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How Do Social Capital and HIV/AIDS Outcomes Geographically Cluster and Which Sociocontextual Mechanisms Predict Differences Across Clusters?

Ransome, Yusuf DrPH*; Dean, Lorraine T. ScD; Crawford, Natalie D. PhD; Metzger, David S. PhD§; Blank, Michael B. PhD§; Nunn, Amy S. ScD

JAIDS Journal of Acquired Immune Deficiency Syndromes: September 1st, 2017 - Volume 76 - Issue 1 - p 13–22
doi: 10.1097/QAI.0000000000001463
Epidemiology

Background: Place of residence has been associated with HIV transmission risks. Social capital, defined as features of social organization that improve efficiency of society by facilitating coordinated actions, often varies by neighborhood, and hypothesized to have protective effects on HIV care continuum outcomes. We examined whether the association between social capital and 2 HIV care continuum outcomes clustered geographically and whether sociocontextual mechanisms predict differences across clusters.

Methods: Bivariate Local Moran's I evaluated geographical clustering in the association between social capital (participation in civic and social organizations, 2006, 2008, 2010) and [5-year (2007–2011) prevalence of late HIV diagnosis and linkage to HIV care] across Philadelphia, PA, census tracts (N = 378). Maps documented the clusters and multinomial regression assessed which sociocontextual mechanisms (eg, racial composition) predict differences across clusters.

Results: We identified 4 significant clusters (high social capital–high HIV/AIDS, low social capital–low HIV/AIDS, low social capital–high HIV/AIDS, and high social capital–low HIV/AIDS). Moran's I between social capital and late HIV diagnosis was (I = 0.19, z = 9.54, P < 0.001) and linkage to HIV care (I = 0.06, z = 3.274, P = 0.002). In multivariable analysis, median household income predicted differences across clusters, particularly where social capital was lowest and HIV burden the highest, compared with clusters with high social capital and lowest HIV burden.

Discussion: The association between social participation and HIV care continuum outcomes cluster geographically in Philadelphia, PA. HIV prevention interventions should account for this phenomenon. Reducing geographic disparities will require interventions tailored to each continuum step and that address socioeconomic factors such as neighborhood median income.

*Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA;

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD;

Department of Behavioral Sciences and Health Education, Emory University Rollins School of Public Health, Atlanta, GA;

§Department of Psychiatry, University of Pennsylvania Pearlman School of Medicine, Pennsylvania, PA; and

Department of Behavioral and Social Sciences, Brown University School of Public Health, Rhode Island Public Health Institute, Providence, RI.

Correspondence to: Yusuf Ransome, DrPH, Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, 7th Floor, Boston, MA 02215 (e-mail: yransome@hsph.harvard.edu).

Y.R. was supported in part by the Clinical and Community-Based HIV/AIDS Research Training Program at Brown University and the Miriam Hospital (R25 MH083620), The HIV Prevention Trials Network (HPTN) Scholars Program, and by the National Institute of Mental Health (K01 MH111374). L.T.D. was supported by the National Institutes of Health/National Cancer Institute (K01 CA184288) and the National Institutes for Allergy and Infection Disease Grant (Johns Hopkins University Center for AIDS Research; P30 AI094189). D.S.M. and M.B.B. was supported in part by the Penn Center for AIDS Research (P30 AI45008), the Penn Mental Health AIDS Research Center (P30 MH097488) and the National Institute on Drug Abuse (R01 DA036503). N.D.C. was supported by the Emory Center for AIDS Research (P30 AI050409) and the HIV/AIDS Substance use and Trauma Training Program. A.S.N. was supported by National Institute of Mental Health (R25 MH083620; 1R34 MH109371).

The authors have no conflicts of interest to disclose.

This article does not contain any studies with human participants performed by any of the authors.

Informed consent was obtained from all individual participants included in the study.

Received January 24, 2017

Accepted May 17, 2017

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BACKGROUND

More than 50% of persons diagnosed with HIV are not retained in care.1 Substantial geographical disparities in HIV care continuum indicators such as diagnosis, linkage, and treatment exists within the United States.2,3 For instance, the average lifetime risk of HIV diagnosis for a US adult is 1% (ie, 1/99), yet the risk is as high as 7.8% (ie, 1/13) for individuals in Washington D.C. and as low as 0.14% (ie, 1/670) for individuals in North Dakota.4 Metropolitan areas and particularly racial and ethnic minorities are disproportionately affected.5

