55098 Federal Register / Vol. 73, No. 186 / Wednesday, September 24, 2008 / Notices 

directs agencies to assess all costs and 

benefits of available regulatory 

alternatives and, if regulation is 

necessary, to select regulatory 

approaches that maximize net benefits 

(including potential economic, 

environmental, public health and safety 

effects, distributive impacts, and 

equity). A regulatory impact analysis 

(RIA) must be prepared for major rules 

with economically significant effects 

($100 million or more in any 1 year). As 

stated in section IV of this notice, we 

estimate that the overall effect of these 

changes in the Part A premium will be 

a cost to voluntary enrollees (section 

1818 and section 1818A of the Act) of 

about $142 million. Therefore, this 

notice is a major rule as defined in Title 

5, United States Code, section 804(2) 

and is an economically significant rule 

under Executive Order 12866. 

The RFA requires agencies to analyze 

options for regulatory relief of small 

businesses, if a rule has a significant 

impact on a substantial number of small 

entities. For purposes of the RFA, small 

entities include small businesses, 

nonprofit organizations, and 

government agencies. Most hospitals 

and most other providers and suppliers 

are small entities, either by nonprofit 

status or by having revenues of $6.5 

million to $31.5 million in any 1 year. 

Individuals and States are not included 

in the definition of a small entity. We 

have determined that this notice will 

not have a significant economic impact 

on a substantial number of small 

entities. Therefore we are not preparing 

an analysis for the RFA. 

In addition, section 1102(b) of the Act 

requires us to prepare a regulatory 

impact analysis if a rule may have a 

significant impact on the operations of 

a substantial number of small rural 

hospitals. This analysis must conform to 

the provisions of section 604 of the 

RFA. For purposes of section 1102(b) of 

the Act, we define a small rural hospital 

as a hospital that is located outside of 

a Metropolitan Statistical Area and has 

fewer than 100 beds. Therefore, the 

Secretary has determined that this 

notice will not have a significant impact 

on the operations of a substantial 

number of small rural hospitals. 

Therefore, we are not preparing an 

analysis for section 1102(b) of the Act. 

Section 202 of the Unfunded 

Mandates Reform Act of 1995 (UMRA) 

also requires that agencies assess 

anticipated costs and benefits before 

issuing any rule whose mandates 

require spending in any 1 year of $100 

million in 1995 dollars, updated 

annually for inflation. In 2008, that 

threshold is approximately $130 

million. This notice has no 

consequential effect on State, local, or 

tribal governments or on the private 

sector. However, States are required to 

pay the premiums for dually-eligible 

beneficiaries. 

Executive Order 13132 establishes 

certain requirements that an agency 

must meet when it promulgates a 

proposed rule (and subsequent final 

rule) that imposes substantial direct 

requirement costs on State and local 

governments, preempts State law, or 

otherwise has Federalism implications. 

This notice will not have a substantial 

effect on State or local governments. 

In accordance with the provisions of 

Executive Order 12866, this notice was 

reviewed by the Office of Management 

and Budget. 

(Catalog of Federal Domestic Assistance 

Program No. 93.773, Medicare—Hospital 

Insurance) 

Dated: August 28, 2008. 

Kerry Weems, 

Acting Administrator, Centers for Medicare 

& Medicaid Services. 

Dated: September 5, 2008. 

Michael O. Leavitt, 

Secretary. 

[FR Doc. E8–22312 Filed 9–19–08; 9:00 am] 

BILLING CODE 4120–01–P 

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, SubSection 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, SubSection 649(k)(1)(A–D)—‘‘Indian Head 

Start Study’’. 

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]—SubSection 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 Starteligible 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 


jlentini on PROD1PC65 with NOTICES
Federal Register / Vol. 73, No. 186 / Wednesday, September 24, 2008 / Notices 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 


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1Pianta, 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. 

2Carter, Alice & Briggs-Gowan, Margaret. Infant 

Toddler Social-Emotional Assessment (ITSEA). San 

Antonio, TX: Pearson Education, Inc., 2005. 

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. 

• 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 SocialEmotional Assessment (ITSEA) (Carter & 

Briggs-Gowan, 2005) 2 for use among AI/ 

AN children. 

• 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. 


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3Tribal 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. 

4The 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. 

5The 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. 

6The 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. 

For purposes of producing these 

estimates, we assume the following 

definitions. 

1. Affiliated with a Federally 

recognized Tribe is defined as selfreported 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 selfreported 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. 

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. 

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

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-todate 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 longform 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 


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7Adapted from U.S. CENSUS BUREAU, Design 

and Methodology, American Community Survey, 

U.S. Government Printing Office, Washington, DC, 

2006. 

