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Vol. 184, No. 1 DOI: 10.1093/aje/kwv334 Advance Access publication: June 6, 2016
Special Article Health Disparities Research Among Small Tribal Populations: Describing Appropriate Criteria for Aggregating Tribal Health Data
Emily R. Van Dyke, Erika Blacksher, Abigail L. Echo-Hawk, Deborah Bassett, Raymond M. Harris*, and Dedra S. Buchwald * Correspondence to Dr. Raymond M. Harris, Initiative for Research and Education to Advance Community Health, Washington State UniversityHealth Sciences Spokane, 1100 Olive Way, Suite 1200, Seattle, WA 98101 (e-mail: [email protected]).
Initially submitted July 2, 2015; accepted for publication November 23, 2015.
In response to community concerns, we used the Tribal Participatory Research framework in collaboration with 5 American-Indian communities in Washington, Idaho, and Montana to identify the appropriate criteria for aggregating health data on small tribes. Across tribal sites, 10 key informant interviews and 10 focus groups (n = 39) were conducted between July 2012 and April 2013. Using thematic analysis of focus group content, we identified 5 guiding criteria for aggregating tribal health data: geographic proximity, community type, environmental exposures, access to resources and services, and economic development. Preliminary findings were presented to focus group participants for validation at each site, and a culminating workshop with representatives from all 5 tribes verified our final results. Using this approach requires critical assessment of research questions and study designs by investigators and tribal leaders to determine when aggregation or stratification is appropriate and how to group data to yield robust results relevant to local concerns. At project inception, tribal leaders should be consulted regarding the validity of proposed groupings. After regular project updates, they should be consulted again to confirm that findings are appropriately contextualized for dissemination. American Indians/Alaska Natives; community-based participatory research; data aggregation; focus groups; health disparities; small populations
Abbreviations: AI, American Indian; AN, Alaska Native.
in non-Hispanic whites (7). Native people also face substantially higher rates of mortality due to diabetes, heart disease, cancer, tuberculosis, suicide, and unintentional injury than non-Hispanic whites (7). Compared with other US groups, AIs and ANs comprise a small population, numbering 5.22 million (1.7% of the allraces total) in 2010. They represent 567 tribes with federal recognition and more than 60 tribes with state recognition only (8, 9). As an example, in Washington, Idaho, and Montana combined, there are 42 federally recognized tribes, of which 35 have fewer than 5,000 enrolled members and 5 have fewer than 250 (10, 11). These small numbers complicate the process of collecting precise, epidemiologically meaningful information. In literature published before 2000, AI/AN data were often aggregated with those on Asians, Pacific Islanders, and multiracial Americans in an undifferentiated “other” category.
National policy statements such as Healthy People 2020 (1) and legislation such as the Affordable Care Act (2) prioritize the elimination of population health disparities. The research needed to achieve this goal requires accurate data on diseases and health in minority communities. Unfortunately, a confluence of factors makes reliable data on American Indians (AIs) and Alaska Natives (ANs) particularly sparse. These factors include geographically dispersed residence, use of nonrepresentative health surveillance methods (3), and widespread racial misclassification of Native people as white in health records (4, 5). Available data suggest that AIs and ANs suffer some of the worst health disparities among racial and ethnic groups in the United States. Native infant mortality rates are the second-highest in the United States (after those in non-Hispanic blacks (6)), whereas allcause mortality rates in AIs and ANs are 46% higher than 1
Am J Epidemiol. 2016;184(1):1–6
2 Van Dyke et al.
Even today, national public health reports typically aggregate data on all AIs/ANs to establish sample sizes large enough for robust findings. However, incidence and mortality rates for many health conditions in Native communities vary significantly by region (12). When data from different geographic and cultural areas are aggregated or stratified without an informed assessment of their comparability, tribal differences are obscured, and it becomes impossible to tailor strategies for research studies or interventions to achieve maximum local impact (13–15). Inevitably, health trends in specific regions and tribes are masked—particularly in tribes with fewer than 250 members. To correct the misunderstandings and distortions that can arise from indiscriminate aggregation and to address the accompanying scientific and ethical issues, we conducted a community-based participatory research project (16–18) to address 2 questions. How can data on small tribal populations be included in regional or national programs of research and health surveillance without sacrificing accuracy? Which factors should be considered when aggregating tribal health data? Our approach followed the principles of Tribal Participatory Research, which emphasizes the social construction of knowledge, the inclusion of community members throughout the research process, and the imperative for research to address local concerns (19). Therefore, we sought the answers to our research questions from AI/AN communities. METHODS
This project was developed at a conference in Seattle in 2009. Thirty tribal health leaders, representing tribes and tribal organizations in 5 western states, were invited to discuss best practices for conducting health research with AI/AN communities. As they heard presentations on the methods used to achieve statistically robust sample sizes, these leaders expressed concern that data aggregation at the national level would obscure important health disparities in small tribes. They requested a research project to investigate how to group tribal data so that local health trends would not be invisible to regional and national policymakers. We responded by conducting key informant interviews and focus groups with members of 5 rural tribes in 3 of the participating states—Washington, Idaho, and Montana—to identify appropriate criteria for data aggregation. Tribal enrollment ranged from fewer than 250 to more than 15,000 (10, 11). Some of these tribal partnerships were newly initiated; others represented long-term research collaborations with our group. This project was reviewed and approved by the institutional review boards of the University of Washington and the Northwest Portland Area Indian Health Board, which serves federally recognized tribes in Washington, Oregon, and Idaho. We also followed all appropriate tribal approval processes, including conducting in-person presentations to tribal health committees and tribal councils, as well as obtaining tribal resolutions and data usage agreements ratified by tribal governments. All key informants and focus group participants provided written informed consent and received gift card incentives of $50 per interview or focus group.
