[Federal Register Volume 73, Number 186 (Wednesday, September 24, 2008)]
[Notices]
[Pages 55098-55106]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: E8-22335]


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DEPARTMENT OF HEALTH AND HUMAN SERVICES

Administration for Children and Families


Notice of Public Comment on Section 635 [42 U.S.C. 9801]--The 
2007 Head Start School Readiness Act, Sub-Section 649(k)(1)(A-D)--
``Indian Head Start Study''

AGENCY: Office of Head Start (OHS), Administration for Children and 
Families (ACF), HHS.

ACTION: Notice of Public Comment on Section 635 [42 U.S.C. 9801]--The 
2007 Head Start School Readiness Act, Sub-Section 649(k)(1)(A-D)--
``Indian Head Start Study''.

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SUMMARY: The following Notice of Public Comment is in response to 
section 649(k) Sub-Section (3) of the 2007 Head Start School Readiness 
Act that requires the Secretary no later than 9 months after the 
effective date of this Sub-Section, publish in the Federal Register a 
plan of how the Secretary will carry out section 649 Sub-Section (k) 
Sub-Paragraph (1) and shall provide a period for public comment.

DATES: To ensure consideration, written comments must be submitted on 
or before 60 days after this notice is published.
    To Comment on This Document, or for Further Information Contact: 
Anne Bergan, Office of Planning, Research and Evaluation, 
Administration for Children and Families, 370 L'Enfant Promenade, SW., 
Washington, DC 20447, 202-546-4273, [email protected].

SUPPLEMENTARY INFORMATION: Pursuant to the Improving Head Start for 
School Readiness Act of 2007, Public Law 110-134, Section 635 [42 
U.S.C. 9801]--Sub-Section 649(k)(1)(A-D), notice is hereby given of a 
plan to conduct a set of studies designed to focus on the American 
Indian and Alaska Native (AI/AN) Head Start-eligible population. There 
are two requirements addressed in this notice: (1) A plan for a set of 
studies that will focus on the American Indian and Alaska Native Head 
Start-eligible population related to the following areas: Curriculum 
development, availability and need for services, appropriate research 
methodologies and measures, and best practices for teaching and 
educating American Indian and Alaska Native Head Start Children, and 
(2) a plan to accurately determine the number of children nationwide 
who are eligible to participate in Indian Head Start programs each year 
and to document how many of these children are receiving Head Start 
services each year.

Consultation and Collaboration

    For the purposes of responding to the requirements in the 
legislation related to consultation and collaboration, ACF conferred 
with the National Indian Head Start Directors Association (NIHSDA), the 
AI/AN Head Start Collaboration Director, AI/AN Head Start Program 
Directors, staff from the U.S. Department of Education, the Bureau of 
Indian Affairs, the Indian Health Service, the U.S. Census Bureau, the 
Annie E. Casey Foundation, the American Indian and Alaska Native Head 
Start Research Center at the University of Colorado--Denver, Dr. C. 
Matthew Snipp of Stanford University, Dr. Angela Willeto of Northern 
Arizona University and participants at the Tribal consultation sessions 
held in Denver, Colorado; Kansas City, Kansas; Seattle, Washington; and 
Phoenix, Arizona.

Section I. A Plan for Carrying Out Section 649 Subsection (k) Paragraph 
(1) Subparagraph (A)

    To address the first requirement, to undertake a study or set of 
studies, the Administration for Children and Families (ACF) intends to 
build upon previous and current efforts to develop a viable research 
and evaluation agenda

[[Page 55099]]

