A County-Level Examination of the Relationship Between HIV and Social Determinants of Health: 40 States, 2006-2008
Gant Z*, 1 , Lomotey M1, Hall H.I1, Hu X1, Guo X2, Song R1
Identifiers and Pagination:Year: 2012
First Page: 1
Last Page: 7
Publisher ID: TOAIDJ-6-1
Article History:Received Date: 10/5/2011
Revision Received Date: 21/10/2011
Acceptance Date: 28/10/2011
Electronic publication date: 21 /2/2012
Collection year: 2012
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
Social determinants of health (SDH) are the social and physical factors that can influence unhealthy or risky behavior. Social determinants of health can affect the chances of acquiring an infectious disease – such as HIV – through behavioral influences and limited preventative and healthcare access. We analyzed the relationship between social determinants of health and HIV diagnosis rates to better understand the disparity in rates between different populations in the United States.
Using National HIV Surveillance data and American Community Survey data at the county level, we examined the relationships between social determinants of health variables (e.g., proportion of whites, income inequality) and HIV diagnosis rates (averaged for 2006-2008) among adults and adolescents from 40 states with mature name-based HIV surveillance.
Analysis of data from 1,560 counties showed a significant, positive correlation between HIV diagnosis rates and income inequality (Pearson correlation coefficient ρ = 0.40) and proportion unmarried – ages >15 (ρ = 0.52). There was a significant, negative correlation between proportion of whites and rates (ρ = -0.67). Correlations were low between racespecific social determinants of health indicators and rates.
Overall, HIV diagnosis rates increased as income inequality and the proportion unmarried increased, and rates decreased as proportion of whites increased. The data reflect the higher HIV prevalence among non-whites. Although statistical correlations were moderate, identifying and understanding these social determinants of health variables can help target prevention efforts to aid in reducing HIV diagnosis rates. Future analyses need to determine whether the higher proportion of singles reflects higher populations of gay and bisexual men.