Data collection: key informant interviews
Tribal councils and site coordinators identified 2 key informants per tribe as appropriate community leaders to provide an overview of local health concerns. This sample included 6 men and 4 women. In each tribe, 1 key informant was a senior health official (generally the medical director of the tribal clinic) and the other was a community leader (for example, the tribal chair or a respected elder). Interviews lasted approximately 30 minutes and were semistructured, following a key informant field guide. Most were conducted by telephone. The interviewer took notes and wrote synopses of all interviews, which were informed by 3 central questions: 1. What are the primary health concerns in this community? 2. What factors should researchers consider when grouping data from multiple tribes of different sizes? 3. What are some ways we can talk about this topic that would be meaningful and relevant to tribal members? The synopses included responses from each key informant that were used to tailor the focus group guide for the corresponding community. Data collection and analysis: focus groups
After research staff extracted pertinent data from these interviews, an individual focus group guide was developed for each tribe by incorporating information on local health concerns and cultural norms for discussing them. For example, if key informants identified environmental contaminants as a primary concern, the corresponding guide showed how data from several tribes could be combined to examine trends in disease incidence due to environmental factors. A sample focus group guide is available in Web Appendix 1 (available at http://aje.oxfordjournals.org/). Trained AI/AN staff used these guides to facilitate 2 successive focus groups per site between July 2012 and April 2013. All groups took the form of talking circles, a methodology specific to AI/AN cultures (20, 21). All participants were tribal members recruited by trained community coordinators, many of whom had previously worked with us on cancer-related research. To the extent possible, the coordinators recruited tribal members who had faced serious illness as patients or caregivers. To conduct each focus group, the facilitator (female) and assistant facilitator (female in 7 out of 10 groups) traveled to the relevant tribal seat of government. All focus group discussions were audio recorded and transcribed, and the assistant facilitator took field notes during each group. Focus groups were organized around a single question: What is the most appropriate way to aggregate data on small tribal populations without losing precision or masking significant health issues? Because the participants were unfamiliar with epidemiologic terminology, this question was rephrased in colloquial terms for each group. A typical rephrasing was, “The reason we have to combine data is because this tribe is pretty small. What we’re looking at is, how do we decide the best way to combine the information from the tribes? What are the things we need to consider?” Subsequent prompts asked participants to suggest any attributes that would make tribes similar enough for appropriate aggregation of their health data. Am J Epidemiol. 2016;184(1):1–6
Aggregating Health Data on Small Tribes 3
After the initial round of focus groups, a second focus group was conducted with each tribe to evaluate our preliminary thematic analyses in a process of “member checking” (22). Across sites, all but 1 participant in the initial groups attended. Altogether, the 10 focus groups yielded 247 pages (105,482 words) of transcripts. We conducted a thematic analysis of the transcripts and field notes, tagging emergent concepts with codes for subsequent grouping into categories (23). In our analyses, we used both inductive and deductive methods and preferentially centered the analyses on group discussions that pertained directly to our goal of findings appropriate ways to aggregate AI/AN health data. Data were coded manually by a trained qualitative analyst, and all materials and data interpretation were shared within the team for review and revision. Analyses in process were discussed by the team each month. Validation was enhanced through triangulation (22) by comparing 3 kinds of data: key informant interviews, focus group results, and field notes. Validation of results: workshop with tribal leaders
In keeping with the qualitative goal of transferability (24), we invited tribal health leaders to assess our results for accuracy and applicability. Twelve tribal leaders and staff participated in a culminating workshop in Seattle in May 2013, during which we examined the consensus themes identified in focus group data. This process generated another 73 pages (28,817 words) of transcripts. After lengthy discussion, tribal leaders confirmed that our analyses accurately represented their views. RESULTS
A total of 39 tribal members (29 women and 10 men) participated in the 10 focus groups, for a mean of 8 participants per meeting. Across groups, the major health concerns were cancer, diabetes, prescription drug abuse, and shortages of affordable, accessible options for screening and treatment. Participants offered a nuanced understanding of the factors that should govern data aggregation and stratification. On one hand, they noted that all AI/AN tribes might be comparable enough to aggregate for certain research questions, given their similarities in traditional subsistence practices, spirituality, and shared historical trauma (25, 26). On the other hand, each tribe had a powerful sense of its own uniqueness, ruling out many potential groupings. Factors as diverse as age, presence or absence of casinos, community values, disease type, educational levels, food, health insurance coverage, history, medical mistrust, religion, sex, stress, traditional political alliances, and water sources were all proposed as potential criteria for aggregation. Qualitative analysis of transcripts enabled us to group these factors into 5 broad criteria for classifying data across tribes: geographic proximity, community type, environmental exposures, access to resources and services, and economic development.
