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This study examined overall and gender-specific associations between place-based characteristics and opposite-sex exchange sex among people who inject drugs (PWID) in the U.S. PWID were recruited from 19 metropolitan statistical areas (MSAs) for the Centers for Disease Control and Prevention’s 2012 National HIV Behavioral Surveillance. Administrative data were used to describe the economic, social, and political features of the ZIP codes, MSAs, counties, and states where PWID lived. Multilevel modeling estimated associations of place characteristics and exchange sex. We found that 52% of women and 23% of men reported past-year opposite-sex exchange sex (N = 7599). Female PWID living in states with stronger policies supporting working caregivers had lower odds of exchange sex (aOR = 0.80; 95% CI 0.69, 0.94). PWID living in ZIP codes with greater economic deprivation had higher odds of exchange sex (aOR = 1.10; 95% CI 1.03, 1.17). We found that a high percentage of male PWID exchanged sex with women; determinants and risks of this group merit exploration. If future research establishes that the relationships identified here are causal, interventions to reduce exchange sex among PWID should include policies supporting working caregivers and reducing poverty rates.

Racial/ethnic homophily in sexual partnerships (partners share the same race/ethnicity) has been associated with racial/ethnic disparities in HIV. Structural racism may partly determine racial/ethnic homophily in sexual partnerships. This study estimated associations of racial/ethnic concentration and mortgage discrimination against Black and Latino residents with racial/ethnic homophily in sexual partnerships among 7847 people who inject drugs (PWID) recruited from 19 US cities to participate in CDC’s National HIV Behavioral Surveillance. Racial/ethnic concentration was defined by two measures that respectively compared ZIP code-level concentrations of Black residents to White residents and Latino residents to White residents, using the Index of Concentration at the Extremes. Mortgage discrimination was defined by two measures that respectively compared county-level mortgage loan denial among Black applicants to White applicants and mortgage loan denial among Latino applicants to White applicants, with similar characteristics (e.g., income, loan amount). Multilevel logistic regression models were used to estimate associations. Interactions of race/ethnicity with measures of racial/ethnic concentration and mortgage discrimination were added to the final multivariable model and decomposed into race/ethnicity-specific estimates. In the final multivariable model, among Black PWID, living in ZIP codes with higher concentrations of Black vs. White residents and counties with higher mortgage discrimination against Black residents was associated with higher odds of homophily. Living in counties with higher mortgage discrimination against Latino residents was associated with lower odds of homophily among Black PWID. Among Latino PWID, living in ZIP codes with higher concentrations of Latino vs. White residents and counties with higher mortgage discrimination against Latino residents was associated with higher odds of homophily. Living in counties with higher mortgage discrimination against Black residents was associated with lower odds of homophily among Latino PWID. Among White PWID, living in ZIP codes with higher concentrations of Black or Latino residents vs. White residents was associated with lower odds of homophily, but living in counties with higher mortgage discrimination against Black residents was associated with higher odds of homophily. Racial/ethnic segregation may partly drive same race/ethnicity sexual partnering among PWID. Future empirical evidence linking these associations directly or indirectly (via place-level mediators) to HIV/STI transmission will determine how eliminating discriminatory housing policies impact HIV/STI transmission.

A barrier in the children’s environmental health field has been the lack of early-warning systems to identify risks of childhood illness and developmental disorders. We aimed to develop a methodology to identify an accessible biomarker measured in a small amount of blood to distinguish newborns at elevated risk from a toxic prenatal exposure, using air pollutants as a case study. Because air pollutants are associated with altered DNA methylation, we developed a pipeline using DNA methylation signatures measured in umbilical cord blood, which could be used as predictors of prenatal exposure. We used air pollution indicators, including modelled trimester-specific and pregnancy average NO(2) and PM(2.5), and DNA methylation signatures from Illumina arrays measured in two New York City-based longitudinal birth cohorts from the Columbia Centre for Children’s Environmental Health. We developed a screening plus three-part pipeline that incorporates selection, testing, and validation to identify whether DNA methylation can be used to predict exposure to prenatal air pollution indicators, NO(2) and PM(2.5). Applying this pipeline, we found that cord blood DNA methylation could be used to predict high vs. low average pregnancy NO(2) (AUC = 0.60, 95% CI: 0.52–0.68, with validation AUC = 0.60). Similar results were found for high vs. low third trimester NO(2). In this proof of concept study using air pollutants as an example, we provide an approach (with a generalizable analytic pipeline) that can be used for prediction of prenatal exposure to contaminants. This approach has potential to identify children at risk of developmental disorders and illness resulting from prenatal exposure.

New York City (NYC) experienced a sharp decline in air pollution during the COVID-19 shutdown period (March 15, 2020 to May 15, 2020)-albeit at high social and economic costs. It provided a unique opportunity to simulate a scenario in which the city-wide air quality improvement during the shutdown was sustained over the five-year period, 2021 through 2025, allowing us to estimate the potential public health benefits to children and adults and their associated economic benefits. We focused on fine particulate matter (PM2.5) and modelled potential future health benefits to children and adults. The analysis considered outcomes in children that have not generally been accounted for in clean air benefits assessments, including preterm birth, term low birthweight, infant mortality, child asthma incidence, child asthma hospital admissions and emergency department visits, autism spectrum disorder, as well as adult mortality. We estimated a city-wide 23% improvement in PM2.5 levels during the COVID-19 shutdown months compared to the average level for those months in 2015-2018 (the business as usual period). Based on the data for 2020, we extrapolated the ambient levels of PM2.5 for the following five-year period. The estimated cumulative benefits for 2021-2025 included thousands of avoided cases of illness and death, with associated economic benefits from $31.8 billion to $77 billion. This "natural experiment," tragic though the cause, resulted in a clean air scenario that can be considered aspirational-one that could be achieved through transportation, climate, and environmental policies that support robust economic recovery with similarly reduced emissions.

