Table 3.
Bivariate associations between place covariates and the odds of past-year HIV testing among self-reported HIV-negative PWID (N = 7477), drawn from the 2012 Centers for Disease Control and Prevention’s National HIV Behavioral Surveillance
| Place-based exposures by geographic scale | Models with place characteristic only | Models with individual race/ethnicity as moderator of the effect of place characteristic | |||||||
|---|---|---|---|---|---|---|---|---|---|
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| Effects in Whites | Interaction: difference of effect in black/whites | Interaction: difference of effect in Latino/whites | 1 SD difference | ||||||
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| OR | p value | OR | p value | OR | p value | OR | p value | ||
| Sociodemographic composition | |||||||||
| Male:female sex ratios: more females versus equity (%)a | |||||||||
| ZIP | 1.25 | 0.09 | 1.01 | 0.91 | 1.33 | 0.08 | 1.38 | 0.08 | – |
| County | 1.41 | 0.04 | 1.21 | 0.31 | 1.17 | 0.25 | 1.40 | 0.03 | – |
| MSA | 1.56 | 0.02 | 1.45 | 0.07 | 1.04 | 0.76 | 1.21 | 0.18 | – |
| Racial/ethnic composition (ZIP) | |||||||||
| % White | 0.99 | 0.64 | 0.99 | 0.89 | 1.06 | 0.39 | 0.95 | 0.53 | 23.89 |
| % Black | 1.07 | 0.06 | 1.09 | 0.20 | 0.94 | 0.49 | 1.11 | 0.34 | 32.42 |
| % Latino | 0.96 | 0.21 | 0.96 | 0.45 | 0.97 | 0.68 | 0.96 | 0.63 | 23.45 |
| Residential isolation (MSA) | |||||||||
| Black | 1.16 | 0.16 | 1.11 | 0.34 | 1.05 | 0.49 | 1.05 | 0.53 | 20.64 |
| Latino | 0.96 | 0.74 | 0.97 | 0.81 | 1.04 | 0.57 | 0.89 | 0.12 | 16.81 |
| Principal component analysis (PCA) | |||||||||
| MSA | |||||||||
| Social componentb | 1.14 | 0.22 | 1.10 | 0.40 | 1.01 | 0.93 | 1.16 | 0.10 | – |
| Economic disadvantage | |||||||||
| Median income (USD) | |||||||||
| ZIP | 0.99 | 0.65 | 0.98 | 0.65 | 1.10 | 0.17 | 0.91 | 0.23 | 18,869 |
| County | 0.96 | 0.07 | 0.87 | 0.96 | 0.92 | 0.18 | 1.07 | 0.40 | 13,636 |
| MSA | 1.24 | 0.02 | 1.32 | 0.02 | 0.88 | 0.07 | 1.17 | 0.08 | 14,522 |
| Poverty rate (%) | |||||||||
| ZIP | 1.03 | 0.39 | 1.04 | 0.42 | 0.91 | 0.16 | 1.09 | 0.25 | 11.41 |
| County | 1.05 | 0.55 | 0.99 | 0.96 | 1.09 | 0.12 | 1.04 | 0.61 | 5.19 |
| MSA | 0.88 | 0.21 | 0.85 | 0.16 | 1.08 | 0.31 | 0.92 | 0.34 | 4.26 |
| Economic disadvantage | |||||||||
| Unemployment (%) | |||||||||
| ZIP | 1.00 | 0.99 | 1.00 | 0.96 | 0.97 | 0.67 | 1.04 | 0.68 | 6.97 |
| County | 1.08 | 0.84 | 0.98 | 0.84 | 1.05 | 0.53 | 1.07 | 0.49 | 2.89 |
| MSA | 0.88 | 0.28 | 0.84 | 0.20 | 1.06 | 0.50 | 0.97 | 0.81 | 2.60 |
| No high-school diploma or general equivalency diploma (%) | |||||||||
| ZIP | 0.99 | 0.68 | 0.99 | 0.88 | 0.93 | 0.29 | 1.01 | 0.84 | 11.27 |
| County | 1.08 | 0.34 | 1.06 | 0.55 | 1.05 | 0.49 | 1.01 | 0.89 | 5.08 |
| MSA | 0.94 | 0.60 | 0.91 | 0.45 | 1.10 | 0.15 | 0.98 | 0.79 | 4.21 |
| Gini coefficient of income inequality (MSA) (%) | 1.03 | 0.77 | 0.93 | 0.52 | 1.16 | 0.04 | 1.16 | 0.07 | 2.40 |
| Principal component analysis (PCA) | |||||||||
| ZIP | |||||||||
| Economic disadvantage componentc | 0.93 | 0.07 | 1.02 | 0.70 | 0.92 | 0.22 | 1.07 | 0.40 | – |
| County | |||||||||
| Economic disadvantage componentc | 1.05 | 0.60 | 1.01 | 0.93 | 1.10 | 0.22 | 1.01 | 0.89 | – |
| MSA | |||||||||
| Economic disadvantage componentc | 0.86 | 0.15 | 0.80 | 0.08 | 1.12 | 0.14 | 0.92 | 0.37 | – |
| Health and law enforcement interventions | |||||||||
| Spatial access to HIV testing sites (y/n) (ZIP) (%) | 1.20 | 0.01 | 1.18 | 0.12 | 1.16 | 0.23 | 1.39 | 0.04 | – |
| Spatial access to substance use disorder treatment programs (ZIP)d | 1.30 | <0.0005 | 1.15 | 0.01 | 1.07 | 0.32 | 1.27 | 0.002 | 2.20 |
| Spatial access to MTPs (y/n/) (ZIP) (%) | 1.14 | 0.06 | 1.05 | 0.65 | 1.50 | 0.71 | 1.33 | 0.06 | – |
| Health and law enforcement interventions | |||||||||
