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Covariates of condom use in South Africa: findings from a national population-based survey in 2008 ab
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Leickness C. Simbayi , Gladys Matseke , Njeri Wabiri , Nolusindiso Ncitakalo , Mercy d
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Banyini , Cily Tabane & Dynah Tshebetshebe a
HIV/AIDS, STIs & TB, Human Sciences Research Council of South Africa, Cape Town, South Africa b
Department of Psychiatry & Mental Health, University of Cape Town, Cape Town, South Africa c
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HIV/AIDS, STIs & TB, Human Sciences Research Council of South Africa, Pretoria, South Africa d
Department of Psychology, University of Venda, Thohoyandou, South Africa
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Department of Social Work, University of the Witwatersrand, Johannesburg, South Africa
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Energy Policy and Planning Branch, Department of Energy, Pretoria, South Africa Published online: 31 Mar 2014.
To cite this article: Leickness C. Simbayi, Gladys Matseke, Njeri Wabiri, Nolusindiso Ncitakalo, Mercy Banyini, Cily Tabane & Dynah Tshebetshebe (2014) Covariates of condom use in South Africa: findings from a national populationbased survey in 2008, AIDS Care: Psychological and Socio-medical Aspects of AIDS/HIV, 26:10, 1263-1269, DOI: 10.1080/09540121.2014.902419 To link to this article: http://dx.doi.org/10.1080/09540121.2014.902419
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AIDS Care, 2014 Vol. 26, No. 10, 1263–1269, http://dx.doi.org/10.1080/09540121.2014.902419
Covariates of condom use in South Africa: findings from a national population-based survey in 2008 Leickness C. Simbayia,b*, Gladys Matsekec, Njeri Wabiric, Nolusindiso Ncitakaloa, Mercy Banyinid, Cily Tabanee and Dynah Tshebetshebef a
HIV/AIDS, STIs & TB, Human Sciences Research Council of South Africa, Cape Town, South Africa; bDepartment of Psychiatry & Mental Health, University of Cape Town, Cape Town, South Africa; cHIV/AIDS, STIs & TB, Human Sciences Research Council of South Africa, Pretoria, South Africa; dDepartment of Psychology, University of Venda, Thohoyandou, South Africa; eDepartment of Social Work, University of the Witwatersrand, Johannesburg, South Africa; fEnergy Policy and Planning Branch, Department of Energy, Pretoria, South Africa
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(Received 22 February 2013; accepted 4 March 2014) Condom use has increased significantly over the past decade among all adult age groups in South Africa, and it is widely believed to have played a major role in the recent significant decline in HIV incidence in the country, especially among young people. This study investigated the demographic, behavioural and psychosocial correlates of condom use at last sex among a national random probability sample of sexually experienced respondents aged 15 years and older (n = 7817, 42.9% males and 57.1% females) using data from the 2008 South African national HIV population-based household survey. Multivariate logistic regression analyses revealed that for both sexes, being aged 15–24 years and 25–49 years old, Black African, never married and unemployed were significantly associated with condom use at last sex. In addition, for males, condom use was associated with having had two or more sexual partners, whereas for females it was associated with living in urban formal, urban informal and rural informal areas, and having been in a current relationship for less than a year. Based on these findings, it was concluded that there is a need to further promote condom use especially among the subgroups of people with lower rates of condom use in order to reduce their risk of HIV infection. Keywords: condom use; covariates; HIV prevention; South Africa
Introduction The use of condoms both correctly and consistently reduces the risk of STI/HIV transmission by over 90% (UNAIDS, 2013). South Africa, the country with the largest burden of HIV/AIDS in the world with an estimated 5.6 million of its citizens living with HIV/ AIDS (UNAIDS, 2013), boasts one of the largest condom distribution programmes across the globe (SANAC, 2011). Consequently, it is not surprising that the rates of condom use have increased tremendously over the past decade, especially among the youth (Beksinska, Smit, & Mantell, 2012; UNAIDS, 2013). Importantly, condom use is believed to be partly responsible for the recent decline in HIV incidence in the country, especially among youth aged 15–24 years (UNAIDS, 2013). Although there have been many studies on condom use in South Africa, many of them are now dated (e.g., Hendriksen, Pettifor, Lee, Coates, & Rees, 2007; Lane, 2004; Shisana & Simbayi, 2002; Shisana et al., 2005), while more recent studies are of relatively smaller scale (e.g., Hargreaves et al., 2009) or limited in geographic scope (e.g., Chimbindi, McGrath, Herbst, *Corresponding author. Email: [email protected] © 2014 Taylor & Francis
Tint, & Newell, 2010). This paper presents findings from an investigation into demographic, behavioural and psychosocial correlates of condom use at last sex among people aged 15 years and older nationally in South Africa who were sexually active during the previous 12 months prior to the 2008 national population-based survey.