Background The 2008 Recession was a global event that led to funding cuts for programs and services in the United States; though this recession officially ended in 2009, its aftershocks continued through 2012. We evaluated the relationship between the severity of the Great Recession's aftermath and spatial access to combined prevention services (i.e. HIV testing, syringe service programs, substance use disorder treatment program) for people who inject drugs (PWID) living in 19 metropolitan statistical areas (MSAs) in the United States. Methods The unit of analysis was the ZIP code; we sampled ZIP codes in these 19 MSAs where ≥1 PWID lived in 2009 and 2012, according to the CDC's National HIV Behavioral Surveillance. We used administrative data to describe the combined prevention environment (i.e., spatial access to HIV testing) for each ZIP code, and measured the severity of the recession's aftermath in each ZIP code, and in the counties and MSAs where these ZIP codes were located. Multilevel modeling estimated associations between changes in the aftermath of the Great Recession and ZIP code-level changes in spatial access to combined prevention services from 2009 to 2012. Results 675 ZIP codes located in 36 counties and 19 MSAs were included in this analysis. From 2009 to 2012, 21% of ZIP code areas lost access to combined prevention services and 14% gained access. ZIP codes with higher poverty rates relative to their respective MSAs were less likely to lose access (aOR: 0.91; 95% CI: 0.88, 0.95) and more likely to gain access (aOR: 1.05; 95% CI: 1.01, 1.09); there is some evidence to suggest the former association was attenuated for ZIP codes with higher percentages of non-Hispanic white residents. Conclusion Combined prevention services for PWID living in these 675 ZIP codes demonstrated resilience in the aftermath of the Great Recession. Future research should explore whether community-based and federal HIV prevention initiatives contributed to this resilience, particularly in areas with higher concentrations of people of color.

We evaluated the associations of exposure to fine particulate matter (particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) at concentrations of

As a key step in advancing the sustainable development goals, the World Health Organisation (WHO) has placed emphasis on building capacity for measuring and monitoring health inequalities. A number of resources have been developed, including the Health Equity Assessment Toolkit (HEAT), a software application that facilitates the assessment of within-country health inequalities. Following user demand, an Upload Database Edition of HEAT, HEAT Plus, was developed. Launched in July 2017, HEAT Plus allows users to upload their own databases and assess inequalities at the global, national or subnational level for a range of (health) indicators and dimensions of inequality. The software is open-source, operates on Windows and Macintosh platforms and is readily available for download from the WHO website. The flexibility of HEAT Plus makes it a suitable tool for both global and national inequality assessments. Further developments will include interactive graphs, maps and translation into different languages.

Background Despite abruption's elusive etiology, knowledge of triggers that precede it by just a few days prior to delivery may help to understand the underpinnings of this acute obstetrical complication. We examine whether air pollution exposures immediately preceding delivery are associated with acute-onset abruptions. Methods We applied a bidirectional, time-stratified, case-crossover design to births with an abruption diagnosis in New York City, 2008-2014. We measured ambient fine particulate matter (PM2.5) and nitrogen dioxide (NO2). We fit distributed lag nonlinear models based on conditional logistic regression to evaluate individual exposure and cumulative exposures over lags 0-7 days before abruption, adjusted for temperature and relative humidity (similar lags to the main exposures). Results We identified 1,190 abruption cases. We observed increased odds of abruption for exposure to PM2.5 (per 10 μg/m) on lag day 3 (odds ratio [OR] 1.19, 95% confidence interval [CI] = 0.98, 1.43), lag day 4 (OR 1.21, 95% CI = 1.01, 1.46), and lag day 5 (OR 1.17, 95% CI = 1.03, 1.33). Similarly, the odds of abruption increased with exposure to NO2 (per 5 ppb) on lag day 3 (OR 1.16, 95% CI = 0.98, 1.37), lag day 4 (OR 1.19, 95% CI = 1.02, 1.39), and lag day 5 (OR 1.16, 95% CI = 1.05, 1.27). Exposures to PM2.5 and NO2 at other lags, or cumulative exposures, were not associated with abruption of acute onset. Conclusions This case-crossover study showed evidence of an association between short-term ambient air pollution exposures and increased abruption risk of acute onset.

Objectives The frequency and intensity of extreme heat events are increasing in New York State (NYS) and have been linked with increased heat-related morbidity and mortality. But these effects are not uniform across the state and can vary across large regions due to regional sociodemographic and environmental factors which impact an individual's response or adaptive capacity to heat and in turn contribute to vulnerability among certain populations. We developed a heat vulnerability index (HVI) to identify heat-vulnerable populations and regions in NYS. Study design Census tract level environmental and sociodemographic heat-vulnerability variables were used to develop the HVI to identify heat-vulnerable populations and areas. Methods Variables were identified from a comprehensive literature review and climate-health research in NYS. We obtained data from 2010 US Census Bureau and 2011 National Land Cover Database. We used principal component analysis to reduce correlated variables to fewer uncorrelated components, and then calculated the cumulative HVI for each census tract by summing up the scores across the components. The HVI was then mapped across NYS (excluding New York City) to display spatial vulnerability. The prevalence rates of heat stress were compared across HVI score categories. Results Thirteen variables were reduced to four meaningful components representing 1) social/language vulnerability; 2) socioeconomic vulnerability; 3) environmental/urban vulnerability; and 4) elderly/ social isolation. Vulnerability to heat varied spatially in NYS with the HVI showing that metropolitan areas were most vulnerable, with language barriers and socioeconomic disadvantage contributing to the most vulnerability. Reliability of the HVI was supported by preliminary results where higher rates of heat stress were collocated in the regions with the highest HVI. Conclusions The NYS HVI showed spatial variability in heat vulnerability across the state. Mapping the HVI allows quick identification of regions in NYS that could benefit from targeted interventions. The HVI will be used as a planning tool to help allocate appropriate adaptation measures like cooling centers and issue heat alerts to mitigate effects of heat in vulnerable areas.