| Spatial access to SSPs (y/n) (ZIP) (%) | 1.32 | <0.0005 | 1.11 | 0.32 | 1.16 | 0.23 | 1.60 | 0.001 | – |
| Percent of residents living in a medically underserved area (County) (%) | 1.10 | 0.37 | 0.99 | 0.95 | 1.15 | 0.03 | 1.32 | 0.03 | 24.30 |
| Percent of residents without health insurance (County) (%) | 0.88 | 0.14 | 0.88 | 0.20 | 1.06 | 0.40 | 0.83 | 0.01 | 8.70 |
| Per capita expenditures on health (MSA) (USD) | 1.09 | 0.44 | 1.09 | 0.43 | 0.97 | 0.52 | 1.08 | 0.29 | 170.02 |
| Arrest rate for hard drug possession, per 1000 | |||||||||
| County | 1.17 | 0.10 | 1.28 | 0.03 | 0.88 | 0.14 | 0.88 | 0.31 | 3.09 |
| MSA | 1.05 | 0.68 | 1.09 | 0.48 | 0.95 | 0.53 | 0.93 | 0.30 | 1.41 |
| Arrest rate for possession of any drug, per 1000 | |||||||||
| County | 1.24 | 0.02 | 1.26 | 0.02 | 0.95 | 0.51 | 1.01 | 0.93 | 6.04 |
| MSA | 1.21 | 0.06 | 1.23 | 0.06 | 0.98 | 0.74 | 0.96 | 0.59 | 2.82 |
| Jail incarceration rate, per 1000 (MSA) | |||||||||
| Overall | 0.99 | 0.96 | 0.95 | 0.65 | 1.10 | 0.09 | 0.91 | 0.32 | 0.16 |
| Black | 0.96 | 0.72 | 0.93 | 0.55 | 1.09 | 0.28 | 0.96 | 0.72 | 0.44 |
| White | 0.88 | 0.22 | 0.86 | 0.20 | 1.07 | 0.34 | 0.83 | 0.04 | 0.08 |
| Latino | 1.02 | 0.84 | 1.03 | 0.80 | 1.01 | 0.84 | 0.96 | 0.11 | 0.25 |
| Per capita expenditures on police (MSA) (USD) | 1.22 | 0.03 | 1.20 | 0.06 | 0.99 | 0.84 | 1.11 | 0.15 | 95.0 |
| Per capita expenditures on corrections (MSA) (USD) | 1.15 | 0.17 | 1.03 | 0.80 | 1.15 | 0.01 | 1.38 | 0.001 | 44.0 |
| Principal component analysis (PCA) | |||||||||
| County | |||||||||
| Poor access to general healthcaree | 0.96 | 0.66 | 0.91 | 0.40 | 1.15 | 0.04 | 0.88 | 0.19 | – |
| Criminal justice componentf | 1.23 | 0.04 | 1.15 | 0.22 | 1.08 | 0.25 | 1.20 | 0.02 | – |
| HIV burden | |||||||||
| AIDS diagnosis per 1000 PWID (MSA) | 1.04 | 0.24 | 0.92 | 0.77 | 1.07 | 0.77 | 1.78 | 0.05 | 0.91 |
| AIDS-related mortality rates for PWID during the HAART era, (MSA) | 1.04 | 0.31 | 0.82 | 0.68 | 1.20 | 0.67 | 1.89 | 0.17 | 1.81 |
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 exposures with these indicator variables. All bivariate models were hierarchical generalized linear models with three levels (individual nested in ZIP code, ZIP code nested in county, and county nested in MSA) When independent variables are continuous, the odds ratio (OR) is calculated for a 1 standard deviation difference in that variable USD United States Dollar; MTPs Methadone Treatment Programs; SSPs Syringe Service Programs
Male:female sex ratios were initially categorized into 3 levels: equal sex ratios (commonly defined as ranging from 0.95–1.05), more males (>1.05), and more females (<0.95); equity was used as the reference category to assess whether sex ratios were imbalanced. There were, however, no MSAs that had sex ratios indicating more males, thus in the end the measure included only 2 categories: more females and equity
Component variables: black isolation; Latino isolation
Component variables: Median income; Percent in poverty; Percent unemployed; Percent of adults without a high-school degree/GED
We used gravity-based methods to estimate spatial access to drug- and HIV-related health services for PWID. The measure was created using a 3-mile radius around each ZIP code’s centroid. This method generates a unit-less measure, with higher values indicating better spatial access. Measures of spatial access to MTPs, SSPs, and HIV testing sites had many zero values, and so we dichotomized them (0 = no access vs. >0 = some spatial access, according to the measure)
Component variable: Percent of residents who are uninsured; Percent of residents living in a medically underserved area
Component variables: Expenditures on policing per capita; Expenditures on corrections per capita; Hard drug arrest rates, per 1000 adults