We analyzed relationships between place characteristics and being HIV-negative among black, Latino, and white people who inject drugs (PWID) in the US.
Data on PWID (N = 9077) were from the Centers for Disease Control and Prevention’s 2009 National HIV Behavioral Surveillance. Administrative data were analyzed to describe the 968 ZIP codes, 51 counties, and 19 metropolitan statistical areas (MSAs) where they lived. Multilevel multivariable models examined relationships between place characteristics and HIV status. Exploratory population attributable risk percents (e-PAR%s) were estimated.
Black and Latino PWID were more likely to be HIV-negative if they lived in less economically disadvantaged counties, or in MSAs with less criminal-justice activity (i.e., lower drug-related arrest rates, lower policing/corrections expenditures). Latino PWID were more likely to be HIV-negative in MSAs with more Latino isolation, less black isolation, and less violent crime. E-PAR%s attributed 8–19% of HIV cases among black PWID and 1–15% of cases among Latino PWID to place characteristics.
Citation: Cooper HLF, Linton S, Kelley ME, Ross Z, Wolfe ME, Chen Y-T, et al. (2016) Risk Environments, Race/Ethnicity, and HIV Status in a Large Sample of People Who Inject Drugs in the United States. PLoS ONE 11(3): e0150410. https://doi.org/10.1371/journal.pone.0150410
Editor: Ruan Yuhua, National Center for AIDS/STD Control and Prevention, China CDC, CHINA
Received: September 9, 2015; Accepted: February 13, 2016; Published: March 14, 2016
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Data Availability: National HIV Behavioral Surveillance system data are available from the CDC. Due to ethical restrictions regarding potentially identifying information, data for the place-based variables are available upon request. Requests for all data may be made to Gabriela Paz-Bailey, MD, PhD, MSc (Team Lead for the Behavioral Surveillance Team, BCSB/DHAP/NCHHSTP/CDC) at firstname.lastname@example.org.
Funding: This research was supported by two NIH grants. One from the National Institute on Drug Abuse (http://www.drugabuse.gov/) entitled “Place Characteristics & Disparities in HIV in IDUS: A Multilevel Analysis of NHBS” (DA035101; Cooper, PI), and another from the National Institutes of Health (http://www.nih.gov/) funding the Emory Center for AIDS Research (P30 AI050409; Curran, PI). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ZevRoss SpatialAnalysis provided support in the form of salaries for author ZR but did not have any additional role in the study design, data collection or analysis, decision to publish, or preparation of the manuscript. The specific role of this author is articulated in the ‘author contributions’ section.
Competing interests: Zev Ross is employed by ZevRoss SpatialAnalysis, a commercial entity that created geospatial measures. ZevRoss Spatial Analysis served as a consulting agency that created geospatial measures for the study. There are no patents, products in development, or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.
HIV epidemics are heterogeneous across populations and places [1,2]. In the United States (US) in 2011, estimated rates of newly diagnosed HIV cases among people who inject drugs (PWID) were eleven times as high among black PWID (230/100,000), and six times as high among Latino PWID (121/100,000), as among white PWID (21/100,000) . The Centers for Disease Control and Prevention (CDC) and the National HIV/AIDS Strategy prioritized eliminating racial/ethnic disparities in HIV incidence among PWID and other key populations, and highlighted the role of place characteristics (a term used interchangeably here with “environmental features”) in creating disparities [3,4]. To illustrate, PWID are more vulnerable to HIV when they are exposed to environments with more economic disadvantage, drug-related criminal justice activities, and violent crime and other physical and social disorder (e.g., abandoned housing) [5–11], and are less vulnerable to HIV when they live in environments with laws permitting access to sterile syringes without a prescription and with more healthcare services [6,12–15].
HIV-related outcomes, moreover, are heterogeneous within racial/ethnic groups of PWID [16,17]. Most notably, these outcomes vary across geographic areas among black, white, and Latino PWID [17,18]. Few studies, though, have investigated place-based determinants of variations in HIV-related outcomes within specific racial/ethnic groups of PWID. Advancing this line of research can help develop structural interventions to address the particular constellation of place characteristics shaping vulnerability and resilience to HIV within each racial/ethnic group.
We cross-sectionally analyzed relationships between features of the environments where PWID live and the odds of being HIV-negative, and explored whether these relationships varied across racial/ethnic groups. Because we analyzed a large (N = 9077) sample of PWID in 19 metropolitan statistical areas (MSAs), we had the rare opportunity to study place-based correlates of HIV within each of three geographic scales: ZIP codes, counties, and MSAs. Our outcome is the odds of being HIV-negative because these MSAs had high AIDS prevalences at the time of data collection; being HIV-negative in this context would require ongoing engagement in low-risk behaviors or participation in low-risk networks. The analysis is guided by the Risk Environment Model, a multilevel conceptual framework that highlights the roles that contextual factors play in creating vulnerability and resilience to HIV transmission among PWID [19–23].
We also estimated exploratory racial/ethnic-specific population attributable risk percents (e-PAR%s) for place-based exposures. A previous analysis found large differences in characteristics of the places PWID lived, across and within racial/ethnic groups . By combining data on racial/ethnic-specific exposure to place characteristics with data on the magnitudes of these exposures’ relationships to HIV status, racial/ethnic-specific e-PAR%s estimate the percent of cases of HIV that could potentially have been prevented within each racial/ethnic group if exposure to harmful place-based characteristics were minimized [25,26].
Materials and Methods
Study description and analytic sample
We combined 2009 National HIV Behavioral Surveillance (NHBS) data about PWID in 19 US MSAs with data from existing administrative sources to describe the places where PWID lived. NHBS assesses HIV status, HIV-related behaviors, and service use in high-risk populations, including PWID .