most participants, proximity in space implied similarities in culture, lifestyle, and history, although many tribal members were quick to note exceptions. The following remarks from 3 different sites are characteristic of those we received. • Well, we have a Coast Salish group that’s very—we practice the same religion, we eat the same foods, we live in longhouses, travel by canoes. All very, very, very similar lifestyles. • Pacific Northwest, that’s a good group . . . because California would be a completely different story I bet. • I look at that as kind of like the rules of real estate: location, location, location. You know context, stress, access, it’s all location related. • So if you’re comparing outside the region, you’re comparing apples and oranges, because the attitudes, the stress factor, and the access all change. The established approach of the Indian Health Service is to aggregate tribal data within defined geographic regions. Although participants universally considered the regional categories in current use to be inadequate, we noted a consensus view that most Pacific Northwest tribes are similar enough to aggregate. Participants from these tribes noted that they were already involved in associations with neighboring tribes, including pan-tribal powwows and canoe races. Across sites, participating tribes preferred to be grouped with their nearest neighbors, and tribal members had strong opinions on the suitability of specific groupings. For rare diseases, they thought that grouping by Indian Health Service region would be preferable to nationwide aggregation. Many participants agreed that data aggregation based solely on research partnerships established with a disparate set of tribes by a single organization did not represent a meaningful grouping. Community type
Community type, defined in terms of local population density, landforms, and natural resources, was also deemed an important factor. The 2 dimensions that received the most discussion were urban versus rural residence and coastal versus inland tribes. Even within a coherent geographic region, participants felt that these considerations trumped physical proximity. The following statements from 3 different sites illustrate widely endorsed views:
Geographic proximity
• If someone was raised as an urban Indian and grew up in Chicago, they would be different than somebody who grew up here and so would their health. • I see where rural versus city, I don’t think they’re the same at all. I mean because you’ve got the travel distance. • I think the Plains Indians east of the Rocky Mountains here, I think they have a very different lifestyle than we have on the coast. • The coastal, I think it would be a lot more different than our people here because . . . over there it’s wet and a lot of things cool down. Here we’ve got so much in the environment with all the insecticides and [nuclear waste].
All 5 tribes identified geographic proximity as an appropriate way to aggregate tribal health data. Across groups, this criterion received extensive, thoughtful discussion. For
In these excerpts, factors beyond the simple dichotomies of urban/rural and coastal/inland were clearly significant to tribal members.
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4 Van Dyke et al.
Environmental exposures
Concerns about exposure to local environmental contaminants were emphasized by key informants at 1 site and by focus groups at all 5 sites. Participants spoke at length about empirically verified contaminants (e.g., mercury, petroleum, pesticides, and nuclear waste) that originated in nearby extraction, refining, industrial, and agricultural activities. These contaminants were thought to affect the community’s soil, water, and food and to exacerbate the incidence of disease, especially cancer, in tribal members. Accordingly, all groups expressed eagerness for research on potential environmental causes of illness. During a discussion of escalating cancer diagnoses on an inland reservation, one participant said, “I mean is this like an isolated thing here? I’d like to see a comparison maybe from [place names redacted] and maybe [redacted] as a comparison to here. Because I know we have got a profound amount of insecticides in our valley.” During a similar conversation on a different reservation, a participant noted that selecting an appropriate sample for such a population study raised significant issues: “Why would we wanna be grouped with somebody that’s from an entirely different environment than we come from?” Access to resources and services
Participants from all 5 tribes described barriers to obtaining health services and other essential resources, emphasizing the limited availability of resources needed to sustain or improve health, as well as the necessity of traveling long distances for access. Particular concerns included disparities in the quality and cultural sensitivity of care provision; difficulties in obtaining affordable, healthful food; limited educational opportunities; and a scarcity of local options for physical activity. Participants considered differential access to such resources as a key criterion in aggregating or stratifying health data, as in this representative excerpt: Another thing be aware of is . . . how sensitive that [healthcare] facility might be towards the population that they’re serving, and that would affect, you know, the amount of people that you guys have at the facility. So I think that would be one thing to consider in terms of measuring one [tribe] against the other, or grouping them.
Access issues often overlapped with the criteria of community type and economic development, such that poorer and more rural communities typically perceived that they had poorer access. Nevertheless, participants also noted that certain rural communities had better access to resources than others, whereas even urban AIs and ANs might need to travel to a rural tribal facility for healthcare. Economic development
Along with access to key resources, participants identified the importance of a tribe’s overall affluence and economic development as factors in data aggregation. For instance, if one tribe had a lucrative casino and another did not, this disparity could affect health outcomes: You have tribes that through economic development or casino development are very wealthy. We don’t fall in that category, so I
would think that we would partner with somebody with the same economic development.