for American Indian and Alaska Native (AI/AN) Head Start. Specifically, 
ACF will support and work with the AI/AN Head Start Research Center 
(AI/ANHSRC) at the University of Colorado--Denver to develop and expand 
a set of studies that target issues of interest to the AI/AN Head Start 
community.
    Background. Research in AI/AN communities must take into account 
the unique characteristics of those communities. Stakeholders typically 
voice concerns about community participation and oversight of research 
conducted in Tribal settings; the cultural appropriateness of methods 
and measures used; the relevance of the research topics to community 
needs and interests; and the process of reviewing and publishing 
findings within and outside the community research sites. In Fiscal 
Year 2002, a project funded by ACF undertook to document the existing 
knowledge base concerning early childhood programming and assessment in 
Tribal settings, and to collect information on the research needs and 
priorities of Tribal Head Start programs. Listening sessions with AI/AN 
Head Start stakeholders resulted in a documentation of the topics of 
particular interest in Tribal communities, as well as concerns about 
the processes of implementing research and disseminating findings.
    These and other efforts documented the scarcity and lack of rigor 
of existing research for American Indian and Alaska Native children and 
families, the need to develop the capacity for early childhood research 
in Tribal settings, and the need to increase the number of qualified 
individuals who have the ability to effectively partner with Tribes to 
implement methodologically sound empirical research.
    In recognition of these needs, ACF announced in Fiscal Year 2005 a 
competitive funding opportunity for an American Indian Alaska Native 
Head Start Research Center, the purposes of which were to (1) support 
local research projects that focus on the development of young children 
and families in AI/AN Head Start and Early Head Start programs, and (2) 
offer training opportunities and on-site support to build capacity for 
research in Tribal communities. A cooperative agreement was awarded to 
the University of Colorado at Denver, Health Sciences Center, to lead 
this work. The AI/ANHSRC has worked to identify existing data on 
American Indian Alaska Native Head Start, to locate gaps in the 
available literature and reporting on programs, to generate policy-
relevant findings, to give shape to research and training priorities, 
and to build a national network of programs for future research efforts 
and participate in data collection and developing research partnerships 
between researchers and AI/AN Head Start programs.
    The AI/ANHSRC is guided by a steering committee that includes AI/AN 
Head Start program directors, other Tribal representatives, NIHSDA 
representatives, the Head Start Collaboration Director, staff from the 
ACF's Office of Planning, Research and Evaluation and the Office of 
Head Start, and researchers who are working in Tribal settings. The 
first years of this cooperative agreement were focused on establishing 
local research partnerships, developing community participatory models 
to identify research needs, and agreeing on processes for conducting 
research in local sites. Over the past 3 years, the AI/ANHSRC competed 
and awarded three subcontracts to Arizona State in partnership with the 
Gila River Tribe, Michigan State University in partnership with the 
Inter-Tribal Council of Michigan and to the University of Oregon in 
partnership with the Confederated Tribes of Warm Springs to develop and 
conduct research in collaboration with local Tribal Head Start programs 
and Tribal communities. These projects place significant emphasis on 
Tribal participation in the research and on the implementation of 
methodologically sound studies. The AI/ANHSRC has also supported the 
professional development of researchers by awarding three training 
fellowships to doctoral level individuals who are now conducting 
research in conjunction with the Seneca, Inter-Tribal Council of 
Michigan and Jemez Head Start programs. The AI/ANHSRC, through the 
building of a network of AI/AN Head Start program staff and 
researchers, and through the development of the local research 
partnership projects and the training fellowships, has laid the 
foundation for addressing study areas identified in legislation, 
including studies of professional development to enhance best practices 
for teaching, culturally appropriate curricula, and appropriate 
research methodologies and measures.
    ACF intends to support and work with the AI/ANHSRC to build on its 
network of partnerships, its research portfolio, and its training 
activities to target more specifically the research aims that are 
described in the Head Start School Readiness Act. These aims will be 
addressed by the establishment of a Research Consortium that includes 
the ongoing AI/ANHSRC local research partnership projects, the training 
fellowships, and direct participation of a number of additional Head 
Start American Indian and Alaska Native programs. The Research 
Consortium includes the Seneca Nation of Indians, the Rosebud Sioux 
Tribe, the South Central Foundation of Alaska, the Blackfeet Nation, 
Rincon Band of Luiseno Indians, Turtle Mountain Chippewa Tribe of 
Indians, Red Cliff Band of Lake Superior Chippewa, and the Cherokee 
Nation of Oklahoma. Discussions with additional Tribal communities are 
also underway. The inclusion of these Tribes represents an expansive 
representation of AI/AN Head Start programs and a commitment by many 
Tribes and Tribal Head Starts to conduct in-depth research on the areas 
identified by the Act. Below are descriptions of ongoing and planned 
studies as they relate to the areas prescribed by the legislation:
    Curriculum Development. The issue of how to incorporate the unique 
and important aspects of native culture into pre-existing curricula, as 
well as the development and validation of the efficacy of new cultural 
curricula has been a priority for the AI/ANHSRC Steering Committee. The 
following studies will address this topic:
     A study by the Confederated Tribes of Warm Springs and the 
University of Oregon examining the implementation of a staff training 
model that incorporates culture and heritage into developmentally 
appropriate pedagogy for children from birth through age 5, while also 
focusing on strategies for children's cultural learning between home 
environments and the Head Start program.
     Development and evaluation of a culturally based 
curriculum for use in a Tribal Head Start program; the curriculum will 
foster the maintenance of Tribal language and cultural knowledge and 