In 2009, NHBS recruited PWID living in 20 MSAs with the highest AIDS burden in 2006 [18,28,29]. San Juan-Bayamon was excluded from this analysis because the sample was ethnically homogenous (98% were Latino). NHBS used respondent-driven sampling (RDS) to recruit approximately 500 adult (≥18 years old) PWID in each MSA . In total, 9,884 PWID were enrolled across the 19 MSAs. Our analytic sample was 9077: we excluded participants who had an incomplete survey; lacked racial/ethnic information or a valid HIV test or ZIP code; or (because of small numbers) identified as transgender or non-Hispanic race other than white or black (alone or in combination).
NHBS offered anonymous HIV testing. Participants with nonreactive screening test results were considered HIV-negative. Participants were considered HIV-positive if their screening test was reactive and confirmed by Western blot or immunofluorescence assay.
As described elsewhere [16,24], we used self-report data to construct three mutually exclusive racial/ethnic groups: Latino, non-Hispanic white, and non-Hispanic black (hereafter referred to as white and black, respectively).
Participants reported the ZIP code and county where they lived. Homeless participants’ ZIP codes and counties were based on where they usually slept. Participants were linked to MSAs via data collection site.
Other individual-level covariates.
Information about participant sociodemographic characteristics, years since first injection, and injection frequency were drawn from NHBS.
We measured features of PWID’s environments in 2009 in ZIP codes, counties, and/or MSAs in four domains: social, economic, healthcare service/criminal justice intervention, and physical environment (Table 1). Methods used to create these measures are described elsewhere .
Features were selected based on past research about place-based exposures and HIV-related outcomes among PWID and other high-risk populations. The geographic scale at which we operationalized each feature was determined by our conceptualization of the feature itself and data availability. For example, we measured black and Latino isolation (forms of racial/ethnic segregation) within MSAs because segregation develops, in part, when white residents live in the suburbs and work in central cities. Likewise, alcohol outlet density was measured within ZIP codes because it is a local phenomenon with local effects. While violent crime, incarceration, and arrest rates may be salient within ZIP codes,[34–41] ZIP-level data on these constructs was unavailable across the 19 MSAs, and so these constructs were assessed in counties and MSAs.
Because measures of place characteristics were often correlated, we used principal components analysis (PCA) with varimax rotation to reduce potential multicollinearity in multivariable models. PCAs combine correlated variables to form uncorrelated components . PCAs were conducted within each geographic scale and domain, and components created for subsets of correlated variables; resulting component scores were standardized (Table 2).
We used descriptive statistics to characterize the sample and the places where PWID lived. Racial/ethnic-specific coefficients of variation were calculated for place-based exposures to quantify dispersion around the mean within racial/ethnic groups. Model building occurred in four stages:
Bivariate hierarchical generalized linear models (HGLMs) were constructed to examine the relationship of each feature (whether a PCA-derived component or a single variable) to the odds of being HIV-negative, and to determine whether individual race/ethnicity moderated this association. (We use the term “bivariate” here to describe models that include a single place-based covariate, indicator variables for individual race/ethnicity, and the interactions of the place-based covariate with these indicator variables.). In all HGLMs, four-level models were constructed (individuals nested in ZIPs; ZIPs in counties; and counties in MSAs) that included random intercepts for each scale. Features associated with the outcome at p<0.05 (as main effects or interacted with race/ethnicity) were carried forward into Stage 2. The race by variable interactions were coded with whites as the reference group, thus the p-values and estimates for blacks and Latinos represent ratios of odds ratios (ORs) and the significance of the difference in ORs compared to whites. To facilitate interpretation, we also calculated some racial/ethnic-specific ORs using model estimates (presented in text). The racial/ethnic-specific estimate was calculated by multiplying the ratio by the OR in whites: OR black/OR white * OR white = OR black.
To further reduce the number of place-based variables analyzed within each geographic scale, we created three four-level multivariable HGLMs, one for each geographic scale, and used backward selection to eliminate variables (p<0.05 cutpoint). Backward selection was used because the Risk Environment Model does not specify which variables to include in each domain.
A multivariable HGLM was constructed containing all significant environmental features (within ZIP codes, counties, and MSAs) from Stage 2, individual-level race/ethnicity, and possible individual-level confounders (e.g., age, gender). Backward selection (p<0.05 cutpoint) was used to make the model more parsimonious. We re-ran this multivariable model with select possible individual-level mediators of relationships between place-based exposures and HIV status (e.g., income, injection frequency) to learn whether individual characteristics mediate relationships.
We estimated racial/ethnic-specific PAR%s for each place-based exposure that was significantly associated with the outcome in the model that controlled for age and gender. PAR%s require that the sample represent the underlying population and that the exposure cause the outcome . Because we can make neither claim, we call these PAR%s “exploratory PAR%s” (e-PAR%s) and did not calculate them when we suspected reverse causation. All analyses were run on Stata version 13.
The Emory University Institutional Review Board (IRB) approved study protocols. All state and local jurisdictions participating in NHBS obtained human subject protections approval before conducting the 2009 NHBS survey among PWID. Activities for NHBS were approved by local IRBs for each of the 20 participating cities and by the CDC as research in which the CDC was not engaged.[43–45] All participants provided verbal informed consent to take part in the interview and to be tested for HIV. Verbal rather than written consent procedures were used to protect participants. Verbal consent was documented electronically on the survey instrument by interviewers for all participants and on hard copy as required by local IRBs. All consent procedures, including verbal consent, were approved by local IRBs
The 9077 PWID lived in 19 MSAs, 51 counties, and 968 ZIP codes. Approximately half (51.6%) were black, 30.3% were white, and 18.1% were Latino (Table 3). Most (71.1%) were men and average age was 45.7 (standard deviation [SD] = 10.6). Participants were impoverished: 60.9% subsisted on <$10,000 a year and 39.8% were homeless. Approximately 9% tested positive for HIV. Prevalence varied by race/ethnicity: 10.7% of black PWID tested positive, as did 7.6% of Latino PWID, and 6.3% of white PWID.