This exploratory analysis investigates relationships of place characteristics to HIV testing among people who inject drugs (PWID). We used CDC's 2012 National HIV Behavioral Surveillance (NHBS) data among PWID from 19 US metropolitan statistical areas (MSAs); we restricted the analytic sample to PWID self-reporting being HIV negative (N = 7477). Administrative data were analyzed to describe the 1. Sociodemographic Composition; 2. Economic disadvantage; 3. Healthcare Service/Law enforcement; and 4. HIV burden of the ZIP codes, counties, and MSAs where PWID lived. Multilevel models tested associations of place characteristics with HIV testing. Fifty-eight percent of PWID reported past-year testing. MSA-level per capita correctional expenditures were positively associated with recent HIV testing among black PWID, but not white PWID. Higher MSA-level household income and imbalanced sex ratios (more women than men) in the MSA were associated with higher odds of testing. HIV screening for PWID is suboptimal (58%) and needs improvement. Identifying place characteristics associated with testing among PWID can strengthen service allocation and interventions in areas of need to increase access to HIV testing.

Neighborhood social and physical factors shape sexual network characteristics in HIV-seronegative adults in the United States. This multilevel analysis evaluated whether these relationships also exist in a predominantly HIV-seropositive cohort of women. This cross-sectional multilevel analysis included dm 734 women enrolled in the Women's Interagency HIV Study's sites in the U S South. Center track-level contextual data captured socioeconomic disadvantage (e.g., tract poverty), number of alcohol outlets, and number of nonprofits in the census tracts where women lived; participant-level data, including perceived neighborhood cohesion, were gathered via survey. We used hierarchical generalized linear models to evaluate relationships between tract characteristics and two outcomes: perceived main sex partner risk level (e.g., partner substance use) and perceived main sex partner non-monogamy. We tested whether these relationships varied by women's HIV. Greater tract-level socioeconomic disadvantage was associated with greater sex partner risk (OR = 1.29, 95 % CI = 1.06-1.58) among HIV-seropositive women and less partner non-monogamy among HIV-seronegative women (OR = 0.69, 95 % CI = 0.51-0.92). Perceived neighborhood trust and cohesion was associated with lower partner risk (OR = 0.83, 95 % CI = 0.69-1.00) for HIV-seropositive and HIV-seronegative women. The tract-level number of alcohol outlets and non-profits were not associated with partner risk characteristics. Neighborhood characteristics are associated with perceived sex partner risk and non-monogamy among women in the South; these relationships vary by HIV status. Future studies should examine causal relationships and explore the pathways through which neighborhoods influence partner selection and risk characteristics.

Aims To identify geographic “hotspots” for potential transmission of HIV and HCV and for drug overdose among persons who use heroin and cocaine in New York City and to examine historical continuities in problem drug use hotspots in the city. Methods A total of 2714 study participants were recruited among persons entering Beth Israel substance use treatment programs. A structured questionnaire was administered and blood samples for HIV and HCV testing were collected. Hotspots for potential virus transmission were defined as ZIP codes with 10+ participants, 2+ persons infected with the virus and engaging in transmission behavior, and 2+ persons not infected and engaging in acquisition behavior. ZIP codes with 3+ persons with previous overdoses were considered potential hotspots for future overdoses. Results Participants resided in 166/178 (93%) of the ZIP codes in New York City. Injecting drug use was reported in 150/178 (84%) of the ZIP codes. No zip codes were identified for injecting-related HIV transmission, 5 zip codes were identified for sexual HIV transmission, 3 for HCV transmission, and 8 for drug overdose. Many of the ZIP code potential hotspots were in neighborhoods long associated with drug use: Lower Eastside and Harlem in Manhattan, the South Bronx, and Central Brooklyn. Discussion Heroin and cocaine use requiring treatment were reported from almost all ZIP codes in New York City, indicating needs for widely dispersed harm reduction services. Identified hotspots should be targeted for reducing sexual transmission of HIV, transmission of HCV, and drug overdoses. Some of the hotspots have persisted as problem drug use areas for 40 to over 100 years. Monitoring of drug use patterns in historical hotspot neighborhoods may permit early identification of and response to emerging drug use-related health problems. Persistent historical hotspots for problem drug use present a complex problem for implementing harm reduction services that deserve additional research.

This study examined overall and gender-specific associations between place-based characteristics and opposite-sex exchange sex among people who inject drugs (PWID) in the U.S. PWID were recruited from 19 metropolitan statistical areas (MSAs) for the Centers for Disease Control and Prevention's 2012 National HIV Behavioral Surveillance. Administrative data were used to describe the economic, social, and political features of the ZIP codes, MSAs, counties, and states where PWID lived. Multilevel modeling estimated associations of place characteristics and exchange sex. We found that 52% of women and 23% of men reported past-year opposite-sex exchange sex (N = 7599). Female PWID living in states with stronger policies supporting working caregivers had lower odds of exchange sex (aOR = 0.80; 95% CI 0.69, 0.94). PWID living in ZIP codes with greater economic deprivation had higher odds of exchange sex (aOR = 1.10; 95% CI 1.03, 1.17). We found that a high percentage of male PWID exchanged sex with women; determinants and risks of this group merit exploration. If future research establishes that the relationships identified here are causal, interventions to reduce exchange sex among PWID should include policies supporting working caregivers and reducing poverty rates.