skill building (Jemez Pueblo in New Mexico).
     Collaboration within the Research Consortium sites in 
Fiscal Year 2009 and Fiscal Year 2010 to promote community dialogues on 
cultural values, existing curricula, and the processes through which 
new curricula and the science to support them can be developed.
    Professional Development. Several studies will focus on best 
practices for teaching and educating young children in American Indian 
and Alaska Native Head Start.
     Evaluation of a model for individualized educational 
planning for early childhood employees, including three education 
approaches (individual online mentoring, face-to-face tutorials, cohort 
model mentoring), while

[[Page 55100]]

developing and testing an approach to staff training in children's 
cultural learning (the Confederated Tribes of Warm Springs and the 
University of Oregon).
     Examination of Head Start teacher recruitment, retention 
and professional development at the local level and how they intersect 
and interact with efforts to indigenize the curriculum (the Gila River 
Indian Community and Arizona State University).
     Evaluation of a program designed to increase the number of 
American Indian teachers in Early Head Start (EHS) and Head Start (HS) 
classrooms, to increase teacher's academic credentials and to infuse 
cultural knowledge into EHS/HS curricula (the InterTribal Council of 
Michigan and Michigan State University).
    Availability and Need for Services. In consultation with AI/AN Head 
Start Directors, the AIANHSRC is working with communities to analyze 
existing data to determine where there are service needs and to 
identify and evaluate approaches to service provision:
     A partnership between the AIANHSRC and interested members 
of the Consortium to examine the cultural appropriateness of approaches 
offered by the Center on Social and Emotional Foundations for Early 
Learning (CSEFEL) at Vanderbilt University and the kinds of supports 
required for teachers in AI/AN Head Starts to implement some of the 
practices recommended by CSEFEL.
     A project to better understand behavior problems among AI/
AN children with speech and language delays/problems, with the goal of 
developing/adapting an intervention targeting these behaviors among 
children with speech and language delays/problems (Seneca Tribal Head 
Start program).
     Systematic coordinated data collection strategies for 
assessing the needs of parents and children, as well as data on service 
utilization, is under development by the Research Consortium and should 
be ready for pilot work in early 2009.
    Appropriate Research Methodologies and Measures. In addition to 
building on the partnerships seeded in the first phase of the AI/ANHSRC 
(2005-2008), the work sponsored by ACF will expand to include 
coordinated data collections on program and classroom quality (2008-
2009) and children's outcomes (2009-2010) within the broader 
Consortium. Existing measures of classroom quality, teacher 
effectiveness, and child outcomes were developed without consideration 
of the goals of American Indian and Alaska Native Head Start teachers, 
programs and communities. Studies in this domain include:
     Using Head Start Family and Child Experiences Survey 
(FACES) measures as a point of departure, research under this component 
will conduct focus groups, using a common protocol, to determine the 
acceptability and appropriateness of these measures and will then pilot 
both the original and modified versions of these measures to evaluate 
their performance in AI/AN Head Start Programs. The coordinating center 
for the AI/ANHSRC will serve as a data repository for these efforts, 
analyzing cross-site data for final reports to inform the Office of 
Head Start and the AI/AN Head Start community.
     Development of a proposed common measurement strategy for 
assessing child and family needs, teacher effectiveness, and children's 
outcomes. AIANHSRC staff and collaborators will complete training on 
the Classroom Assessment Scoring System (CLASS) measure (Pianta, La 
Paro & Hamre, 2008) \1\ which has been proposed for use in Head Start's 
(FACES) study and conforms to the monitoring section requirements in 
the Head Start School Readiness Act. Work groups within the Consortium 
have now formed to identify additional measures required for the 
appropriate assessment of family and children's outcomes. The goal is 
to establish this common measurement approach (with local additions 
that reflect the unique characteristics of each participating AI/AN 
Head Start program) in early 2009.
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    \1\ Pianta, Robert C., La Paro, Karen M., & Hamre, Bridget K., 
Classroom Assessment Scoring System (Class) Manual, Pre-K. 
Baltimore, MD: Paul H Brookes Pub Co., 2008.
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     A cultural critique and analysis of the Nursing Child 
Assessment Satellite Training (NCAST) (Barnard, 1978) for the 
assessment of American Indian caregiver-child relationships and 
interactions.
     Examination of the reliability and validity of the Infant 
Toddler Social-Emotional Assessment (ITSEA) (Carter & Briggs-Gowan, 
2005) \2\ for use among AI/AN children.
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    \2\ Carter, Alice & Briggs-Gowan, Margaret. Infant Toddler 
Social-Emotional Assessment (ITSEA). San Antonio, TX: Pearson 
Education, Inc., 2005.
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     Finalization of standard ``partnership'' measures, using 
data collected across local research partnership sites. Results from 
these measures, in conjunction with the employment of the community 
participatory research model by all three sites will inform the field 
on how to effectively engage with Tribes to conduct research.
    Plan for Dissemination. ACF will sponsor development and 
enhancement of the AIANHSRC website, which will include areas for 
interactive discussions of measurement and research strategies, both 
within the Research Consortium and nationally. AIANHSRC collaborators 
have formed the nucleus of the new Native Children's Research Exchange, 
sponsored by the Society for Research on Child Development (SRCD), 
which is designed to foster research on AI/AN children's development 
over the first two decades of life. Finally, the Principal Investigator 
for the AIANHSRC, Dr. Paul Spicer, has been invited to serve on the 
board of Zero to Three, which will facilitate the dissemination of the 
AIANHSRC's work in infant and toddler service settings. The involvement 
of the AI/ANHSRC in these organizations will promote a national 
presence for the AI/AN Head Start research agenda.