As reported elsewhere , across most measures of place-based exposures, black PWID lived in more disadvantaged areas than white and (in most cases) Latino PWID (Table 4). Coefficients of variation reveal moderate (25%-75%) to high (>75%) variation in exposure to place characteristics within each racial/ethnic group.
Findings are discussed by domain, below. Overall, multivariable results indicated that several place-based exposures were related to the odds of being HIV-negative among black and Latino PWID, while few were related to HIV status among white PWID.
Healthcare Service/Criminal Justice Intervention Environment
Bivariate and multivariable models, which controlled for significant participant characteristics, suggested an association between the criminal justice component and HIV status in black (p = 0.001) and Latino PWID (p = 0.04) that was different from whites (Table 5; Table 6; Table 7, Model B). More specifically, black PWID were less likely to be HIV-negative if they lived in MSAs that scored higher on the criminal-justice component than if they lived elsewhere (race-specific OR = 0.68; adjusted OR [AOR] = 0.64); multivariable analyses suggest the same for Latino PWID (AOR = 0.65). Specifically, multivariable models indicate that black and Latino PWID were about 35% less likely to be HIV-negative if they lived in an MSA that was 1 SD above the mean on the criminal-justice component. MSAs that were 1 SD above the mean on this component spent $92.8 per capita more on policing, $74.4 per capita more on corrections, and had drug-related arrest rates that were 2.6/1000 higher than the mean MSA. In MSAs ≥1 SD above the mean on this component, e-PAR%s suggest that 8.35% of HIV infections among black PWID possibly might have been prevented if drug-related arrest rates and spending on corrections and policing were at their mean values for MSAs in the sample, as might 15.23% of infections among Latino PWID.
These cross-sectional analyses indicated an association between HIV status and healthcare access in Latino PWID that was different from whites (p < 0.0005). More specifically, Latino PWID living in counties with worse general healthcare access (i.e., higher percentages of residents were uninsured or lived in medically underserved areas) were more likely to be HIV-negative (racial/ethnic-specific OR = 1.72; AOR = 2.38). They also suggest that PWID, regardless of race/ethnicity, living in ZIP codes with ≥1 HIV testing sites were less likely to be HIV-negative (AOR = 0.59, p = 0.006).
Bivariate and multivariable analyses also indicated that the relationship between the social component and HIV status was different in Latino PWID compared to whites (p = 0.01). The data showed that Latino PWID living in MSAs with lower values on the social component were more likely to be HIV-negative than Latino PWID living elsewhere (racial/ethnic-specific OR = 0.47; AOR = 0.50. Specifically, Latino PWID were 50% more likely to be HIV-negative if they lived in an MSA that was 1 SD below the mean on the social component. In MSAs that were 1 SD below the mean on this component, the crime rate was 3.4/1000 incidents lower, the black isolation index was 27.6 points lower, and the Latino isolation index was 18.8 points higher than in the mean MSA. However, E-PAR%s suggest that only 0.89% of HIV infections among Latino PWID in MSAs scoring ≥1 SD above the mean on this component could have been prevented if rates of violent crime, black isolation, and Latino isolation were at mean levels, given the low prevalence of this characteristic in Latino PWID.
Regardless of individual race/ethnicity, PWID living in ZIP codes with higher percentages of black residents were less likely to be HIV-negative (AOR = 0.99, p = 0.005). Among PWID living in ZIPs above the 75th percentile on this variable (≥64.2% of residents were black), e-PAR%s suggest that 19.11% of infections among black PWID might be attributable to the racial/ethnic segregation of black residents, as might 3.12% of infections among white and Latino PWID.
Relationships between ZIP-level sex ratios and HIV status varied by race/ethnicity. For example, living in a ZIP code with >105 men for every 100 women was associated with higher odds of being HIV-negative among black PWID (racial/ethnic-specific AOR = 1.25), but with lower odds of being HIV-negative among white PWID (AOR = 0.54).
Bivariate and multivariable models suggest a significant difference in association with economic disadvantage and HIV status in black (p = 0.04) and Latino PWID (p = 0.04) compared to whites. Black and Latino PWID living in counties that scored higher on the economic disadvantage component were less likely to be HIV-negative than black and Latino PWID living elsewhere (black PWID: OR = 0.82; AOR = 0.75; Latino PWID: OR = 0.81; AOR = 0.68). Specifically, black and Latino PWID were 25% and 32% less likely, respectively, to be HIV-negative if they lived in a county that scored 1 SD above the mean on the economic disadvantage component. In counties scoring 1 SD above the mean on this component, the poverty rate was 10.3 percentage points higher, the unemployment rate was 4.5% percentage points higher, and the high-school dropout rate was 7.7 percentage points higher than in the mean county, and the median income was $26,494 lower. In counties that were ≥1 SD above the mean on this component, e-PAR%s suggest that 10.04% of HIV infections among black PWID might have been prevented if these counties’ economic conditions were at the mean, as might 12.62% of infections among Latino PWID.
A one-unit increase in ZIP-code unemployment rates was associated with a 3% increase in the odds of being HIV-negative, regardless of race/ethnicity (AOR = 1.03, p = 0.03).
The physical environment was unrelated to the odds of being HIV-negative in all racial/ethnic groups.
Including possible individual-level mediators in the multivariable model did not substantively alter the magnitudes of relationships between place-based exposures and the outcome (i.e., no AOR for place-based exposures differed by ≥10% across Models B and C, Table 7), suggesting that these covariates did not mediate these associations.
