Objectives. To examine food insecurity and cardiovascular disease–related health outcomes among American Indians (AIs) in rural Oklahoma.Methods. We surveyed a cross-sectional sample of 513 AI adults to assess food insecurity domains (i.e., food quality and quantity) and obesity, diabetes, and hypertension.Results. Among AIs surveyed, 56% reported inadequate food quantity and 62% reported inadequate food quality. The unadjusted prevalence of diabetes (28.4% vs 18.4%), obesity (60.0% vs 48.3%), and hypertension (54.1% vs 41.6%) was higher among participants with inadequate food quantity than among those with adequate food quantity. These associations did not reach statistical significance after adjustment for age, gender, study site, education, and income. The unadjusted prevalence of obesity (60.7% vs 45.8%), diabetes (27.3% vs 18.8%), and hypertension (52.5% vs 42.5%) was higher among those with inadequate food quality than among those with adequate food quality, even after adjustment for age, gender, study site, education, and income.Conclusions. Tribal, federal, and state policymakers, as well as businesses and nonprofit organizations, must collaboratively take aggressive action to address food insecurity and its underlying causes, including improving tribal food environments, reducing barriers to healthy foods, and increasing living wages.

Structural equation modeling (SEM) is a multivariate analysis method for exploring relations between latent constructs and measured variables. As a theory-guided approach, SEM estimates directional pathways in complex models based on longitudinal or cross-sectional data where randomized control trials would either be unethical or cost prohibitive. However, this method is infrequently used in nutrition research, despite recommendations by epidemiologists for its increased use. The aim of this study was to explore 3 key methodologic areas for consideration by researchers when conducting SEM with complex survey datasets: the use of sampling weights, treatment of missing data, and model estimation techniques. With the use of data from NHANES waves 2005-2010, we developed an SEM to estimate the relation between the latent construct of depression and measured variables of food security, tobacco use (serum cotinine), and age. We used a hierarchic approach to compare 5 SEM model iterations through the use of: 1 and 2) complete cases without and with the application of sampling weights; 3) an applied missingness dataset to test the accuracy of multiple imputation (MI); 4) the full NHANES dataset with imputed data and sampling weights; and 5) a final respecified model. Each iteration was conducted with maximum likelihood (ML) and quasimaximum likelihood with the Satorra-Bentler correction (QML) to compare path coefficients, standard errors, and model fit statistics. Path coefficients differed between 15.68% and 19.17% among model iterations. Nearly one-third of the cases had missing values, and MI reliably imputed values, allowing all cases to be represented in the final model iterations. QML provided better model fit statistics in all iterations. Nutrition epidemiologists should use complex weights, MI, and QML as a best-practices approach to SEM when conducting analyses with complex design survey data.

American Indians (AIs) have significantly higher rates of diet-related chronic diseases than other racial/ethnic groups, and many live in environments with limited access to healthy food. As part of the Tribal Resilience in Vulnerable Environments (THRIVE) study, we examined the relations between the perceived food environment, utilization of food retailers, fruit and vegetable intake, and chronic diseases, including obesity, hypertension, and type 2 diabetes among AI adults. Through a community-based participatory research partnership, we surveyed a cross-sectional sample of 513 AIs living within the Chickasaw Nation and the Choctaw Nation of Oklahoma. Only 57% of participants reported that it was easy to purchase fruits and vegetables in their town, and fewer (35%) reported that available fruits and vegetables were of high quality. Additionally, over half (56%) reported traveling ≥20 miles round trip to shop for food. Few participants met the recommended daily intake for fruit (44%) or vegetables (25%). Obesity (55%), hypertension (49%), and diabetes (25%) were commonly reported. Obesity was significantly higher among participants who reported that the price of fruits and vegetables were cost-prohibitive (prevalence proportion ratio (PPR): 1.24; 95% CI: 1.02, 1.50) and those who shopped frequently for food at nontraditional food retailers, such as Dollar Stores (PPR: 1.35; 95% CI: 1.08, 1.69) and small markets (PPR: 1.38; 95% CI: 1.02, 1.86). Diabetes was significantly higher among participants who frequently shopped at convenience stores/gas stations (PPR: 2.26; 95% CI: 1.22, 4.19). Our study found that the use of nontraditional food retailers, including convenience stores, gas stations, and Dollar Stores, as a regular source of food was associated with obesity and diabetes. These results underscore the importance of interventions to improve rural Tribal food environments. Healthy retail interventions in nontraditional retail settings, such as those implemented through the THRIVE study, may contribute to reducing AI health disparities.

Food pantries serve populations with high rates of chronic disease and thus may be ideal settings for community-based health promotion programs. This study aimed to describe the readiness of food pantry organizations (n = 69) and their personnel (n = 129) to engage in nutrition-focused food pantry services, including nutrition education and the application of dietary guidelines for disease prevention and management as components of existing food assistance programs. Among survey respondents, few personnel reported asking clients about chronic disease diagnoses or providing clients with nutrition education, and over half reported lower confidence in performing these activities. Yet, the majority were interested in training to address client health needs. However, organizational capacity is currently restricted by lack of nutrition policies and limited healthy food inventory, including fruits and vegetables. To address the nutritional needs of clients, food pantries may benefit from in-person training and additional resources to obtain and distribute healthier foods.