Section II. A Plan for Carrying Out Section 649 Subsection (k) 
Paragraph (1) Subparagraphs (B-D)

    To address section II, a plan that will accurately determine the 
number of children nationwide who are eligible to participate in 
American Indian/Alaskan Native (AI/AN) Head Start programs each year 
and to document how many of these children are receiving Head Start 
services each year; the Administration for Children and Families 
contracted with National Opinion Research Center (NORC) to propose an 
initial estimation methodology. The following plan details the 
population of interest for AI/AN Head Start, lays out the process and 
criteria that will be used to assess the data sources, describes the 
data sources which have been examined and the results of the 
evaluation, and describes the proposed process for producing the 
estimates. Alternate methods that were examined are also described, 
along with the reasons they were not selected.
    Definition of Population of Interest. The goal of the estimation 
process is to produce population estimates of the number of American 
Indian and Alaskan Native (AI/AN) children birth to age 5 who are 
eligible for the Indian Head Start program. Designation as an Indian 
Head Start program requires that the grantee must be affiliated with a 
Federally recognized Tribe and at least 51% of the children must fall 
at or below the Federal poverty level. Therefore, eligible children 
must be affiliated with a Federally recognized Tribe and living on or 
near Reservations.

[[Page 55101]]

    For purposes of producing these estimates, we assume the following 
definitions.
    1. Affiliated with a Federally recognized Tribe is defined as self-
reported affiliation with 1 of the 562 AI/AN Tribes officially 
recognized by the Federal Government. Though we recognize some children 
may be affiliated with a State-recognized Tribe, for the purposes of 
the current estimate only Federally recognized Tribes at the time of 
the estimate will be included in the count. However, as a practical 
matter, these kinds of data are not available.\3\ It is only possible 
to use self-reported AI/AN racial identification as a substitute. We 
include any child whose reported race is AI/AN, either alone or in 
combination with other races.
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    \3\ Tribal affiliation is asked as part of the Census, but 20% 
of AI/AN respondents do not list a Tribe, and the data are generally 
considered unreliable.
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    2. Living on or near a reservation is defined as residence on or in 
a county adjacent to a recognized American Indian Reservation.\4\ 
Specifically, we use the Indian Health Service (IHS) definition of on 
or near a reservation, which includes the counties served by the IHS 
Contract Health Service Delivery Areas, or CHSDAs.\5\ We refer to these 
groups as county clusters.
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    \4\ The Census Bureau recognizes AIRs (American Indian 
Reservations) as Territory over which American Indians have primary 
governmental authority. These entities are known as Colonies, 
Communities, Pueblos, Rancherias, Ranches, Reservations, Reserves, 
Tribal towns, and Tribal Villages. The Bureau of Indian Affairs 
(BIA) maintains a list of Federally recognized Tribal Governments.
    \5\ The list of CHSDAs we use comes from, ``Geographic 
Composition of the Contract Health Service Delivery Areas (CHSDA) 
and Service Delivery Areas (SDA) of the Indian Health Service'' 72 
Federal Register 119 (21 June 2007), pp. 34262-34267.
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    General Estimation Approach. There are three primary 
characteristics that define the eligible population that is the object 
of this estimation process.
    1. Children ages 5 and under of American Indian or Alaskan Native 
ancestry;
    2. And who live on or near a Reservation;
    3. And at least 51% of the age- and race-eligible children fall at 
or below the Federal poverty level.\6\
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    \6\ The eligibility requirements for an Indian Head Start 
program are more complex than the 51% rule, and include provisions 
for non-AI/AN children who meet the low-income guideline, children 
with disabilities, and others. However, producing estimates that 
account for all these possibilities is outside the scope of this 
estimation. A complete assessment of eligible children would require 
data that do not currently exist, and thus we are forced to draw a 
compromise between the text of the law and what data are available. 
As a result, we define eligibility based only on the AI/AN 
population, according to income.
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    Therefore, the basis for these estimates is a count for each county 
cluster defined above that enumerates all AI/AN children ages five and 
under that fall above and below the Federal poverty level.
    To produce these counts, we employ several data sources that in 
combination produce the most accurate and up-to-date estimates 
feasible. Unfortunately no single source of data contains all the 
elements needed to estimate the eligible population, with the possible 
exception of the U.S. Census. The 2000 Census data have other 
disadvantages (primarily that they will be 9 years out of date when the 
estimates are produced) that make it desirable to employ multiple data 
sources.
    Evaluation Criteria for Data Sources. ACF has evaluated each data 
source in comparison to the criteria described in this section. The 
criteria are chosen in order to provide guidance as to the benefits and 
limitations of each source, as well as guidance in using the sources in 
the estimation process. Because a multi-year recommendation will be 
made, the data sources employed in the first year may change in later 
years, although the initial emphasis is on the first year.
    Precision. One of the key criteria for each data source is the 
precision of the estimates that can be produced with the data. Our 
estimation methodologies are based on statistical models and data 
derived from the Census Bureau and other administrative sources. The 
accuracy of the estimates will be limited by the accuracy of the 
assumed models and by the error structure of the various data inputs. 
We attempt to provide a description of all of the known limitations in 
the estimates.
    Geographic Representation. Although some data sources under 
consideration can provide estimates at the national level, there are 
others, such as State data sources, which are representative of only a 
smaller geography. It is necessary to assess the scope and completeness 
of geographic coverage of each data source, as well as what levels of 
sub-geography are available. In addition, the desired geographic units 
of analysis must be determined in conjunction with the achievable 
precision.
    Coverage. Data sources have different rates of coverage of the 
target population, not only by geography, but in subgroups based on 
important demographic characteristics, such as low-income, urban/rural, 
or others. We evaluate each data source, with particular attention to 
any issues that may arise due to insufficient coverage of crucial 
subgroups within the population.
    Timeliness. Data sources are updated on different schedules, some 
annually and others much less frequently. The more recently updated 
data sources may be preferred to more outdated sources, even if their 
estimates may be less precise, for example. The schedule of updates for 
each data source will guide us as to when and how they may be employed 
not only in the first year, but in the future during the 5 years the 
plan will cover. There will also be implications for precision and 
coverage for some sources as additional years of data become available.
    Data Sources. As part of the evaluation process each of the 
following data sources was reviewed against the criteria listed above. 
Here a description is presented of each data source and the results of 
the evaluation.
    Census. The decennial Census is the premier source of population 
data for the United States. It has been used successfully in past 
Census studies of the AI/AN population and provides the highest levels 
of precision and coverage available. The data gathered on the Census 
long form also allow estimates of children by income to be constructed, 
and thus the estimates could in principle be constructed from the 
Census data alone.
    The Census data suffer from one primary drawback that leads us to 
consider alternate approaches. The data which are currently available 
date from 2000, which will make them nearly 9 years out of date at the 
time the first estimates will be produced. To produce more up to date 
numbers the data would require substantial adjustment to account for 
changes over time. This is especially challenging given the young age 
of the target population. Fortunately, data from the 2010 Census will 
start to become available in 2011 and may provide updated figures in 
later estimates, although the detailed data files needed for the 
estimation may not be available until 2012 or after.
    American Community Survey. The American Community Survey (ACS) is a 
new survey conducted by the U.S. Census Bureau. This survey uses a 
series of monthly samples to produce annually updated data for the same 
small areas (Census tracts and block groups) as the decennial census 
long-form sample formerly surveyed. Initially, 5 years of samples are 
required to produce these small-area data. Once the Census Bureau has 
collected 5 years of data, new small-area data are produced annually. 
The Census Bureau will also produce 3-year and 1-year data products for 
larger geographic areas.
    With full implementation beginning in 2005, population and housing

[[Page 55102]]