8The Chickasaw data can potentially be used for 

purposes of evaluating the population estimates we 

will produce for the corresponding county cluster. 

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. 

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

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-butnot-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 Tribespecific 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. 

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. 

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. 


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10The 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]. 

11For the reference year of 2005, these years form 

the range of birthdates of all children ages five and 

under. 

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/ 

NCHSlDataRelease.htm. 

13This definition attempts to avoid 

undercounting AI/AN children, at the suggestion of 

Angela Willeto. 

14The State level is the most detailed level of 

reporting for these statistics that is available. 

15These rates are available only at the national 

level for all races combined. 

16The 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/resourlimel005l020508.html). 

17Each 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. 

18As 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. 

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: 

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

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. 

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. 

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. 

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. 

Using the PUMA Equivalency files, 

PUMAs are grouped into the defined 


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19The 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. 

20As noted above, this rule is more simplistic 

than the guidelines actually allow. However, given 

the data that are available this is a reasonable 

simplification. 

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 lowincome group. This proportion is used 

in the next step. 

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}, 

Where 

E = total estimated eligible children, 

L = total estimated low-income children, 

H = total estimated high-income children, 

and 

R = L

L+H .

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

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. 

Note that B B i ia

a

= ∑=1

6

.

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 


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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 ∈ U , is 

given by 

NB d IO i ia ib ij

j U

ij

b= j U

a

a

= − ( ) + − = ∈ ∈

∑ ∏ 1 ∑ ∑ 1

, 1

6

or more simply Ni = survivors among 

births in the county-cluster plus inmigrants 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 

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 inmigrants 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 countycluster 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 

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 outmigrants are obtained similarly as 

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 

N N B d IO U i

iU iU

ia

a=

ib

b=

a

i U

ij

j U

ij

j U

== − ( ) + − 



 

∈ ∈ ∈∈ ∈

∑ ∑∑ ∏ ∑∑ ∑ 1 1

1

6



 = − ( ) ∈

∑ ∑ ∏ i U

ia

a=

ib

b=

a

B d

1 1

1

6

.

For the aggregate set of areas on or 

near Reservations, the population size is 

N B d IO C

i C

ia

a=

ib

b=

a

i

ij

j U

ij

j U

i C

= − ( ) + − 



 



 = ∈ ∈∈ ∈

∈

∑ ∑ ∏ ∑∑ ∑

∑

1 1

1

6

C

B d IO I ia

a=

ib

b=

a

i U

ij

j U

ij

j U i C

ij

j c 1 1

1

6

∑ ∏( ) − + − ∑∑ ∑ ∑ 



 



 − ∈∈ ∈ ∈ ∈U

ij

j U

i C

ia

a=

ib

b=

a

i C

ij

j U

O

B d IO c

∑ ∑

∑ ∑ ∏ ∑ ∑

− 



 



 =

( ) − − −

∈

∈ ∈ ∈

,

1 1

1

6

ij

j U∈

∑ 



 





and the corresponding estimator is 


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21See IHS Statistical Note Number 1, American 

Indian and Alaska Native Population Figures Used 

by the Indian Health Service. 

where Cc is the set of areas that are not 

on or near Reservations and U = C È Cc. 

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. 

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 

DEPARTMENT OF HEALTH AND 

HUMAN SERVICES 

Administration for Children and 

Families 

Submission for OMB Review; 

Comment Request 

Proposed Project: 

Title: Evaluation of Pregnancy 

Prevention Approaches—Phase 1. 

OMB No.: New collection. 

Description: The Administration for 

Children and Families (ACF), U.S. 

Department of Health and Human 

Services (HHS), is proposing a data 

collection activity as part of the 

Evaluation of Pregnancy Prevention 

Approaches study. This study will 

assess the effectiveness of a range of 

programs designed to prevent or reduce 

sexual risk behavior and pregnancy 

among older adolescents. Knowing what 

types of programs are effective will 

inform programmatic decisions by 

policymakers and practitioners. 

The proposed activity involves the 

collection of information from 

observations of program activities and 

interviews with a range of 

knowledgeable experts about various 

aspects of existing prevention programs 

and topics the experts view as important 

to address through evaluation. These 

data will be used to help inform 

decisions about the types of programs to 

be evaluated in the study. 

Respondents: The respondents will be 

researchers and policy experts, program 

directors, program staff, or school 

administrators. Data will be collected 

from observations of program activities 

as well. 

ANNUAL BURDEN ESTIMATES

Instrument 

Annual number of respondents Number of responses per 

respondent 

Average burden 

hours per response 

Estimated annual burden 

hours 

Discussion Guide for Use with Researchers and Policy Experts ............... 100 1 1 100 

Discussion Guide for Use with Program Directors ...................................... 50 1 1 50 


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