High income inequality was associated with a priority cluster for late HIV diagnosis and linkage to HIV care. To better understand those findings, which run contrary to the negative effects inequality usually has on health,73 we conducted an exploratory analysis of income inequality in a model without median income, and then median income without income inequality, and then with both excluded. Those analyses revealed that income inequality adjusted for other covariates was not significantly associated with being in a priority cluster. Next, the effect size of median income was attenuated when income inequality was added to the model (results not displayed).
We ground our findings within the local geographic context of Philadelphia, PA, in addition to being data driven. For example, we juxtaposed a zoning map of the city to provide visual cues of space. This most recent zoning map (year 2012) shows that areas with high social capital and high HIV diagnosis were mainly industrial zones. These areas may have small population density and thus HIV prevalence may be amplified with one new case of a small denominator.
A secondary aim of this study was to identify which sociocontextual factors differentiate any clusters found, which could aid in identifying mechanisms to potentially target. We found that areas with higher median income and income inequality were more likely to be in a high “priority” cluster compared with a low priority cluster (ie, poor HIV diagnosis and care). That finding is consistent with another Philadelphia study,79 which showed that hotspots with poor viral suppression were more likely to be in economically deprived neighborhoods.
The income inequality finding, however, seemed contrary to the theory given that income inequality erodes social capital.80 Income inequality was not collinear with median household income, so we think the paradoxical findings could be that the GINI coefficient does not allow one to distinguish whether it is driven by a higher proportion of high-income residents compared with low-income residents or vice versa. Alternately, the findings may be due to unobserved confounding or interaction with other sociocontextual factors. For example, a recent study in New York City showed that late HIV diagnosis was high in areas with low black racial concentration and high income inequality and areas with high black racial concentration and low income inequality.81
Our aggregate data contained cases diagnosed in the correctional setting which dominates one area of Northeast Philadelphia where jails for both men and women are located, which previous estimates indicate is approximately 8%.78 However, the Northeast location of the major jail in Philadelphia, PA, (Curran-Fromhold Correctional Facility) is situated in an area which exhibited low social capital–low late HIV diagnosis and low linkage to care. One would expect a higher rate of HIV diagnosis and linkage to care among individuals in an institutional setting. Therefore, we believe that the impact of HIV prevalence in the tracts that includes this group is minimal, especially because an estimated 92% of the sample would be noninstitutional and population based.78 We could not distinguish between noninstitutional and institutional populations in our data.
We only analyzed social participation, which is one of several indicators that measure social capital, although others suggest it may not reflect the underlying construct.85 Thus, we cannot generalize the findings to other indicators such as social cohesion or collective efficacy. However, in previous HIV and social capital research, social/civic/political participation was highly positively correlated with other social capital indicators such as collective efficacy, social cohesion, and informal social control.32 We also could not analyze participation by the type of organizations (eg, athletic club compared with religious or other associations). Next, although we focused on a core group of sociocontextual mechanisms highlighted in the literature to impact both social capital and HIV care continuum indicators, we did not examine access to transportation, ethnic density, or other measures of social disorganization such as crime rates,33,49 which are also potentially modifiable determinants. Nevertheless, despite these potential limitations, this is the first study to analyze the geographic clustering in the association between social capital and care outcomes.
We selected Philadelphia, PA, for this analysis because ours and other research demonstrated geographic disparities in HIV outcomes. We also had access to social capital and HIV outcomes data across neighborhoods. However, the spatial and regression methods we used can be replicated across other settings and using other social capital indicators, care continuum outcomes, and sociocontextual mechanisms. For instance, AIDSVu.com 86—an online interactive website—displays HIV data at the census tract, ZIP code, or neighborhood level for several other US cities including Atlanta, GA, and Chicago, IL. HIV data on AIDSVu.com are publicly available, and researchers can download and combine these data with external sources that contain social capital measures to replicate this analysis.
Understanding and responding to geographic clustering in the association between social capital and HIV care continuum indicators may have important implications for HIV prevention in urban areas with high rates of HIV infection. For example, some communities may need interventions focused on reducing late HIV diagnosis, whereas others may need interventions focused on enhancing retention and linkage to HIV care. Interventions will also require understanding the distribution of sociocontextual factors such as neighborhood median income across communities. Last, the association between social capital and health outcomes such as HIV diagnosis is context dependent.87 However, given recent studies documenting an association with HIV care continuum outcomes net other traditional contextual factors such as income inequality,32,88 we recommend that social capital questions be included and routinely collected in both national and local health or other demographic surveys when possible.
The authors the Pennsylvania City Planning Commission Geographic Information Systems (GIS) Division for an updated zoning map.
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