Social and structural factors at the neighborhood or census tract level may play an important role in these outcomes. For example, geographic areas (eg, neighborhoods) with high prevalence of persons diagnosed with HIV in the advanced stages of AIDS (ie, late HIV diagnosis) and suboptimal rates of retention in HIV care may elevate HIV acquisition risks for residents.6,7 Persons diagnosed late and not linked to care miss timely opportunities to benefit from antiretroviral therapy; this may also hinder community-level virologic suppression,8–11 which impacts future HIV incidence and acquisition.12,13

Social capital, defined here as features of social organization that improve efficiency of society by facilitating coordinated actions,14 has been hypothesized to have a protective effect on HIV population level HIV transmission dynamics.15,16 Indicators based on this theoretical perspective include membership in social and civic community organizations (ie, social participation) and trust among neighbors.17

Social capital can improve HIV prevention by facilitating the political capital and power18 necessary to address substandard housing, access to HIV testing and other prevention services, and improving economic conditions,19 all factors which structure the risk environments that ultimately affect HIV infection in the population.20,21 Social capital can also improve HIV prevention by promoting positive psychological health among individuals22 and communities.23 For instance, higher social capital (eg, participation in formal community groups) has been linked to changed social norms from HIV stigma to solidarity and support,24,25 which has enabled families of HIV-infected individuals to access HIV care and also empower infected individuals to improve antiretroviral therapy adherence.26

The preponderance of evidence documenting a protective association between social capital, HIV risks, and HIV care continuum outcomes has been among international populations,25,27–31 and there are only a handful of ecological studies on the topic in the United States.32–35 One state-level ecological study using data from year 1999 showed that higher social capital was associated with lower AIDS case rates.34 A census tract ecological study in Philadelphia, PA, using aggregate data between 2007 to 2011 showed that higher social participation was associated with higher linkage to HIV care but, paradoxically, to higher late HIV diagnosis.33

Whether and how social capital affects outcomes in the HIV care continuum in the United States is not sufficiently understood. Given recent studies documenting the important role that place of residence may play in elevating HIV acquisition risks,36,37 geographic analysis investigating how social capital in relation to HIV varies across place and space can inform strategies to reduce racial and geographic disparities in HIV/AIDS.38,39

Place in health research considers the distribution of aggregate characteristics of individuals, opportunity structures in the physical and social environment, and shared norms, typically within rigid geographic boundaries.40 Research focused on place alone is limited because rigid boundaries assume individuals in an area or an area's characteristics are static.41 Space in health research considers the dynamic relations of individuals across geographic areas and an area's characteristics can be influenced by relative position and proximity with another area's characteristics.41–43

One primary method that incorporates place and space is cluster analysis.44 Once clusters are identified between social capital and HIV care continuum, the next step is to analyze what sociocontextual mechanisms distinguish those clusters. Sex distribution, racial composition, socioeconomic position, access to treatment, and elements of the built environment such as alcohol outlets, which can suggest neighborhood disorder, are sociocontextual factors that influence the distribution of social capital45–49 as well as HIV/AIDS outcomes.50–52 Therefore, those are important mechanisms to analyze in relation to geographic clustering.

To explore the relationship between social capital and HIV care continuum outcomes, in this study, we characterize bivariate geographic cluster patterns between social capital and late HIV diagnosis, and linkage to HIV care, in the city of Philadelphia, PA. We then analyze whether and which sociocontextual mechanisms predict differences across clusters. We selected Philadelphia, PA, because the city has high HIV infection rates; in 2014, among Blacks (66.7 per 100,000) was 5 times higher than the US national average (12.3 per 100,000).53,54 Philadelphia also has wide geographic variations in HIV care continuum outcomes and has a late HIV diagnosis rate as high as the US national average.55 Finally, in Philadelphia, PA, we had access social capital and HIV data for neighborhoods.

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METHODS

HIV/AIDS Outcomes

Through HIVcontinuum.org, we obtained deidentified aggregate ZIP code level HIV surveillance data from Philadelphia, PA, on the 5-year (2007–2011) prevalence of late HIV diagnosis and linkage to HIV care, among adults and adolescents. Displayed through maps, HIVcontinuum.org contains HIV surveillance data from local health departments across 5 cities with high HIV burden.56 These are population-based data reported as of December 31, 2012. Cases without ZIP codes at HIV diagnosis were excluded. Following methods established from ours33 and other research,57 we spatially interpolated the data onto the Census 2010 tract boundaries (N = 384) using areal interpolation and reaggregation techniques.58

The 2 study outcomes are (1) late HIV diagnosis (defined as an AIDS diagnosis within 3 months of a newly received HIV diagnosis) and (2) linkage to HIV care (defined as newly diagnosed with HIV and a reported CD4/viral load within 3 months of HIV diagnosis).