Methodology Secondary data analysis was performed using data from the third cross-sectional national HIV population-based household survey conducted in 2008 (see Shisana et al., 2009 for more details). A complex (multistage stratified cluster) national probability sample of respondents was employed with a design effect of two. The second-generation HIV surveillance approach was used to allow for the linking of HIV sero-status and behaviour (UNAIDS/WHO Working Group on Global HIV/AIDS and STI Surveillance, 2013). Appropriate ethical approval was obtained from the Human Sciences Research Council’s (HSRC) Research Ethics Committee (REC 2/23/10/07). Weighted data for respondents aged 15 years and older were analysed using STATA Version 10 to account for
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the complex multilevel sampling design. Data were disaggregated by sex because of different dynamics involved when making decisions on condom use (Leclerc-Madlala, Simbayi, & Cloete, 2009). Analyses first used the chisquared test statistics to determine the relationship between condom use and various demographic, behavioural and psychosocial factors. Then, both univariate and multivariate logistic regression methods were conducted to determine significance of selected variables using a p-value of < 0.05.
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Results Table 1 presents the distribution of condom use by demographic characteristics of 7817 respondents who reported that they had had sexual intercourse (defined as penetrative vaginal or anal sex) during the previous 12 months (out of 13,839 respondents and 9635 of whom reported having ever had sexual intercourse). The majority of the respondents was female (57.1%), aged 25–49 years (57.1%), Black African (60.5%), married (49.6%) and lived in urban formal areas (60.7%). The largest proportion of the respondents had attained Grades 10–12 education (51.3%), while 37.1% were employed. Overall, 41.7% of the sample reported condom use during last sexual encounter (“Did you use a condom the last time you had sex?” Yes/No). A further examination of Table 1 shows that reported levels of condom use significantly varied by age, race, locality type, marital status, education, employment status and province (all p-values < 0.001). Table 2 presents the prevalence of condom use in relation to different risk behaviours. It shows that condom use was higher or highest for both sexes among those that reported that they had had two sexual partners (73.1% vs. 65.4% for males and females, respectively) and three or more sexual partners (60.8% vs. 59.3%) during the past 12 months, had two or more regular (72.7% vs. 59.8%) and non-regular (56.7% vs. 56.8%) sexual partners, had a number of sexual partners who were commercial sex workers (68.7% vs. 29.8%), had recent partners who had other sexual partners (64.2% vs. 49.0%) and had a current relationship that was less than a year old (74.3% vs. 69.4%). Table 3 shows the results of both univariate logistic regression analyses that identify some individual significant correlates of condom use separately for the two sexes. When significant variables were entered into a multivariate regression analysis (see Table 4), the following factors were found to be significantly associated with condom use for both sexes: being youth aged 15–24 years and adult 25–49 years, Black African, never married, less educated and unemployed. For males only, having had two or more regular sexual partners was found to be significantly associated with condom use,