Background People who inject drugs (PWID) lag behind other key populations in HIV care continuum outcomes. The impacts of criminal justice reform and increasing drug treatment access on HIV have been underexplored. Methods We developed agent-based models (ABM) of sexual partnerships among PWID and non-PWID, and injection equipment-sharing partnerships among PWID in five US cities (Baltimore, Boston, Miami, New York City, San Francisco) over 3 years. The first set of ABM projected changes in partnership discordance among PWID as a function of decreasing ZIP code-level incarceration rates. The second set projected discordance as a function of increasing ZIP code-level drug treatment access. ABM were parameterized and validated overall, and by city and PWID race/ethnicity (Black, Latino, White) using National HIV Behavioral Surveillance data, administrative ZIP code-level data, surveillance reports and prior literature. Informed by research on prisoner release and community-level HIV prevalence, reductions in incarceration rates were fixed at 5% and 30% and respectively projected to increase ZIP code-level HIV prevalence by 2% and 12%. Increases in drug treatment access were fixed at 30% and 58%. Results In each city, a 30% reduction in ZIP code-level incarceration rates and 12% increase in ZIP code-level HIV prevalence significantly increased sero-discordance among at least one racial/ethnic group of PWID by 1–3 percentage points. A 5% reduction in incarceration rates, and 30% and 58% increases in drug treatment access, led to isolated significant changes in sero-discordance among Black and White PWID that were less than 1 percentage point. Conclusion Reductions in incarceration rates may lead to short-term increases in sero-discordant partnerships among some PWID by increasing community-level HIV prevalence. Efforts to increase HIV testing, engagement in care and community reintegration post release, should be strengthened in the wake of incarceration reform. Additional research should confirm these findings and explore the lack of widespread impacts of drug treatment in this study.

Introduction. Cross-sectional research suggests that neighborhood characteristics and transportation access shape unmet need for medical care. This longitudinal analysis explores relationships of changes in neighborhood socioeconomic disadvantage and trans- portation access to unmet need for medical care. Methods. We analyzed seven waves of data from African American adults (N = 172) relocating from severely distressed public housing complexes in Atlanta, Georgia. Surveys yielded individual-level data and admin- istrative data characterized census tracts. We used hierarchical generalized linear models to explore relationships. Results. Unmet need declined from 25% pre-relocation to 12% at Wave 7. Post-relocation reductions in neighborhood disadvantage were inversely associated with reductions in unmet need over time (OR = 0.71, 95% CI = 0.51-0.99). More frequent transportation barriers predicted unmet need (OR = 1.16, 95% CI = 1.02-1.31). Conclusion. These longitudinal findings support the importance of neighborhood environments and transportation access in shaping unmet need and suggest that improvements in these exposures reduce unmet need for medical care in this vulnerable population.

Determining the relationship between within-city spatial variation of ambient temperatures and land-use characteristics would be useful for developing mitigation strategies for urban heat island effects in the warming climate, but relatively few published studies collected high-density ambient temperature data in large cities to date. We applied land-use regression (LUR) modeling to the ambient temperature data collected during 2009–2016 as part of the New York City Community Air Survey (NYCCAS), a high density air pollution monitoring network with up to 150 locations within New York City (NYC). The 3–5 AM average summer temperature, seasonally adjusted, was selected to avoid the influence of variation in shading across monitoring units. Environmental indicators, including normalized difference vegetation index (NDVI), high resolution land-cover, imperviousness and building density, were characterized at multiple buffer distances around each monitoring site, screened for explanatory value, and combined in a stepwise multivariable regression model using the first 7 years of data. We validated the model against 2016 data. Summer average temperature over the 8 years ranged from 17.1 to 25.8 °C and the spatial variation was well-explained by a nonlinear term of km2 tree, shrub, and grass cover at 200 m (negative association), a measure of live green vegetation (NDVI) within 1000 m (negative), interior built space within 600 m (positive), the summer average daily minimum temperature at the National Weather Service station at LaGuardia airport, and a nonlinear term based on the XY coordinates to model unexplained spatial autocorrelation (R2 = 0.82). This model predicted temperature very well at NYCCAS sites for summer 2016 (R2 = 0.87). We observed that there was a range of baseline vegetative cover where increasing cover was associated with the greatest decreases in temperature, suggesting specific urban micro-environments where increased tree canopy may be the most effective in reducing ambient temperature. Within-city variation of nighttime temperatures is well-explained by features of the built and natural environment. Interventions that target this combination of factors, such as converting paved vacant lots and derelict buildings into vegetated space or replacing city infrastructure that uses impermeable surfaces with vegetated ones wherever possible, may help to decrease ambient temperature in NYC. Our findings suggest that evaluations of neighborhood greening may not show temperature benefits until a certain level of live vegetative cover is established.

Background Substantial racial/ethnic disparities exist in HIV infection among people who inject drugs (PWID) in many countries. To strengthen efforts to understand the causes of disparities in HIV-related outcomes and eliminate them, we expand the "Risk Environment Model" to encompass the construct "racialized risk environments," and investigate whether PWID risk environments in the United States are racialized. Specifically, we investigate whether black and Latino PWID are more likely than white PWID to live in places that create vulnerability to adverse HIV-related outcomes. Methods As part of the Centers for Disease Control and Prevention's National HIV Behavioral Surveillance, 9170 PWID were sampled from 19 metropolitan statistical areas (MSAs) in 2009. Self-reported data were used to ascertain PWID race/ethnicity. Using Census data and other administrative sources, we characterized features of PWID risk environments at four geographic scales (i.e., ZIP codes, counties, MSAs, and states). Means for each feature of the risk environment were computed for each racial/ethnic group of PWID, and were compared across racial/ethnic groups. Results Almost universally across measures, black PWID were more likely than white PWID to live in environments associated with vulnerability to adverse HIV-related outcomes. Compared to white PWID, black PWID lived in ZIP codes with higher poverty rates and worse spatial access to substance abuse treatment and in counties with higher violent crime rates. Black PWID were less likely to live in states with laws facilitating sterile syringe access (e.g., laws permitting over-the-counter syringe sales). Latino/white differences in risk environments emerged at the MSA level (e.g., Latino PWID lived in MSAs with higher drug-related arrest rates). Conclusion PWID risk environments in the US are racialized. Future research should explore the implications of this racialization for racial/ethnic disparities in HIV-related outcomes, using appropriate methods.