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Social Capital

Social capital data were collected in the Southeastern Pennsylvania Household Health Survey (SPHHS), administered by the Public Health Management Corporation.59 SPHHS is a random digit dialing household landline and cell phone survey that contains health, social, and behavioral items asked of persons 18 years and older in the 5 major counties in the greater Philadelphia, PA, area. We only used data from individuals living in Philadelphia County, which coincides exactly with the city limits. We combined data from survey years 2006, 2008, and 2010 yielding a total of 12, 986 participants from whom social capital was calculated. The sample characteristics across the years were similar, so combining these data did not threaten temporal variability. SPHHS data are deidentified and available through the University of Pennsylvania Library.60

Social participation is one unique indicator within the broader construct of social capital.61 Although the SPHHS contains other social capital indicators, we selected social participation, which is derived from a single-item question: “How many local groups or organizations in your neighborhood do you currently participate in such as social, political, religious, school-related, or athletic organizations?” We created the measure following the Empirical Bayes regression procedures described previously33 by aggregating individual's responses to the census tract. We selected social participation because it is a valid indicator of features of the social organization within the city that can facilitate collective action toward a community goal, which is a primary pathway proposed between social capital and health.14,62 Second, we wanted to better understand the findings in our recent study33 where social participation, among 2 other social capital variables (social cohesion and collective engagement), was positively associated with the late HIV diagnosis and with linkage to care. Hereafter, we use the term social capital to reflect the broader concept through which the measure is derived.

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Sociocontextual Mechanisms

We included male-to-female ratio, black racial composition (percent of persons identifying as black/African American), unemployment rate (percent of individuals 16 years and older, unemployed), poverty level (percentage of families in poverty), education level (percent 25 years older with college education or higher), median household income (2011 inflation adjusted dollars), and income inequality (GINI coefficient). All the aforementioned continuous variables were retrieved from Social Explorer63 based on the American Community Survey 5-year estimates for 2007–2011 for Philadelphia Census tracts.

HIV testing/treatment access was created through methods described previously.33 Briefly, HIV testing sites were retrieved from the National HIV and STD Testing website64 and geographic locations of Ryan White HIV treatment centers were retrieved from OpenDataPhilly.org.65 The Philadelphia AIDS Activities Coordinating Office validated the centers that were present before year 2007, yielding a list of N = 75 centers after removing duplicates. Because of a strong association with HIV risk, alcohol outlets are being considered as potential sites for HIV interventions.66 However, higher alcohol outlet density may hinder development of social capital.67

The count of alcohol outlets for year 2007 was retrieved from Business Analyst68 for both on- and off-premises establishments (North American Industry Classification Codes: 722410 and 445310). We created density of HIV testing/treatment sites and density of alcohol outlets per square mile in each census tract using the Kernel Density Tool in ArcGIS 10.2. All sociocontextual factors were standardized with a mean of zero and SD of 1.

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Statistical Analysis

Spatial Statistics

After merging the social capital, HIV/AIDS, and sociocontextual variables, a total of N = 378 census tracts were available for analysis. To achieve the first study aim, we performed bivariate spatial cluster analysis of social capital (x) with the 2 HIV care continuum outcomes (y) separately, using the Local Moran's I tool in GeoDa software.69 A positive Moran's I indicates that high values are surrounded by high values, whereas a negative coefficient indicates high values are surrounded by low values. We assessed statistical significance of the clusters and Moran's I at the alpha = 0.05 level based on 499 permutations. A spatial weights matrix of k = 7 nearest neighbors was used. The bivariate cluster analysis identifies patterns, which are locations where groups of neighboring census tracts cluster. The cluster indicators have 5 values: 0 for not significant, 1 for high–high, 2 for low–low, 3 for high–low, and 4 for low–high.70 The clusters represent high social capital–high late HIV diagnosis, low social capital–low late HIV diagnosis, high social capital–low late HIV diagnosis, and low social capital–high late HIV diagnosis. The coding pattern is similar for linkage to HIV care. The cluster indicators were then imported into STATA 14.1 for statistical analysis.71 We considered the reference or “priority” clusters as census tracts with high social capital–low late HIV diagnosis and high social capital–high linkage to HIV care.

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Descriptive Statistics

We computed mean values and interquartile ranges for social capital, care continuum indicators, and sociocontextual variables across the cluster types. We used Spearman and Pearson correlations to examine potential multicollinearity and to identify variables to include in the multivariable analysis. We mapped the cluster patterns for each social capital and HIV/AIDS indicators in Arc Map 10.2. Each cluster is represented by a different colored shade on the legend. In addition, we included a map of median income and one that displays the geographic context of Philadelphia, PA.

