The current study extends SDOH knowledge by empirically testing the four leading SDOH models (World Health Organization, Health Peoples Model, Kaiser Permanente, and the County Health Rankings Model) while analyzing their influence on diabetes risk factors. These finding elucidate the complex relationship between BMI, HbA1c, and social and environmental risk factors.
Black participants had higher odds of HbA1c risk within every SDOH model, poverty and literacy challenges were significant across two of the models. Regarding BMI, Black participants were again at increased risk for BMI across each SDOH model. Additional main effects that were commonly predictive for BMI were poverty, lack of access to higher education, and social status. These findings elucidate the complex and profound influence regarding racism on diabetes risk. Future research may perform path analysis or mediation models to determine mechanisms through which racial discrimination influences diabetes risk.
The composition of SDOH models was influential in determining which SDOH variables were significant. For example, poverty was included within each SDOH model but only significant for BMI in the County Health and Healthy People models. Similarly, social cohesion was included within three of the SDOH models but was only significant for BMI in the Health Peoples Model. This finding likely reveals interaction effects of SDOH variables. SDOH variables may be operating as suppressor variables, mediators, or moderators. Future research may utilize complex mediation models and path analyses to further elucidate the mechanisms through which SDOH influence diabetes risk.
Understanding the overall impact of SDOH models and their unique main effects may help us build better SDOH models to empirically test which SDOH models will create better policy and intervention for patients with diabetes. The Kaiser model was the most comprehensive and had the largest effect size for BMI even when using the adjusted R2. We will examine the operationalization of these two SDOH models while identifying recommendations for future research.
Kaiser Model
The Kaiser model was most comprehensive including constructs such as social support, discrimination, and stress within their SDOH model. Individuals had higher BMIs when participants were Black, had debt, lacked access to higher education, did not have a spouse to rely on, and lacked access to quality healthcare. The Kaiser model was the only SDOH model to include variables of debt, stress, quality of care, and cultural competency of the healthcare provider. Framing SDOH in a comprehensive way increased the statistical impact of the Kaiser model regarding BMI even when using the adjusted R square.
Healthy People Model
The Healthy People model was the only one to include incarceration which was a significant predictor of HbA1c. In addition, the Healthy People model was more concrete in the way they operationalized their concepts. For example, they include poverty and housing instability rather than general categories of income and housing status. This supports the empirical construction of variables and therefore more readily allows for a standardized way to detect SDOH predictors of HbA1c.
BMI and HbA1c
SDOH influencing HbA1c risk include poverty, race, literacy issues, and lack of healthcare quality. Previous literature have examined the role of race and poverty on diabetes outcomes demonstrating racial minorities experienced higher risk of diabetes due to poverty and housing instability 19. Our findings support those while adding additional context by testing housing and economic variables along with the other variables included within our statistical models. Regarding BMI, there were more main effects including social status, debt, education, and not having people to rely on. These findings suggest that there may be distinct risk factors for BMI and HbA1c. Social status, debt, lack of higher education, and lack of social support may influence HbA1c indirectly. Future research may examine the influence of these SDOH identifying whether these risk factors increase risk of developing diabetes over time.
Intersections of Poverty and Race.
Racial discrimination has been identified as a SDOH 30. Much of the existing research evaluates the effect of racial discrimination on HbA1c 31, health behaviors 32, and BMI 33. Few studies include other SDOH within their respective research models 34. Our analysis demonstrated that Black individuals have increased risk of elevated HbA1c even when including several other SDOH variables. There were, in addition, significant findings for income, literacy challenges, and healthcare quality. These SDOH factors may be influential in how Black individuals experience increased risk. Research and policy practice may consider analyzing potential SDOH factors which are associated between racial identity and diabetes outcomes.
Nonsignificant SDOH Variables.
Testing these models also demonstrated SDOH variables that were not statistically significant. For example, discrimination was included within two of the SDOH models, but was not significant for either BMI or HbA1c. In addition, social cohesion was included within 3 of the SDOH models but was only significant in the Health People Model for BMI. Some of these nonsignificant findings are contrary to existing research. For example, there are studies demonstrating the role of discrimination on diabetes outcomes 31, while other studies demonstrate a lack of significance when adding in additional control variables to include demographics and health behaviors 35. There may be suppressor or mediating variables within the SDOH models. In addition, relying on subjective self-report data may be a limitation. Future research may build on existing research efforts to utilize more objective indicators of racial minority stress to include cortisol and C-reactive protein 36.
Limitations
One limitation of the current study was that proxies were used to represent each SDOH construct, and that the Add Health dataset did not have a proxy for every construct included. In addition, this study was cross sectional. Therefore, we cannot determine the longitudinal or causal relationship between the SDOH models and the diabetes outcomes. Relying on self-report data may also be a limitation, and future research may utilize geospatial information system and social network analysis to better analyze environmental influences. Despite these limitations, this research provides a unique approach regarding methods to empirically test SDOH models.
Future Research
Longitudinal analyses may be performed to assess the influence of SDOH models and factors associated overtime. In addition, studies may include approaches to address social determinants of health (e.g., food as medicine programs) and identify how these interventions may impact diabetes outcomes. Future research may empirically test the association between the SDOH models and other disease states such as cardiovascular disease, chronic pain, and cancer. Since social workers are often utilized to address SDOH, randomized control trials (RCTs) may be employed which utilize social workers to assess SDOH and various healthcare concerns. Interventions to address these SDOH could be performed while assessing their impact on the SDOH indirectly and various healthcare outcomes indirectly. This may provide future direction for healthcare practice and policy.