A Census Tract–Level Examination of Social Determinants of Health among Black/African American Men with Diagnosed HIV Infection, 2005–2009—17 US Areas

Abstract

BackgroundHIV disproportionately affects black men in the United States: most diagnoses are for black gay, bisexual, and other men who have sex with men (collectively referred to as MSM). A better understanding of the social conditions in which black men live and work may better explain why HIV incidence and diagnosis rates are higher than expected in this population.MethodsUsing data from the National HIV Surveillance System and the US Census Bureau's American Community Survey, we examined the relationships of HIV diagnosis rates and 5 census tract–level social determinants of health variables for 21,948 black MSM and non-MSM aged ≥15 years residing in 17 areas in the United States. We examined federal poverty status, marital status, education level, employment status, and vacancy status and computed rate ratios (RRs) and prevalence odds ratios (PORs), using logistic regression with zero-inflated negative binomial modeling.ResultsAmong black MSM, HIV diagnosis rates decreased as poverty increased (RR: 0.54). At the time of HIV diagnosis, black MSM were less likely than black non-MSM to live in census tracts with a higher proportion below the poverty level (POR: 0.81) and with a higher proportion of vacant houses (POR: 0.86). In comparison, housing vacancy was positively associated with HIV diagnosis rates among black non-MSM (RR: 1.65). HIV diagnosis rates were higher for black MSM (RR: 2.75) and non-MSM (RR: 4.90) whose educational level was low. Rates were significantly lower for black MSM (RR: 0.06) and non-MSM (RR: 0.26) as the proportion unemployed and the proportion married increased.ConclusionsThis exploratory study found differences in the patterns of HIV diagnosis rates for black MSM and non-MSM and provides insight into the transmission of HIV infection in areas that reflect substantial disadvantage in education, housing, employment, and income.

Characteristics of Black Men Who Have Sex With Men in Baltimore, Philadelphia, and Washington, D.C.: Geographic Diversity in Socio-Demographics and HIV Transmission Risk

“…In DC, HIV prevalence was significantly higher among those with lower education, unemployment, and incarceration history and in both DC and Philadelphia, those with public insurance were substantially more likely to be HIV-positive. These findings support previous research that shows a connection between high poverty, low education and elevated HIV prevalence among BMSM 6 and make clear .the importance of employing a social determinants perspective in HIV planning for BMSM, as others have noted. 4,25 …”

“…At the same time, population differences at multiple levels as well as local context influence lifetime risk of HIV exposure and transmission risk among BMSM. 5,6,1 There is a lack of understanding of the complex interplay between prevailing and local factors in the HIV epidemic among BMSM and a dearth of comprehensive HIV interventions for BMSM 7 . These gaps undermine municipal ability to effectively address regional and local HIV transmission.…”

Racial Disparities in Invasive Methicillin-resistant Staphylococcus aureus Infections, 2005–2014

“…Observed racial disparities might result from underlying patient-level disparities, such as differences in underlying health, access to medical care, health-seeking behavior, and environmental factors [20, 21]. For example, our data showed that blacks with invasive MRSA associated with healthcare exposure (HO and HACO cases) had more documented chronic medical comorbidities than whites.…”

Developing intermediate zones for analysing the social geography of Auckland, New Zealand

“…; Gant et al . ). However, they are not designed for investigating such data, and their use may produce misleading and inaccurate results (Shortt ).…”

“…The analysis of health and social data requires consistent geographical units of analysis in order to make fair comparisons between areas. Historically, researchers and policy analysts have used readily available census and administrative boundaries to report small-area or 'neighbourhood' statistics (Pearce et al 2006a,b;Greer et al 2010;Gant et al 2014). However, they are not designed for investigating such data, and their use may produce misleading and inaccurate results (Shortt 2009).…”