[Federal Register: September 24, 2008 (Volume 73, Number 186)]
[Notices]
[Page 55098-55106]
From the Federal Register Online via GPO Access [wais.access.gpo.gov]
[DOCID:fr24se08-108]
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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, abergan@acf.hhs.gov.
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\
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\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.
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\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.
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\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\
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\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.
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\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