Employment rates were also noted as an important factor, because unemployment was perceived as reducing people’s ability to care for their health. Participants recommended aggregating data only among tribes with similar economic conditions: You’d want to group us with the more rural, less economically developed tribes . . . or it would make us look like we don’t have a disparity here because we’re being grouped with them. DISCUSSION
The results of this collaboration with 5 northwestern AI tribes emphasize the value of tribal consultation and the need for tribal approval of any grouping of communities proposed to augment sample sizes for health research. Using interview and focus group data, we distilled 5 broad criteria for data aggregation and stratification, all of which can be tailored to individual communities: geographic proximity, community type, environmental exposures, access to resources and services, and economic development. In practice, these criteria will likely overlap, because environmental exposures tend to vary by geography; access to services varies by geography, community type, and economic conditions; and the same geographic region can be home to a range of community types with differential access to services and economic development. Conceptually, these criteria can be understood on 2 levels: those based on external data without implied effects on health (geography, community type) and those based on communitylevel data on the social determinants of health (economic status, access to resources and services, and environmental exposures) (27–29). In practice, epidemiologists use all 5 factors to organize and analyze population health data (6, 28, 30, 31). To the best of our knowledge, this project was the first academic/community collaboration in the United States to elicit tribal recommendations for aggregating health data on small tribes. Literature searches revealed little previous work in this area. For the United States, we found a single conference presentation from 2006 by an AI/AN health researcher who noted the limitations of the current approaches to aggregating tribal data and emphasized the need for respectful dialogue with community members to improve data collection (32). For Canada, we learned of an effort in which the First Nations of Manitoba contracted an academic team to assist in designing and implementing a longitudinal survey of aboriginal health in a sample of First Nations communities (33). In that project, a steering committee comprising First Nations health officials developed 4 criteria to achieve a truly representative selection of communities: political affiliations, tribal affiliations within larger political units, geographic factors, and community size. We note broad similarities between our work and both of these studies, ranging from the emphasis on community dialogue to the generation of selection criteria. Ironically, in the First Nations project, the criterion regarding community size excluded communities with fewer than 250 members—a reminder of the ethical imperative for researchers to make small tribes visible to policymakers. Am J Epidemiol. 2016;184(1):1–6
Aggregating Health Data on Small Tribes 5
Our project was designed to establish trusting relationships with participating tribes by learning about each tribe’s needs and concerns regarding data aggregation. To this end, we made repeated site visits to present the project to tribal councils and convene focus groups. We also sponsored a culminating workshop with tribal representatives in Seattle. As our tribal partners concluded, any grouping of tribal data for research purposes is meaningful only if it is determined through an ethical process of consultation with the tribes affected. The present study has notable limitations. Although our tribal collaborators are diverse in many ways, they are a small fraction of AI/AN communities nationwide and cannot represent the full Native population. In addition, focus group participants at some sites were individually recruited by community coordinators involved in cancer research instead of enrolled at random; as a consequence, cancer survivors or their family members were overrepresented at 2 sites. This factor likely biased some discussions in favor of cancerrelated issues. Finally, our participant population was skewed toward women, who outnumbered men 3:1. According to many participants, this imbalance in the sexes mirrored the differential use of health services by AI/AN men and women. Despite these shortcomings, we believe that our results will be useful to tribes and researchers in a range of settings, and we have created a resource summarizing the methods and results of the present study for future dissemination to tribes nationwide. Meaningful data on disease incidence and mortality are urgently needed to effectively channel resources and improve outcomes in AI/AN communities. Such data must accurately reflect the lived experience of tribal members. In the present study, participants observed that just 1 cancer diagnosis has a major impact on morale, perception of risk, and tribal health finances, even in a community of a few thousand people. They also affirmed that they did not wish to be excluded from research simply because their tribal population was small. To ensure that data aggregation is acceptable to tribes and effective in leading to desired outcomes, paradigms offered by Tribal Participatory Research and communitybased participatory approaches advise academic researchers to engage in regular consultation with tribal elders (19, 34). Consultation should continue throughout the project, from proposal development through drafting of presentations and manuscripts for publication. Tribal leaders should be consulted again before any public dissemination of research to confirm that findings have been interpreted and contextualized accurately. This process that has already been implemented by the Elders-in-Residence programs at the University of Alaska Fairbanks (35) and the University of Washington (36), and we encourage its uptake as a best practice.
ACKNOWLEDGMENTS
Author affiliations: Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington (Emily R. Van Dyke, Abigail L. Echo-Hawk, Deborah Bassett, Raymond M. Harris, Dedra S. Buchwald); and Department of Am J Epidemiol. 2016;184(1):1–6
Bioethics and Humanities, School of Medicine, University of Washington, Seattle, Washington (Erika Blacksher). Raymond M. Harris is currently at the Department of Community Health, Health Sciences Spokane, Washington State University, Seattle, Washington. This work was supported by the National Cancer Institute (grant U54 CA153498) and the National Center for Advancing Translational Health Sciences (grant UL1TR000423) at the National Institutes of Health. We thank Dr. Stephen Schwartz of the Fred Hutchinson Cancer Research Center in Seattle, Washington, for helpful conversations during manuscript development. Conflict of interest: none declared.
