Translational Research to Address American Indian Health Disparities in Arizona
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1 Translational Research to Address American Indian Health Disparities in Arizona Inter Tribal Council of Arizona Donald Warne, MD, MPH (Oglala Lakota) Director, Master of Public Health Program North Dakota State University August 15, 2013
2 Translational Research Overview Demographics Definitions Disease Patterns & Health Disparities Potential Benefits to American Indian Communities Barriers to Conducting Research Linking Health Needs and Future Directions
3 AI Demographics Over 3 million American Indian Only in 2010 Census Over 5 million AI and other in 2010 Census >60% of AI people live in urban areas Over 560 federally recognized AI/AN tribes Nine AI Tribes in SD, Four in ND, 22 in AZ Significant poverty & Social Determinants of Health
4 AI/AN Population by County
5 AIs in ARIZONA >1/4 of Land Base 22 Tribes ~300,000 Residents TIMELINE: Statehood 1912 Citizenship 1924 Voting Rights 1948
6 American Indian Health Disparities Life Expectancy in Years: Men Women Total U.S AI/AN Disparity: Average age of death in AZ: 72.2 General Population 54.7 AI Population
7 American Indian Health Disparities and Disease Patterns Differences in the incidence, prevalence, mortality, and burden of diseases and other adverse conditions that exist among specific population groups in the United States. NIH Working Group on Health Disparities
8 Translational Research Translational research is scientific research that helps to make findings from basic science useful for practical applications that enhance human health and well-being. It is practiced in the medical, behavioral, and social sciences. For example, in medicine it is used to "translate" findings in basic research quickly into medical practice and meaningful health outcomes.
9 IHS Areas Portland Billings Aberdeen Bemidji California Phoenix Nashville Tucson Navajo Alaska Albuquerque Oklahoma
10 2009 IHS Expenditures Per Capita and Other Federal Health Care Expenditures Per Capita - $10,000 $8,000 - Per Capita spending in the year for which data are published most recently see base of each bar. $6,000 $11, $4,000 $2,000 $ Medicare per beneficiary $6,732 National Health Expenditures $6,130 Veterans Administration $5, Medicaid per enrollee $4,817 FEHB Medical Benchmark - $3,242 Medical for Federal Prisons >decade old IHS Medical - $2,696 IHS Other $648 Indian Health Service 2009 See page 2 notes on reverse for data sources and extrapolation assumptions. 9/5/2013
11 American Indian Health Disparities and Disease Patterns Diabetes Death Rates US All Races IHS Total Phoenix Area Rate/100,000 Population
12 American Indian Health Disparities and Disease Patterns Alcoholism Death Rates Rate/100,000 Population 0 US All Races IHS Total Phoenix Area
13
14 Medical Behavioral
15 Leading Causes of Death Ages 1-4 Pneu/Infl Heart Homicide US All AIAN Anomaly Injuries Death Rates Per 100,000 Population
16 Leading Causes of Death Ages 5-14 Anomaly Suicide Homicide US All AIAN Cancer Injuries Death Rates Per 100,000 Population
17 American Indian Health Disparities and Disease Patterns Potential Causes of Racial Disparities in Health: Socioeconomic Status Cultural Factors Access to & Utilization of Services Genetic Predisposition
18 Potential Benefits of Genetics Research in AI Communities Identify markers for disease risk (ESRD) Identify risk for emerging diseases (multiple myeloma, RCC, Invasive BC, MVID) New treatments Treatment efficacy specific to AI/AN populations Economic Development Intellectual Property Educational Opportunities
19 Potential Barriers to Conducting Genetics Research Trust Ownership and care of samples Ownership of data Intellectual Property Spiritual/Cultural Property Perception of Benefit Misuse / Unusual use of data Genetics Testing for Tribal Enrollment? Research Paradigm Degree of Community Participation
20 Current/Historic Research Paradigm Research Institution Funding Agency LAB (COMMUNITY) Results
21 Community Participatory Research Paradigm Research Institution Funding Agency Results Community
22 IRB Processes and Challenges in AI/AN Health ASU AI/AN Faculty member / Researcher is not a proxy for the community Research institution needs a formal process for tribal community engagement AI/AN Researchers need to follow the same processes for approval Tribes need to give formal approval prior to IRB approval Each tribe has its own process for research approval Human Subjects Protections v Community Protections
23 What is Tribally Driven Research? Research questions and agenda are generated by the tribe Results intended to improve tribal health programs, services and community health Translational Data and samples are owned by the tribe Community participates in all phases of research Research/academic institutions provide technical assistance
24 CBPR v Tribally Driven Research Research question is generated by the researcher Results have the potential to improve tribal health programs/services Data is jointly owned by the tribes & researchers Community participation Technical assistance from community leaders Research question is generated by the tribe Results have the potential to improve tribal health programs/services Data is owned by the tribes Community participation Technical assistance from research institutions
25 Benefits of Tribally Driven, Translational Research? Results stay in the community and are applied in the community Improve understanding of health status, services & quality of care in community Results can be used to advocate for more resources to improve health programs Can focus on applied benefits in the community health promotion and disease prevention
26 Examples of Tribally Based Research Questions What is the best way to treat diabetes in my community? (treatment) How can we get our children to exercise more? (prevention) Am I getting quality care from my doctor? How good are the tribal health programs in my community and how can they be improved? (policy)
27 Research and Policy Data Drives Policy Data Informs Policy Black Box Theory Need Political Champions
28 Strategies to Promote Health Parity / Equity Community Engagement Cultural Competence vs One-size fits all Research CPBR, Translational, Agenda Setting, Oral Health considerations Education URM under-representation Policy Development Institutionalized program change
29 Race and Ethnicity
30 RUDOLPH VIRCHOW Medical statistics will be our standard of measurement: we will weigh life for life and see where the dead lie thicker, among the workers or among the privileged. 1848
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