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Multivariable Statistics

To achieve the second study aim, we fit a multivariable model by including, in one block, all the sociocontextual mechanisms that were significant in the correlation analysis. For all models, a priori, we included black racial composition and income inequality given a specific interest based on empirical evidence from previous research.50 We used multinomial logistic regression and selected the “priority” cluster described above as the reference group. In addition, we excluded the insignificant cluster because comparing this to the priority cluster was uninformative. Relative risk (RR) ratios and 95% confidence intervals (CIs) are reported.72 Relationships are statistically significant at P < 0.05.

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RESULTS

Spatial Statistics and the Local Setting

Maps showing geographical clustering of social capital and each HIV/AIDS outcome are given in Figure 1. Significant clustering was observed between social capital and late HIV diagnosis (Moran's I = 0.19, z = 9.54, P < 0.001) and linkage to HIV care (Moran's I = 0.06, z = 3.274, P = 0.002), (Moran's I data not shown). Areas with high social capital and low HIV were zoning areas with residential single and 2-family attached homes as well as active and passive parks and open space. Areas with high social capital and high HIV were mainly industrial areas.

FIGURE 1

FIGURE 1

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Descriptive

The prevalence of each HIV care continuum indicator was highest in the “high-high” clusters. The mean social capital levels were highest in the “high-high” clusters for late HIV diagnosis and linkage to HIV care (Table 1). The highest correlation was between median household income and education level (r = 0.65, P < 0.001) (Table 2), and the remaining correlations were lower, suggesting that multicollinearity is not a problem among the sociocontextual variables.

TABLE 1

TABLE 1

TABLE 2

TABLE 2

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Multivariable

Clustering of Social Capital and Late HIV Diagnosis

The reference group (ie, “priority” cluster) in this multinomial model is high social capital–low late HIV diagnosis (N = 27 tracts). We consider low social capital–high late HIV diagnosis the “high-need” cluster (N = 23 tracts) because those communities may have limited social resources to address the high HIV burden. A 1 SD increase in black racial composition (RR = 0.33, 95% CI: 0.13 to 0.85), median household income (RR = 0.02, 95% CI: 0.00 to 0.15), and income inequality (RR = 0.16, 95% CI: 0.05 to 0.50) decreased the RR of belonging to a high-need cluster, controlling for covariates. A 1 SD increase in education level (RR = 0.17, 95% CI: 0.03 to 0.97) and median household income (RR = 0.04, 95% CI: 0.01 to 0.32) is associated with a decreased risk of being in a low social capital–low late HIV diagnosis cluster (N = 67 tracts) (Table 3).

TABLE 3

TABLE 3

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Clustering of Social Capital and Linkage to HIV Care

The reference group was high social capital–high linkage to HIV care (N = 35 tracts). We considered low social capital–low linkage to HIV care a high-need cluster (N = 48 tracts). A 1 SD increase in median household income (RR = 0.01, 95% CI: 0.01 to 0.03) and income inequality (RR = 0.28, 95% CI: 0.10 to 0.77) decreased the RR of belonging to a high-need cluster compared with a priority cluster, controlling for covariates. Similar associations were found for belonging to a low social capital–high linkage to HIV cluster (Table 3).

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Across the Models

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).

Both variables, however, contributed to the model fit. For instance, in the social capital and late HIV diagnosis model, without median income and inequality, the pseudo-R2 was 26%, which rose to 36% after median income and inequality were included. Next, the model with social capital and linkage to care, the pseudo-R2 was 12%, which rose to 33% after included (R2 results not displayed). We present the results of the fully adjusted model based on our a priori decisions. McFadden pseudo-R squared for categorical outcomes is reported in STATA; however, it does not have the same direct interpretation of variance explained as an R squared from OLS regression.74

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DISCUSSION

This is the first ecological study to document that the association between social capital (specifically, participation in local community groups) and HIV diagnosis and linkage to care—2 upstream HIV care continuum outcomes—varies across space (ie, cluster geographically), in Philadelphia, PA. Specifically, we saw that some neighborhoods were characterized by high social capital and high late HIV diagnosis while others were characterized by high social capital and low late HIV diagnosis. There were relatively few areas that met the criteria we consider a “priority” cluster—that is where social capital is high and late HIV diagnosis is low. We think the higher proportion of areas that were not a priority cluster could potentially explain the findings from our previous work where high social participation was related to higher late HIV diagnosis prevalence.33 The broader implication of this study is that HIV prevention interventions that seek to activate social capital in communities may be needed with stronger intensity in nonpriority areas. We show where these areas are in the choropleth maps.