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27. Schulz A, Northridge ME. Social determinants of health: implications for environmental health promotion. Health Educ Behav. 2004;31(4):455–471. 28. Commission on the Social Determinants of Health. Closing the Gap in a Generation: Health Equity Through Action on the Social Determinants of Health. Geneva, Switzerland: World Health Organization; 2008. 29. Solar O, Irwin A. A Conceptual Framework for Action on the Social Determinants of Health. Geneva, Switzerland: World Health Organization; 2010. 30. Diaz JH. The legacy of the Gulf oil spill: analyzing acute public health effects and predicting chronic ones in Louisiana. Am J Disaster Med. 2011;6(1):5–22. 31. Centers for Disease Control and Prevention. CDC Health Disparities and Inequalities Report—United States, 2011. MMWR Surveill Summ. 2011;60(suppl):1–2. 32. Chino M. Small Numbers, Big Issues: Perspectives from a U.S. Data User. International Group for Indigenous Health Measurement. Canberra, Australia: Australian Institute of Health and Welfare; November 28, 2006. http://www.aihw.gov. au/WorkArea/DownloadAsset.aspx?id=60129544140. Accessed November 19, 2015. 33. O’Neil J, Reading J, Leader A. Changing the relations of surveillance: the development of a discourse of resistance in aboriginal epidemiology. Hum Organ. 1998;57(2):230–237. 34. Manson SM, Garroutte E, Goins RT, et al. Access, relevance, and control in the research process: lessons from Indian country. J Aging Health. 2004;16(5 suppl):58S–77S. 35. Alaska Native Knowledge Network. Elders and cultural camps as educational resources. Fairbanks, Alaska: University of Alaska Fairbanks; 2013. http://www.ankn.uaf.edu/NPE/Elders. html. Updated May 15, 2013. Accessed March 18, 2015. 36. Bach D. Elders-in-residence program brings traditional learning to campus. UW Today. March 18, 2015. http://www.washington. edu/news/2015/02/06/elders-in-residence-program-bringstraditional-learning-to-campus/. Accessed October 28, 2015.
Am J Epidemiol. 2016;184(1):1–6
Vol. 184, No. 1 DOI: 10.1093/aje/kwv334 Advance Access publication: June 6, 2016
Special Article Health Disparities Research Among Small Tribal Populations: Describing Appropriate Criteria for Aggregating Tribal Health Data
Emily R. Van Dyke, Erika Blacksher, Abigail L. Echo-Hawk, Deborah Bassett, Raymond M. Harris*, and Dedra S. Buchwald * Correspondence to Dr. Raymond M. Harris, Initiative for Research and Education to Advance Community Health, Washington State UniversityHealth Sciences Spokane, 1100 Olive Way, Suite 1200, Seattle, WA 98101 (e-mail: [email protected]).
Initially submitted July 2, 2015; accepted for publication November 23, 2015.
In response to community concerns, we used the Tribal Participatory Research framework in collaboration with 5 American-Indian communities in Washington, Idaho, and Montana to identify the appropriate criteria for aggregating health data on small tribes. Across tribal sites, 10 key informant interviews and 10 focus groups (n = 39) were conducted between July 2012 and April 2013. Using thematic analysis of focus group content, we identified 5 guiding criteria for aggregating tribal health data: geographic proximity, community type, environmental exposures, access to resources and services, and economic development. Preliminary findings were presented to focus group participants for validation at each site, and a culminating workshop with representatives from all 5 tribes verified our final results. Using this approach requires critical assessment of research questions and study designs by investigators and tribal leaders to determine when aggregation or stratification is appropriate and how to group data to yield robust results relevant to local concerns. At project inception, tribal leaders should be consulted regarding the validity of proposed groupings. After regular project updates, they should be consulted again to confirm that findings are appropriately contextualized for dissemination. American Indians/Alaska Natives; community-based participatory research; data aggregation; focus groups; health disparities; small populations
Abbreviations: AI, American Indian; AN, Alaska Native.
in non-Hispanic whites (7). Native people also face substantially higher rates of mortality due to diabetes, heart disease, cancer, tuberculosis, suicide, and unintentional injury than non-Hispanic whites (7). Compared with other US groups, AIs and ANs comprise a small population, numbering 5.22 million (1.7% of the allraces total) in 2010. They represent 567 tribes with federal recognition and more than 60 tribes with state recognition only (8, 9). As an example, in Washington, Idaho, and Montana combined, there are 42 federally recognized tribes, of which 35 have fewer than 5,000 enrolled members and 5 have fewer than 250 (10, 11). These small numbers complicate the process of collecting precise, epidemiologically meaningful information. In literature published before 2000, AI/AN data were often aggregated with those on Asians, Pacific Islanders, and multiracial Americans in an undifferentiated “other” category.
National policy statements such as Healthy People 2020 (1) and legislation such as the Affordable Care Act (2) prioritize the elimination of population health disparities. The research needed to achieve this goal requires accurate data on diseases and health in minority communities. Unfortunately, a confluence of factors makes reliable data on American Indians (AIs) and Alaska Natives (ANs) particularly sparse. These factors include geographically dispersed residence, use of nonrepresentative health surveillance methods (3), and widespread racial misclassification of Native people as white in health records (4, 5). Available data suggest that AIs and ANs suffer some of the worst health disparities among racial and ethnic groups in the United States. Native infant mortality rates are the second-highest in the United States (after those in non-Hispanic blacks (6)), whereas allcause mortality rates in AIs and ANs are 46% higher than 1
Am J Epidemiol. 2016;184(1):1–6
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Even today, national public health reports typically aggregate data on all AIs/ANs to establish sample sizes large enough for robust findings. However, incidence and mortality rates for many health conditions in Native communities vary significantly by region (12). When data from different geographic and cultural areas are aggregated or stratified without an informed assessment of their comparability, tribal differences are obscured, and it becomes impossible to tailor strategies for research studies or interventions to achieve maximum local impact (13–15). Inevitably, health trends in specific regions and tribes are masked—particularly in tribes with fewer than 250 members. To correct the misunderstandings and distortions that can arise from indiscriminate aggregation and to address the accompanying scientific and ethical issues, we conducted a community-based participatory research project (16–18) to address 2 questions. How can data on small tribal populations be included in regional or national programs of research and health surveillance without sacrificing accuracy? Which factors should be considered when aggregating tribal health data? Our approach followed the principles of Tribal Participatory Research, which emphasizes the social construction of knowledge, the inclusion of community members throughout the research process, and the imperative for research to address local concerns (19). Therefore, we sought the answers to our research questions from AI/AN communities. METHODS
This project was developed at a conference in Seattle in 2009. Thirty tribal health leaders, representing tribes and tribal organizations in 5 western states, were invited to discuss best practices for conducting health research with AI/AN communities. As they heard presentations on the methods used to achieve statistically robust sample sizes, these leaders expressed concern that data aggregation at the national level would obscure important health disparities in small tribes. They requested a research project to investigate how to group tribal data so that local health trends would not be invisible to regional and national policymakers. We responded by conducting key informant interviews and focus groups with members of 5 rural tribes in 3 of the participating states—Washington, Idaho, and Montana—to identify appropriate criteria for data aggregation. Tribal enrollment ranged from fewer than 250 to more than 15,000 (10, 11). Some of these tribal partnerships were newly initiated; others represented long-term research collaborations with our group. This project was reviewed and approved by the institutional review boards of the University of Washington and the Northwest Portland Area Indian Health Board, which serves federally recognized tribes in Washington, Oregon, and Idaho. We also followed all appropriate tribal approval processes, including conducting in-person presentations to tribal health committees and tribal councils, as well as obtaining tribal resolutions and data usage agreements ratified by tribal governments. All key informants and focus group participants provided written informed consent and received gift card incentives of $50 per interview or focus group.