Objectives. We explored how variance in HIV infection is distributed across multiple geographical scales among people who inject drugs (PWID) in the United States, overall and within racial/ethnic groups. Methods. People who inject drugs (n = 9077) were recruited via respondent-driven sampling from 19 metropolitan statistical areas (MSAs) for the Centers for Disease Control and Prevention’s 2009 National HIV Behavioral Surveillance system. We used multilevel modeling to determine the percentage of variance in HIV infection explained by zip codes, counties, and MSAs where PWID lived, overall and for specific racial/ethnic groups. Results. Collectively, zip codes, counties, and MSAs explained 29% of variance in HIV infection. Within specific racial/ethnic groups, all 3 scales explained variance in HIV infection among non-Hispanic/Latino White PWID (4.3%, 0.2%, and 7.5%, respectively), MSAs explained variance among Hispanic/Latino PWID (10.1%), and counties explained variance among non-Hispanic/Latino Black PWID (6.9%). Conclusions. Exposure to potential determinants of HIV infection at zip codes, counties, and MSAs may vary for different racial/ethnic groups of PWID, and may reveal opportunities to identify and ameliorate intraracial inequities in exposure to determinants of HIV infection at these geographical scales.

Objective We identified potential geographic “hotspots” for drug-injecting transmission of HIV and hepatitis C virus (HCV) among persons who inject drugs (PWID) in New York City. The HIV epidemic among PWID is currently in an “end of the epidemic” stage, while HCV is in a continuing, high prevalence (> 50%) stage. Methods We recruited 910 PWID entering Mount Sinai Beth Israel substance use treatment programs from 2011–2015. Structured interviews and HIV/ HCV testing were conducted. Residential ZIP codes were used as geographic units of analysis. Potential “hotspots” for HIV and HCV transmission were defined as 1) having relatively large numbers of PWID 2) having 2 or more HIV (or HCV) seropositive PWID reporting transmission risk—passing on used syringes to others, and 3) having 2 or more HIV (or HCV) seronegative PWID reporting acquisition risk—injecting with previously used needles/syringes. Hotspots for injecting drug use initiation were defined as ZIP codes with 5 or more persons who began injecting within the previous 6 years. Results Among PWID, 96% injected heroin, 81% male, 34% White, 15% African-American, 47% Latinx, mean age 40 (SD = 10), 7% HIV seropositive, 62% HCV seropositive. Participants resided in 234 ZIP codes. No ZIP codes were identified as potential hotspots due to small numbers of HIV seropositive PWID reporting transmission risk. Four ZIP codes were identified as potential hotspots for HCV transmission. 12 ZIP codes identified as hotspots for injecting drug use initiation. Discussion For HIV, the lack of potential hotspots is further validation of widespread effectiveness of efforts to reduce injecting-related HIV transmission. Injecting-related HIV transmission is likely to be a rare, random event. HCV prevention efforts should include focus on potential hotspots for transmission and on hotspots for initiation into injecting drug use. We consider application of methods for the current opioid epidemic in the US.

Theories of social causation and social influence, which posit that neighborhood and social network characteristics are distal causes of substance use, are frequently used to interpret associations among neighborhood characteristics, social network characteristics and substance use. These associations are also hypothesized to result from selection processes, in which substance use determines where people live and who they interact with. The potential for these competing selection mechanisms to co-occur has been underexplored among adults. This study utilizes path analysis to determine the paths that relate census tract characteristics (e.g., economic deprivation), social network characteristics (i.e., having ≥ 1 illicit drug-using network member) and illicit drug use, among 172 African American adults relocated from public housing in Atlanta, Georgia and followed from 2009 to 2014 (7 waves). Individual and network-level characteristics were captured using surveys. Census tract characteristics were created using administrative data. Waves 1 (pre-relocation), 2 (1st wave post-relocation), and 7 were analyzed. When controlling for individual-level sociodemographic factors, residing in census tracts with prior economic disadvantage was significantly associated with illicit drug use at wave 1; illicit drug use at wave 1 was significantly associated with living in economically-disadvantaged census tracts at wave 2; and violent crime at wave 2 was associated with illicit drug-using social network members at wave 7. Findings from this study support theories that describe social causation and neighborhood selection processes as explaining relationships of neighborhood characteristics with illicit drug use and illicit drug-using social networks. Policies that improve local economic and social conditions of neighborhoods may discourage substance use. Future studies should further identify the barriers that prevent substance users from obtaining housing in less disadvantaged neighborhoods.

Exposure to environmental noise from traffic is common in urban areas and has been linked to increased risks of adverse health effects including cardiovascular disease. Because traffic sources also produce air pollutants that increase the risk of cardiovascular morbidity, associations between traffic exposures and health outcomes may involve confounding and/or synergisms between air pollution and noise. While prior studies have characterized intraurban spatial variation in air pollution in New York City (NYC), limited data exists on the levels and spatial variation in noise levels. We measured 1-week equivalent continuous sound pressure levels (Leq) at 56 sites during the fall of 2012 across NYC locations with varying traffic intensity and building density that are routinely monitored for combustion-related air pollutants. We evaluated correlations among several noise metrics used to characterize noise exposures, including Leq during different time periods (night, day, weekday, weekend), Ldn (day-night noise), and measures of intermittent noise defined as the ratio of peak levels to median and background levels. We also examined correlations between sound pressure levels and co-located simultaneous measures of nitric oxide (NO), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and black carbon (BC) as well as estimates of traffic and building density around the monitoring sites. Noise levels varied widely across the 56 monitoring sites; 1-week Leq varied by 21.6 dBA (range 59.1–80.7 dBA) with the highest levels observed during the weekday, daytime hours. Indices of average noise were well correlated with each other (r > 0.83), while indices of intermittent noise were not well correlated with average noise levels (r