profiles for 2005 first became available in the summer of 2006 and 
every year thereafter for specific geographic areas with populations of 
65,000 or more. Three-year period estimates will be available in 2008 
for specific areas with populations of 20,000 or more, and 5-year 
period estimates will be available in 2010 for areas down to the 
smallest block groups, census tracts, small towns and rural areas. 
Beginning in 2010, and every year thereafter, the Nation will have a 5-
year period estimate available as an alternative to the decennial 
census long-form sample, a community information resource that shows 
change over time, even for neighborhoods and rural areas.\7\
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    \7\ Adapted from U.S. CENSUS BUREAU, Design and Methodology, 
American Community Survey, U.S. Government Printing Office, 
Washington, DC, 2006.
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    As the American Community Survey is designed to provide estimates 
comparable to the Census, the data collected contain all the elements 
necessary to produce the desired figures for the target population. In 
principle, the ACS could be used as the only data source, but there are 
other drawbacks that lead us to consider using the ACS in conjunction 
with other sources described below.
    Vital Statistics. A technique that has been used on other studies 
that concern populations of young children requires the use of National 
Center for Health Statistics (NCHS) vital statistics data on births. 
This allows very up-to-date estimates of age-eligible children based on 
births, with adjustments for deaths and estimated migration in the AI/
AN population.
    There are two chief advantages that the vital statistics data bring 
to the process. First, the data on births is in principle a complete 
census of all births in the U.S. and therefore is not subject to 
sampling variability. Second, the data are produced on an annual basis 
for the entire U.S., and thus can be updated in a timely fashion with 
an exact count of births.
    Natality data require adjustments to account for deaths, and 
possibly migration, to compute an accurate count of children within a 
certain age range in a geographic area. These adjustments take into 
account infant mortality, which is also reported in the NCHS vital 
statistics. Adjustments for migration after birth are also made, using 
estimates from the Census.
    Although the vital statistics data provide very accurate counts of 
children, they contain no data on income, and thus cannot be used to 
compute all the figures necessary for the estimates. This limitation 
will be addressed in the detailed estimation methodology section 
described below.
    Program Information Report (PIR) Office Of Head Start Data Base. 
The PIR data will be used only for computing the numbers of AI/AN 
children enrolled in Head Start programs. These data cannot be used to 
estimate the overall population of enrolled and eligible-but-not-
enrolled children.
    Other Sources. Chickasaw Nation Tribal Census. In 2005 the 
Chickasaw Nation conducted a Tribal census. Information of this kind is 
extremely valuable for studying specific Tribes. However, for a Nation-
wide estimation, it is difficult to incorporate one Tribe-specific data 
source with other data for the rest of the nation. It would be 
impossible to assess the comparability of the data for the Chickasaw 
with the remainder of the U.S. Given that the goal is to produce 
national estimates, rather than Tribal estimates, we recommend using a 
single source for all of the U.S. when possible. We will attempt to 
compare our estimates to those obtained from other sources, such as the 
Chickasaw census \8\ where possible.
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    \8\ The Chickasaw data can potentially be used for purposes of 
evaluating the population estimates we will produce for the 
corresponding county cluster.
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    Detailed Estimation Methodology. This section describes in detail 
ACF's recommended methodology for producing the estimates of the target 
population, including the data sources to be used, the method for 
combining the data, and the implementation of the eligibility rule. In 
the estimating the number of AI/AN children section, we recommend 
methodology for estimating the number of age- and race-eligible 
children not living on or near a Reservation.
    Overview. There are four primary tasks to perform in order to 
produce the estimates. They are:
    1. Construct the geographic areas, or county clusters, that will be 
used;
    2. Estimate the total number of AI/AN children under 6 living in 
these areas;
    3. Estimate the proportion of age- and race-eligible children 
living in these areas that meet the income criterion; and
    4. Use these counts and the eligibility rule to compute the final 
estimates.
    All steps of the estimation methodology assume that the target year 
of estimation is 2005, (the most recent year that data are available 
from all sources as of this writing). However, at the time the 
estimates are produced more recent data may be available; for example, 
the 2006 Vital Statistics data are scheduled to be released late in 
2008. Adjustments to the procedure should be made to take advantage of 
the most recent data at the time the estimates are produced.
    Construct Geographic Areas Using Contract Health Delivery System 
Areas (CHDSA) Definitions. The eligibility requirements for an Indian 
Head Start program include children living on or near a Reservation. As 
described in the definitions above, the Indian Health Services (IHS) 
uses a similar definition for establishing their Contract Health 
Delivery System Areas by creating clusters of counties that include all 
or part of a Reservation, and any county or counties that have a common 
boundary with a Reservation. The same areas are used for the estimation 
process in order to account in an accepted way for programs that serve 
American Indian and Alaskan Native (AI/AN) children who do not live on 
or near a Reservation, such as in the Alaska Native Regional 
Corporations and the Oklahoma Tribal Statistical Areas.
    The definitions used in this plan were published in the Federal 
Register on June 21, 2007, cited in footnote 5 above. Some areas 
overlap at the county level with more than one Reservation. In these 
cases, we combine the joint set of counties together into one county 
cluster.\9\ For example, a simple cluster would consist of a set of 
counties linked to one reservation, such as the Poarch Band of Creek 
Indians, which are linked to Baldwin, AL; Escambia, AL; Escambia, FL; 
Elmore, AL; Mobile, AL; and Monroe, AL. An example of a more complex 
cluster is the overlapping areas of the Miccosukee Tribe (Broward, FL; 
Collier, FL; and Miami-Dade, FL) and the Seminole Tribe of Florida 
(Broward, FL; Collier, FL; Glades, FL; and Hendry, FL). Together these 
form one cluster of counties that includes Broward, FL; Collier, FL; 
Glades, FL; Hendry, FL; and Miami-Dade, FL.
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    \9\ It is possible in these instances that more than one Head 
Start program provides services in these areas, but for purposes of 
the estimates they are treated as a group. As the final estimates 
are at the national level, this doesn't pose any significant 
difficulties.
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    Four States are included in their entirety as Contract Health 
Delivery System Areas Alaska, Nevada, Oklahoma, and South Carolina as 
part of the Catawba Indian Nation area. California is also included in 
part as a separate area.
    For the rest of the estimation process, all numbers are computed 
within county clusters, until the final national estimate is produced 
from the sum over all clusters.

[[Page 55103]]

    Estimate Number of AI/AN Childrean Under Six Using Vital Statistics 
Data. The number of children ages five and under of AI/AN descent in 
each county cluster is estimated using the Centers for Disease Control 
and Prevention's National Center for Health Statistics (NCHS) vital 
statistics natality data, with a series of adjustments. The steps are:
    1. Defining the reference period;
    2. Counting Births (NCHS Vital Statistics Natality Data);
    3. Adjustment for Infant Mortality (National Vital Statistics 
Reports); and
    4. Adjustment for Migration between States (Public-Use Microdata 
Samples Data).
    Each step is described in detail in this section.

1. Defining the Reference Period

    This step involves choosing the exact date at which child age will 
be determined and the corresponding range of birth dates to be included 
in the time period of estimation. For example, for the reference date 
of December 31, 2005 (the most recent Vital Statistics data available 
as of this writing), the range of eligible birth dates is from January 
1, 2000 through December 31, 2005.

2. Counting Births

    Data on births are reported by the National Center for Health 
Statistics Division of Vital Statistics annually.\10\ The number of AI/
AN births nationally from 2000 through 2005 \11\ according to Vital 
Statistics data is:
---------------------------------------------------------------------------

    \10\ The representative figures reported here are from tables 
available from the VitalStats reporting system, Centers for Disease 
Control and Prevention, National Center for Health Statistics, 
VitalStats. http://www.cdc.gov/nchs/vitalstats.htm. [07/22/2008].
    \11\ For the reference year of 2005, these years form the range 
of birthdates of all children ages five and under.