Data collection: key informant interviews
Tribal councils and site coordinators identified 2 key informants per tribe as appropriate community leaders to provide an overview of local health concerns. This sample included 6 men and 4 women. In each tribe, 1 key informant was a senior health official (generally the medical director of the tribal clinic) and the other was a community leader (for example, the tribal chair or a respected elder). Interviews lasted approximately 30 minutes and were semistructured, following a key informant field guide. Most were conducted by telephone. The interviewer took notes and wrote synopses of all interviews, which were informed by 3 central questions: 1. What are the primary health concerns in this community? 2. What factors should researchers consider when grouping data from multiple tribes of different sizes? 3. What are some ways we can talk about this topic that would be meaningful and relevant to tribal members? The synopses included responses from each key informant that were used to tailor the focus group guide for the corresponding community. Data collection and analysis: focus groups
After research staff extracted pertinent data from these interviews, an individual focus group guide was developed for each tribe by incorporating information on local health concerns and cultural norms for discussing them. For example, if key informants identified environmental contaminants as a primary concern, the corresponding guide showed how data from several tribes could be combined to examine trends in disease incidence due to environmental factors. A sample focus group guide is available in Web Appendix 1 (available at http://aje.oxfordjournals.org/). Trained AI/AN staff used these guides to facilitate 2 successive focus groups per site between July 2012 and April 2013. All groups took the form of talking circles, a methodology specific to AI/AN cultures (20, 21). All participants were tribal members recruited by trained community coordinators, many of whom had previously worked with us on cancer-related research. To the extent possible, the coordinators recruited tribal members who had faced serious illness as patients or caregivers. To conduct each focus group, the facilitator (female) and assistant facilitator (female in 7 out of 10 groups) traveled to the relevant tribal seat of government. All focus group discussions were audio recorded and transcribed, and the assistant facilitator took field notes during each group. Focus groups were organized around a single question: What is the most appropriate way to aggregate data on small tribal populations without losing precision or masking significant health issues? Because the participants were unfamiliar with epidemiologic terminology, this question was rephrased in colloquial terms for each group. A typical rephrasing was, “The reason we have to combine data is because this tribe is pretty small. What we’re looking at is, how do we decide the best way to combine the information from the tribes? What are the things we need to consider?” Subsequent prompts asked participants to suggest any attributes that would make tribes similar enough for appropriate aggregation of their health data. Am J Epidemiol. 2016;184(1):1–6
Aggregating Health Data on Small Tribes 3
After the initial round of focus groups, a second focus group was conducted with each tribe to evaluate our preliminary thematic analyses in a process of “member checking” (22). Across sites, all but 1 participant in the initial groups attended. Altogether, the 10 focus groups yielded 247 pages (105,482 words) of transcripts. We conducted a thematic analysis of the transcripts and field notes, tagging emergent concepts with codes for subsequent grouping into categories (23). In our analyses, we used both inductive and deductive methods and preferentially centered the analyses on group discussions that pertained directly to our goal of findings appropriate ways to aggregate AI/AN health data. Data were coded manually by a trained qualitative analyst, and all materials and data interpretation were shared within the team for review and revision. Analyses in process were discussed by the team each month. Validation was enhanced through triangulation (22) by comparing 3 kinds of data: key informant interviews, focus group results, and field notes. Validation of results: workshop with tribal leaders
In keeping with the qualitative goal of transferability (24), we invited tribal health leaders to assess our results for accuracy and applicability. Twelve tribal leaders and staff participated in a culminating workshop in Seattle in May 2013, during which we examined the consensus themes identified in focus group data. This process generated another 73 pages (28,817 words) of transcripts. After lengthy discussion, tribal leaders confirmed that our analyses accurately represented their views. RESULTS
A total of 39 tribal members (29 women and 10 men) participated in the 10 focus groups, for a mean of 8 participants per meeting. Across groups, the major health concerns were cancer, diabetes, prescription drug abuse, and shortages of affordable, accessible options for screening and treatment. Participants offered a nuanced understanding of the factors that should govern data aggregation and stratification. On one hand, they noted that all AI/AN tribes might be comparable enough to aggregate for certain research questions, given their similarities in traditional subsistence practices, spirituality, and shared historical trauma (25, 26). On the other hand, each tribe had a powerful sense of its own uniqueness, ruling out many potential groupings. Factors as diverse as age, presence or absence of casinos, community values, disease type, educational levels, food, health insurance coverage, history, medical mistrust, religion, sex, stress, traditional political alliances, and water sources were all proposed as potential criteria for aggregation. Qualitative analysis of transcripts enabled us to group these factors into 5 broad criteria for classifying data across tribes: geographic proximity, community type, environmental exposures, access to resources and services, and economic development.