In the 19 US MSAs with the highest AIDS burden in 2006, features of social, economic, and healthcare service/criminal justice environments were associated with odds of being HIV-negative among black and Latino PWID, and were rarely associated with this outcome among white PWID. Features of PWID residential environments varied both across and within racial/ethnic groups of PWID . E-PAR%s suggest that percentages of cases attributable to place characteristics were higher for black and Latino PWID than white PWID.
As others have noted [23,46,47], almost all multilevel public health studies of individuals nested in places exclusively focus on exposures operating within neighborhoods (e.g., census tracts). Neighborhoods, however, do not exist in isolation, and factors operating within other geographic scales may also influence health. By analyzing CDC data on 9o77 PWID living in 19 MSAs, we were able to explore characteristics of multiple geographic scales simultaneously, and found that features of ZIP codes, counties, and MSAs were related to the odds of being HIV-negative, controlling for characteristics of other geographic scales.
Consistent with past research , we found that black and Latino PWID were more likely to be HIV-negative if they lived in MSAs that scored lower on the criminal-justice component–that is, if they lived in MSAs that spent less on police and corrections and had lower drug-related arrest rates; this relationship persisted after controlling for characteristics of individuals, ZIP codes and counties. Risk behaviors and networks may mediate these relationships. PWID living in New York City health districts with lower drug-related arrest rates are less likely to engage in receptive syringe sharing [6,12]. Additionally, PWID’s sexual and injecting networks may experience less turnover when fewer network members are cycling through jail/prison [48,49], and may have lower background HIV seroprevalence . Several jurisdictions have relaxed drug-related laws and their enforcement, or sought to reduce incarceration rates . Longitudinal research should explore whether these changes affect HIV transmission among black and Latino PWID. The persistence of the relationship between the criminal-justice component and being HIV-negative in multivariable models controlling for violent crime suggests that perhaps some MSAs addressed violent crime using community-based strategies or alternatives to incarceration, rather than relying on criminal-justice approaches.
Controlling for possible confounders operating at other geographic scales, we found that black and Latino PWID living in less economically disadvantaged counties were more likely to be HIV-negative, as were Latino PWID living in MSAs with less violent crime and black isolation, and more Latino isolation. Residents of places that are less economically disadvantaged or less violent report less psychological distress [51–57]; better psychological well-being may have protected participants in these areas from engaging in sexual and injecting risk behavior [58–60]. Additionally, black and Latino PWID’s injecting and sexual networks may have lower HIV prevalence in less economically disadvantaged and violent areas [10,11,61–65]. The positive association between ZIP-code unemployment rates and being HIV-negative is likely a measurement artifact: the unemployment rate’s numerator excluded unemployed people who stopped actively seeking work.
Relationships between residential segregation and health are complex. Latino isolation can indicate Latino enclaves, which may provide social resources that promote resilience [66,67], including resilience to HIV transmission. Additionally, recent Latino immigrants are more likely to live in enclaves, and may have lower HIV prevalence . While predominately black neighborhoods in MSAs with high black isolation can also foster resilience , they tend to have fewer economic resources and higher rates of violent crime, and may be targeted by aggressive policing [33,71–73], each of which might foster HIV transmission [6,7,10,11,61–65,74]. Additionally, racial/ethnic assortativity in injecting and sexual partnerships among black adults [75,76], combined with historically high HIV prevalence in this population [77,78], increase the chances that black PWID will have HIV-positive partners. Perhaps for these reasons (and because final models did not control for these factors within ZIP codes), the percent of residents who were black in PWID’s ZIP codes was inversely associated with being HIV-negative, regardless of PWID race/ethnicity.
Past cross-sectional NHBS analyses identified substantial geographic variation in HIV status among white PWID [16,79], suggesting that variations in place-based exposures might be associated with HIV infection in this group. Few place characteristics, however, were associated with HIV among white PWID, perhaps because HIV prevalence was relatively low (6.3%).
PAR%s are powerful but underused tools [25,80,81]: by combining information on effect size and exposure prevalence, they estimate the percentage of cases in a population attributable to an exposure, and thus help prioritize intervention targets . PAR%s can help illuminate the role of place in shaping HIV (and other health outcomes) across and within racial/ethnic groups. Black, Latino, and white PWID live in markedly different environments in the US . Racial/ethnic-specific PAR%s incorporate this variation in exposure prevalence, while ORs and other effect estimates ignore it, though we caution that we report exploratory racial/ethnic-specific PAR%s because we could not claim causality or a representative sample. E-PAR%s preliminarily suggest that the environmental features studied here account for few HIV cases among whites, 8%-19% of cases among black PWID, and 1%-15% among Latino PWID. They suggest that structural interventions to eliminate HIV incidence among black and Latino PWID might prioritize alleviating economic disadvantage (black e-PAR% = 10%; Latino e-PAR% = 12%), and accelerating the transition from applying criminal-justice approaches to drug activity to public health approaches (black e-PAR% = 8%, Latino e-PAR% = 15%). The percentage of ZIP-code residents who are black likely reflects local socioeconomic deprivation [33,71–73] and the self-perpetuating nature of historically high HIV prevalence among black adults (black e-PAR% = 19%; Latino and white e-PAR% = 3%). Structural interventions should be developed and evaluated to improve socioeconomic conditions and increase access to HIV testing and treatment in predominately black ZIP codes.
We attribute the inverse relationships between access to healthcare services and being HIV-negative to the analyses’ cross-sectional design, and to successful efforts to locate health services in high-need areas (e.g., ).
