International Journal of Drug Policy - 2014

Volume 25 Issue 3 May 2014

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W.M. Wechsberg et al. / International Journal of Drug Policy 25 (2014) 583–590 585 women had to report being in the relationship with their current partner for at least 12 months. Both partners had to report that they planned to stay together for at least another year and they were not planning to conceive a child within the next year. If both partners met the study eligibility criteria and were interested in participat- ing in the study, they were given an appointment for a baseline interview. Data collection and assessment At the baseline appointment, participants were rescreened and asked to provide informed consent to participate in the study. Once enrolled, an interviewer administered the baseline questionnaire using computer-assisted personal interviewing (CAPI) technol- ogy. The CAPI instrument was programmed in English; however, interviewers were fluent in Afrikaans and isiXhosa (indigenous lan- guages of South Africa) and could translate as needed. Field staff then conducted biological testing for recent AOD use as well as for HIV. Women were also tested for pregnancy. Participants were pro- vided with refreshments and a grocery voucher valued at ZAR 100 (USD 11.50) for their time. Measures The CAPI baseline questionnaire is a modified version of the Revised Risk Behavior Assessment (RRBA) (Wechsberg, 1998) adapted for use in earlier South African studies (Johnson, Carney, Kline, Browne, & Wechsberg, 2012; Wechsberg et al., 2008, 2012). We collected measures of socioeconomic status (age, mar- ital status, education, and employment) and living conditions including type of roof materials and whether the dwelling has electricity, or running water or both. We combined self-reported behaviour with biological drug measures to create drug use vari- ables. We used individual responses and combined responses from each member of the couple to create other "partner" covari- ates. For example, we controlled for the HIV serostatus of the participant's partner because the likelihood of being infected increases if one's partner is also infected. On the basis of the frequency of alcohol use and the number of drinks consumed when drinking, we created gender-specific variables to indi- cate abstinence from alcohol, light-moderate drinking, and heavy drinking. HIV testing Participants provided a finger stick blood sample for rapid HIV testing using the Unigold Rapid Test and the Determine Rapid Test. If either of these results were positive or indeterminate, a confirma- tory test (Reveal G3 Rapid HIV-1 Antibody Test l) was performed. A participant was classified as being HIV positive if any two of these tests were positive. Neighbourhood HIV prevalence The prevalence of HIV for each neighbourhood was computed as the number of HIV-positive participants divided by the total number enrolled from the given neighbourhood. This formula was also used to compute the prevalence of methamphetamine use. A categorical variable was created for HIV prevalence: low (0–10%), medium (11–20%) or high (21–50%). As methamphetamine use was low across most neighbourhoods, with many neighbourhoods having a prevalence of 0%, a dichotomous variable was created to indicate any neighbourhood methamphetamine use (preva- lence ≥ 1%) versus no methamphetamine use (prevalence < 1%). Analyses We conducted descriptive analyses and used chi-square and t-tests to identify gender differences for sociodemograpic char- acteristics and risk behaviours. The geospatial autocorrelation of HIV prevalence was modelled among shebeens within neighbour- hoods and across neighbourhoods using the GIS-mapped longitude and latitude data. A spatial exponential covariance structure was used to capture neighbourhood-level nesting that took into account (a) similarity of individuals who were nested within neighbour- hoods/shebeens and (b) greater similarity among individuals who were in shebeens that were in greater geospatial proximity to each other. We used Poisson regression to estimate prevalence odds ratios (POR) and 95% confidence intervals (95% CI) to identify factors asso- ciated with HIV infection. The regression analyses were stratified by gender and incorporated characteristics of participants, their partner, and neighbourhood. Bivariate regressions were clustered by neighbourhood to account for the correlation introduced using HIV prevalence and methamphetamine use. For the multivariable analysis, we used random effects Poisson regression to adjust for the correlation of HIV prevalence across neighbourhoods due to geospatial proximity. The multivariable models included the same variables for each gender to allow for direct comparison. All sta- tistical analyses were conducted using Stata Version 12 (College Station, TX) and SAS Ver 9.3 (Cary, NC). Results Outreach staff screened 363 couples of which 337 met eligi- bility and 300 couples enrolled. The sample therefore included 600 individuals (300 men and 300 women). This analysis includes 580 individuals among 290 partnerships because we subsequently chose to exclude two neighbourhoods that were outside the boundaries of Khayelitsha. Four males did not complete baseline interviews. Sample description The average participant was approximately 25 years old, with the typical female participant being two years younger than the typical male participant (24 vs. 26 years) (See Table 1). Only about one quarter of the sample had completed high school, with a slightly greater proportion of males (28%) completing high school than females (26%). Significantly more females were unemployed than males (80% vs. 69%; p < 0.01). About 50% of participants lived in makeshift dwellings (made of cardboard, sheet metal or wood), 46% had access to running water in their homes, and 85% had access to electricity. There were no differences in access to these basic services and resources between men and women. One fifth (20%) of the sample was HIV-infected, with twice as many females (26%) infected with HIV as males (13%; p < 0.001). Significantly more males than females reported having multiple sex partners (36% vs. 10%; p < 0.001). A quarter of all participants reported using drugs while having sex, with significantly more males (31%) reporting this behaviour than females (20%; p < 0.01). On the basis of self-reported use and biological drug screening, alcohol, methamphetamine, and other drug use was more preva- lent among males than females (See Table 1). In the overall sample, half of the participants reported drinking at levels consistent with alcohol abuse. Compared with females (31%), a significantly larger proportion of males in the sample reported abusing alcohol (70%; p < 0.001). In comparison to other drugs, more partici- pants used cannabis (23%) than any other drug, and significantly more males (37%) than females used this drug (10%; p < 0.001).

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