most participants, proximity in space implied similarities in culture, lifestyle, and history, although many tribal members were quick to note exceptions. The following remarks from 3 different sites are characteristic of those we received. • Well, we have a Coast Salish group that’s very—we practice the same religion, we eat the same foods, we live in longhouses, travel by canoes. All very, very, very similar lifestyles. • Pacific Northwest, that’s a good group . . . because California would be a completely different story I bet. • I look at that as kind of like the rules of real estate: location, location, location. You know context, stress, access, it’s all location related. • So if you’re comparing outside the region, you’re comparing apples and oranges, because the attitudes, the stress factor, and the access all change. The established approach of the Indian Health Service is to aggregate tribal data within defined geographic regions. Although participants universally considered the regional categories in current use to be inadequate, we noted a consensus view that most Pacific Northwest tribes are similar enough to aggregate. Participants from these tribes noted that they were already involved in associations with neighboring tribes, including pan-tribal powwows and canoe races. Across sites, participating tribes preferred to be grouped with their nearest neighbors, and tribal members had strong opinions on the suitability of specific groupings. For rare diseases, they thought that grouping by Indian Health Service region would be preferable to nationwide aggregation. Many participants agreed that data aggregation based solely on research partnerships established with a disparate set of tribes by a single organization did not represent a meaningful grouping. Community type
Community type, defined in terms of local population density, landforms, and natural resources, was also deemed an important factor. The 2 dimensions that received the most discussion were urban versus rural residence and coastal versus inland tribes. Even within a coherent geographic region, participants felt that these considerations trumped physical proximity. The following statements from 3 different sites illustrate widely endorsed views:
Geographic proximity
• If someone was raised as an urban Indian and grew up in Chicago, they would be different than somebody who grew up here and so would their health. • I see where rural versus city, I don’t think they’re the same at all. I mean because you’ve got the travel distance. • I think the Plains Indians east of the Rocky Mountains here, I think they have a very different lifestyle than we have on the coast. • The coastal, I think it would be a lot more different than our people here because . . . over there it’s wet and a lot of things cool down. Here we’ve got so much in the environment with all the insecticides and [nuclear waste].
All 5 tribes identified geographic proximity as an appropriate way to aggregate tribal health data. Across groups, this criterion received extensive, thoughtful discussion. For
In these excerpts, factors beyond the simple dichotomies of urban/rural and coastal/inland were clearly significant to tribal members.
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Environmental exposures
Concerns about exposure to local environmental contaminants were emphasized by key informants at 1 site and by focus groups at all 5 sites. Participants spoke at length about empirically verified contaminants (e.g., mercury, petroleum, pesticides, and nuclear waste) that originated in nearby extraction, refining, industrial, and agricultural activities. These contaminants were thought to affect the community’s soil, water, and food and to exacerbate the incidence of disease, especially cancer, in tribal members. Accordingly, all groups expressed eagerness for research on potential environmental causes of illness. During a discussion of escalating cancer diagnoses on an inland reservation, one participant said, “I mean is this like an isolated thing here? I’d like to see a comparison maybe from [place names redacted] and maybe [redacted] as a comparison to here. Because I know we have got a profound amount of insecticides in our valley.” During a similar conversation on a different reservation, a participant noted that selecting an appropriate sample for such a population study raised significant issues: “Why would we wanna be grouped with somebody that’s from an entirely different environment than we come from?” Access to resources and services
Participants from all 5 tribes described barriers to obtaining health services and other essential resources, emphasizing the limited availability of resources needed to sustain or improve health, as well as the necessity of traveling long distances for access. Particular concerns included disparities in the quality and cultural sensitivity of care provision; difficulties in obtaining affordable, healthful food; limited educational opportunities; and a scarcity of local options for physical activity. Participants considered differential access to such resources as a key criterion in aggregating or stratifying health data, as in this representative excerpt: Another thing be aware of is . . . how sensitive that [healthcare] facility might be towards the population that they’re serving, and that would affect, you know, the amount of people that you guys have at the facility. So I think that would be one thing to consider in terms of measuring one [tribe] against the other, or grouping them.
Access issues often overlapped with the criteria of community type and economic development, such that poorer and more rural communities typically perceived that they had poorer access. Nevertheless, participants also noted that certain rural communities had better access to resources than others, whereas even urban AIs and ANs might need to travel to a rural tribal facility for healthcare. Economic development
Along with access to key resources, participants identified the importance of a tribe’s overall affluence and economic development as factors in data aggregation. For instance, if one tribe had a lucrative casino and another did not, this disparity could affect health outcomes: You have tribes that through economic development or casino development are very wealthy. We don’t fall in that category, so I
would think that we would partner with somebody with the same economic development.