On average, HIV-positive PWID reported being diagnosed 12 years before participating in NHBS. While HIV-positive participants reported living in the same MSA for 33 years, on average, they may have moved to new ZIP codes or counties post-infection. If HIV-positive individuals were able to qualify for services post-diagnosis that moved them to “better” counties or ZIP codes, ORs for county- and ZIP-level exposures may be biased toward the null. If their infection led them to “worse” areas, ORs may be inflated. HIV-positive individuals may have been more or less likely to move than HIV-negative individuals for many reasons, including depression and discrimination; the possible effect of differential relocations on the statistical relationships identified here is unknown.
ZIP code areas are designed to facilitate mail delivery and may not capture PWID activity spaces. The resulting exposure misclassification likely biased ORs to the null.
The NHBS sample may not represent the underlying population of PWID in the 19 MSAs. While characteristics of the true underlying PWID populations are unknown, RDS generates samples different from those generated using other methods [83,84]. Additionally, four-level HGLMs could not adjust for possible clustering of HIV within RDS recruitment chains.
Past HIV prevalence in a population predicts individual serostatus.[85,86] While we were unable to control for racial/ethnic-specific seroprevalence, we re-ran models controlling for MSA-level racial/ethnic-specific AIDS-related mortality rates among PWID and found no differences in the magnitudes of relationships between place characteristics and HIV (data available upon request).
Features of the social, economic, healthcare service/criminal justice intervention environments in ZIP codes, counties, and MSAs were associated with the odds of being HIV-negative among black and Latino PWID. E-PAR%s suggest that 7%-19% of HIV cases among black PWID and 3%-24% of cases among Latino PWID might be attributable to these features. To help eliminate HIV transmission among black and Latino PWID, structural interventions could be implemented that reduce economic disadvantage, accelerate the transition away from criminal-justice approaches to drug activity to public health approaches, and target mediators of relationships between these place-based exposures and HIV transmission. Evaluating effects of such interventions on being HIV-negative is important for addressing racial disparities in HIV status and for reducing HIV incidence. Focusing these efforts in predominately black ZIP codes may be particularly beneficial for black PWID.
The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
This research was supported by the Centers and Disease Control and Prevention, and the National HIV Behavioral Surveillance Study Group: Atlanta, GA: Jennifer Taussig, Shacara Johnson, Jeff Todd;Baltimore, MD: Colin Flynn, Danielle German; Boston, MA: Debbie Isenberg, Maura Driscoll, Elizabeth Hurwitz; Chicago, IL: Nikhil Prachand, Nanette Benbow; Dallas, TX: Sharon Melville, Richard Yeager, Jim Dyer, Alicia Novoa; Denver, CO: Mark Thrun, Alia Al-Tayyib; Detroit, MI: Emily Higgins, Eve Mokotoff, Vivian Griffin; Houston, TX: Aaron Sayegh, Jan Risser, Hafeez Rehman; Los Angeles, CA: Trista Bingham, Ekow Kwa Sey; Miami, FL: Lisa Metsch, David Forrest, Dano Beck, Gabriel Cardenas; Nassau-Suffolk, NY: Chris Nemeth, Lou Smith, Carol-Ann Watson; New Orleans, LA: William T. Robinson, DeAnn Gruber, Narquis Barak; New York City, NY: Alan Neaigus, Samuel Jenness, Travis Wendel, Camila Gelpi-Acosta, Holly Hagan; Newark, NJ: Henry Godette, Barbara Bolden, Sally D’Errico; Philadelphia, PA: Kathleen A. Brady, Althea Kirkland, Mark Shpaner; San Diego, CA: Vanessa Miguelino-Keasling, Al Velasco; San Francisco, CA: H. Fisher Raymond; San Juan, PR: Sandra Miranda De Leo´n, Yadira Rolo´n-Colo´n; Seattle, WA: Maria Courogen, Hanne Thiede, Richard Burt; St Louis, MO: Michael Herbert, Yelena Friedberg, Dale Wrigley, Jacob Fisher; Washington, DC: Marie Sansone, Tiffany West-Ojo, Manya Magnus, Irene Kuo; Behavioral Surveillance Team. We would like to thank the NHBS participants for making this study possible. We also thank and acknowledge Mr. Scott Burris and Ms. Mona Bennett for their help documenting laws governing syringe access.
Conceived and designed the experiments: HLFC SL MK SF DCDJ BT GP-B ED DB CW ZR NHBS. Performed the experiments: GP-B ED DB CW ZR JJ NHBS. Analyzed the data: MW MK YC ZR SL MZ. Wrote the paper: HLFC SL MK ZR MZ JJ SRF DCDJ BT DB ED CW GP-B.
- 1. Lansky A, Finlayson T, Johnson C, Holtzman D, Wejnert C, et al. (2014) Estimating the number of persons who inject drugs in the united states by meta-analysis to calculate national rates of HIV and hepatitis C virus infections. PLoS ONE [Electronic Resource] 9: e97596. pmid:24840662
- 2. Tempalski B, Lieb S, Cleland CM, Cooper H, Brady JE, et al. (2009) HIV prevalence rates among injection drug users in 96 large US metropolitan areas, 1992–2002. Journal of Urban Health 86: 132–154. pmid:19015995
The White House Office of National AIDS Policy (2010) National HIV/AIDS Strategy. Washington, DC: The White House Office of National AIDS Policy.
Centers for Disease Control and Prevention (2011) Strategic Plan: The Division of HIV/AIDS Prevention 2011 through 2015. Atlanta, GA: CDC.
- 5. Friedman S, Cooper H, Tempalski B, Keem M, Friedman R, et al. (2006) Relationships of deterrence and law enforcement to drug-related harms among drug injectors in US metropolitan areas. AIDS 20: 93–99. pmid:16327324
- 6. Cooper H, Des Jarlais DC, Tempalski B, Bossak B, Ross Z, et al. (2012) Drug-related arrest rates and spatial access to syringe exchange programs in New York City health districts: Combined effects on the risk of injection-related infections among injectors. Health and Place 18: 218–228. pmid:22047790
- 7. Cooper H, Moore L, Gruskin S, Krieger N (2005) The impact of a police drug crackdown on drug injectors' ability to practice harm reduction: a qualitative study. Social Science & Medicine 61: 673–684.
