American Indian Center

Abstract Negative health impacts of water insecurity are often felt most in poor and rural communities and communities of color, who are more likely to be underserved by water infrastructure and disproportionately subject to socioeconomic stressors. Despite mandated efforts to allocate significant federal resources to infrastructure funding in ‘disadvantaged communities,’ communities with the most need risk systematic exclusion from access to resources, decision-making structures, and even benefits of research intended to address inequitable infrastructure services and health outcomes in their own communities. This project aims to describe groundwork and preliminary findings from community-engaged environmental research conducted within an ongoing community-based participatory research partnership in Robeson County, NC, a majority–minority county with the lowest median household income of NC’s 100 counties. Semi-structured interviews conducted with community members were analyzed to identify concerns about drinking water security (including safety, affordability, and reliability), perceptions of water quality, averting behaviors taken due to water insecurity, and ideas for improving water security. Findings suggest that there is a high level of mistrust in community water supplies, with perceptions of poor water quality driving a reliance on bottled water. Those relying on private wells expressed greater trust in their water and lower reliance on bottled water. Concerns about affordability were less prominent than those about water quality. Insufficient water reliability (low flow) was mentioned by many respondents, including those with community water service and those relying on private wells. Most supported increasing taxes to improve water security and also recommended increasing communications between water service providers and the public to improve trust. Overall, this work suggests the need for a comprehensive assessment of the quality and reliability of community water services in Robeson County, interventions to address problems identified, and much more engagement with the community about identifying and allocating funding to solve water security problems.

ABSTRACT The generalized estimating equations method (GEE) is commonly applied to analyze data obtained from family studies. GEE is well known for its robustness on misspecification of correlation structure. However, the unbalanced distribution of family sizes and complicated genetic relatedness structure within each family may challenge GEE performance. We focused our research on binary outcomes. To evaluate the performance of GEE, we conducted a series of simulations, on data generated adopting the kinship matrix (correlation structure within each family) from the Strong Heart Family Study (SHFS). We performed a fivefold cross-validation to further evaluate the GEE predictive power on data from the SHFS. A Bayesian modeling approach, with direct integration of the kinship matrix, was also included to contrast with GEE. Our simulation studies revealed that GEE performs well on a binary outcome from families having a relatively simple kinship structure. However, data with a binary outcome generated from families with complex kinship structures, especially with a large genetic variance, can challenge the performance of GEE.

¶ a Citizen of Navajo Nation and the Dine ´community. ¶ b Kanaka 'O ¯iwi diaspora from Micronesia.

Cancer disparities continue among American Indian and Alaska Native (AI/AN) populations while they have decreased among other racial and ethnic groups. No studies were found that utilized the Community Readiness Model (CRM) to ascertain the readiness of Tribal and American Indian organizations to participate in cancer research and cancer prevention and control initiatives. The Partnership for Native American Cancer Prevention conducted an assessment of the status of American Indian communities' readiness to implement activities for prevention, early detection, and treatment to improve AI/AN cancer rates. The assessment was a component of the Community Outreach Core of the grant. Thirty-four key Informants participated in the interview process. The Community Readiness Assessment (CRA) provided a baseline assessment of community partners' readiness to participate in cancer research and programming. Despite years of cancer intervention programs, the communities were classified as being in the early stages of readiness [1-5] of the nine-stage model. Additionally, findings showed low levels of awareness of previous or ongoing cancer research. The findings in prevention and control efforts indicated a need for technical assistance and funding to support community projects in prevention and control. This supported the implementation of a community grants initiative. They also indicated that communities were not ready to conduct research, despite ongoing cancer related research in at least two communities. Communication tools and social media methods and messages were developed to increase awareness of cancer as a health concern and cancer research in the community. The CRM informed these and other engagement activities to meet the appropriate stage of readiness for each Tribe/community, and to build their capacity to participate in cancer research and programming activities.

