically Underserved 
Populations (MUPs) and Health Professional Shortage Areas (HPSAs)
Sec.
5.1 Purpose.
5.2 Definitions.
5.3 Procedures for designation and withdrawal of designation.
5.4 Notice and publication of designation and withdrawals.
5.5 Transition provisions.
5.6 Provisions related to Automatic HPSA designation of certain 
Federally Qualified Health Centers (FQHC) and Rural Health Clinics 
(RHC)

Subpart A--General Procedures for Designation of Medically 
Underserved Populations (MUPs) and Health Professional Shortage 
Areas (HPSAs)


Sec.  5.1  Purpose.

    This part establishes criteria and procedures for the designation 
and withdrawal of designations of medically underserved populations 
pursuant to section 330(b)(3) of the Public Health

[[Page 11271]]

Service Act and of health professional shortage areas pursuant to 
section 332 of the Act.


Sec.  5.2  Definitions.

    As used in this part:
    (a) Act means the Public Health Service Act, as amended (42 U.S.C. 
201 et seq.).
    (b) Department means the Department of Health and Human Services.
    (c) Frontier Area means those areas identified by the Secretary 
(through the Frontier Work Group of the Office for the Advancement of 
Telehealth) as frontier areas, or, until an official list of frontier 
areas is issued, those U.S. counties or county-equivalent units with a 
population density less than or equal to 6 persons per square mile.
    (d) FTE means full-time equivalent, and shall be computed using 
such guidance as the Secretary may provide.
    (e) Governor means the Governor or other chief executive officer of 
a State.
    (f) Health professional shortage area (or HPSA) means any of the 
following which the Secretary determines in accordance with this part 
has a shortage of health professionals:
    (1) A rational, geographic service area;
    (2) A population group; or
    (3) A public or nonprofit private medical facility or other public 
facility that provides primary medical, dental or mental health 
services.
    (g) Inner portions of urban areas means core areas of urbanized 
central places areas as defined by HRSA, based on data from the Bureau 
of the Census.
    (h) Population Center means the census area (tract, division, town, 
etc.) with the largest population within a proposed rational service 
area.
    (i) Medical facility (or other public facility that provides 
primary medical, dental or mental health services) includes:
    (1) A health center, as defined in Section 330(a) of the Public 
Health Service Act, means an entity that serves a population that is 
medically underserved, or a special medically underserved population 
comprised of migratory and seasonal agricultural workers, the homeless, 
and residents of public housing, by providing, either through the staff 
and supporting resources of the center or through contracts or 
cooperative arrangements, required primary health services and, as may 
be appropriate for particular centers, additional health services 
necessary for the adequate support of the primary health services 
required for all residents of the area served by the center (including 
a community health center, migrant health center, health center for the 
homeless, or health center for residents of public housing);
    (2) Any Federally qualified health center (FQHC), as defined in 
Section 1861(aa)(4) of the Social Security Act term ``Federally 
qualified health center'' means an entity which is receiving a grant 
under section 330 (other than subsection (h)) of the Public Health 
Service Act, or is receiving funding from such a grant under a contract 
with the recipient of such a grant, and meets the requirements to 
receive a grant under section 330 (other than subsection (h)) of such 
Act; based on the recommendation of the Health Resources and Services 
Administration within the Public Health Service, is determined by the 
Secretary to meet the requirements for receiving such a grant; was 
treated by the Secretary, for purposes of part B, as a comprehensive 
Federally funded health center as of January 1, 1990; or is an 
outpatient health program or facility operated by a tribe or tribal 
organization under the Indian Self-Determination Act or by an urban 
Indian organization receiving funds under Title V of the Indian Health 
Care Improvement Act.
    (3) A rural health clinic [RHC] as defined in Section 1861(aa)(2) 
of the Social Security Act is primarily engaged in furnishing to 
outpatients services which is located in an area that is not an 
urbanized area (as defined by the Bureau of the Census) and in which 
there are insufficient numbers of needed health care practitioners 
which is located in an area that is not an urbanized area (as defined 
by the Bureau of the Census) and in which there are insufficient 
numbers of needed health care practitioners; a public health center or 
other medical, dental or mental health facility operated by a city or 
county or State health department; or a community mental health center 
(see Section 520 of the Act);
    (4) An ambulatory or outpatient clinic of a hospital;
    (5) An Indian Health Service facility, or a health program or 
facility operated under the Indian Self-Determination Act by a tribe or 
tribal organization; or an Urban Indian Health Program; or
    (6) A facility for delivery of health services to inmates in a U.S. 
penal or correctional institution (under section 323 of the Act), or a 
State correctional institution; or
    (7) A State mental hospital.
    (j) Medically underserved population (or ``MUP'') means:
    (1) The population of a geographic area designated by the Secretary 
in accordance with this part as having a shortage of personal health 
services (also called a medically underserved area or MUA); or
    (2) A population group designated by the Secretary in accordance 
with this part as having a shortage of such services.
    (k) Metropolitan statistical area means an area that has been 
designated by the Office of Management and Budget as a metropolitan 
statistical area. All other areas are ``micropolitan'' or ``non-
metropolitan'' areas.
    (l) Poverty level means the current poverty threshold as defined by 
the Bureau of the Census, which uses a set of money income thresholds 
that vary by family size and composition to determine who is in 
poverty. If a family's total income is less than the family's 
threshold, then that family and every individual in it is considered in 
poverty. The thresholds are updated annually.
    (m) Primary care clinician means a physician, nurse practitioner, 
physician assistant, or certified nurse midwife who practices in a 
primary care specialty as defined in Sec.  5.104(e)(2) of this part, 
provides direct patient care, and practices in a primary care setting, 
as defined in paragraph (n) of this section.
    (n) Primary care setting means a setting where integrated, 
accessible health care services are provided by clinicians who are 
accountable for addressing a large majority of personal health care 
needs, developing a sustained partnership with patients, practicing in 
the context of family and community, and providing continuity and 
integration of health care. It includes but is not limited to health 
centers as defined in Sec.  5.2(i)(2) of this part, health maintenance 
organizations, generalist physicians' offices, and ambulatory care 
facilities operated by hospitals including outpatient facilities that 
are separate but a part of inpatient facilities; it excludes inpatient 
facilities, non-primary care physician specialist's offices, and 
facilities for long term care.
    (o) Secretary means the Secretary of Health and Human Services, or 
any officer or employee of the Department to whom the Secretarial 
authority involved has been delegated.
    (p) State includes the 50 States, the District of Columbia, the 
Commonwealth of Puerto Rico, American Samoa, Guam, the Northern Mariana 
Islands, the U.S. Virgin Islands, the Federated States of Micronesia, 
the Marshall Islands, and the Republic of Palau.


Sec.  5.3  Procedures for designation and withdrawal of designation.