USA is experiencing a paradigm shift in public housing policy: while policies used to place people who qualified for housing assistance into spatially concentrated housing complexes, they now seek to geographically disperse them, often to voucher-subsidized rental units in the private market. Programs that relocate residents from public housing complexes tend to move them to neighborhoods that are less impoverished and less violent. To date, studies have reached conflicting findings about the relationship between public housing relocations and depression among adult relocaters. The present longitudinal multilevel analysis tests the hypothesis that pre-/postrelocation improvements in local economic conditions, social disorder, and perceived community violence are associated with declines in depressive symptoms in a cohort of African-American adults; active substance misusers were oversampled. We tested this hypothesis in a cohort of 172 adults who were living in one of seven public housing complexes scheduled for relocation and demolition in Atlanta, GA; by design, 20 % were dependent on substances and 50 % misused substances but were not dependent. Baseline data captured prerelocation characteristics of participants; of the seven census tracts where they lived, three waves of postrelocation data were gathered approximately every 9 months thereafter. Surveys were administered at each wave to assess depressive symptoms measured using the Center for Epidemiologic Studies Depression Scale (CES-D), perceived community violence, and other individual-level covariates. Participants' home addresses were geocoded to census tracts at each wave, and administrative data sources were used to characterize tract-level economic disadvantage and social disorder. Hypotheses were tested using multilevel models. Between waves 1 and 2, participants experienced significant improvements in reported depressive symptoms and perceived community violence and in tract-level economic disadvantage and social disorder; these reductions were sustained across waves 2–4. A 1 standard deviation improvement in economic conditions was associated with a 1-unit reduction in CES-D scores; the magnitude of this relationship did not vary by baseline substance misuse or gender. Reduced perceived community violence also predicted lower CES-D scores. Our objective measure of social disorder was unrelated to depressive symptoms. We found that relocaters who experienced greater pre-/postrelocation improvements in economic conditions or in perceived community violence experienced fewer depressive symptoms. Combined with past research, these findings suggest that relocation initiatives should focus on the quality of the places to which relocaters move; future research should also identify pathways linking pre-/postrelocation changes in place characteristics to changes in mental health.

Building on a unique exposure assessment project in New York, New York, we examined the relationship of particulate matter with aerodynamic diameter less than 2.5 μm and nitrogen dioxide with birth weight, restricting the population to term births to nonsmokers, along with other restrictions, to isolate the potential impact of air pollution on growth. We included 252,967 births in 2008-2010 identified in vital records, and we assigned exposure at the residential location by using validated models that accounted for spatial and temporal factors. Estimates of association were adjusted for individual and contextual sociodemographic characteristics and season, using linear mixed models to quantify the predicted change in birth weight in grams related to increasing pollution levels. Adjusted estimates for particulate matter with aerodynamic diameter less than 2.5 μm indicated that for each 10-µg/m(3) increase in exposure, birth weights declined by 18.4, 10.5, 29.7, and 48.4 g for exposures in the first, second, and third trimesters and for the total pregnancy, respectively. Adjusted estimates for nitrogen dioxide indicated that for each 10-ppb increase in exposure, birth weights declined by 14.2, 15.9, 18.0, and 18.0 g for exposures in the first, second, and third trimesters and for the total pregnancy, respectively. These results strongly support the association of urban air pollution exposure with reduced fetal growth.

Land use regression (LUR) models are widely employed in health studies to characterize chronic exposure to air pollution. The LUR is essentially an interpolation technique that employs the pollutant of interest as the dependent variable with proximate land use, traffic, and physical environmental variables used as independent predictors. Two major limitations with this method have not been addressed: (1) variable selection in the model building process, and (2) dealing with unbalanced repeated measures. In this paper, we address these issues with a modeling framework that implements the deletion/substitution/addition (DSA) machine learning algorithm that uses a generalized linear model to average over unbalanced temporal observations. Models were derived for fine particulate matter with aerodynamic diameter of 2.5 microns or less (PM 2.5 ) and nitrogen dioxide (NO 2 ) using monthly observations. We used 4119 observations at 108 sites and 15,301 observations at 138 sites for PM 2.5 and NO 2 , respectively. We derived models with good predictive capacity (cross-validated- R 2 values were 0.65 and 0.71 for PM 2.5 and NO 2 , respectively). By addressing these two shortcomings in current approaches to LUR modeling, we have developed a framework that minimizes arbitrary decisions during the model selection process. We have also demonstrated how to integrate temporally unbalanced data in a theoretically sound manner. These developments could have widespread applicability for future LUR modeling efforts.

Although intra-urban air pollution differs by season, few monitoring networks provide adequate geographic density and year-round coverage to fully characterize seasonal patterns. Here, we report winter intra-urban monitoring and land-use regression (LUR) results from the New York City Community Air Survey (NYCCAS). Two-week integrated samples of fine particles (PM(2.5)), black carbon (BC), nitrogen oxides (NO(x)) and sulfur dioxide (SO(2)) were collected at 155 city-wide street-level locations during winter 2008-2009. Sites were selected using stratified random sampling, randomized across sampling sessions to minimize spatio-temporal confounding. LUR was used to identify GIS-based source indicators associated with higher concentrations. Prediction surfaces were produced using kriging with external drift. Each pollutant varied twofold or more across sites, with higher concentrations near midtown Manhattan. All pollutants were positively correlated, particularly PM(2.5) and BC (Spearman's r=0.84). Density of oil-burning boilers, total and truck traffic density, and temporality explained 84% of PM(2.5) variation. Densities of total traffic, truck traffic, oil-burning boilers and industrial space, with temporality, explained 65% of BC variation. Temporality, built space, bus route location, and traffic density described 67% of nitrogen dioxide variation. Residual oil-burning units, nighttime population and temporality explained 77% of SO(2) variation. Spatial variation in combustion-related pollutants in New York City was strongly associated with oil-burning and traffic density. Chronic exposure disparities and unique local sources can be identified through year-round saturation monitoring.