- 8. Nandi A, Glass TA, Cole SR, Chu H, Galea S, et al. (2010) Neighborhood poverty and injection cessation in a sample of injection drug users. American Journal of Epidemiology 171: 391–398. pmid:20093307
- 9. Genberg BL, Gange SJ, Go VF, Celentano DD, Kirk GD, et al. (2011) The effect of neighborhood deprivation and residential relocation on long-term injection cessation among injection drug users (IDUs) in Baltimore, Maryland. Addiction 106: 1966–1974. pmid:21592251
- 10. Latkin C, Williams CT, Wang J, Curry AD (2005) Neighborhood Social disorder as a determinant of drug injection behaviors: a structural equation modelling approach. Health Psychology 24: 96–100. pmid:15631567
- 11. Latkin CA, Curry AD, Hua W, Davey MA (2007) Direct and indirect associations of neighborhood disorder with drug use and high-risk sexual partners. American Journal of Preventive Medicine 32: S234–S241. pmid:17543716
- 12. Cooper H, Des Jarlais D, Ross Z, Tempalski B, Bossak B, et al. (2011) Spatial access to syringe exchange programs and pharmacies selling over-the-counter syringes predicts drug injectors’ use of sterile syringes. American Journal of Public Health 101: 1118–1125. pmid:21088267
- 13. Williams C, Metzger D (2010) Race and distance effects on regular syringe exchange program use and injection risks: A geobehavioral analysis. American Journal of Public Health 100: 1068–1074. pmid:20395589
- 14. Friedman SR, Perlis T, Des Jarlais DC (2001) Laws prohibiting over-the-counter syringe sales to injection drug users: relations to population density, HIV prevalence, and HIV incidence. American Journal of Public Health 91: 791–793. pmid:11344889
- 15. Bramson H, Des Jarlais DC, Arasteh K, Nugent A, Guardino V, et al. (2015) State laws, syringe exchange, and HIV among persons who inject drugs in the United States: History and effectiveness. Journal of Public Health Policy 36: 212–230. pmid:25590514
Linton S, Cooper HL, Kelley M, Karnes C, Wolfe ME, et al. (2014) Geographic variations in racial/ethnic disparities in HIV among injection drug users in the United States. 142nd meeting of the American Public Health Association. New Orleans, LA: APHA.
- 17. Pouget ER, West BS, Tempalski B, Cooper HL, Hall HI, et al. (2014) Persistent racial/ethnic disparities in AIDS diagnosis rates among people who inject drugs in U.S. metropolitan areas, 1993–2007. Public Health Reports 129: 267–279. pmid:24791025
- 18. Broz D, Wejnert C, Pham HT, DiNenno E, Heffelfinger JD, et al. (2014) HIV infection and risk, prevention, and testing behaviors among injecting drug users—National HIV Behavioral Surveillance System, 20 U.S. cities, 2009. MMWR Surveillance Summaries 63: 1–51.
- 19. Rhodes T (2002) The ‘risk environment’: a framework for understanding and reducing drug-related harm. International Journal of Drug Policy 13: 85–94.
- 20. Rhodes T (2009) Risk environments and drug harms: A social science for harm reduction approach. International Journal of Drug Policy 20: 193–201. pmid:19147339
- 21. Rhodes T, Mikhailova L, Sarang A, Lowndes C, Rylkov A, et al. (2003) Situational factors influencing drug injecting, risk reduction and syringe exchange in Togliatti City, Russian Federation: a qualitative study of micro risk environment. Social Science & Medicine 57: 39–54.
- 22. Rhodes T, Singer M, Bourgois P, Friedman SR, Strathdee SA (2005) The social structural production of HIV risk among injecting drug users. Social Science & Medicine 61: 1026–1044.
- 23. Strathdee SA, Hallett TB, Bobrova N, Rhodes T, Booth R, et al. (2010) HIV and risk environment for injecting drug users: the past, present, and future. Lancet 376: 268–284. pmid:20650523
- 24. Cooper HL, Linton S, Kelley ME, Zev R, Wolfe ME, et al. (In press) Racialized risk environments in a large sample of people who inject drugs In the United States. International Journal of Drug Policy.
- 25. Northridge ME (1995) Annotation: Public Health Methods-Attributable Risk as a Link between Causality and Public Health Action. American Journal of Public Health 85: 1205–1206.
Hennekens CH, Buring JE (1987) Epidemiology in Medicine; Mayrent SL, editor. Boston, MA: Little, Brown and Company.
- 27. Gallagher K, Sullivan PS, Lansky A, Onorato I (2007) Behavioral surveillance among people at risk for HIV infection in the U.S.: the National HIV Behavioral Surveillance System. Public Health Reports 122: 32–38. pmid:17354525
- 28. Centers for Disease Control and Prevention (2012) HIV infection and HIV-associated behaviors among injecting drug users—20 cities, United States, 2009. MMWR 61: 133–138. pmid:22377843
Centers for Disease Control and Prevention (2012) Diagnoses of HIV infection among adults and adolescents, by sex and transmission category, 2010–46 states and 5 U.S. dependent areas. Atlanta, GA: CDC.
Office of Applied Studies in the Substance Abuse and Mental Health Services Administration (2010) National Directory of Drug and Alcohol Abuse Treatment Programs 2010. Bethesda, MD: SAMHSA.