    (a) Any agency or individual may request the Secretary to designate 
(or withdraw the designation of) an area,

[[Page 11272]]

population group, or facility as an MUP and/or as a HPSA. Requests by 
State agencies participating in the Department's electronic shortage 
designation system should be made electronically.
    (b) The Applicant will forward a copy of (or relevant electronic 
information on) each such designation request to the officials and 
entities listed below in each State affected by the request, asking 
that they review the request and offer their recommendations, if any, 
to the Secretary within 30 days:
    (1) The Governor;
    (2) The head of the State health department or State health agency 
designated by the Governor, or other health official to whom this 
reviewing authority has been delegated (such as the Director of the 
Primary Care Office), and the Director of the State Office of Rural 
Health;
    (3) Appropriate local officials within the State, such as health 
officers of counties or cities affected;
    (4) The State primary care association or other State organization, 
if any, that represents federally qualified health centers and other 
community-based primary care organizations in the State;
    (5) Affected State medical, dental, and other health professional 
societies; and
    (6) Where a public facility (including a Federal medical facility) 
is proposed for designation or withdrawal of designation, the chief 
administrative officer of such facility.
    (c) The Secretary may propose the designation, or withdrawal of the 
designation, of an area, population group, or facility under this part. 
Where such a designation or withdrawal is proposed, the Secretary will 
notify the agencies, officials, and entities described in paragraph (b) 
of this section and request comment as therein provided.
    (d) Using data available to the Secretary from national and State 
sources, and based upon the applicable criteria in the remaining 
subparts and appendices to this part, the Secretary will annually 
prepare listings (by State) of currently designated MUPs and HPSAs, 
together with relevant data available to the Secretary, and will 
identify those MUPs and HPSAs within the State whose designations, 
because of age or other factors, are required to be updated. The 
Secretary will provide the listing for each State and a description of 
any required information to the entities in that State identified in 
paragraph (b)(2) and (4) of this section, either electronically or in 
hard copy, and will request review and comment within 90 days.
    (e) The Secretary will furnish, upon request, an information copy 
of a request made pursuant to paragraph (a) of this section or 
applicable portions of the materials provided pursuant to paragraph (c) 
of this section to other interested persons and groups for their review 
and comment. Resulting comments or recommendations may be provided to 
the Secretary, the Governor, and/or the State health official 
identified in paragraph (b)(2) of this section.
    (f) In the case of a proposed withdrawal of a designation, the 
Secretary shall afford other interested persons and groups in the 
affected area an opportunity to submit data and information concerning 
the proposed action, including entities directly dependent on the 
designation and primary care associations and State health professional 
associations, to the extent practicable.
    (g) The Secretary may request such further data and information as 
he/she deems necessary to evaluate particular proposals or requests for 
designation or withdrawal of designation under paragraph (a) of this 
section. Any data so requested must be submitted within 30 days of the 
request, unless a longer period is approved by the Secretary. If the 
information requested under paragraph (c) of this section or under this 
section is not provided, the Secretary will evaluate the proposed 
designation (including continuation of designation) or withdrawal of 
designation of the areas, population groups, and/or facilities for 
which the information was requested on the basis of the information 
available to the Secretary.
    (h) After review and consideration of the available information and 
the comments and recommendations submitted, the Secretary will 
designate those areas, population groups, and facilities as MUPs and/or 
HPSAs, as applicable, which have been determined to meet the applicable 
criteria under this part, and will withdraw the designations of those 
which have been determined no longer to meet the applicable criteria 
under this part.
    (i) Urgent Review. If a clinician dies, retires, or leaves an area 
that is not already designated as an MUP or HPSA with no or limited 
notice, causing a sudden and dramatic change in primary medical care, 
dental or mental health services available to that area's population, 
the State health agency or official identified in paragraph (b)(2) of 
this section may submit an urgent request to the Secretary on behalf of 
the affected community that the area be immediately designated as an 
MUP and/or HPSA. Such urgent requests will be reviewed on an expedited 
basis, within 30 days of receipt. If
    (j) The Secretary fails to complete review of the request within 30 
days after receipt, the area as defined by the State agency will be 
considered designated as an MUP and/or HPSA, as applicable, until and 
unless subsequent review by the Secretary indicates that inaccurate 
data were provided or that the situation has changed. Each year, each 
State may invoke this urgent procedure for processing no more than five 
percent of the total number of designations the State had at the end of 
the preceding calendar year.


Sec.  5.4  Notice and publication of designations and withdrawals.

    (a) In the case of a request under Sec.  5.3(a) of this part, the 
Secretary will give written or electronic notice of the determination 
made to the individual or agency that made the request. The date of 
this notice will reflect the actual date of determination.
    (b) The Secretary will also give written or electronic notice of a 
designation (or withdrawal of designation) under this part on or not 
later than 60 days after the effective date, as noted in paragraph (a) 
of this section , of the designation (or withdrawal), to:
    (1) The Governor of each State in which the designated or withdrawn 
MUP or HPSA is located in whole or in part;
    (2) The State health department or other agency or official 
identified under paragraph Sec.  5.3(b)(2) of this part of the affected 
State or States, and any other State agency deemed appropriate by the 
Secretary; and
    (3) Other appropriate public or nonprofit private entities which 
are located in or which the Secretary determines have a demonstrated 
interest in the area designated or withdrawn, including entities 
directly dependent on the designation and primary care associations and 
State health professional associations.
    (c) The Secretary will publish updated lists of designated MUPs and 
HPSAs in the Federal Register after the end of each fiscal year, 
reflecting designations current at the end of each fiscal year, and 
make the complete list available on-line, by type of designation and by 
State, and will maintain a regularly updated Web site of current 
designations between Federal Register list publications. Such listings 
will distinguish between first and second tier designations as 
determined pursuant to Sec.  5.103 of this part.

[[Page 11273]]

    (d) The effective date of the designation of an MUP or HPSA shall 
be the date of the notification letter or electronic notice provided 
pursuant to paragraph (a) or (b) of this section, or the date of 
publication in the Federal Register, whichever occurs first.
    (e) The effective date of the withdrawal of the designation of an 
MUP or HPSA shall be the date of the notification letter or electronic 
notice provided pursuant to paragraph (a) or (b) of this section, or 
the date on which notification of the withdrawal is published in the 
Federal Register, or the date of publication in the Federal Register of 
an updated list of designations of the type concerned which does not 
include the designation, whichever occurs first.


Sec.  5.5  Transition provisions.

    (a) Continuation of currently designated MUPs and primary care 
HPSAs. Except as otherwise provided in this section and Sec.  5.6 of 
this part, these new criteria for the designation of a MUP or a primary 
care HPSA will be phased in over a period of three years from the date 
of publication of the final rule in the Federal Register, with the 
oldest MUP and HPSA designations being reviewed first. Existing 
designations will remain in effect until reviewed under the new 
criteria on the schedule set by the Secretary after consultation with 
State entities as described below.
    (b) Revision of MUPs and primary care HPSAs.
    (1) The Secretary will, within 90 days after publication of this 
final rule in the Federal Register, submit to the entities in each 
State identified pursuant to Sec.  5.3(b)(2) and (4) of this part a 
listing of the adjusted population-to-primary care clinician ratio 
computed under Sec.  5.104 of this part for each currently designated 
MUP and primary care HPSA within its boundaries, based on the data and 
information available to the Secretary.
    (2) The State health agency or other designee of the Governor shall 
have 90 days from receipt of such listing, or such longer time period 
as the Secretary may approve, to provide comments to the Secretary. 
Such comments should take into account the effects on local communities 
and any comments by affected entities and should include 
recommendations on the following topics:
    (i) Where the boundaries of a currently designated MUP and primary 
care HPSA overlap but do not coincide--
    (A) Which service area boundaries the State recommends be continued 
in effect;
    (B) Whether the State proposes to have any remaining area 
separately designated, either on its own or as part of another service 
area; or
    (C) If the State wishes to identify and consider for designation a 
new service area instead of either area currently designated, 
identification of the boundaries recommended.
    (ii) Any other service area boundaries (of existing designated 
areas) that the State recommends be revised;
    (iii) The State's suggestions as to which areas should be updated 
in the first transition year, which in the second, and which in the 
third;
    (iv) The State's recommendations concerning those areas it suggests 
be updated during the first transition year; and
    (v) The accuracy of the FTE primary care clinician data and other 
data used in scoring.
    (3) Where a current MUP and a primary care HPSA designation 
overlap, and the State makes an election under paragraph (b)(2)(i)(A) 
of this section, the MUP or primary care HPSA that is not selected will 
be deemed to be automatically withdrawn.
    (4) If part of the area of a currently designated MUP or primary 
care HPSA is revised under this part and the State does not request 
designation of the remaining area, the current designation covering the 
remaining area will be deemed to be automatically withdrawn.
    (5) If a State does not provide recommendations to resolve 
overlapping area situations under paragraph (a) of this section, the 
Secretary may revise the areas involved, based on the applicable 
criteria and data and information available.


