[Federal Register Volume 87, Number 228 (Tuesday, November 29, 2022)]
[Notices]
[Pages 73311-73313]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2022-25992]


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DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

[30-Day-23-22BC]


Agency Forms Undergoing Paperwork Reduction Act Review

    In accordance with the Paperwork Reduction Act of 1995, the Centers 
for Disease Control and Prevention (CDC) has submitted the information 
collection request titled ``Enhancing Data-driven Disease Detection in 
Newborns (ED3N)'' to the Office of Management and Budget (OMB) for

[[Page 73312]]

review and approval. CDC previously published a ``Proposed Data 
Collection Submitted for Public Comment and Recommendations'' notice on 
December 6, 2021 to obtain comments from the public and affected 
agencies. CDC received one comment related to the previous notice. This 
notice serves to allow an additional 30 days for public and affected 
agency comments.
    CDC will accept all comments for this proposed information 
collection project. The Office of Management and Budget is particularly 
interested in comments that:
    (a) Evaluate whether the proposed collection of information is 
necessary for the proper performance of the functions of the agency, 
including whether the information will have practical utility;
    (b) Evaluate the accuracy of the agencies estimate of the burden of 
the proposed collection of information, including the validity of the 
methodology and assumptions used;
    (c) Enhance the quality, utility, and clarity of the information to 
be collected;
    (d) Minimize the burden of the 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; and
    (e) Assess information collection costs.
    To request additional information on the proposed project or to 
obtain a copy of the information collection plan and instruments, call 
(404) 639-7570. Comments and recommendations for the proposed 
information collection should be sent within 30 days of publication of 
this notice to www.reginfo.gov/public/do/PRAMain. Find this particular 
information collection by selecting ``Currently under 30-day Review--
Open for Public Comments'' or by using the search function. Direct 
written comments and/or suggestions regarding the items contained in 
this notice to the Attention: CDC Desk Officer, Office of Management 
and Budget, 725 17th Street NW, Washington, DC 20503 or by fax to (202) 
395-5806. Provide written comments within 30 days of notice 
publication.

Proposed Project

    Enhancing Data-driven Disease Detection in Newborns (ED3N)--New--
National Center for Environmental Health (NCEH), Centers for Disease 
Control and Prevention (CDC).

Background and Brief Description

    The Newborn Screening and Molecular Biology Branch (NSMBB), in the 
National Center for Environmental Health (NCEH) Division of Laboratory 
Science (DLS), has the only laboratory in the world devoted to ensuring 
the accuracy of newborn screening (NBS) tests in every state and more 
than 78 countries. NSMBB supports NBS programs by conducting research, 
developing methods, and performing analyses by using complex, state-of-
the-art molecular and biochemical techniques for identifying risk 
factors for diseases of public health importance.
    Both NSMBB and state NBS programs are experiencing increased data 
analytic challenges associated with continued expansion of the number 
of newborn screening diseases, increased complexity of disease 
detection, and difficulties in correlating disease markers with disease 
risk. Further, the addition of late-onset diseases to NBS panels 
necessitates a better way to routinely capture clinical information and 
outcomes so that NBS programs can fully appreciate the spectrum of 
disease they are detecting.
    The NSMBB is requesting a three-year Paperwork Reduction Act (PRA) 
clearance for Enhancing Data-driven Disease Detection in Newborns 
(ED3N), a new national NBS data platform, that will address these 
analytic and post-analytic challenges and promote sharing of molecular, 
biochemical, and clinical information amongst NBS partners. The 
information will better equip NSMBB and newborn screening partners to 
assess disease risk and will help harmonize approaches for disease 
detection in newborns. Given the rarity of newborn screening diseases, 
it is imperative that data be collected and analyzed at a national 
level in order to glean useful insights and to analyze trends. The 
NSMBB is best suited to oversee this work given its role in providing 
technical assistance to NBS programs nationally. Numerous studies along 
with presentations by NBS programs suggest that gaps in programmatic 
resources and expertise are hampering the ability to perform more 
complex data analytics resulting in low positive predictive values for 
a number of conditions (which subsequently results in higher false 
positive and negative rates and downstream burden to families and the 
medical system). Smaller-scale work on the use of post-analytical tools 
such as machine learning algorithms have shown that incorporation of 
these elements into newborn screening can improve detection rates, 
while reducing false positives. These studies, however, have been 
limited to single sites and have not been integrated into the daily 
workflow of high-throughput NBS programs. Without this project, NBS 
programs will continue to be unable to keep up with the increasing 
complexity and future demands of screening, perpetuating inequities in 
screening across the nation.
    There are 53 domestic NBS programs in the United States. A 
``respondent'' refers to a single NBS program. Given that data 
submission will ultimately be accomplished through automatic electronic 
data transfer, each respondent's burden hours were split into two 
estimates: (1) the one-time need to set-up, test, and implement the 
electronic data transfer mechanism; and (2) the ongoing automatic 
electronic data transfer occurring after initial set-up. Initial set-up 
time burden was estimated based on analysis of similar data transfer 
projects embarked upon by NBS programs as well as brief discussions 
with NBS Program Laboratory Information Management System vendors. The 
one-time burden to set-up the data transfer interface was estimated to 
be 40 hours total. For purposes of annualizing this component of burden 
over the three-year period of this request, the 53 respondents are 
represented as 18 respondents in the table below (53/3 = 17.67, rounded 
to 18). Ongoing daily data submission burden was estimated assuming 
automatic transfer thereafter, 365 days per year. The estimated burden 
per response is one minute.
    CDC requests OMB approval for an estimated 1,042 annualized burden 
hours. There are no costs to respondents other than their time to 
participate.

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                                        Estimated Annualized Burden Hours
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                                                                                                      Average
                                                                     Number of       Number of      burden per
          Type of respondent                    Form name           respondents    responses per   response  (in
                                                                                    respondent          hr)
----------------------------------------------------------------------------------------------------------------
Newborn Screening Programs............  Set-up of ED3N Data                   18               1              40
                                         Elements.
                                        Ongoing transfer of ED3N              53             365            1/60
                                         Data Elements.
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Jeffrey M. Zirger,
Lead, Information Collection Review Office, Office of Scientific 
Integrity, Office of Science, Centers for Disease Control and 
Prevention.
[FR Doc. 2022-25992 Filed 11-28-22; 8:45 am]
BILLING CODE 4163-18-P