Social capital can be intentionally generated through creating new or enhancing current relationships between local community groups and government or other bureaucratic agencies.23 Next, creating social capital to address HIV can be strengthened by forging partnerships between health care organizations and community organizations.25

In addition, social capital can be created, especially in urban cities such as Philadelphia, PA, and other poor communities in urban and rural areas by supporting environments that increase political participation,75 which can include fostering stronger relationships to citywide political processes.18 Increased political participation can balance power relations, for instance, by creating political representation of individuals who reflect the communities disadvantaged by HIV and economic opportunities.75 Social capital can also be leveraged through investments in infrastructure such as mixed-income and mixed-use housing and other aspects of the built environment such as walkability, which provide opportunities for formal and informal social interactions among individuals.76,77

Next, we document that the geographic cluster pattern between social capital and late HIV diagnosis do not overlap with the cluster pattern between social capital and linkage to HIV care. The nonoverlap in clusters among HIV care continuum outcomes was also found in one previous Philadelphia, PA, study.78 Together, both study findings suggest that HIV prevention interventions in communities will vary by geography and the HIV care continuum outcome in question. 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.

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

There are some study limitations. We used HIV/AIDS prevalence data originally obtained at the ZIP code level. Larger areas lose geographic detail and may limit usefulness in public health planning.82 However, we used spatial reaggregation techniques to produce a surface map across census tracts. With spatial reaggregation, we still could not avoid sudden changes in prevalence across boundaries because we did not have raw counts to create smoothed rates based on population denominators.82 Findings are still subject to the modifiable areal unit problem (MAUP), which posits that associations found at one ecological unit may not be the same at another unit.83 Associations could therefore be different across Congressional Districts or Wards, which also designate geographic boundaries in Philadelphia, PA.84 Related, the social capital and HIV/AIDS data were cross-sectional, thus we cannot draw causal inference conclusions.

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.

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CONCLUSION

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.

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ACKNOWLEDGMENTS

The authors the Pennsylvania City Planning Commission Geographic Information Systems (GIS) Division for an updated zoning map.