Sec.  5.6  Provisions related to Automatic HPSA designation of certain 
Federally Qualified Health Centers (FQHC) and Rural Health Centers 
(RHC).

    (a) The Health Care Safety Net Amendments of 2002, as amended by 
Public Law 108-163, provide automatic HPSA designation for at least six 
years, for all entities that:
    (1) Were deemed or certified as an FQHC or RHC, Sec.  5.2(h) of 
this part, on or after October 26, 2002;
    (2) Meet the requirements of section 334 of the Act (concerning the 
provision of services regardless of ability to pay); and
    (3) Do not lose their FQHC or RHC status and/or cease to meet the 
requirements of section 334 of the Act during that time period.
    (b) After the date these regulations take effect, some of the FQHC 
and RHC entities with automatic HPSA designation as described under 
paragraph (a) of this section, [or some of the clinical sites of these 
entities], may also be found to:
    (1) Be located in a geographic area that has been designated under 
the criteria for geographic primary care designations in Subpart B of 
this part;
    (2) Be located in an area containing a population group that has 
been designated under the population group criteria in Subpart C of 
this part and serving the designated population group, as determined by 
the Secretary (e.g., a migrant health center serving a designated 
migrant population; a homeless health center serving a designated 
homeless population; a public housing or community health center 
serving a designated low-income population group); or
    (3) Have met the criteria for designation as a safety-net facility 
in Subpart D of this part.
    (c) A list of FQHC and RHC clinical sites that are automatically 
designated pursuant to paragraph (a) of this section, excluding any 
clinical sites that have also been found to be covered by another HPSA 
designation as set forth in paragraph (b) of this section, shall be 
maintained. This list of automatically designated clinical sites, with 
their addresses, shall be appended to each list of designated HPSAs 
published in the Federal Register or posted on the web in accordance 
with Sec.  5.4 (c) of this part.
    (d) To maintain HPSA designation after six years of automatic 
designation, FQHC or RHC clinical sites remaining on the appended list 
of ``automatic'' HPSAs (or the most recent previous date that the HPSA 
list was published in the Federal Register or posted on the web) will 
be required to demonstrate that their area meets the criteria in 
subpart B of this part, that they are serving a population group which 
meets the criteria in subpart C of this part, or that they meet the 
facility criteria in subpart D of this part. At or near the end of the 
six-year period of automatic designation, the FQHCs and RHCs involved 
will be informed of this requirement by mail, and shall then have 90 
days to provide evidence that the criteria are met for the sites in 
question.
    (e) If an FQHC or RHC is notified as described in paragraph (d) of 
this section that it needs to demonstrate that one or more of its 
clinical sites meet the designation criteria herein, and fails to 
submit materials in support of such a finding within 90 days, the sites 
involved shall then be removed from the HPSA list, unless additional 
time to provide further information is granted

[[Page 11274]]

by the Secretary on a case-by-case basis. Sites so removed can reapply 
for HPSA/MUP designation under the criteria herein at a later date if 
their situation changes so that they are able to provide such evidence.
    (f) If evidence in support of designation of an FQHC or RHC site 
under the criteria herein is provided within the 90 day timeframe 
specified in paragraph (d) of this section, or during such additional 
time as the Secretary may allow in paragraph (e) of this section, the 
Secretary will review the evidence submitted and make a determination, 
within 60 days of receipt. Such sites will remain on the HPSA list 
until this determination is made.
    (g) After review of any information provided as described in 
paragraph (f) of this section, any FQHC or RHC clinical site which the 
Secretary determines does not meet the criteria herein shall be removed 
from the HPSA list. The FQHC or RHC involved will be so notified, and 
subsequent published or posted HPSA lists will not include such sites.
    4. Subpart B is added to read as follows:
Subpart B--Criteria and Methodology for Designation of Geographic Areas 
as Medically Underserved Areas (MUAs) and Primary Care HPSAs
Sec.
5.101 Applicability.
5.102 Criteria for designation of geographic areas as MUAs and 
Primary Care HPSAs.
5.103 Identification of rational service areas for the delivery of 
primary medical care.
5.104 Determination of adjusted population-to-primary care clinician 
ratio.
5.105 Contiguous area considerations.

Subpart B--Criteria and Methodology for Designation of Geographic 
Areas as Medically Underserved Areas (MUAs) and Primary Care HPSAs


Sec.  5.101  Applicability.

    The following criteria and methodology shall be used to designate 
geographic areas as medically underserved (under section 330(b) of the 
Public Health Service Act) and as primary care HPSAs (under section 332 
of the Act).


Sec.  5.102  Criteria for designation of geographic areas as MUAs and 
Primary Care HPSAs.

    A geographic area will be designated both as a medically 
underserved area (pursuant to section 330(b) of the Act) and as a 
primary care HPSA (under Section 332 of the Act) if it is demonstrated, 
by such data and information as the Secretary may require, that the 
area meets the following criteria:
    (a) The area meets the requirements for a rational service area for 
the delivery of primary medical care services under Sec.  5.103 of this 
part; and
    (b) The area's adjusted population-to-primary care clinician ratio/
score, computed under Sec.  5.104 of this part, equals or exceeds 
3,000:1; and
    (c) In the case of specific types of areas identified in Sec.  
5.105 of this part, resources in contiguous areas are shown to be 
overutilized or otherwise inaccessible, as defined in Sec.  5.105 of 
this part.


Sec.  5.103  Identification of rational service areas for the delivery 
of primary medical care.