2000: 41,668
2001: 41,872
2002: 42,368
2003: 43,052
2004: 43,927
2005: 44,813

    Data at the individual level are available from NCHS for all 
births, including county of mother's residence, mother and father's 
race, and other demographic characteristics.\12\ Following IHS 
definitions, we classify children as AI/AN based on either father or 
mother's race including AI/AN on the birth certificate.\13\
---------------------------------------------------------------------------

    \12\ Data including geographic identifiers have restricted 
access and require special agreement with NCHS to obtain. For more 
details, see http://www.cdc.gov/nchs/about/major/dvs/NCHS_DataRelease.htm.
    \13\ This definition attempts to avoid undercounting AI/AN 
children, at the suggestion of Angela Willeto.
---------------------------------------------------------------------------

    It is important to note that while we have information on the 
mother's residence at time of birth, we assign births based on place of 
birth because the Census data only has place of birth, and doesn't have 
mother's residence. Therefore, the migration step 3 described below is 
a combination of switching from place of birth to mother's residence 
and the migration of one resident State to another.

3. Adjustment for Infant Mortality

    In order to account for infant mortality, the birth counts are 
first adjusted using one-year infant mortality rates for the AI/AN 
race/ethnicity group within each State.\14\ The most recent rates are 
available from Table 3 of Infant Mortality Statistics from the 2004 
Period Linked Birth/Infant Death Data Set. NVSR Volume 55, Number 14. 
33 pp. (PHS) 2007-1120.
---------------------------------------------------------------------------

    \14\ The State level is the most detailed level of reporting for 
these statistics that is available.
---------------------------------------------------------------------------

    These rates are applied to the counts of births. However, this is 
an overestimate of the survivors to age five because it does not 
consider infant deaths between one year and age five. In order to 
account for this, adjustments are made by year up to age 5.\15\ The 
most recent rates come from Table 1 of United States Life Tables, 2004. 
NVSR Volume 56, Number 9. 40 pp. (PHS) 2008-1120.
---------------------------------------------------------------------------

    \15\ These rates are available only at the national level for 
all races combined.
---------------------------------------------------------------------------

4. Adjustment for Migration between States

    In Step 4, ACF used State of birth to estimate migration between 
States. This adjustment, however, necessarily combines migration with 
an adjustment for babies born in a different State from the mother's 
residence because the births were assigned based on mother's residence, 
but the Census Public Use Microdata Samples (PUMS) data only contain 
State of Birth. The State with the largest percentage gain is 
surprisingly Rhode Island (+ 6.48%). It is not surprising to see Nevada 
in third place. At the bottom, Washington, DC loses the highest 
percentage (-9.20%). Washington, DC has hospitals with many Maryland 
and Virginia births.
    Estimate Proportion of Children in Different Income Groups Using 
ACS/CENSUS. Once the counts of AI/AN children in the appropriate age 
range are computed, they must be allocated into two groups above and 
below the Federal poverty level.\16\ Direct computation of these 
figures is not possible since income information is not available from 
the Centers for Disease Control and Prevention's Vital Statistics. Here 
we describe how these groups are allocated.
---------------------------------------------------------------------------

    \16\ The income guidelines that determine eligibility for Head 
Start are complex. For example, section 645(a)(3)(A) of the new Head 
Start Act requires that certain types of pay and allowance to 
members of the uniformed services not be counted as income for 
purposes of determining Head Start eligibility. In addition, under 
37 U.S.C. 402a(g), the child or spouse of a member of the armed 
forces receiving a ``supplemental subsistence allowance'' who, 
except on account of such allowance, would be eligible to receive a 
service provided under the Head Start Act, shall be considered 
eligible for such benefits notwithstanding the receipt of the 
allowance.
    Likewise, the definition of family used in the guidelines has 
several complexities that make exact implementation difficult. Due 
to limitations in the data that are available regarding income, we 
use family income to divide children into the two groups, above and 
below the Federal poverty level.
    For further explanation, see the 2008 Family Income Guidelines. 
ACF-IM-HS-08-05-R. HHS/ACF/OHS. 2008 (http://eclkc.ohs.acf.hhs.gov/hslc/Program Design and Management/Fiscal/ProgramManagement/
Management Systems Procedures/resour--ime--005--020508.html).
---------------------------------------------------------------------------

    The ACS Public Use Microdata Samples are used to produce estimates 
of the proportion of AI/AN children living in families at or below the 
Federal poverty level. These data are available at the Public Use 
Microdata Area, or PUMA level, which can be mapped to counties using 
the PUMS Equivalency files.\17\ PUMS data allows the researcher to 
create custom tabulations of information that are not published by the 
Census Bureau in standard reports.\18\ The most recent data file 
available is the 2006 single-year PUMS file, but in the fall of 2008 
multi-year data will become available, as well as the 2007 data. When 
the multi-year data become available ACF will include them in the 
estimates in order to increase precision.
---------------------------------------------------------------------------

    \17\ Each PUMA has a minimum population of 100,000; as a result 
there are PUMAs which contain more than one county and counties with 
more than one PUMA. For example, Cowlitz County, Washington is part 
of a PUMA that also includes Klickitat, Skamania, and Wahkiakum 
counties; in contrast Miami-Dade County, Florida consists of 12 
PUMAs. In instances where multiple counties are part of one PUMA, we 
will allocate children according to the proportion of AI/AN age-
eligible children in the county. Due to the small sizes of these 
counties, the proportions will most likely need to be taken from the 
2000 Census. We expect the number of counties for which this 
adjustment needs to be made will be small.
    \18\ As an additional option, we will attempt to obtain 
clearance from the Census Bureau to access restricted data files for 
the ACS. These data permit the tabulation of data at levels lower 
than the PUMA, and thus more closely match the county clusters, 
especially for small counties. Due to the time required to obtain 
clearance and the potential impact to the delivery schedule, we 
include this as an option. This option was added at the suggestion 
of Matthew Snipp.
---------------------------------------------------------------------------

    Using the PUMA Equivalency files, PUMAs are grouped into the 
defined

[[Page 55104]]

county clusters. The records are limited to children of AI/AN ages 5 
and under. Using the family income, State of residence, and family 
size, we assign the children to the two groups.\19\ ACF can then 
compute the proportion of children in each cluster that fall in the 
low-income group. This proportion is used in the next step.
---------------------------------------------------------------------------

    \19\ The income guidelines for the reference year of 2005 are 
found in Head Start Family Income Guidelines for 2005. ACYF-IM-HS-
05-01. DHHS/ACF/ACYF/HSB. 2005.
---------------------------------------------------------------------------