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REFERENCES

1. Mugavero MJ, Amico KR, Horn T, et al. The state of engagement in HIV care in the United States: from cascade to continuum to control. Clin Infect Dis. 2013;58:1164–1171.
2. Centers for Disease Control and Prevention. Southern States Lag Behind Rest of the Nation in HIV Treatment, Testing. Atlanta, GA: National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention; 2015.
3. Rebeiro PF, Gange SJ, Horberg MA, et al. Geographic variations in retention in care among HIV-infected adults in the United States. PLoS One. 2016;11:e0146119.
4. Centers for Disease Control and Prevention. HIV in the United States by Geographic Distribution. Atlanta, GA: CDC; 2016.
5. Hall HI, Espinoza L, Benbow N, et al; for the Urban Areas HIVSW. Epidemiology of HIV infection in large urban areas in the United States. PLoS One. 2010;5:e12756.
6. Mugavero MJ, Amico KR, Westfall AO, et al. Early retention in HIV care and viral load suppression: implications for a test and treat approach to HIV prevention. J Acquir Immune Defic Synd. 2012;59:86–93.
7. Wilson DP, Law MG, Grulich AE, et al. Relation between HIV viral load and infectiousness: a model-based analysis. Lancet. 2008;372:314–320.
8. Sabin CA, Smith CJ, Gumley H, et al. Late presenters in the era of highly active antiretroviral therapy: uptake of and responses to antiretroviral therapy. AIDS. 2004;18:2145–2151.
9. Mukolo A, Villegas R, Aliyu M, et al. Predictors of late presentation for HIV diagnosis: a literature review and suggested way forward. AIDS Behav. 2013;17:5–30.
10. Dombrowski J, Kent J, Buskin S, et al. Population-based metrics for the timing of HIV diagnosis, engagement in HIV care, and virologic suppression. AIDS. 2012;26:77–86.
11. United Nations. On the Fast Track to Ending the AIDS Epidemic. Geneva, Switzerland: UNAIDS; 2016.
12. Tanser F, Bärnighausen T, Grapsa E, et al. High coverage of ART associated with decline in risk of HIV acquisition in rural KwaZulu-Natal, South Africa. Science. 2013;339:966–971.
13. Das M, Chu PL, Santos G-M, et al. Decreases in community viral load are accompanied by reductions in new HIV infections in San Francisco. PLoS One. 2010;5:e11068.
14. Putnam R. Making Democracy Work: Civic Traditons in Modern Italy. Princeton, NJ: Princeton University Press; 1993.
15. Poundstone K, Strathdee S, Celentano D. The social epidemiology of human immunodeficiency virus/acquired immunodeficiency syndrome. Epidemiol Rev. 2004;26:22–35.
16. Campbell C, Williams B, Gilgen D. Is social capital a useful conceptual tool for exploring community level influences on HIV infection? An exploratory case study from South Africa. AIDS Care. 2002;14:41–54.
17. Putnam R. Bowling alone: America's declining social capital. J Democr. 1995;6:65–78.
18. Fuchs ER, Shapiro RY, Minnite LC. Social capital, political participation, and the urban community. In: Social Capital and Poor Communities. Seagert SJ, Thompson P, Warren MR, eds. New York, NY: Russell Sage Foundation; 2001:290–324.
19. James SA, Schulz AJ, van Olphen J. Social capital, poverty, and community health: an exploration of linkages. In: Social Capital and Poor Communities. Seagert SJ, Thompson P, Warren MR, eds. New York, NY: Russell Sage Foundation; 2001:165–188.
20. Rhodes T, Singer M, Bourgois P, et al. The social structural production of HIV risk among injecting drug users. Soc Sci Med. 2005;61:1026–1044.
21. Cooper HLF, Linton S, Kelley ME, et al. Risk environments, race/ethnicity, and HIV status in a large sample of people who inject drugs in the United States. PLoS One. 2016;11:e0150410.
22. Cattell V. Poor people, poor places, and poor health: the mediating role of social networks and social capital. Soc Sci Med. 2001;52:1501–1516.
23. Gibbs A, Campbell C, Akintola O, et al. Social contexts and building social capital for collective action: three case studies of volunteers in the context of HIV and AIDS in South Africa. J Community Appl Soc Psychol. 2015;25:110–122.
24. Campbell C, Scott K, Nhamo M, et al. Social capital and HIV competent communities: the role of community groups in managing HIV/AIDS in rural Zimbabwe. AIDS Care. 2013;25(suppl 1):S114–S122.
25. Sivaram S, Zelaya C, Srikrishnan A, et al. Associations between social capital and HIV stigma in Chennai, India: considerations for prevention intervention design. AIDS Educ Prev. 2009;21:233–250.
26. Campbell C, Skovdal M, Mupambireyi Z, et al. Building adherence-competent communities: factors promoting children's adherence to anti-retroviral HIV/AIDS treatment in rural Zimbabwe. Health Place. 2012;18:123–131.
27. Gregson S, Mushati P, Grusin H, et al. Social capital and women's reduced vulnerability to HIV infection in rural Zimbabwe. Popul Dev Rev. 2011;37:333–359.
28. Frumence G, Killewo J, Kwesigabo G, et al. Social capital and the decline in HIV transmission–A case study in three villages in the Kagera region of Tanzania. SAHARA J. 2010;7:9–20.
29. Ware NC, Idoko J, Kaaya S, et al. Explaining adherence success in sub-Saharan Africa: an ethnographic study. PLoS Med. 2009;6:e1000011.