    (a) General definition: A rational service area (RSA) is a 
geographically delimited, continuous and cohesive area around one or 
more population centers within which a preponderance of the population 
normally seeks and can reasonably expect to receive primary medical 
care services.
    (b) Each rational service area should be large enough to sustain 
services and small enough to ensure that primary medical care resources 
within the RSA are accessible to the population of the RSA within a 
reasonable travel time, assumed to be 40 minutes for a frontier area 
and 30 minutes for all other areas unless the provisions of paragraph 
(g) of this section are invoked by a State.
    (1) Travel times in most areas shall be measured by the estimated 
time required to get from point A to point B by principal roads in an 
automobile traveling at the speed limit, in typical traffic for the 
area, taking into consideration the area's terrain.
    (2) Travel times within inner portions of urban areas may be 
computed in terms of travel by public transportation, in areas with at 
least 20% of the population under 100% of the poverty level and/or a 
significant reliance on public transportation (e.g. at least over 30% 
dependent according to the U.S. Census.)
    (c) Individual RSAs shall be defined in terms of one or more 
contiguous U.S. Census Bureau geographic units for which census data 
are available (e.g. counties, census tracts, census divisions (MCDs/
CCDs), or zip code tabulation areas (ZCTAs), the boundaries of which do 
not overlap with the boundaries of another rational service area.
    (d) Cohesiveness for paragraph (a) of this section can be 
established by demonstrating that the area:
    (1) Is isolated from contiguous areas due to topography, market or 
transportation patterns or other physical barriers, or
    (2) Has a homogeneous socioeconomic composition different from 
those in contiguous areas, and is isolated from or has limited 
interaction with contiguous communities and/or access barriers to 
resources in those areas, or
    (3) Has a tradition of primarily internal interaction or 
independence as defined by transportation or market patterns, or
    (4) Is a single whole county.
    (e) Size of an RSA shall be limited, where an RSA has more than one 
population center (towns of equivalent size), by a maximum of 30 
minutes travel time between population centers within a single RSA.
    (f) Geographic separation of RSAs
    (1) Geographic separation of RSAs shall be measured by the travel 
times between the population center(s) of one RSA and those of 
contiguous RSAs, normally involving a minimum of 30 minutes travel time 
between population centers of different RSAs.
    (2) Travel time from the population center of an RSA to the 
population center of a contiguous RSA may be less than 30 minutes 
within metropolitan statistical areas where established neighborhoods 
and communities display a strong self-identity (as indicated by a 
homogeneous socioeconomic or demographic structure and/or a tradition 
of interaction or interdependence), have limited interaction with 
contiguous areas, and, in general, have a population density equal to 
or greater than 100 persons per square mile.
    (g) RSA parameters determined by State--
    (1) RSA parameters different from those defined in paragraph (f) of 
this section, but within the ranges defined in paragraph (g)(2) of this 
section, may be used for RSA delineation within a State if:
    (i) Such parameters and the method for defining RSAs to be used 
with those parameters are adopted by the State through a partnership 
approach with affected State and community officials/stakeholders and 
in consultation with the Secretary, (ii) The RSA parameters and method 
selected have the approval of the State health department or other 
designee of the Governor identified in Sec.  5.3(b)(2) of this part, 
and
    (iii) The final RSA approach to be used has been reviewed by the 
Secretary in advance of the State submitting particular RSA definitions 
using its approach.
    (2) Permissible Ranges for RSA parameters adopted by States:

[[Page 11275]]

    (i) The maximum travel time to assure access to care within the RSA 
is set at 30 minutes in paragraph (b) of this section and the maximum 
travel time between population centers within the RSA, set generally at 
30 minutes in paragraph (e) of this section, may be set at any value 
greater than or equal to 20 minutes but less than or equal to 40 
minutes, for non-frontier RSAs.
    (ii) Maximum travel time to assure access to care within a frontier 
or other sparsely-populated RSA, set generally at 40 minutes in 
paragraph (e) of this section, may be set at any value greater than 30 
minutes but less than or equal to 60 minutes, where topography, market, 
transportation, or other conditions and patterns lead to utilization of 
providers at greater distances.
    (iii) Separation between RSAs--Minimum travel time from the 
population center(s) of the RSA to the population center of a 
contiguous RSA may be set at any value greater than or equal to 20 
minutes and less than or equal to 40 minutes.
    (h) State-wide system. Each State is encouraged to develop a State-
wide system which divides the territory of the State into rational 
service areas (RSAs) for the delivery of primary care services within 
the State.
    (1) This may be done all at once or incrementally, by developing 
State RSA criteria using the parameter ranges defined above and a 
process for defining the State's RSAs according to those criteria over 
a period of time. A full statewide plan is encouraged to maximize its 
effectiveness in improving the designation process.
    (2) Each State-wide system of rational service areas or process for 
developing State RSAs shall be developed in consultation with the 
Secretary and be approved by the State health department or other 
designee of the Governor.


Sec.  5.104  Determination of adjusted population-to-primary care 
clinician ratio.

    The adjusted population-to-primary care clinician ratio is computed 
as the sum of the ``barrier-free'' population-to-primary care clinician 
ratio of an area, calculated as in paragraph (a) of this section, and 
the area's High Need Indicator score, calculated as paragraph (b) of 
this section:
    (a) Effective Barrier-Free Population-to Clinician Ratio for an 
area is computed as follows:
    (1) Estimate the primary care utilization of the area's population 
if no barriers to accessing health care existed, in total expected 
visits per year. This shall be done by applying current national 
utilization rates for populations without access barriers, to current 
data on the population composition of each area by age and gender. The 
national utilization rates to be used for this purpose (in visits per 
year, by age group and gender) will be published in tabular form by the 
Secretary from time to time. The utilization rate table applicable at 
the time of publication of this regulation will be included in the 
preamble; later updates will be made available periodically but no more 
often than annually.
    (2) Divide the resulting total estimated number of annual barrier-
free visits for the area by the national mean utilization rate 
(consistent with the tabular utilization data used and published along 
with it) to obtain the area's effective (barrier-free) population.
    (3) Where an area has a significant number of migratory workers, 
homeless persons, or seasonal residents, the effective population 
calculated in paragraph (a)(2) of this section may be adjusted further 
by multiplying by the factor [Resident Civilian Pop. + Migratory 
workers & families + Homeless + Seasonal Residents] / Resident Civilian 
Pop., where these quantities are defined as in paragraph (c)(1) of this 
section. The resident-civilian population does include some components 
of the homeless population, so any additions should avoid duplication.
    (4) Calculate the ratio of the final effective population to the 
area's number of FTE primary care clinicians, calculated as discussed 
in paragraph (c)(2) of this section, to determine the area's barrier-
free population-to-primary care clinician ratio.
    (b) High Need Indicator Score.
    (1) The High Need Indicator score for an area is computed as the 
sum of the area's partial scores for each of the nine variables listed 
in this paragraph (b)(1):
    (i) Percentage of population below 200% of the federal poverty 
level;
    (ii) Unemployment rate;
    (iii) Percentage of population that is non-White;
    (iv) Percentage of population that is Hispanic;
    (v) Percentage of population that is over age 65;
    (vi) Population density;
    (vii) Actual/expected death rate
    (viii) Low birth weight birth rate
    (ix) Infant mortality rate
    (2) A current national Percentiles Table IV-6 (relating raw scores 
for each indicator to the national percentile distribution of that 
indicator at the county level) shall be used to determine an area's 
percentile rank for each high need indicator at the time of proposed 
designation or update. HRSA will publish revised percentile tables as a 
Notice in the Federal Register if there are significant changes in the 
indicators in paragraph (b)(1) in this section.
    (3) The percentile rank for each indicator shall then be converted 
to a partial score, using the Scores Table IV-7.
    (4) The total High Need Indicator score is computed as the sum of 
the nine partial scores computed in paragraph (b)(3) of this section 
for each indicator.
    (c) The barrier-free population-to-primary care clinician ratio/
score, as computed in paragraph (a) of this section, is added to the 
High Need Indicator Score, as computed in paragraph (b) of this 
section, to obtain the final adjusted population-to-primary care 
clinician ratio.
    (d) The threshold for designation is an adjusted population-to-
primary care clinician ratio/score that exceeds 3,000:1.
    (e) Calculation of specific variables
    (1) Population counts. The population of an area is the total 
resident civilian population, excluding inmates and residents of 
institutions, based on the most recent U.S. Census data, adjusted for 
increases/decreases to the current year using the best available 
intercensus projections, and making the following adjustments, as 
appropriate:
    (i) Migratory workers and their families may be added to the 
adjusted resident civilian population, if significant numbers of 
migratory workers are present in the area, using the latest Migrant 
Health Atlas or best available Federal or State estimates. Estimates 
used must be adjusted to reflect the percentage of the year that 
migratory workers are present in the area.
    (ii) If an area includes significant numbers of homeless 
individuals not reflected in the census figures, and reasonable 
estimates of their numbers are available, these data may be submitted 
for consideration as an adjustment to the population of the area.
    (iii) Where seasonal residents significantly affect the effective 
total population of an area, seasonal residents (not including 
tourists) may be added to the adjusted resident civilian population, if 
supported by acceptable State or local estimates. Estimates used must 
be adjusted to reflect the percentage of the year that seasonal 
residents are present in the area.
    (iv) Significant numbers of these populations are indicated when 
the numbers are large enough to reflect an additional burden on the 
health care

