Inequality in State-Level Income Distribution As a Social Determinant of HIV Risk

Background and Purpose: Income inequality is a recognized social determinant of many detrimental health behaviors and negative health outcomes (e.g., obesity, substance misuse, heart disease) (Kondo et al., 2009). Comparatively less is known about income inequality as a determinant of sexual health behaviors, especially those that elevate the risk of contracting HIV, which is U.S. and global health initiative. This study aimed to understand the effect of income inequality as an upstream, policy-based determinant of HIV risk in order to develop targeted policy solutions and practice insights to enhance the sexual health of those who are vulnerable to HIV.

Methods: We measured income inequality utilizing a previously published and publicly available secondary data set [Frank series] on the Gini coefficient (scale 0-100 where 100 is most unequal) for US states. Separately, we utilized an HIV risk factor indicator variable and other demographic variables from publicly available CDC Behavioral Risk Factor Surveillance System (BRFSS) data. These data were concatenated into a single dataset for analysis. We then employed multi-variable logistic regressions to assess the relationship between income inequality at the level of US states and HIV-related sexual risk behavior, with adjustments for confounds (e.g., age, sex, race, individual income). We supplemented this main analysis with sensitivity checks for varying formulations of key control variables as well the HIV sexual risk behavior indicator.

Results: On average, a single point increase in inequality (0-100 scale) was associated with an approximately 9% percent increase in the risk of engaging in an HIV risk behavior (OR=1.09, p<.001). The range of income inequality across states is approximately 10, meaning that the probability of engaging in sexual risk behavior for HIV in the highest inequality states is approximately double that of the lowest inequality states. We did not detect sensitivity to alternative formulations of the HIV indicator variable or the income variable in our modeling, with results that were robust to alternative variable and model formulations.

Conclusions and Implications: The findings of a notable relationship between income inequality and HIV risk behavior contribute to the literature on both income inequality and social determinants of sexual risk behaviors. This enhances to our knowledge about the wide-ranging impact of income inequality as a determinant of downstream health effects, including to HIV risk specifically. We suggest that policy solutions that remediate the unequal and growingly disparate distribution of income in the US can meaningfully address sexual health risk, in addition to other known health outcomes. Specifically, at a policy level, social work researchers, educators, and policymakers should target income disparities that intensify sexual vulnerabilities, while social worker practitioners involved in sexual health services should have opportunities to understand and investigate how income inequality contributes to clients’ vulnerabilities at an individual level (e.g., via continuing education).