30. Sen S, Aguilar JP, Goldbach J. Does social capital act as a buffer against HIV risk among migrant men in Sub-Saharan Africa? J HIV AIDS Soc Serv. 2010;9:190–211.
31. Kerrigan D, Mbwambo J, Likindikoki S, et al. Project Shikamana: baseline findings from a community empowerment–based combination HIV prevention trial among female sex workers in Iringa, Tanzania. JAIDS. 2017;74(suppl 1):S60–S68.
32. Ransome Y, Galea S, Pabayo R, et al. Is social capital associated with late HIV diagnosis?: an ecological analysis. J Acquir Immune Defic Syndr. 2016;73:213–221.
33. Ransome Y, Kawachi I, Dean LT. Neighborhood social capital in relation to late HIV diagnosis, linkage to HIV care, and HIV care engagement. AIDS Behav. 2017;21:891–904.
34. Holtgrave DR, Crosby RA. Social capital, poverty, and income inequality as predictors of gonorrhoea, syphilis, chlamydia and AIDS case rates in the United States. Sex Transm Infect. 2003;79:62–64.
35. Semaan S, Sternberg M, Zaidi A, et al. Social capital and rates of gonorrhea and syphilis in the United States: spatial regression analyses of state-level associations. Soc Sci Med. 2007;64:2324–2341.
36. Sullivan PS, Peterson J, Rosenberg ES, et al. Understanding racial HIV/STI disparities in black and white men who have sex with men: a multilevel approach. PLoS One. 2014;9:e90514.
37. Bauermeister JA, Eaton L, Andrzejewski J, et al. Where you live matters: structural correlates of HIV risk behavior among young men who have sex with men in metro detroit. AIDS Behav. 2015;19:2358–2369.
38. Nunn A, Yolken A, Cutler B, et al. Geography should not be destiny: focusing HIV/AIDS implementation research and programs on microepidemics in US neighborhoods. Am J Public Health. 2014;104:775–780.
39. Castel AD, Kuo I, Mikre M, et al. Feasibility of using HIV care-continuum outcomes to identify geographic areas for targeted HIV testing. J Acquir Immune Defic Synd. 2017;74(suppl 2):S96–S103.
40. Macintyre S, Ellaway A, Cummins S. Place effects on health: how can we conceptualise, operationalise and measure them? Soc Sci Med. 2002;55:125–139.
41. Rainham D, McDowell I, Krewski D, et al. Conceptualizing the healthscape: contributions of time geography, location technologies and spatial ecology to place and health research. Soc Sci Med. 2010;70:668–676.
42. Jones K, Moon G. Medical geography: taking space seriously. Prog Hum Geogr. 1993;17:515–524.
43. Cummins S, Curtis S, Diez-Roux AV, et al. Understanding and representing “place” in health research: a relational approach. Soc Sci Med. 2007;65:1825–1838.
44. Grubesic TH, Wei R, Murray AT. Spatial clustering overview and comparison: accuracy, sensitivity, and computational Expense. Ann Assoc Am Geogr. 2014;104:1134–1156.
45. Subramanian S, Lochner K, Kawachi I. Neighborhood differences in social capital: a compositional artifact or a contextual construct? Health Place. 2003;9:33–44.
46. Portes A. Social capital: its origins and applications in modern sociology. Annu Rev Sociol. 1998;24:1–24.
47. Theall KP, Scribner R, Ghosh-Dastidar B, et al. Neighborhood alcohol availability and gonorrhea rates: impact of social capital. Geospat Health. 2007;3:241–255.
48. Hutchinson RN, Putt MA, Dean LT, et al. Neighborhood racial composition, social capital and black all-cause mortality in Philadelphia. Soc Sci Med. 2009;68:1859–1865.
49. Goswami ND, Schmitz MM, Sanchez T, et al. Understanding local spatial variation along the care continuum: the potential impact of transportation vulnerability on HIV linkage to care and viral suppression in high-poverty areas, Atlanta, Georgia. J Acquir Immune Defic Synd. 2016;72:65–72.
50. Ransome Y, Kawachi I, Braunstein S, et al. Structural inequalities drive late HIV diagnosis: the role of black racial concentration, income inequality, socioeconomic deprivation, and HIV testing. Health Place. 2016;42:148–158.
51. Buot MLG, Docena JP, Ratemo BK, et al. Beyond race and place: distal sociological determinants of HIV disparities. PLoS One. 2014;9:e91711.
52. Rossheim ME, Thombs DL, Suzuki S. Association between alcohol outlets and HIV prevalence in US counties. J Stud Alc Drugs. 2016;77:898–903.
53. Centers for Disease Control and Prevention. HIV Surveillance Report, Volume 27: Diagnosis of HIV Infection in the United States and Dependent Areas. Atlanta, GA: CDC; 2015.
54. Philadelphia Department of Public Health. AIDS Activities Coordinating Office Surveillance Report 2014: HIV/AIDS in Philadelphia, PA. Philadelphia, PA: The City of Philadelphia; 2015.
55. AIDSvu. City and State Profiles. Philadelphia, PA: AIDSVu; 2016.
56. Sanchez TH, Sullivan P. HIV Continuum-mapping the HIV Care Continuum. Atlanta, GA: AIDSVu; 2015.
57. Stopka TJ, Geraghty EM, Azari R, et al. Is crime associated with over-the-counter pharmacy syringe sales? Findings from Los Angeles, California. Int J Drug Policy. 2014;25:244–250.
58. Environmental Systems Research Institute (ESRI). ArcGIS Help: Release 10.1 Using Areal Interpolation to Perform Polygon to Polygon Predictions. Redlands, CA: Environmental Systems Research Institute; 2015.