Background: Dyslipidemia is an established risk factor for CVD. Standard lipid panel lacks description of the full spectrum of blood lipids. A longitudinal profiling of blood lipidome in relation to the risk of CVD is lacking in any racial/ethnic groups. Objective: Identify novel lipid species associated with risk of CVD in American Indians, independent of traditional risk factors (TRFs). Method: Using untargeted LC-MS, we repeatedly measured 1,542 lipid species in 3,916 fasting plasma samples from 1,958 participants who attended two exams (~5.5 years apart) and were followed up to 18 years in the Strong Heart Study. Elastic net was used to select baseline lipids and change in lipids (baseline to 5-year follow-up) associated with 18-year risk of CVD. Multivariate and network analyses were conducted to identify discriminatory lipidomic signatures and differential lipid networks associated with risk of CVD. Results: Of 1,911 participants free of CVD at both baseline (2001-2003) and 5-year follow-up (2006-2009), 222 developed CVD after 18 years of follow-up. High-resolution LC-MS detected 1,542 lipid species (518 known). After adjusting for TRFs (age, sex, center, smoking, BMI, hypertension, diabetes, LDL-c, and albuminuria), elevated baseline levels of 21 known lipids, including cholesterol esters, sphingolipids, glycerophospholipids, and fatty acids, were significantly associated with increased (HR: 1.08 to 1.26) or decreased (HR: 0.81 to 0.91) risk of CVD. Longitudinal change in 13 lipids, including cholesterol esters, sphingolipids, glycerophospholipids, and fatty acids, were either positively (HR: 1.03 to 1.28) or inversely (HR: 0.75 to 0.92) associated with risk of CVD at 18-year follow-up. A lipid score comprising 10 baseline lipids significantly improved risk prediction beyond TRFs (ROC increases from 0.823 to 0.837, P = 0.009). Multivariate and network analyses identified distinct lipidomic signatures and lipid networks associated with risk of CVD. Conclusion: Novel molecular lipids significantly predict risk of CVD beyond TRFs in American Indians. Our findings provide novel lipid molecules for risk stratification and shed light on the mechanism linking lipid dysregulation to CVD in American Indians.

The Action for Health in Diabetes (Look AHEAD) study previously reported that intensive lifestyle intervention (ILI) reduced incident depressive symptoms and improved health-related quality of life (HRQOL) over nearly 10 years of intervention compared with a control group (the diabetes support and education group [DSE]) in participants with type 2 diabetes and overweight or obesity. The present study compared incident depressive symptoms and changes in HRQOL in these groups for an additional 6 years following termination of the ILI in September 2012. A total of 1,945 ILI participants and 1,900 DSE participants completed at least one of four planned postintervention assessments at which weight, mood (via the Patient Health Questionnaire-9), antidepressant medication use, and HRQOL (via the Medical Outcomes Scale, Short Form-36) were measured. ILI participants and DSE participants lost 3.1 (0.3) and 3.8 (0.3) kg [represented as mean (SE); p=0.10], respectively, during the 6-year postintervention follow-up. No significant differences were observed between groups during this time in incident mild or greater symptoms of depression, antidepressant medication use, or in changes on the physical component summary or mental component summary scores of the Short Form-36. In both groups, mental component summary scores were higher than physical component summary scores. Prior participation in the ILI, compared with the DSE group, did not appear to improve subsequent mood or HRQOL during 6 years of postintervention follow-up.

Objectives. To determine the impact of disaggregated mortality and health surveillance data on the ability to identify health disparities for American Indian and Alaska Native (AI/AN) subpopulations. Methods. We conducted a systematic review of reporting categories for AI/AN decedents on official death certificates for all 50 US states. Using public data from the 2017-2018 California Health Interview Survey (CHIS), we conducted bivariate and multivariate analyses to assess disparities in health conditions and outcomes for tribally enrolled and non‒tribally enrolled AI/AN persons compared with non-Hispanic Whites. Results. There was no standard for the collection of tribal enrollment data or AI/AN race on death certificates across all 50 states. There were stark differences in the incidence and prevalence of various health risk factors and chronic diseases for the tribally enrolled AI/AN subpopulation, non‒tribally enrolled AI/AN subpopulation, and non-Hispanic White comparison group. Conclusions. The collection of tribal enrollment data in vital statistics and health surveillance systems is necessary to identify and respond to health disparities among AI/AN subpopulations. These efforts must be conducted in partnership with tribal nations and consider Indigenous data sovereignty.