    Combine Estimates and Compute Eligible Child Counts Using 
Eligibility Rule. The proportions derived from the ACS data are 
multiplied by the counts of children computed from the vital statistics 
data to estimate the number of children in the low and high-income 
groups in each cluster. The total number of eligible children in each 
cluster is then estimated as:

E = min {L/0.51,L/R{time} ,
Where
E = total estimated eligible children,
L = total estimated low-income children,
H = total estimated high-income children, and
[GRAPHIC] [TIFF OMITTED] TN24SE08.018

    The logic of the formula is that Head Start guidelines specify that 
at least 51% of children served by the program must meet the income 
eligibility guideline, and therefore the maximum number of children 
that could be served must be no more than the number of low-income 
children divided by 0.51, or the number of all AI/AN children, 
whichever is less.\20\
---------------------------------------------------------------------------

    \20\ As noted above, this rule is more simplistic than the 
guidelines actually allow. However, given the data that are 
available this is a reasonable simplification.
---------------------------------------------------------------------------

    Strengths and Limitations of the Methodology. Due to the 
complexities of the rules and regulations that govern Head Start 
eligibility and the exact nature of what data are available, this plan 
makes some difficult choices in both what data sources to employ and 
how they are used. Both the strengths and limitations of the plan are 
discussed here, along with an overview of what alternatives were 
considered and the reasons for their ultimate rejection.
    Strengths. The estimation plan described in this document has 
several key benefits that cause us to recommend it above the 
alternatives. First, it provides the best achievable combination of 
accuracy, coverage, and timeliness in the estimation of the number of 
children of AI/AN descent in the U.S. Because the NCHS Vital Statistics 
natality data are a census of all births in the U.S., they represent 
the definitive source of data for young populations. The natality data 
are also more up to date than alternatives such as the Census.
    Second, by using the ACS it allows a very accurate estimate of the 
income distribution of families with AI/AN children in specific 
geographic areas, yet unlike the Census is updated on an annual basis. 
By design, the ACS is rapidly becoming the primary source of 
demographic data for researchers, particularly when dealing with areas 
below the State level. Continued data collection will allow for even 
more precise estimates in the future as additional multi-year data 
become available.
    A further strength of this approach is the close alignment of the 
county clusters with the Indian Health Services service areas. This 
method provides both a recognized way of identifying areas where Indian 
services are provided and avoids complexities associated with areas 
such as Alaska and Oklahoma, where defining Reservations is difficult.
    A fourth consideration in its favor is that it is based on publicly 
available data sources, and thus brings a measure of transparency to 
the estimation process. This allows stakeholders to feel confident that 
the estimates are reasonable and can be replicated by outside analysts 
if desired.
    One additional strength is that the multi-stage estimation method 
allows the substitution of other data, specifically the 2010 Census, in 
circumstances when superior data become available. Because the 
estimation relies on analytical units that are well-defined in Census 
data sources, it is straightforward to substitute 2010 Census data for 
the ACS to estimate the income distribution, for example, in the 
future.
    Limitations. Any estimation method that could be chosen will suffer 
from some drawbacks as well as advantages and although the recommended 
strategy is sound and defensible, ACF would like to point out the 
following considerations listed below:
    1. The NCHS Vital Statistics natality data has the advantage of 
being a census, rather than a sample, of births, but the mortality 
statistics used to adjust the population counts are reported based on 
rates, rather than counts of actual deaths, with the exception of the 
first year of life. In addition, the best rates available are at the 
State level for all races, and thus are not as precise as the Census 
might provide for a given year. However, these adjustments are 
ultimately small and do not cause the estimate to change in a 
substantial way.
    2. A limitation that arises from using ACS data is that sampling 
variability is introduced, since the ACS by design is a sample survey. 
This limitation is true of nearly all data we might employ with the 
exception of the Census, but as a practical matter up to date estimates 
even from the Census will require adjustments that introduce similar 
variation. As a consequence of the ACS sample design, the mapping from 
PUMA to county is not exact in some cases, particularly when sparsely 
populated counties are combined into a single PUMA.
    3. One final limitation to consider is that the estimates are 
produced from multiple sources of data; population counts from the 
vital statistics and income distributions from the ACS. All else being 
equal, it would be preferable to estimate these from a single source. 
In principle this could be done entirely with the Census or the ACS 
(see below for a further discussion of these approaches) but we believe 
the benefits in terms of timeliness and precision outweigh the costs.
    Precision of the Estimates. The counts produced at the first stage 
from the Centers for Disease Control and Prevention's Vital Statistics 
natality data are based on a complete census of all births in the US, 
and thus within the limitations of the data collection process are the 
actual numbers of AI/AN children and are not subject to sampling 
variation. Children under age 6 at the time of estimation will have 
been born within the defined reference period.
    Let Bi denote the number of AI/AN births to mothers living in the 
i-th county-cluster during the reference period. Let Bia be the number 
of AI/AN births to mothers living in the i-th county-cluster during 
year a of the reference period, with a coded as follows:

------------------------------------------------------------------------
                a                 Age of child at the time of estimation
------------------------------------------------------------------------
1...............................  Age < 1.
2...............................  1 <= Age < 2.
3...............................  2 <= Age < 3.
4...............................  3 <= Age < 4.
5...............................  4 <= Age < 5.
6...............................  5 <= Age < 6.
------------------------------------------------------------------------

                                  [GRAPHIC] [TIFF OMITTED] TN24SE08.019
                                  
    Let dia be the death rate to AI/AN children in the i-th county 
cluster in the a-th year of life, for a = 1, ..., 6. Let Iij be the 
number of survivors at the reference date among AI/AN children who 
lived in county-cluster j at birth and now live in county-cluster i at 
the reference date (the in-migrants). And let

[[Page 55105]]