- 31. McKnight C, Des Jarlais C, Perlis T, Eigo K, Krim M, et al. (2005) Update: Syringe Exchange Programs—United States, 2002. Morbidity and Mortality Weekly Report 54: 673–676. pmid:16015218
- 32. Massey DS, Denton NA (1989) Hypersegregation in U.S. metropolitan areas: black and Hispanic segregation along five dimensions. Demography 26: 373–391. pmid:2792476
Massey D, Denton N (1993) American Apartheid: Segregation and the Making of the Underclass. Cambridge, Massachusetts: Harvard University Press.
- 34. Cooper H, Bonney L, Ross Z, Karnes C, Hunter-Jones J, et al. (2013) The aftermath of public housing relocation: relationship to substance misuse. Drug and Alcohol Dependence 133: 37–44. pmid:23850372
- 35. Cohen DA, Ghosh-Dastidar B, Scribner R, Miu A, Scott M, et al. (2006) Alcohol outlets, gonorrhea, and the Los Angeles civil unrest: a longitudinal analysis. Social Science and Medicine 62: 3062–3071. pmid:16423436
- 36. Cohen DA, Spear S, Scribner R, Kissinger P, Mason K, et al. (2000) "Broken windows" and the risk of gonorrhea. American Journal of Public Health 90: 230–236. pmid:10667184
- 37. Schonlau M, Scribner R, Farley TA, Theall K, Bluthenthal RN, et al. (2008) Alcohol outlet density and alcohol consumption in Los Angeles county and southern Louisiana. Geospatial Health 3: 91–101. pmid:19021112
- 38. Scribner RA, Cohen DA, Farley TA (1998) A geographic relation between alcohol availability and gonorrhea rates. Sexually Transmitted Diseases 26: 544–548.
- 39. Scribner RA, Cohen DA, Fisher W (2000) Evidence of a structural effect for alcohol outlet density: a multilevel analysis. Alcoholism: Clinical & Experimental Research 24: 188–195.
- 40. Theall KP, Scribner R, Cohen D, Bluthenthal RN, Schonlau M, et al. (2009) The neighborhood alcohol environment and alcohol-related morbidity. Alcohol & Alcoholism 44: 491–499.
- 41. Dauria E, Elifson K, Arriola K, Wingood G, Cooper HL (In press) Male incarceration rates and rates of sexually transmitted infections: Results from a longitudinal analysis in a southeastern US city. Sexually Transmitted Diseases.
Kim J-O, Mueller CW (1978) Factor Analysis: Statistical Methods and Practical Issues; Uslaner E, editor. Newbury Park: Sage Publications.
(2009) Code of Federal Regulations, Public Welfare, Department of Health and Human Services, Protection of Human Subjects. 45(46). USA: Code of Federal Regulations.
National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research (1978) Protection of Human Subjects, Belmont Report: Ethical principles and guidelines for the protection of human subjects research. Washington, DC: US Government Printing Office.
Office of the Associate Director for Science (2010) Human Research Protections. Atlanta, GA: Centers for Disease Control and Prevention.
- 46. Diez Roux AV, Mair C (2010) Neighborhoods and health. Annals of the New York Academy of Medicine 1186: 125–145.
- 47. Putnam S, Galea S (2008) Epidemiology and the macrosocial determinants of health. Journal of Public Health Policy 29: 275–289. pmid:18701897
- 48. Adimora AA, Schoenbach VJ (2005) Social context, social networks, and racial disparities in rates of sexually transmitted infections. Journal of Infectious Diseases 191: S115–S122. pmid:15627221
- 49. Cooper HL, Caruso B, Barham T, Embry V, Dauria E, et al. (2015) Partner Incarceration and African-American Women’s Sexual Relationships and Risk: A Longitudinal Qualitative Study. Journal of Urban Health 92: 527–547. pmid:25694224
Silver D (2014) Feds may be rethinking the drug war, but states have been leading the way. In: Center PR, editor. FactTank: News in the Numbers. Washington, DC: Pew Research Center.
- 51. Cooper HL, Hunter-Jones J, Kelley ME, Karnes C, Haley DF, et al. (2014) The aftermath of public housing relocations: relationships between changes in local socioeconomic conditions and depressive symptoms in a cohort of adult relocaters. Journal of Urban Health 91: 223–241. pmid:24311024
- 52. Kling J, Liebman JB, Katz LF (2007) Experimental Analysis of Neighborhood Effects. Econometrica 75: 83–119.
- 53. Boardman J, Finch B, Ellison CG, Williams DR, Jackson J (2001) Neighborhood disadvantage, stress, and drug use among adults. Journal of Health and Social Behavior 42: 151–165. pmid:11467250
- 54. Galea S, Ahern J, Nandi A, Tracy M, Beard J, et al. (2007) Urban neighborhood poverty and the incidence of depression in a population-based cohort study. Annals of Epidemiology 17: 171–179. pmid:17320784
- 55. Yen IH, Kaplan GA (1999) Poverty area residence and changes in depression and perceived health status: evidence from the Alameda County Study. International Journal of Epidemiology 28: 90–94. pmid:10195670
- 56. Kim D (2008) Blues from the neighborhood? Neighborhood characteristics and depression. Epidemiologic Reviews 30: 101–117. pmid:18753674
- 57. Curry A, Latkin C, Davey-Rothwell M (2008) Pathways to depression: the impact of neighborhood violent crime on inner-city residents in Baltimore, Maryland, USA. Social Science & Medicine 67: 23–30.
- 58. Alvy LM, McKirnan DJ, Mansergh G, Koblin B, Colfax GN, et al. (2011) Depression is associated with sexual risk among men who have sex with men, but is mediated by cognitive escape and self-efficacy. AIDS & Behavior 15: 1171–1179.