[[Page 11276]]

system that the census data do not capture effectively.
    (2) Counting of primary care clinicians.
    (i) In determining an area's adjusted population-to-primary care 
clinician ratio for designation as a tier 1 shortage area, clinicians 
shall be counted as follows:
    (A) All non-Federal doctors of medicine (M.D.) and doctors of 
osteopathy (D.O.) who provide direct patient care and practice 
principally in one of the four primary care specialties (general or 
family practice, general internal medicine, pediatrics, and obstetrics 
and gynecology), shall be included in clinician counts.
    (B) All non-Federal nurse practitioners, physician's assistants, 
and certified nurse midwives practicing in primary care settings shall 
be included in clinician counts, but with a multiplier of:
    (1) 0.5, or, at the applicant's option,
    (2) 0.8 times an additional factor whose value is between 0.5 and 
1.0, depending on the scope of practice allowed for each type of non-
physician clinician in the State involved. A table of these factors for 
each State and for each type of non-physician clinician will be 
provided in the final regulation. HRSA will publish an updated table of 
these factors as a Notice in the Federal Register if such updates 
become available.
    (C) Where clinicians are practicing less than full-time, or have 
more than one practice address, their contribution to the total count 
may be reduced based on their estimated full-time-equivalency (FTE) 
practicing within the area being considered, using available data.
    (D) Each intern or resident physician shall be 0.1 FTE physician
    (E) Hospital staff physicians practicing in organized outpatient 
departments and primary care clinics shall be counted only on an FTE 
basis, based on their time in outpatient/ambulatory settings, not in 
inpatient care.
    (F) The following shall be excluded from primary care clinician 
counts:
    (1) Practitioners who are engaged solely in administration, 
research, or teaching;
    (2) Hospital staff physicians involved exclusively in inpatient 
and/or in emergency room care; and
    (3) Clinicians who are suspended under provisions of the Medicare-
Medicaid Anti-Fraud and Abuse Act, during the period of suspension.
    (ii) In determining an area's adjusted population-to-primary care 
clinician ratio for designation as a tier 2 shortage area, clinicians 
shall be counted as provided for above, except that the following 
clinicians shall also be excluded:
    (A) Primary care clinicians who are members of the National Health 
Service Corps (NHSC), established by section 331(a) of the Act, are 
fulfilling a service obligation incurred under the NHSC Scholarship or 
Loan Repayment Program (sections 338A and 338B of the Act) or are 
fulfilling a service obligation incurred under the State Loan Repayment 
program (section 338I of the Act);
    (B) Physicians who are practicing in the United States under a 
waiver of their J-1 Visa requirements; and
    (C) Primary care clinicians who are providing services at a health 
center receiving a grant under section 330 of the Act and who are not 
otherwise excluded under paragraphs (e)(2)(ii)(A) or (B) of this 
section.
    (iii) Counting of FTEs.
    (A) Clinician count data in the Department's electronic designation 
database (from national data, augmented by State data where approved by 
the Secretary) may be used by applicants without adjustments for 
designation purposes.
    (B) If applicants prefer, they may conduct surveys of the 
clinicians in area(s) requested for designation. When this is done, 
FTEs shall be computed using such guidance as the Secretary may 
provide.
    (3) Data Sources for High Need Indicators
    (i) The Unemployment Rate, High Need Indicator at paragraph 
(b)(1)(i)(B) of this section, shall be calculated based on the latest 
Bureau of Labor Statistics unemployment data available for the lowest-
level area (county, city, place, or other labor statistics area) that 
comprises or includes the area.
    (ii) Data for the percent below poverty and demographic High Need 
Indicators at paragraphs (b)(1)(i)(A) and (ii) of this section, for an 
area shall be aggregated from the latest available U.S. Census data for 
the counties, census tracts, census divisions or ZCTAs which comprise 
the area, or from more recent updates thereof if available and approved 
by the Secretary.
    (iii) The health status High Need Indicators at paragraph 
(b)(1)(iii) of this section shall be calculated based on the latest 
available five-year average data available, from DHHS or the State 
involved, for the county of which the service area is a part, unless 
the area is a subcounty area and statistically significant five-year 
average subcounty data on these variables are available for that 
subcounty area. For service areas which cross county lines, a 
population-weighted combination of the rates for the counties involved 
shall be used.


Sec.  5.105  Contiguous area considerations.

    (a) An analysis of resources in areas contiguous to the area being 
considered for designation shall be required only if the State involved 
has not developed a system of RSAs, or has a partially-developed system 
which does not include all areas contiguous to the requested area, and 
the population center of the area for which designation (or update of 
designation) is sought is less than 30 minutes from the nearest 
providers.
    (b) Where contiguous area analysis is required under paragraph (a) 
of this section, resources in a particular contiguous area will be 
deemed to be overutilized or otherwise inaccessible if any of the 
following conditions exists:
    (1) All primary care clinicians in the contiguous area are located 
more than 30 minutes travel time from the population center(s) of the 
requested area;
    (2) The adjusted (or unadjusted) population-to-FTE primary care 
clinician ratio within the contiguous area is in excess of 2000:1; or
    (3) Primary care clinician(s) located in the contiguous area appear 
to be inaccessible to the population of the requested area because of 
specific access barriers, such as:
    (i) A lack of economic access to contiguous area resources, 
particularly where a very high proportion of the requested area's 
population is poor, and Medicaid-covered or public (sliding-fee-
schedule or free) primary care services are not available in the 
contiguous area; or
    (ii) Significant differences exist between the demographic 
characteristics of the requested area and those of the contiguous area 
(and its clinicians), indicative of isolation of the requested area's 
population from the contiguous area, such as language or cultural 
difference.
    5. Subpart C is added to read as follows:
Subpart C--Criteria and Methodology for Designation of Population 
Groups as MUPs and/or Primary Care HPSAs
Sec.
5.201 Applicability.
5.202 General criteria for designation of specific population groups 
as MUPs and/or primary care HPSAs.
5.203 Criteria for designation of migratory and seasonal 
agricultural workers as primary care HPSAs.
5.204 Criteria for designation of homeless populations as primary 
care HPSAs.
5.205 Criteria for designation of Native American populations as 
primary care HPSAs and MUPs.

[[Page 11277]]

5.206 Requirements for ``permissible'' designation of other 
population groups as MUPs.

Subpart C--Criteria and Methodology for Designation of Population 
Groups as MUPs and/or Primary Care HPSAs


Sec.  5.201  Applicability.

    (a) Certain specific population groups will be designated as both 
MUPs and primary care HPSAs if it is demonstrated that the criteria in 
Sec.  5.202 of this part are met when applied to data on these 
population groups. These specific population groups are:
    (1) The low income population, defined as that portion of an area's 
population whose incomes are below 200% of the poverty level.
    (2) The Medicaid-eligible population of the area.
    (3) Linguistically-isolated populations, defined as the Secretary 
may with reference to census definitions of linguistically-isolated 
households and/or populations for whom English is not spoken at all or 
is a second language not spoken well.
    (b) Migratory and seasonal agricultural workers and their families 
within specific service areas are defined in law as ``special medically 
underserved populations''. They will also be designated as primary care 
HPSAs if it is demonstrated that the criteria in Sec.  5.203 of this 
part are met.
    (c) Homeless populations are defined in law as ``special medically 
underserved populations''. They will also be designated as primary care 
HPSAs if it is demonstrated that the criteria in Sec.  5.204 of this 
part are met.
    (d) Residents of Public Housing are defined in law as ``special 
medically underserved populations''. They will also be designated as 
primary care HPSAs if it is demonstrated that the criteria in Sec.  
5.202 of this part are met when computed for the low income population 
group residing in a particular Public Housing community.
    (e) Native American population groups (including American Indian 
tribes or Alaska Native entities) will be designated as both MUPs and 
primary care HPSAs if it is demonstrated that the criteria in Sec.  
5.205 of this part are met.
    (f) If an FQHC, RHC, or other public or nonprofit private clinical 
site has been designated as a safety-net facility primary care HPSA 
under Subpart D, Sec.  5.301 of this part (based on service to 
significant numbers of uninsured and Medicaid-eligible patients), the 
population group of uninsured and Medicaid-eligible patients served by 
the clinical site shall be considered designated as an MUP.
    (g) Other population groups recommended by State and local 
officials may be designated as MUPs under unusual local conditions 
which are a barrier to access to or availability of health services, 
under procedures described in Sec.  5.206.


