International Journal of Drug Policy - 2014

Volume 25 Issue 3 May 2014

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586 W.M. Wechsberg et al. / International Journal of Drug Policy 25 (2014) 583–590 Table 1 Sample characteristics, Western Cape Couples' Health CoOp, Khayelitsha Western Cape Province, South Africa, 2010–2012. Female Male Total p Chi-sq/t n* (%) n a (%) n (%) HIV-infected 76 (26.2) 38 (13.2) 114 (19.7) 0.000 15.45 Sociodemographic descriptors Age in years at baseline (SD) 24.19 (5.10) 26.06 (4.77) 25.11 (5.02) 0.000 Completed high school (%) b 74 (25.5) 79 (27.6) 153 (26.6) 0.570 0.327 Unemployed (%) 232 (80.0) 201 (69.3) 433 (74.7) 0.003 8.757 Walls 0.617 0.250 Bags-cardboard-metal-wood 148 (51.0) 140 (49.0) 288 (50.0) Bricks-tiles-cement 142 (49.0) 146 (51.0) 288 (50.0) Running water 126 (43.4) 141 (49.3) 267 (46.4) 0.159 1.983 Electricity 254 (87.6) 233 (81.5) 487 (84.5) Marital status 0.453 0.564 Not married 211 (72.8) 203 (70.0) 414 (71.4) Married-cohabitating 79 (27.2) 87 (30.0) 166 (28.6) Alcohol use 0.000 154.0 Abstinence 112 (38.6) 0 (0.0) 112 (19.4) Light/moderate 89 (30.7) 87 (30.4) 176 (30.6) Frequent binger/abuse 89 (30.7) 199 (69.6) 288 (50.0) Cannabis use c 28 (9.7) 108 (37.2) 136 (23.4) 0.000 61.5 Number of days used cannabis in last 30 days 8.83 (9.6) 13.64 (11.5) 12.65 (0.90) 0.02 Methamphetamine use c 11 (3.8) 43 (14.8) 54 (9.3) 0.000 20.9 Opiate use c 1 (0.3) 3 (1.0) 4 (0.7) 0.316 1.00 Mandrax use c 10 (3.4) 36 (12.4) 46 (7.9) 0.000 16.0 Crack/cocaine use c 0 (0.0) 6 (2.1) 6 (1.0) 0.014 6.06 Sexual risk factors Multiple partners in the past 3 months d 30 (10.4) 102 (35.7) 132 (23.0) 0.000 51.7 Used drugs during sex c 58 (20.0) 90 (31.0) 148 (25.5) 0.002 9.29 Ever engaged in transactional sex (woman selling sex/men buying sex) c 5 (1.7) 9 (3.1) 14 (2.4) 0.279 1.17 Casual partners in the past 6 months b 26 (9.0) 61 (21.3) 87 (15.1) 0.000 17.2 Ever treated for an STD 56 (19.3) 60 (21.3) 116 (20.3) 0.056 0.35 *n = 290 for female sample unless otherwise noted. a n = 288 for male sample. b n = 286 for male sample. c n = 290 for male sample. d n = 288 for females, n = 286 for males. Although methamphetamine use was low overall (9%), a signif- icantly greater proportion of males (15%) used this drug than females (4%). Similarly, although Mandrax use was low overall (8%), compared with females a significantly greater proportion of males tested positive for the use of this drug (3% vs 12%; p < 0.0001). Biological testing for opiate and crack/cocaine use indi- cated that less than 1% of all participants used any one of these substances. Neighbourhood prevalence of HIV and methamphetamine use The number of couples recruited per neighbourhood ranged from 6 to 13 (mean 9.5). HIV was present in 28 of the 30 neigh- bourhoods, and prevalence ranged from 0.0% to 46.0% (mean 19%; median 17%). Methamphetamine use was present in 20 of the 30 neighbourhoods, and prevalence ranged from 0% to 46% (mean 10%; median 6%). Fig. 1 depicts, the geographic distributions of HIV prevalence (Fig. 1a) and methamphetamine use preva- lence separately (Fig. 1b) and together with both overlaid in the same map (Fig. 1c). Most of the neighbourhoods with the high- est HIV prevalence were concentrated in the northern portion of Khayelitsha, which includes informal settlements. HIV tended to be lower in the southern portion, which has paved streets, tra- ditional houses and running water. In contrast, neighbourhoods with methamphetamine use appeared to be distributed equally across the northern and southern portions of the township. Over- all, there was no clear visual or statistical correlation (r = 0.000, p = 0.999) between neighbourhood HIV prevalence and neighbour- hood methamphetamine use. Correlates of HIV infection In bivariate analyses (see Table 2), being married or cohabitat- ing was associated with HIV infection in both men and women, however, the effect estimate was substantially higher among men (POR = 3.21, 95% CI [1.62, 6.37]) than women (POR = 1.56, 95% CI [1.01, 2.41]). Likewise, increasing age was positively associated with HIV (men: POR = 1.23 [1.13, 1.34]; women: POR = 1.09 [1.06, 1.13]). In addition, having an HIV-positive partner increased the likelihood of infection for both genders; the effect estimate was much larger among women (POR = 6.10 [2.97, 12.55]) than men (POR = 3.45 [2.38, 5.00]). Whereas none of the characteristics of participants' dwellings (i.e. roof materials, running water, and elec- tricity) were associated with HIV infection among women, men were half as likely to be infected if their dwelling had electricity (POR = 0.47 [0.24, 0.94]). Although Mandrax use among women and having multiple sex partners among men seemed to be negatively correlated with HIV, they are likely an artefact of very low cell sizes and thus suspect to interpretation. The multivariable analysis was conducted with and without the four male participants who did not complete baseline interviews and their female partners to assess whether this changed effect estimates to such an extent that they altered inference; exclusion did not change findings. In the multivariable analysis, some correlates of HIV infection were similar for males and females; however, important gender differences emerged (see Table 3). Adjusting for the geospatial cor- relation as a fixed effect increased the standard errors of prevalence odds ratios that had been associated with HIV in the bivariate

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