Oij be the number of survivors at the time of estimation among eligible 
children who lived in county-cluster i at birth and now live in county-
cluster j at the reference date (the out-migrants).
    Let C denote the set of county-clusters that represent areas on or 
near Reservations. Define one additional county-cluster for each State 
(except for AK and OK) that represents all other counties in the State 
not on or near reservations. And let U be the union of C and these 
rest-of-State pieces, or in other words, let U be the set of all areas 
in the U.S.
    Then, by definition, the total number of AI/AN children age under 6 
living in the i-th county-cluster, for i [isin] U , is given by
[GRAPHIC] [TIFF OMITTED] TN24SE08.020

or more simply Ni = survivors among births in the county-cluster plus 
in-migrants less out-migrants.
    Earlier in this report we outlined a demographic-analysis procedure 
for estimating the number of children in the population. Our procedure 
is equivalent to the expression
[GRAPHIC] [TIFF OMITTED] TN24SE08.021

where births are known without error (or virtually without error) from 
the U.S. Vital Statistics system, the death rates are estimated, the 
numbers of in-migrants are estimated, and the numbers of out-migrants 
are estimated. There is error in the estimated population size by 
virtue of error in the estimated death rates and error in the estimated 
counts of in- and out-migrants.
    The estimated death rates are obtained from the U.S. Centers for 
Disease Control and Prevention, National Center for Health Statistics, 
Vital Statistics system. Because all deaths are registered in this 
country, death rates are not subject to sampling error. In the 
procedure, ACF uses death rates calculated at the State by race/
ethnicity level. Error in the estimated death rates arises because the 
AI/AN specific rates are calculated at the State level and then applied 
at the county-cluster level within State. Individual county-clusters 
may experience a higher or lower death rate than the State in which 
they are located, resulting in some over or under-estimation of the 
population in the county cluster. Because infant mortality is 
relatively low and rates do not vary extensively from cluster to 
cluster, ACF expects this component of error to be relatively small.
    The estimated numbers of in-migrants are derived from registered 
births and from estimated migration rates derived from the American 
Community Survey (ACS). The estimator is of the form
[GRAPHIC] [TIFF OMITTED] TN24SE08.022

where mij is an estimator derived from ACS data of the rate of 
migration from county-cluster j to county-cluster i. The ACS data are 
based upon a sample, not a complete enumeration. Moreover, because of 
ACS sample size limitations, ACF estimates the migration rate at a 
higher level of aggregation than the county-cluster level. Thus, the 
estimated numbers of in-migrants are subject to both sampling error and 
error due to failure of the ``synthetic'' assumption.
    The estimated numbers of out-migrants are obtained similarly as
    [GRAPHIC] [TIFF OMITTED] TN24SE08.023
    
and are similarly subject to sampling error and error due to failure of 
the synthetic assumption.
    It is worth noting that the main goal of the estimation is to 
obtain an estimate of the number of AI/AN children under 6 for the 
aggregate set of areas that are on or near Reservations. The goal is 
not strictly to estimate the number of children at the county-cluster 
level. Indeed, at the national level, the numbers of in-migrants must 
equal the numbers of out-migrants, except for deviations due to 
international migration, which are likely to be trivially small for the 
AI/AN population. Thus, at the national level, ACF can write the number 
of AI/AN children under 6 as
[GRAPHIC] [TIFF OMITTED] TN24SE08.024

    For the aggregate set of areas on or near Reservations, the 
population size is
[GRAPHIC] [TIFF OMITTED] TN24SE08.025

and the corresponding estimator is

[[Page 55106]]

[GRAPHIC] [TIFF OMITTED] TN24SE08.026

where C\c\ is the set of areas that are not on or near Reservations and 
U = C [cup] C\c\.

    Thus, error in the estimate of the population in the aggregate set 
of areas on or near Reservations is due to error in the estimated death 
rates and error in the estimated net migration into areas that are not 
on or near Reservations. While migration in or out of any one county-
cluster may be nontrivial, the net migration into the aggregate of 
clusters that are not on or near Reservations is likely to be quite 
small.
    The income proportions estimated from the ACS are subject to 
sampling variability, as the ACS is a sample survey. This variation can 
be estimated using standard statistical techniques when the estimates 
are produced and will be included with the final estimates.
    Alternate Plans Considered. In devising this plan we considered 
several alternative strategies, which are discussed here, along with 
the reasons why they were rejected.
    Census Data at All Stages. Because of the sheer size and scope of 
the decennial Census, it is a natural choice for consideration as the 
primary data source for the estimates. Using the Census PUMS data it 
would be possible to directly compute the estimated counts of children 
within each income group, and thus from there the eligible population. 
However, given the data collection schedule of the Census, it is 
difficult to produce estimates for any given point in time in the 
intercensal years without relying on the Census Bureau population 
projections and adjustments, most of which are not produced at the fine 
level necessary for this estimation. Past experience has also shown 
that these projections tend to undercount the number of Indians in the 
population.\21\ These considerations in conjunction with the young age 
of the population lead ACF to propose the use of Vital Statistics data 
instead.
---------------------------------------------------------------------------

    \21\ See IHS Statistical Note Number 1, American Indian and 
Alaska Native Population Figures Used by the Indian Health Service.
---------------------------------------------------------------------------

    ACS DATA at All Stages. Similarly to the Census, the ACS PUMS data 
contain all the elements necessary to produce the estimates. However, 
although they are produced in a more timely way than the Census, the 
actual counts obtained from the ACS are adjusted using the intercensal 
population estimates produced by the Census Bureau. This is done to 
adjust the ACS sample estimates to match the population estimates using 
population weights. The implication of this is that although 
proportions calculated from the ACS are accurate (for example, based on 
income), the population counts are based on population estimates and 
suffer from similar drawbacks.
    In addition, the ACS data are collected annually, but due to the 
sample design, estimates are available for small geographic areas only 
by combining multiple years of data. These multi-year figures are 
therefore a kind of ``moving average'' of the area, spread over three 
or 5 years for the smallest areas. As a result, although the data are 
more up to date than the 2000 Census, they are less recent than they 
might first appear.
    The Current Population Survey (CPS) is another commonly used source 
of demographic data, particularly on labor force characteristics. It 
includes data on race and income and thus is a potential source for 
income estimates. However, the CPS is not designed to collect reliable 
data at any level below the State, and even State data can suffer 
issues with precision. This limits the usefulness of the data for our 
estimates.

Naomi Goldstein,
Director, Office of Planning, Research and Evaluation.
 [FR Doc. E8-22335 Filed 9-23-08; 8:45 am]
BILLING CODE 4120-01-P