Sec.  5.202  General criteria for designation of specific population 
groups as MUPs and/or primary care HPSAs.

    (a) Any of the specific population groups identified in Sec.  
5.201(a) of this part may be designated if it is demonstrated, using 
such documentation as the Secretary may require, that the following 
criteria are met when applied to data for the population group:
    (1) The area in which the population group resides meets the 
requirements for a rational service area under Sec.  5.103 of this 
part;
    (2) The rational service area in which the population group resides 
does not meet the criteria for designation as a geographic area under 
Sec.  5.102 of this part;
    (3) There are access barriers that prevent the population group 
from accessing primary medical care services available to the general 
population of the area, as demonstrated by an adjusted population-to-
primary care clinician ratio computed for the population group that 
equals or exceeds the 3000:1 designation threshold in Sec.  5.104 of 
this part.
    (b) In calculating the adjusted population-to-primary care 
clinician ratio for a population group, the methodology described in 
Sec.  5.104 of this part shall be used, except that:
    (1) The group's population shall be used instead of the area's 
population,
    (2) The FTE clinicians available to the population group shall be 
used rather than those available to the area in general (i.e. Medicaid 
FTE/claims and sliding fee scale FTE for a low income population), and
    (3) High Need Indicators shall be calculated based as nearly as 
possible on their values for the applicable population group within the 
service area, using such approximations as the Secretary may allow.


Sec.  5.203  Criteria for designation of migratory and seasonal 
agricultural workers as primary care HPSAs.

    (a) Where data availability permits, the method described in Sec.  
5.202 of this part may be used to calculate an adjusted population-to-
primary care clinician ratio for a population group composed of 
migratory and seasonal agricultural workers, and to compare this ratio 
with the 3000:1 designation threshold, with these additional 
conditions:
    (1) For a migratory and seasonal agricultural worker population 
group, an agricultural area (as defined by the Secretary) may be used 
as a rational service area.
    (2) The population of the migratory and seasonal population group 
identified must be adjusted by a factor representing the fraction of 
the year that this population is present in the area.
    (b) Alternatively, a simplified designation procedure may be used, 
as follows:
    (1) Define the boundaries of the agricultural area or other service 
area within which the migratory and seasonal agricultural worker 
population reside or temporarily reside for a portion of the year.
    (2) Provide data on the number of individuals in the population 
group (including workers and their families) and the number of months 
they are present in the area during a typical year.
    (3) If the number of individuals times the number of months divided 
by 12 exceeds 1000, this special medically underserved population group 
will also be considered a primary care HPSA, with its population-to-
primary care clinician ratio assumed equal to 3000:1.


Sec.  5.204  Criteria for designation of homeless populations as 
primary care HPSAs.

    (a) Where data availability permits, the method described in Sec.  
5.202 of this part may be used to calculate an adjusted population-to-
primary care clinician ratio for a homeless population group (or for a 
combined homeless and other low-income population group), and compare 
this ratio with the 3000:1 designation threshold. For such population 
groups, the area in which homeless populations congregate and/or are 
sheltered may be used as a rational service area.
    (b) Alternatively, a simplified designation procedure may be used, 
as follows:
    (1) Define the boundaries of the area in which homeless populations 
congregate and/or are sheltered.
    (2) Provide data on the average number of homeless individuals in 
the defined area during a typical year, and the average number of 
months they are homeless.
    (3) If the average number of homeless individuals during a typical 
year exceeds 1000, this special medically underserved population group 
will also be considered a primary care HPSA, with its population-to-
primary care clinician ratio assumed equal to 3000:1.

[[Page 11278]]

Sec.  5.205  Criteria for designation of Native American population 
groups as primary care HPSAs and MUPs.

    (a) Those American Indian tribes or Alaska Native entities 
identified by the Department of the Interior as federally recognized 
are automatically designated as population group primary care HPSAs and 
MUPs and will be given a baseline ratio of 3000:1.
    (b) Where data availability permits, the method described in Sec.  
5.202(b) of this part may be used to calculate a higher population-to-
primary care clinician ratio for a Native American population group 
and/or to facilitate scoring such a designation for purposes of 
allocating program resources. For such designations, a reservation may 
be used as a rational service area.


Sec.  5.206  Requirements for ``permissible'' designation of other 
population groups as MUPs.

    The population of a service area that does not meet the criteria at 
Sec.  5.102 of this part, or a population group that does not meet the 
criteria in Sec. Sec.  5.202 through 5.205 of this part, may 
nevertheless be designated as an MUP if the following requirements are 
met:
    (a) The area or population group is recommended for designation by 
the Governor of the State in which the area is located and by at least 
one local official of the area. A local official for this purpose may 
be--
    (1) The chief executive of the local governmental entity which 
includes all or a substantial portion of the requested area or 
population group (such as the county executive of a county, mayor of a 
town, mayor or city manager of a city); or
    (2) A city or county health official (such as the head of a city or 
county health department) of the local governmental entity which 
includes all or a substantial portion of the requested area or 
population group.
    (b) The request for designation is based on the presence of unusual 
local conditions, not covered by the criteria at Sec.  5.102 and/or 
Sec. Sec.  5.202 through 5.205 of this part, which are a barrier to 
access to or the availability of personal health services in the area 
or for the population group for which designation is sought.
    (c) The request contains such documentation as the Secretary may 
require.
    6. Subpart D is added to read as follows:
Subpart D--Criteria and Methodology for Designation of Facilities as 
Primary Care Health Professional Shortage Areas
Sec.
5.301 Criteria for designation of public and nonprofit private 
medical facilities as safety-net facility primary care HPSAs.
5.302 Criteria for designation of Federal and State correctional 
institutions as primary care HPSAs.

Subpart D--Criteria and Methodology for Designation of Facilities 
as Primary Care Health Professional Shortage Areas


Sec.  5.301  Criteria for designation of public and nonprofit private 
medical facilities as safety-net-facility primary care HPSAs.