Routine air monitoring provides data to assess urban scale temporal variation in pollution concentrations in relation to regulatory standards, but is not well suited to characterizing intraurban spatial variation in pollutant concentrations from local sources. To address these limitations and inform local control strategies, New York City developed a program to track spatial patterns of multiple air pollutants in each season of the year. Monitor locations include 150 distributed street-level sites chosen to represent a range of traffic, land-use and other characteristics. Integrated samples are collected at each distributed site for one 2-week session each season and in every 2-week period at five reference locations to track city-wide temporal variation. Pollutants sampled include PM(2.5) and constituents, nitrogen oxides, black carbon, ozone (summer only) and sulfur dioxide (winter only). During the first full year of monitoring more than 95% of designed samples were completed. Agreement between colocated samples was good (absolute mean % difference 3.2-8.9%). Street-level pollutant concentrations spanned a much greater range than did concentrations at regulatory monitors, especially for oxides of nitrogen and sulfur dioxide. Monitoring to characterize intraurban spatial gradients in ambient pollution usefully complements regulatory monitoring data to inform local air quality management.

Objectives Neighbourhood characteristics (eg, high poverty rates) are associated with STIs among HIV-uninfected women in the USA. However, no multilevel analyses investigating the associations between neighbourhood exposures and STIs have explored these relationships among women living with HIV infection. The objectives of this study were to: (1) examine relationships between neighbourhood characteristics and current STI status and (2) investigate whether the magnitudes and directions of these relationships varied by HIV status in a predominantly HIV-infected cohort of women living in the Southern USA. Methods This cross-sectional multilevel analysis tests relationships between census tract characteristics and current STI status using data from 737 women enrolled at the Women's Interagency HIV Study's southern sites (530 HIV-infected and 207 HIV-uninfected women). Administrative data (eg, US Census) described the census tract-level social disorder (eg, violent crime rate) and social disadvantage (eg, alcohol outlet density) where women lived. Participant-level data were gathered via survey. Testing positive for a current STI was defined as a laboratory-confirmed diagnosis of chlamydia, gonorrhoea, trichomoniasis or syphilis. Hierarchical generalised linear models were used to determine relationships between tract-level characteristics and current STI status, and to test whether these relationships varied by HIV status. Results Eleven per cent of participants tested positive for at least one current STI. Greater tract-level social disorder (OR=1.34, 95% CI 0.99 to 1.87) and social disadvantage (OR=1.34, 95% CI 0.96 to 1.86) were associated with having a current STI. There was no evidence of additive or multiplicative interaction between tract-level characteristics and HIV status. Conclusions Findings suggest that neighbourhood characteristics may be associated with current STIs among women living in the South, and that relationships do not vary by HIV status. Future research should establish the temporality of these relationships and explore pathways through which neighbourhoods create vulnerability to STIs. Trial registration number NCT00000797; results.

Few past studies have collected and analyzed within-city variation of fine particulate matter (PM2.5) elements. We developed land-use regression (LUR) models to characterize spatial variation of 15 PM2.5 elements collected at 150 street-level locations in New York City during December 2008-November 2009: aluminum, bromine, calcium, copper, iron, potassium, manganese, sodium, nickel, lead, sulfur, silicon, titanium, vanadium, and zinc. Summer- and winter-only data available at 99 locations in the subsequent 3 years, up to November 2012, were analyzed to examine variation of LUR results across years. Spatial variation of each element was modeled in LUR including six major emission indicators: boilers burning residual oil; traffic density; industrial structures; construction/demolition (these four indicators in buffers of 50 to 1000 m), commercial cooking based on a dispersion model; and ship traffic based on inverse distance to navigation path weighted by associated port berth volume. All the elements except sodium were associated with at least one source, with R(2) ranging from 0.2 to 0.8. Strong source-element associations, persistent across years, were found for residual oil burning (nickel, zinc), near-road traffic (copper, iron, and titanium), and ship traffic (vanadium). These emission source indicators were also significant and consistent predictors of PM2.5 concentrations across years.

We investigated the implications of one structural intervention--public housing relocations--for partnership dynamics among individuals living areas with high sexually transmitted infection (STI) prevalence. High-prevalence areas fuel STI endemicity and are perpetuated by spatially assortative partnerships.We analyzed 7 waves of data from a cohort of black adults (n = 172) relocating from 7 public housing complexes in Atlanta, Georgia. At each wave, data on whether participants' sexual partners lived in the neighborhood were gathered via survey. Participant addresses were geocoded to census tracts, and measures of tract-level STI prevalence, socioeconomic conditions, and other attributes were created for each wave. "High-prevalence tracts" were tracts in the highest quartile of STI prevalence in Georgia. Descriptive statistics and hierarchical generalized linear models examined trajectories of spatially assortative partnerships and identified predictors of assortativity among participants in high-prevalence tracts.All 7 tracts containing public housing complexes at baseline were high-prevalence tracts; most participants relocated to high-prevalence tracts. Spatially assortative partnerships had a U-shaped distribution: the mean percent of partners living in participants' neighborhoods at baseline was 54%; this mean declined to 28% at wave 2 and was 45% at wave 7. Participants who experienced greater postrelocation improvements in tract-level socioeconomic conditions had a lower odds of having spatially assortative partnerships (adjusted odds ratio, 1.55; 95% confidence interval [95% CI], 1.06-2.26).Public housing relocation initiatives may disrupt high-prevalence areas if residents experience significant postrelocation gains in tract-level socioeconomic conditions.

This analysis investigates changes in spatial access to safety-net primary care in a sample of US public housing residents relocating via the HOPE VI initiative from public housing complexes to voucher-subsidized rental units; substance misusers were oversampled. We used gravity-based models to measure spatial access to care, and used mixed models to assess pre-/post-relocation changes in access. Half the sample experienced declines in spatial access of ≥ 79.83%; declines did not vary by substance misuse status. Results suggest that future public housing relocation initiatives should partner with relocaters, particularly those in poor health, to help them find housing near safety-net clinics.

