Data on Health and Well-being of American Indians, Alaska Natives, and Other Native Americans. Catalog Description

12/01/2006

As described above, this catalog is meant to provide overview information on a wide variety of data sources that can address health and well-being issues for AI/AN/NA populations. It is not meant to be an exhaustive listing of all data available on AI/AN/NA populations concerning health and well-being. Time and resource limitations prevented coverage of the entire universe of data sources that might be used to address these topics. To ensure that the catalog would provide broad coverage of the major topics of health and well-being, as a first step, the project staff, in consultation with ASPE, representatives of the DHHS Data Council's Racial and Ethnic Data Working Group (a workgroup for this project), and a small AI/AN/NA workgroup developed a detailed list of policy issues within the categories of health and well-being that should be covered in the catalog. The purpose of this policy list was to guide decision making about the content of the data sources that should be included. The project staff attempted to maximize coverage of the policy issues and avoid redundancy in the data sources being reviewed. This list of policy issues is presented in Figure 1.

Figure 1.1
Key Policy Issue Areas Guiding Inclusion of Data Sets

DEMOGRAPHIC AND ECONOMIC INDICATORS (e.g., age distribution, marital status, household composition)

HEALTH POLICY ISSUES

  1. Measurement of health status (e.g., self-reported health, disability rates, mortality/morbidity rates, trends over time)
  2. Disease-specific measurements (e.g., % with diabetes, TB, STDs, cancer)
  3. Key health disparities of priority interest (e.g., prenatal care/birth outcomes, cancer mortality, substance abuse, alcohol use, mental health, suicide)
  4. Factors contributing to measured health disparities (e.g., access to health care, utilization rates, insurance coverage, health care financing, socioeconomic factors, preventative measures (such as immunization rates))
  5. Identification of evidence-based practices and programs that address causes of health disparities, result in positive health outcomes, and are generalizable/replicable
  6. Role of traditional medicine in AI/AN/NA communities

WELL-BEING ISSUES

Economic Well-being

  1. Income status (e.g., household income/poverty status, per capita income)
  2. Unemployment rates
  3. Economic assistance program participation rates (e.g., Temporary Assistance for Needy Families/Tribal Temporary Assistance for Needy Families, Food Stamps)
  4. Economic opportunity (e.g., number of businesses/jobs, work history)
  5. Measurement of economic/employment disparities between AI/AN/NA and general population
  6. Factors contributing to economic disparities (e.g., lack of child care arrangement, transportation barriers)
  7. Identification of evidence-based practices and programs that reduce economic disparities and are generalizable/replicable

Education Levels and Opportunities

  1. Educational attainment (e.g., last grade completed, literacy/numeracy skills)
  2. Educational opportunities (e.g., Head Start, special education programs, school financing)
  3. Factors contributing to educational disparities (e.g., parents' education level, average education in city/county, education spending per capita, and other socioeconomic factors)
  4. Identification of evidence-based practices and programs that produce positive educational outcomes and are generalizable/replicable

Family Well-being

  1. Measures of well-being for families/households (e.g., families with low income levels, homeless families, teen pregnancy/birthrates, household size and composition)
  2. Factors contributing to well-being disparities of families (e.g., socioeconomic factors, education levels of family adults, housing quality, public transportation availability)
  3. Identification of evidence-based practices and programs that improve family well-being and are generalizable/replicable

Child Well-being

  1. Measures of well-being for children (e.g., children in foster care, incarcerated children)
  2. Factors contributing to well-being disparities of children (household composition, martial status of parents, foster care placement)
  3. Identification of evidence-based practices and programs that improve child well-being and are generalizable/replicable

Elder Well-being

  1. Measures of well-being for elders (e.g., elders with low income levels, homeless elders, elder abuse)
  2. Factors contributing to well-being disparities of elders (e.g., socioeconomic factors, living arrangements, activities of daily living and instrumental activities of daily living (ADL/IADL), family members in proximity, services available/used (such as Meals on Wheels/elder transportation)
  3. Identification of evidence-based practices and programs that improve elder well-being and are generalizable/replicable

Housing Issues

  1. Housing quality (e.g., rooms per person, running water, electricity, heat, age of building)
  2. Type of housing
  3. Housing ownership
  4. Rental unit quality and cost
  5. Homelessness

Transportation Quality and Availability Issues

Justice System Issues

  1. Rates of involvement with justice system (e.g., arrest, conviction, probation, parole rates)
  2. Differences in resolution of arrest, by type of court system (e.g., federal, tribal, state, local)
  3. Lifetime probability of being a victim of a violent crime
  4. Lifetime probability of being a victim of a non-violent crime
  5. Domestic violence rates
  6. Child maltreatment rates
  7. Factors contributing to disparities in involvement with justice system and outcomes (e.g., family stability/foster care placement, family members' history of legal system involvement, race/ethnicity, truancy history)
  8. Identification of evidence-based practices or programs that reduce involvement with justice system or reduce recidivism and are generalizable/replicable

Military Service/Veterans' Issues

  1. Military service rates (e.g., % served in military, % retired from military with benefits)
  2. Eligibility and use of Veterans Administration health facilities
  3. Eligibility and use of other Veterans Administration benefits (e.g. housing loans, educational benefits)

Beyond policy parameters, the project team, in consultation with ASPE, members of the DHHS workgroup, and the AI/AN/NA workgroup, also established the following technical parameters for the data catalog:

  • Data should be from a survey, program reporting system, or registry;
  • Data should be quantitative in nature;
  • Data source must allow user to identify people who are AI/AN/NA or focus on a specific geographic region with a large AI/AN/NA population;
  • Data source must be available to researchers (as data or as requested analyses) or have extensive published tables and reports available;
  • Contact and location information regarding the data source must be available;
  • Documentation of data collection methods should be available;
  • Data should not have been collected primarily to fill advocacy needs, lobbying purposes, or to support corporate interests;
  • Total unweighted AI/AN/NA population in data source should be available, and the sample size must be adequate for analyses;
  • Coverage of data source must be either national or focus on a specific AI/AN/NA subpopulation only (e.g., single tribe, Pacific Islanders), or consist of a smaller geographic area of clear relevance to the AI/AN/NA population; and
  • Timeframe of the data source should be mid-1990s or later (unless a strong argument can be made to include older data).

The review of data sources conducted based on these parameters did not include an assessment of the quality of the data source. A careful examination of the actual data to make such an assessment was beyond the scope of this effort. Instead, reviewers sought to provide sufficient information about the data source to allow users to make an initial assessment of the potential usefulness of the data source for their purposes. We strongly recommend that potential users thoroughly examine the documentation and data from these sources to make their own assessments of the quality of the data.

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