Employment rates were also noted as an important factor, because unemployment was perceived as reducing people’s ability to care for their health. Participants recommended aggregating data only among tribes with similar economic conditions: You’d want to group us with the more rural, less economically developed tribes . . . or it would make us look like we don’t have a disparity here because we’re being grouped with them. DISCUSSION
The results of this collaboration with 5 northwestern AI tribes emphasize the value of tribal consultation and the need for tribal approval of any grouping of communities proposed to augment sample sizes for health research. Using interview and focus group data, we distilled 5 broad criteria for data aggregation and stratification, all of which can be tailored to individual communities: geographic proximity, community type, environmental exposures, access to resources and services, and economic development. In practice, these criteria will likely overlap, because environmental exposures tend to vary by geography; access to services varies by geography, community type, and economic conditions; and the same geographic region can be home to a range of community types with differential access to services and economic development. Conceptually, these criteria can be understood on 2 levels: those based on external data without implied effects on health (geography, community type) and those based on communitylevel data on the social determinants of health (economic status, access to resources and services, and environmental exposures) (27–29). In practice, epidemiologists use all 5 factors to organize and analyze population health data (6, 28, 30, 31). To the best of our knowledge, this project was the first academic/community collaboration in the United States to elicit tribal recommendations for aggregating health data on small tribes. Literature searches revealed little previous work in this area. For the United States, we found a single conference presentation from 2006 by an AI/AN health researcher who noted the limitations of the current approaches to aggregating tribal data and emphasized the need for respectful dialogue with community members to improve data collection (32). For Canada, we learned of an effort in which the First Nations of Manitoba contracted an academic team to assist in designing and implementing a longitudinal survey of aboriginal health in a sample of First Nations communities (33). In that project, a steering committee comprising First Nations health officials developed 4 criteria to achieve a truly representative selection of communities: political affiliations, tribal affiliations within larger political units, geographic factors, and community size. We note broad similarities between our work and both of these studies, ranging from the emphasis on community dialogue to the generation of selection criteria. Ironically, in the First Nations project, the criterion regarding community size excluded communities with fewer than 250 members—a reminder of the ethical imperative for researchers to make small tribes visible to policymakers. Am J Epidemiol. 2016;184(1):1–6
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Our project was designed to establish trusting relationships with participating tribes by learning about each tribe’s needs and concerns regarding data aggregation. To this end, we made repeated site visits to present the project to tribal councils and convene focus groups. We also sponsored a culminating workshop with tribal representatives in Seattle. As our tribal partners concluded, any grouping of tribal data for research purposes is meaningful only if it is determined through an ethical process of consultation with the tribes affected. The present study has notable limitations. Although our tribal collaborators are diverse in many ways, they are a small fraction of AI/AN communities nationwide and cannot represent the full Native population. In addition, focus group participants at some sites were individually recruited by community coordinators involved in cancer research instead of enrolled at random; as a consequence, cancer survivors or their family members were overrepresented at 2 sites. This factor likely biased some discussions in favor of cancerrelated issues. Finally, our participant population was skewed toward women, who outnumbered men 3:1. According to many participants, this imbalance in the sexes mirrored the differential use of health services by AI/AN men and women. Despite these shortcomings, we believe that our results will be useful to tribes and researchers in a range of settings, and we have created a resource summarizing the methods and results of the present study for future dissemination to tribes nationwide. Meaningful data on disease incidence and mortality are urgently needed to effectively channel resources and improve outcomes in AI/AN communities. Such data must accurately reflect the lived experience of tribal members. In the present study, participants observed that just 1 cancer diagnosis has a major impact on morale, perception of risk, and tribal health finances, even in a community of a few thousand people. They also affirmed that they did not wish to be excluded from research simply because their tribal population was small. To ensure that data aggregation is acceptable to tribes and effective in leading to desired outcomes, paradigms offered by Tribal Participatory Research and communitybased participatory approaches advise academic researchers to engage in regular consultation with tribal elders (19, 34). Consultation should continue throughout the project, from proposal development through drafting of presentations and manuscripts for publication. Tribal leaders should be consulted again before any public dissemination of research to confirm that findings have been interpreted and contextualized accurately. This process that has already been implemented by the Elders-in-Residence programs at the University of Alaska Fairbanks (35) and the University of Washington (36), and we encourage its uptake as a best practice.
ACKNOWLEDGMENTS
Author affiliations: Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington (Emily R. Van Dyke, Abigail L. Echo-Hawk, Deborah Bassett, Raymond M. Harris, Dedra S. Buchwald); and Department of Am J Epidemiol. 2016;184(1):1–6
Bioethics and Humanities, School of Medicine, University of Washington, Seattle, Washington (Erika Blacksher). Raymond M. Harris is currently at the Department of Community Health, Health Sciences Spokane, Washington State University, Seattle, Washington. This work was supported by the National Cancer Institute (grant U54 CA153498) and the National Center for Advancing Translational Health Sciences (grant UL1TR000423) at the National Institutes of Health. We thank Dr. Stephen Schwartz of the Fred Hutchinson Cancer Research Center in Seattle, Washington, for helpful conversations during manuscript development. Conflict of interest: none declared.
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