- 59. Lemstra M, Rogers M, Thompson A, Moraros J, Buckingham R (2011) Risk indicators of depressive symptomatology among injection drug users and increased HIV risk behaviour. Canadian Journal of Psychiatry—Revue Canadienne de Psychiatrie 56: 358–366. pmid:21756450
- 60. Shrier LA, Schillinger JA, Aneja P, Rice PA, Batteiger BE, et al. (2009) Depressive symptoms and sexual risk behavior in young, chlamydia-infected, heterosexual dyads. Journal of Adolescent Health 45: 63–69. pmid:19541251
- 61. Zierler S, Krieger N, Tang Y, Coady W, Siegfried E, et al. (2000) Economic deprivation and AIDS incidence in Massachusetts. American Journal of Public Health 90: 1064–1073. pmid:10897184
- 62. Fife D, Mode C (1992) AIDS incidence and income. Journal of Acquired Immune Deficiency Syndrome 5: 1105–1110.
- 63. Hu D, Frey R, Costa S (1994) Geographical AIDS rates and sociodemographic variables in Newark, New Jersey metropolitan area. AIDS Public Policy Journal 9: 20–25.
- 64. Simon P, Hu D, Diaz T, Kerndt P (1995) Income and AIDS rates in Los Angeles County. AIDS 9: 281–284. pmid:7755917
- 65. Cooper HL, Linton S, F HD, Kelley ME, Dauria EF, et al. (In press) Changes in exposure to neighborhood characteristics are associated with sexual network characteristics in a cohort of adults relocating from public housing. AIDS and Behavior.
- 66. Ostir GV, Eschbach K, Markides KS, Goodwin JS (2003) Neighbourhood composition and depressive symptoms among older Mexican Americans. Journal of Epidemiology & Community Health 57: 987–992.
- 67. Patel KV, Eschbach K, Rudkin LL, Peek MK, Markides KS (2003) Neighborhood context and self-rated health in older Mexican Americans. Annals of Epidemiology 13: 620–628. pmid:14732301
- 68. Logan JR, Zhang W, Alba RD (2002) Immigrant Enclaves and Ethnic Communities in New York and Los Angeles American Sociological Review 67: 299–322
- 69. Oster AM, Russell K, Wiegand RE, Valverde E, Forrest DW, et al. (2013) HIV infection and testing among Latino men who have sex with men in the United States: the role of location of birth and other social determinants. PLoS ONE [Electronic Resource] 8: e73779. pmid:24147151
- 70. Kramer MR, Hogue CR (2009) Is segregation bad for your health? Epidemiologic Reviews 31: 178–194. pmid:19465747
- 71. Williams DR, Collins C (2001) Racial residential segregation: a fundamental cause of racial disparities in health. Public Health Reports 116: 404–416. pmid:12042604
- 72. Cooper HL, Wypij D, Krieger N (2005) Police drug crackdowns and hospitalization rates for illicit-injection-related infections in New York City. International Journal of Drug Policy 16: 150–160.
Alexander M (2010) The New Jim Crow: Mass Incarceration in the Age of Colorblindness. New York, NY: The New Press.
- 74. Bluthenthal RN, Lorvick J, Kral AH, Erringer EA, Kahn JG (1999) Collateral damage in the war on drugs: injection drug use. International Journal of Drug Policy 10: 25–38.
- 75. Laumann EO, Youm Y (1999) Racial/ethnic group differences in the prevalence of sexually transmitted disesases in the United States: A network explanation. Sexually Transmitted Diseases 26: 250–261. pmid:10333277
- 76. Kottiri BJ, Friedman SR, Neaigus A, Curtis R, Des Jarlais DC (2002) Risk networks and racial/ethnic differences in the prevalence of HIV infection among injection drug users. Journal of Acquired Immune Deficiency Syndromes: JAIDS 30: 95–104. pmid:12048369
- 77. Curran JW, Jaffee H, Hardy A, Morgan W, Selik RM, et al. (1988) Epidemiology of HIV infection and AIDS in the United States. Science 239: 610–616. pmid:3340847
- 78. Laffoon B, Satcher Johnson A, Cohen S, Hu X, Shouse R (2011) Disparities in diagnoses of HIV infection between Blacks/African Americans and other racial/ethnic populations—37 states, 2005–2008. MMWR 60: 93–98. pmid:21293323
- 79. Linton S, Cooper H, Kelley ME, Ross Z, Wolfe ME, et al. (In press) Geographic distribution of HIV-positive injection drug users in the US in 2009. American Journal of Public Health.
- 80. Nakayama T (2000) Under-reporting of attributable risk and reporting of the risk ratio in epidemiologic literature. Epidemiology 11: 366–367. pmid:10784264
- 81. Nakayama T, Zaman MM, Tanaka H (1998) Reporting of attributable and relative risks, 1966–97. Lancet 351: 1179. pmid:9643696
Centers for Disease Control and Prevention (2013) Expanded Testing Initiative Atlanta, GA: CDC.
- 83. Kral AH, Malekinejad M, Vaudrey J, Martinez AN, Lorvick J, et al. (2010) Comparing respondent-driven sampling and targeted sampling methods of recruiting injection drug users in San Francisco. Journal of Urban Health 87: 839–850. pmid:20582573
- 84. Robinson WT, Risser JM, McGoy S, Becker AB, Rehman H, et al. (2006) Recruiting injection drug users: a three-site comparison of results and experiences with respondent-driven and targeted sampling procedures. Journal of Urban Health 83: i29–38. pmid:16933101
- 85. Des Jarlais DC, Braine N, Yi H, Turner C (2007) Residual injection risk behavior, HIV infection, and the evaluation of syringe exchange programs. AIDS Education & Prevention 19: 111–123.
- 86. Des Jarlais DC, Hagan H, Friedman SR, Friedmann P, Goldberg D, et al. (1995) Maintaining low HIV seroprevalence in populations of injecting drug users. Jama 274: 1226–1231. pmid:7563513