In our analysis, HIV diagnosis rates increased as income inequality and the proportion unmarried increased, and rates decreased as proportion of whites increased. For certain individuals within a county, income inequality can play a role in increased economic strain and limited material availability [32
]. One explanation in the observed relationship between income inequality and increased HIV diagnosis rates may be the social hierarchy established within a community’s social and income stratifications, which leads to psychosocial harm due to perceived positioning in society, material availability, limited opportunity, and a truncated sense of control [32
]. This, in turn, contributes to certain coping mechanisms that may be detrimental to good health, such as limited future life chances, more impulsivity, and more risky behavior [32
]. This established infrastructure brings about disparities across different groups, leading to decreased medical care and decreased HIV testing among the population negatively impacted [34
]. Decreased testing leads to persons unaware of their infection status, and those who are unaware have higher transmission rates than those who are aware of their status [35
The data show a statistically significant positive correlation between proportion unmarried and HIV diagnosis rates. The proportion unmarried within an area may have some impact on HIV diagnosis rates as it relates to the structure of sexual networks within an area.
This relationship could be expected based on the nature of the interactions among married versus unmarried individuals. That is, the research shows persons unmarried tend to have multiple sexual partners, and be indirectly or directly linked to a sexual network which can influence the transmission of HIV [3
]. The effects of sexual networks could be compounded by social context, which influences transmission of HIV [3
]. Also, these sexual networks could differ based on sexual orientation, and with same-sex marriage not recognized or accepted in most states, this could possibly have a confounding effect [3
]. Future analyses need to determine whether the higher proportion unmarried reflects higher populations of gay and bisexual men.
The data reflect the higher HIV prevalence among non-whites. Racial segregation and composition at the county level could affect HIV through differential distribution of resources [22
]. Certain communities disproportionately impacted may have fewer resources, restricted medical access and reduced awareness of existing health programs; thereby, increasing the possibility of more risky behavior [22
]. Lack of resources may also have an effect on the social and sexual networks within the communities [22
]. Farley makes the case that race may be a marker for social and environmental factors (e.g., alcohol and drug marketing, social capital, poor education, male incarceration, and chronic joblessness) that are related to an increase in HIV transmission [7
]. Further, the data suggest that the proportion of whites within a county plays a role in the linear relationship between other social determinants of health and HIV diagnosis rates. That is, other social determinants of health variables controlled for in the partial correlation models did not contribute to the explanation of the relationship between proportion white and HIV diagnosis rates. This can partly be explained by the high prevalence of HIV among African Americans and Hispanics/Latinos, which provides greater chances for infection because of the partner pool within those racial/ethnic groups and contributes to the disparity in HIV diagnosis rates between racial/ethnic groups [41
]. Further work is needed to understand the community environment and its contribution to any social and sexual network disparities between the different racial/ethnic groups.
We did not observe a relationship between race-specific social determinants of health variables and HIV diagnosis rates. From the American Community Survey data, there were a number of counties with missing data for blacks/African Americans and Hispanics/Latinos. Because of the lack of sampling of these populations in the American Community Survey, there were counties which contain no blacks/African Americans and Hispanics/Latinos persons. Therefore, there may not be enough power to detect an association.
There are limitations to this study. One limitation is that our analyses are based on residence at the time of diagnosis of HIV infection, which does not necessarily represent incidence or location of HIV infection. Also, the data are based on known diagnoses – it does not include those infected but undiagnosed (which is estimated at 21% nationally) [44
]. Data for this analysis were adjusted for reporting delays, but not for incomplete reporting. This may result in an underestimate of the true number of cases within the given time period. Also, social determinants of health information for each individual person diagnosed with HIV are unknown. We use counties as a surrogate for the conditions of persons diagnosed with HIV. In addition, American Community Survey only surveys two-thirds of counties for its sampling population, which may have potential reliability concerns in the results. Use of county as the unit of measurement may be problematic as it may not accurately represent people’s socio-economic status, which may be more closely tied to smaller areas such as neighborhoods that may reflect the connection of social networks and physical spatial locations [22
]. However, certain area characteristics such as income inequality and residential segregation are potentially relevant to health, and they are more meaningfully defined at larger levels or aggregations. As these larger areas may not fully explain the heterogeneity within them, it will be important to also look at smaller areas such as neighborhoods in future analyses.
This is one of the first studies to examine the relationship between social determinants of health and HIV diagnosis rates at the county level using national data. Overall, our results build on earlier works that have examined social determinants of health and HIV/AIDS infection [13
]. Although statistical correlations for some social determinants of health variables were moderate, these analyses provide a first step to a better identification and understanding of social determinants of health factors in relation to the HIV burden. Future analyses may provide additional insight to help target prevention efforts and provide information on societal factors influencing disparate HIV diagnosis rates.