    (a) A public or nonprofit private medical facility, or a remote 
clinical site of such a facility, which is located in a geographic area 
that is not designated as a geographic primary care HPSA under Subpart 
B of this part, shall be designated as a ``safety-net-facility'' 
primary care HPSA if the following criteria are met:
    (1) The facility or site is or is part of an FQHC, RHC or other 
public or nonprofit private medical facility which provides primary 
medical care services on an ambulatory or outpatient basis, and
    (2) The facility or clinical site is identifiable as a safety-net 
facility based on service to significant numbers of uninsured and 
Medicaid-eligible patients, as determined using payment source data and 
the minimum requirements by type of area described in paragraph (b) of 
this section.
    (b) Methodology. In determining whether public or nonprofit private 
facilities or clinical sites are safety-net facilities for purposes of 
this designation, the following methodology will be used:
    (1) The facility or particular site for which designation is sought 
must meet all of the following requirements:
    (i) Currently provides full-time ambulatory or outpatient primary 
medical care;
    (ii) Provides services regardless of an individual's ability to pay 
for such services; and
    (iii) Has a posted, discounted sliding-fee-scale which is available 
to all uninsured patients with incomes below 200% of the poverty line.
    (2) Payment source criteria. Using such documentation as may be 
required by the Secretary, it must be demonstrated that:
    (i) At least 10% of all patients served at each facility or 
clinical site (or group of such sites, where payment source data are 
available only for the group) are indigent uninsured, receiving 
services free or on a discounted sliding fee scale.
    (ii) The number of patients served that are paid under Medicaid, 
plus the number who receive services free or on a discounted sliding 
fee scale, as a percentage of all patients served at each facility or 
clinical site (or group of such sites, where payment source data are 
available only for the group) must equal or exceed the following:
    (A) 40% in metropolitan areas
    (B) 30% in non-metropolitan, non-frontier areas
    (C) 20% of all patients in frontier, non-metropolitan areas


Sec.  5.302  Criteria for designation of Federal and State correctional 
institutions as primary care HPSAs.

    (a) Medium to maximum security Federal and State correctional 
institutions and youth detention facilities will be designated as 
primary care HPSAs, if both of the following criteria are met:
    (1) The institution has at least 250 inmates; and
    (2) The institution has no primary medical care clinicians, or the 
ratio of the number of inmates per year to the number of FTE primary 
care clinicians, determined in accordance with Sec.  5.104(e)(2) of 
this part, serving the institution is at least 1,000:1.
    (b) For purposes of this paragraph, the number of inmates shall be 
determined as follows:
    (1) If the number of new inmates per year and the average length-
of-stay are not specified, or if the information provided does not 
indicate that intake medical examinations are routinely performed upon 
entry, then the number of inmates is used.
    (2) If the average length-of-stay is specified as one year or more, 
and intake medical examinations are routinely performed upon entry, 
then the number of inmates equals the average number of inmates plus 
0.3 multiplied by the number of new inmates per year; or
    (3) If the average length-of-stay is specified as less than one 
year, and intake examinations are routinely performed upon entry, then 
the number of inmates equals the average number of inmates plus 0.2 
multiplied by (1 + ALOS/2) multiplied by the number of new inmates per 
year, where ALOS is the average length of stay, in fraction of a year.
    (c) Clinicians permanently employed by the Federal Bureau of 
Prisons or by States to provide services to Federal or State prisoners 
shall be counted based on the FTE services they provide, calculated as 
provided for in Sec.  5.104(c)(2).
    7. Subpart E is added to read as follows:

[[Page 11279]]

Subpart E--Identification of Primary Care Health Professional 
Shortage Areas of Greatest Need


Sec.  5.401  Use of methodology for identification of HPSAs of greatest 
need.

    The adjusted population to clinician ratios that are the result of 
the calculations in the methodology will be used as the relative scores 
to identify those HPSAs of Greatest Need. Areas will be ranked 
according to the ratios calculated to determine an area's eligibility 
for designation.
    8. Appendix A to part 5 is revised to read as follows:

Appendix A to Part 5--Scoring Table for High Need Indicators Used in 
MUP and Primary Care HPSA Designation

                                      Table A-1.--Scores for High Need Indicators, Given Their National Percentiles
--------------------------------------------------------------------------------------------------------------------------------------------------------
   Percentile         Poverty           Unemp           Elderly          Density          Hispanic        Non white        Death rate        LBW/IMR
--------------------------------------------------------------------------------------------------------------------------------------------------------
             0             0.00             0.00             0.00           995.20             0.00             0.00             0.00             0.00
             1             3.01             1.18             0.54           831.13             0.81             0.00             0.82             0.72
             2             6.04             2.37             1.09           735.15             1.64             0.00             1.65             1.44
             3             9.11             3.58             1.65           667.05             2.47             0.00             2.49             2.17
             4            12.21             4.79             2.21           614.23             3.31             0.00             3.33             2.91
             5            15.34             6.02             2.77           571.07             4.15             0.00             4.19             3.65
             6            18.50             7.26             3.34           534.58             5.01             0.00             5.05             4.40
             7            21.70             8.52             3.92           502.98             5.88             0.00             5.93             5.17
             8            24.93             9.79             4.51           475.10             6.75             0.00             6.81             5.93
             9            28.20            11.07             5.10           450.16             7.64             0.00             7.70             6.71
            10            31.50            12.37             5.69           427.59             8.53             0.00             8.60             7.50
            11            34.84            13.68             6.30           407.00             9.44             0.00             9.52             8.29
            12            38.22            15.00             6.91           388.05            10.35             0.00            10.44             9.10
            13            41.64            16.35             7.53           370.51            11.28             0.00            11.37             9.91
            14            45.10            17.70             8.15           354.18            12.21             0.00            12.32            10.73
            15            48.59            19.08             8.78           338.90            13.16             0.00            13.27            11.57
            16            52.13            20.46             9.42           324.55            14.12             0.00            14.24            12.41
            17            55.71            21.87            10.07           311.02            15.09             0.00            15.22            13.26
            18            59.34            23.29            10.72           298.22            16.07             0.00            16.21            14.12
            19            63.00            24.73            11.39           286.08            17.07             0.00            17.21            15.00
            20            66.72            26.19            12.06           274.53            18.07             0.00            18.22            15.88
            21            70.48            27.67            12.74           263.52            19.09             0.00            19.25            16.78
            22            74.29            29.16            13.43           253.00            20.12             0.00            20.29            17.68
            23            78.15            30.68            14.12           242.92            21.17             0.00            21.34            18.60
            24            82.06            32.21            14.83           233.26            22.23             0.00            22.41            19.53
            25            86.02            33.77            15.55           223.98            23.30             0.00            23.49            20.48
            26            90.03            35.34            16.27           215.04            24.39             0.00            24.59            21.43
            27            94.10            36.94            17.01           206.43            25.49             0.00            25.70            22.40
            28            98.22            38.56            17.75           198.13            26.61             0.00            26.83            23.38
            29           102.40            40.20            18.51           190.10            27.74             0.00            27.97            24.38
            30           106.64            41.86            19.28           182.34            28.89             0.00            29.13            25.39
            31           110.95            43.55            20.05           174.83            30.05             0.00            30.30            26.41
            32           115.31            45.27            20.84           167.54            31.23             0.00            31.49            27.45
            33           119.74            47.01            21.64           160.47            32.43             0.00            32.70            28.50
            34           124.24            48.77            22.45           153.61            33.65             0.00            33.93            29.57
            35           128.80            50.56            23.28           146.94            34.89             0.00            35.18            30.66
            36           133.44            52.38            24.12           140.46            36.14             0.00            36.45            31.76
            37           138.15            54.23            24.97           134.15            37.42             0.00            37.73            32.88
            38           142.93            56.11            25.83           128.00            38.72             0.00            39.04            34.02
            39           147.79            58.02            26.71           122.00            40.03             0.00            40.37            35.18
            40           152.74            59.96            27.61           116.16            41.37             0.00            41.72            36.36
            41           157.76            61.93            28.51           110.46            42.73             1.39            43.09            37.55
            42           162.87            63.94            29.44           104.89            44.12             2.81            44.48            38.77
            43           168.07            65.98            30.38            99.44            45.53             4.25            45.90            40.01
            44           173.36            68.06            31.33            94.12            46.96             5.71            47.35            41.27
            45           178.75            70.17            32.31            88.92            48.42             7.20            48.82            42.55
            46           184.24            72.33            33.30            83.83            49.90             8.72            50.32            43.86
            47           189.83            74.52            34.31            78.85            51.42            10.27            51.85            45.19
            48           195.52            76.75            35.34            73.97            52.96            11.85            53.40            46.54
            49           201.33            79.03            36.39            69.18            54.53            13.46            54.99            47.92
            50           207.25            81.36            37.46            64.50            56.14            15.10            56.60            49.33
            51           213.29            83.73            38.55            59.90            57.77            16.77            58.25            50.77
            52           219.45            86.15            39.66            55.39            59.44            18.48            59.94            52.24
            53           225.75            88.62            40.80            50.97            61.15            20.22            61.66            53.74
            54           232.18            91.15            41.96            46.62            62.89            22.00            63.41            55.27
            55           238.75            93.73            43.15            42.36            64.67            23.82            65.21            56.83
            56           245.47            96.36            44.37            38.17            66.49            25.68            67.04            58.43
            57           252.34            99.06            45.61            34.05            68.35            27.58            68.92            60.07
            58           259.38           101.82            46.88            30.01            70.26            29.53            70.84            61.74
            59           266.59           104.65            48.18            26.03            72.21            31.53            72.81            63.46
            60           273.97           107.55            49.52            22.11            74.21            33.57            74.83            65.21
            61           281.54           110.52            50.89            18.27            76.26            35.67            76.89            67.02
            62           289.30           113.57            52.29            14.48            78.36            37.82            79.02            68.87