59. Public Health Management Corporation. Southeastern Pennsylvania Household Health Survey (SPHHS). Philadelphia, PA: Public Health Management Corporation (PHMC). Available at: http://http://www.chdbdata.org/index.php/about-us/about-sepa-household-survey. Accessed November 15, 2015.
60. Libraries UoP. Community Health Data Base—Penn Libraries Data Resources: Overview. Philadelphia, PA: Penn Libraries; 2016.
61. Carpiano RM. Toward a neighborhood resource-based theory of social capital for health: can Bourdieu and sociology help? Soc Sci Med. 2006;62:165–175.
62. Sampson R, Raudenbush S, Earls F. Neighborhoods and violent crime: a multilevel study of collective efficacy. Science. 1997;277:918–924.
63. Social Explorer. Social Explorer Tables: ACS 2011 (5-Year Estimates). New York, NY: Social Explorer; 2016.
64. Centers for Disease Control and Prevention. National HIV and STD Testing Resources. Rockville, MD: CDC National Prevention Information Network; 2015.
65. Azavea. OpenDataPhilly. Philadelphia, PA: OpenDataPhilly.org; 2015.
66. Pitpitan EV, Kalichman SC. Reducing HIV risks in the places where people drink: prevention interventions in alcohol venues. AIDS Behav. 2016;20:119–133.
67. Theall KP, Scribner R, Cohen D, et al. Social capital and the neighborhood alcohol environment. Health Place. 2009;15:323–332.
68. Environmental Systems Research Institute (ESRI). ESRI Business Analyst. Redlands, CA: ESRI; 2014.
69. Anselin L, Ibnu S, Youngihn K. GeoDa: an introduction to spatial data analysis. Geogr Anal. 2006;38:5–22.
70. Anselin L. An Introduction to Spatial Autocorrelation Analysis With GeoDa. Champagne-Urbana, IL: Spatial Analysis Laboratory, University of Illinois; 2003.
71. StataCorp. Stata Statistical Software: Release 14.1. College Station, TX: StataCorp LP; 2015.
72. UCLA Statistical Consulting Group. Understanding RR Ratios in Multinomial Logistic Regression. Los Angeles, CA: UCLA Institute for Digital Research and Education. Available at: http://http://www.chdbdata.org/index.php/about-us/about-sepa-household-survey.
73. Kawachi I, Kennedy BP. Socioeconomic determinants of health: health and social cohesion: why care about income inequality? BMJ. 1997;314:1037–1040.
74. UCLA Statistical Consulting Group. FAQ: What Are Pseudo R-squareds? Los Angeles, CA: UCLA Institute for Digital Research and Education. Available at: http://http://www.ats.ucla.edu/stat/mult_pkg/faq/general/Psuedo_RSquareds.htm. Accessed December 13, 2016.
75. Cohen CJ. Social capital, intervening institutions, and political power. In: Social Capital and Poor Communities. Seagert SJ, Thompson P, Warren MR, eds. New York, NY: Russell Sage Foundation; 2001:267–289.
76. Eicher C, Kawachi I. Social capital and community design. In: Making Healthy Places. Dannenberg A, Frumkin H, Jackson R, eds. Washington, DC: Island Press/Center for Resource Economics; 2011:117–128.
77. Leyden KM. Social capital and the built environment: the importance of walkable neighborhoods. Am J Public Health. 2003;93:1546–1551.
78. Eberhart MG, Yehia BR, Hillier A, et al. Behind the cascade: analyzing spatial patterns along the HIV care continuum. J Acquir Immune Defic Synd. 2013;64:S42.
79. Eberhart MG, Yehia BR, Hillier A, et al. Individual and community factors associated with geographic clusters of poor HIV care retention and poor viral suppression. J Acquir Immune Defic Synd. 2015;69(suppl 1):S37–S43.
80. Kawachi I, Kennedy BP. Income inequality and health: pathways and mechanisms. Health Serv Res. 1999;34:215–227.
81. Ransome Y, Kawachi I, Braunstein S, et al. Structural inequalities drive late HIV diagnosis: the role of black racial concentration, income inequality, socioeconomic deprivation, and HIV testing. Health Place. 2016;42:148–158.
82. Oppong JR, Tiwari C, Ruckthongsook W, et al. Mapping late testers for HIV in Texas. Health Place. 2012;18:568–575.
83. Openshaw S, Taylor P. A million or so correlation coefficients: three experiments on the modifiable area unit problem. In: Statistical Applications in the Spatial Sciences. Wrigley N, ed. London, United Kingdom: Pion Ltd; 1979:127–144.
84. Philadelphia City Planning Commission. Map Gallery. Philadelphia, PA: GIS Staff; 2016.
85. Guillen L, Coromina L, Saris WE. Measurement of social participation and its place in social capital theory. Soc Indic Res. 2011;100:331–350.
86. AIDSvu. City and State Profiles. Atlanta, GA: AIDSVu; 2016.
87. Szreter S, Woolcock M. Health by association? Social capital, social theory, and the political economy of public health. Int J Epidemiol. 2004;33:650–667.
88. Ransome Y, Batson A, Galea S, et al. The relationship between higher social trust and lower late HIV diagnosis and mortality differs by race/ethnicity: results from a state-level analysis. J Int AIDS Soc. 2016;20:21442.
Keywords:

social capital; social determinants; HIV/AIDS; neighborhoods; United States; HIV care continuum

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