Drug-related law enforcement activities may undermine the protective effects of syringe exchange programs (SEPs) on local injectors’ risk of injection-related infections. We explored the spatial overlap of drug-related arrest rates and access to SEPs over time (1995-2006) in New York City health districts, and used multilevel models to investigate the relationship of these two district-level exposures to the odds of injecting with an unsterile syringe. Districts with better SEP access had higher arrest rates, and arrest rates undermined SEPs’ protective relationship with unsterile injecting. Drug-related enforcement strategies targeting drug users should be de-emphasized in areas surrounding SEPs.

Objectives. We examined relationships of spatial access to syringe exchange programs (SEPs) and pharmacies selling over-the-counter (OTC) syringes with New York City drug injectors’ harm reduction practices. Methods. Each year from 1995 to 2006, we measured the percentage of 42 city health districts’ surface area that was within 1 mile of an SEP or OTC pharmacy. We applied hierarchical generalized linear models to investigate relationships between these exposures and the odds that injectors (n = 4003) used a sterile syringe for at least 75% of injections in the past 6 months. Results. A 1-unit increase in the natural log of the percentage of a district's surface area within a mile of an SEP in 1995 was associated with a 26% increase in the odds of injecting with a sterile syringe; a 1-unit increase in this exposure over time increased these odds 23%. A 1-unit increase in the natural log of OTC pharmacy access improved these odds 15%. Conclusions. Greater spatial access to SEPs and OTC pharmacies improved injectors’ capacity to engage in harm reduction practices that reduce HIV and HCV transmission.

Traffic-related emissions have been associated with respiratory symptoms in some studies. However, there is limited information on how traffic-related emissions relate to lung function and airway inflammation.To determine the differential association of traffic-related exposures with exhaled nitric oxide (NO) and lung volumes and symptoms in children with and without asthma.We performed a longitudinal study of 200 children from ages 6 to 12 years of whom half had physician-diagnosed asthma. Two-week NO(2) and 48-hour average levels of elemental carbon and particulate matter of less than 2.5 microm (PM(2.5)) were measured at participating schools. Road and traffic densities were determined at schools and at each participant's house.In children with asthma, an interquartile increase in road density within the 50-, 100-, and 200-m home buffer areas was associated with increased exhaled NO (50 m: 28%; P = 0.03; 95% confidence interval [CI], 3-60; 100 m: 27%; P = 0.005; 95% CI, 8-49; 200 m: 17%, P = 0.09, 95% CI, -2 to 40), and reduced FEV(1) (50 m: -0.091 L; P = 0.038; 95% CI, -0.174 to -0.007; 100 m: -0.072 L, P = -0.028, 95% CI, -0.134 to -0.009; 200 m: -0.106 L, P = 0.002, 95% CI, -0.171 to -0.041]). Exposure to NO(2) at schools was marginally associated with reduced FEV(1) (-0.020; P = 0.060; 95% CI, -0.042 to 0.001). We did not observe significant associations with PM(2.5) or elemental carbon on exhaled NO. We did not observe significant reductions in lung volumes or changes in exhaled NO among healthy children.Vehicular traffic exposures are associated with increased levels of exhaled NO and reduced lung volumes in children with asthma.

We developed regression equations to predict fine particulate matter (PM 2.5 ) at air monitoring locations in the New York City region using data on nearby traffic and land use patterns. Three-year averages (1999–2001) of PM 2.5 at US Environmental Protection Agency (EPA) monitors in the 28 counties including and surrounding New York City were calculated using daily data from the EPA's Air Quality Subsystem. As the secondary contribution to PM 2.5 concentrations is lowest in the winter, we also calculated and modeled average winter 2000 PM 2.5 to conduct a preliminary evaluation of model sensitivity to source contribution. Candidate predictor variables included traffic, land use, census and emissions data from local, state and national sources and were tabulated for a series of circular buffer regions at varying distances around the monitors using a geographic information system. In total, more than 25 variables at 5 different buffer distances were considered for inclusion in the model. Before evaluating the variables we removed several samples from the modeling for validation. For comparison and validation purposes we computed both a model using data for the full 28-county region as well as a more urbanized 9-county region. We found that traffic within a buffer of 300 or 500 m explains the greatest proportion of variance (37–44%) in all 3 models. Measures of urbanization, specifically population density, explain a significant amount of the residual variation (7–18%) after including a traffic variable. Finally, a measure of industrial land use further improves the 28-county and 9-county models based on the 3-yr annual averages, explaining an additional 4% and 11% of the variation, respectively, while vegetative land use improves the winter model explaining an additional 6%. The final models predicted well at validation locations. In total, the final land use regression models explain between 61% and 64% of the variation in PM 2.5 .

Background Few studies assess whether place characteristics are associated with social network characteristics that create vulnerability to substance use. Methods This longitudinal study analyzed 7 waves of data (2009–2014) from a predominantly substance-using cohort of 172 African American adults relocated from public housing complexes in Atlanta, GA, to determine whether post-relocation changes in exposure to neighborhood conditions were associated with four network characteristics related to substance use: number of social network members who used illicit drugs or alcohol in excess in the past six months (“drug/alcohol network”), drug/alcohol network stability, and turnover into and out of drug/alcohol networks. Individual- and network-level characteristics were captured via survey and administrative data were used to describe census tracts where participants lived. Multilevel models were used to assess relationships of census tract-level characteristics to network outcomes over time. Results On average, participants relocated to census tracts that had less economic disadvantage, social disorder, and renter-occupied housing. Post-relocation reductions in exposure to economic disadvantage were associated with fewer drug/alcohol network members and less turnover into drug/alcohol networks. Post-relocation improvements in exposure to multiple census tract-level social conditions and reductions in perceived community violence were associated with fewer drug/alcohol network members, less turnover into drug/alcohol networks, less drug/alcohol network stability, and more turnover out of drug/alcohol networks. Conclusion Relocating to neighborhoods with less economic disadvantage and better social conditions may weaken relationships with substance-using individuals.

For pediatric populations, schools are considered effective platforms to establish comprehensive asthma surveillance campaigns, particularly in communities with disproportionate prevalence of disea...