[[Page 11280]]

 
            63           297.28           116.70            53.73            10.75            80.52            40.03            81.19            70.76
            64           305.47           119.92            55.21             7.08            82.74            42.30            83.43            72.71
            65           313.89           123.22            56.73             3.47            85.02            44.63            85.73            74.72
            66           322.56           126.63            58.30            -0.09            87.37            47.03            88.10            76.78
            67           331.49           130.13            59.91            -3.60            89.79            49.50            90.54            78.91
            68           340.69           133.74            61.58            -7.06            92.28            52.05            93.05            81.10
            69           350.18           137.47            63.29           -10.46            94.85            54.68            95.64            83.36
            70           359.98           141.32            65.06           -13.82            97.51            57.39            98.32            85.69
            71           370.12           145.30            66.90           -17.13           100.25            60.20           101.09            88.10
            72           380.61           149.41            68.79           -20.40           103.10            63.11           103.95            90.60
            73           391.49           153.68            70.76           -23.62           106.04            66.12           106.92            93.19
            74           402.77           158.11            72.80           -26.79           109.10            69.24           110.01            95.87
            75           414.50           162.72            74.92           -29.93           112.27            72.49           113.21            98.67
            76           426.70           167.51            77.12           -33.02           115.58            75.87           116.54           101.57
            77           439.43           172.50            79.42           -36.08           119.03            79.39           120.02           104.60
            78           452.72           177.72            81.83           -39.09           122.63            83.07           123.65           107.76
            79           466.63           183.18            84.34           -42.07           126.39            86.93           127.45           111.08
            80           481.22           188.91            86.98           -45.01           130.35            90.97           131.43           114.55
            81           496.55           194.93            89.75           -47.92           134.50            95.21           135.62           118.20
            82           512.72           201.28            92.67           -50.78           138.88            99.69           140.04           122.05
            83           529.81           207.98            95.76           -53.62           143.51           104.42           144.70           126.11
            84           547.94           215.10            99.03           -56.42           148.42           109.44           149.65           130.43
            85           567.23           222.68           102.52           -59.19           153.65           114.79           154.92           135.02
            86           587.86           230.77           106.25           -61.93           159.23           120.50           160.56           139.93
            87           610.02           239.47           110.26           -64.63           165.23           126.64           166.61           145.21
            88           633.95           248.87           114.58           -67.31           171.72           133.26           173.15           150.90
            89           659.97           259.08           119.28           -69.95           178.76           140.47           180.25           157.10
            90           688.47           270.27           124.43           -72.57           186.48           148.36           188.04           163.88
            91           719.97           282.63           130.13           -75.15           195.02           157.08           196.64           171.38
            92           755.19           296.46           136.49           -77.71           204.56           166.84           206.26           179.76
            93           795.11           312.13           143.71           -80.24           215.37           177.89           217.16           189.27
            94           841.20           330.23           152.04           -82.75           227.85           190.66           229.75           200.24
            95           895.72           351.63           161.89           -85.23           242.62           205.75           244.64           213.21
            96           962.43           377.82           173.95           -87.68           260.69           224.23           262.86           229.10
            97          1048.45           411.58           189.50           -90.11           283.99           248.05           286.36           249.57
            98          1169.68           459.18           211.41           -92.51           316.83           281.62           319.47           278.43
            99          1376.93           540.53           248.87           -94.89           372.97           339.02           376.07           327.76
--------------------------------------------------------------------------------------------------------------------------------------------------------

    9. The heading for Appendix B to part 5 is revised to read as 
follows:

Appendix B to Part 5--Criteria for Designation of Areas Having 
Shortages of Dental Professionals

* * * * *

Appendices D, E, F, G [Removed]

    10. Appendices D, E, F, and G of part 5 are removed.

PART 51c--GRANTS FOR COMMUNITY HEALTH SERVICES

    11. The authority citation for part 51c is revised to read as 
follows:

    Authority: 42 U.S.C. 216, 254c.

    12. Section 51c.102 is amended by revising paragraph (e) and adding 
paragraph (k) to read as follows:


Sec.  51c.102  Definitions.

* * * * *
    (e) Medically underserved population means the population of an 
urban or rural area which is designated as a medically underserved 
population by the Secretary under part 5 of this chapter.
* * * * *
    (k) Special medically underserved population means a population 
defined in section 330(g), 330(h), or 330(i) of the Act. These include 
migratory and seasonal agricultural workers, homeless populations, and 
residents of public housing, A special medically underserved population 
is not required to be designated in accordance with part 5 of this 
chapter.
    13. Section 51c.104 is amended by revising paragraph (b)(3) and 
adding paragraph (d) to read as follows:


Sec.  51c.104  Applications.

* * * * *
    (b) * * *
    (3) The results of an assessment of the need that the population 
served or proposed to be served has for the services to be provided by 
the project (or in the case of applications for planning and 
development projects, the methods to be used in assessing such need), 
utilizing, but not limited to, the factors set forth in Sec.  5.104 of 
this chapter.
* * * * *
    (d) If an application funded under this part demonstrates that the 
grantee would serve a designated medically underserved population at 
the time of application, then the grantee will be assumed to be serving 
a medically underserved population for the duration of the project 
period, even if the designation is withdrawn during the project period.
    14. Section 51c.203 is amended by revising paragraph (a) to read as 
follows:


Sec.  51c.203  Project elements.

* * * * *
    (a) Prepare an assessment of the need of the population proposed to 
be served by the community health center for the services set forth in 
Sec.  51c.102(c)(1), with special attention to the need of the 
medically underserved population for such services. Such assessment of 
need shall, at a minimum, consider the factors listed in Sec.  5.103(b) 
of this chapter.
* * * * *


[[Page 11281]]


    Dated: May 23, 2005.
Betty Duke,
Administrator, Health Resources and Services Administration.
    Approved: March 26, 2007.
Michael O. Leavitt,
Secretary, Department of Health and Human Services.

    Editorial Note: This document was received at the Office of the 
Federal Register on February 21, 2008.

 [FR Doc. E8-3643 Filed 2-28-08; 8:45 am]
BILLING CODE 4165-15-P