whereas living in urban formal, urban informal and rural informal areas and having had been in a current relationship for less than a year were significant for females only. Discussion This study found that several demographic and behavioural factors, but not psychosocial ones, were significantly associated with condom use. This is partly in line with predictions by the Integrated Model of behaviour change by Fishbein (2000). First, youth and adults of both sexes reported significantly higher levels of condom use when compared to the elderly. These findings are consistent with those from previous studies (e.g., Hargreaves et al., 2009; Rahamefy et al., 2008). This likely reflects the national successes of a large-scale condom promotion and distribution system as well as HIV communication programmes that have encouraged younger people to internalise risk (SANAC, 2011). Consequently, younger people are more likely to practice safer sex by using condoms. Second, large differences in the rates of condom use were found between Black Africans and the other race groups of both sexes as well as among females living in urban formal, urban informal and rural informal areas compared to those from rural formal areas. These findings were expected as Black Africans, who form the largest racial group nationally and also mostly live in the three locality types identified above, have a HIV prevalence that is 17 times higher than that found among both whites and Indians (Shisana et al., 2009). The lower rates of condom use by female respondents from rural formal areas are most probably due to the both low supply and low demand in rural formal areas (Exavery et al., 2012; SANAC, 2011). Third, the study found that lower socio-economic status (SES) as measured by both low education and unemployment was associated with higher condom use. These findings contradict previous research findings by Chimbindi et al. (2010) in rural KwaZulu-Natal in South Africa and in other countries (e.g., Baker, Leon, & Collins, 2011) which show the opposite relationships which were explained by the belief that poverty leads to risk behaviour such as early sexual debut and less frequent use of condoms which puts poor people at risk of HIV infection (APA, n.d.; Davidoff-Gore, Luke, & Wawire, 2011). However, as HIV prevalence is highest among those who are poor and unemployed with low education and predominantly Black African, it is, therefore, not surprising that the poor also tend to use condoms more in order to protect themselves. Fourth, condom use was found to be significantly higher among males who had more than one regular sex
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Table 1. Demographic characteristics of sexually active respondents who self-reported that they had used condoms during their last sexual encounter by sex, South Africa 2008. Males (3351)
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Variable Age 15–24 25–49 50 + Total Race Black African White Coloured Indian Total Locality type Urban formal Urban informal Rural informal Rural formal Total Marital status Single Married Living together/not married Widower/divorced/separated Total Highest education qualification No schooling Grades 3–9 Grades 10–12 Diploma with Grade 12 Bachelors and postgraduate Total Employment status Housewife/homemaker Unemployed Informal sector Pensioner Sick/disabled Student learner Self-employed Employed (formal) Total Province Western Cape Eastern Cape Northern Cape Free State KwaZulu-Natal Gauteng Mpumalanga Limpopo Total
Females (4466)
Total
%
95% CI
%
95% CI
%
95% CI
84.3 40.7 14.1 45.2
81.0–87.1 37.2–44.3 10.8–18.3 42.6–48.0
63.1 37.8 6.4 38.8
58.8–67.2 34.8–40.9 4.1–9.8 36.4–41.2
73.4 39.0 10.8 41.7
70.5–76.1 36.6–41.5 8.6–13.4 39.8–43.7
52.9 20.7 23.8 28.2 45.3
49.8–56.1 15.6–27.0 19.8–28.3 20.1–38.0 42.6–48.0
45.6 11.9 19.1 10.2 38.8
42.9–48.3 8.3–16.7 15.1–23.8 6.1–16.7 36.4–41.2
48.9 16.2 21.6 18.2 41.8
46.7–51.0 13.0–20.0 18.9–24.6 13.2–24.6 39.9–43.7
43.5 46.4 52.9 33.2 45.2
39.8–47.1 39.6–53.4 47.5–58.3 23.3–44.9 42.6–48.0
36.9 47.6 43.7 21.0 38.8
33.3–40.5 42.2–52.9 39.4–48.1 16.5–26.3 36.4–41.2
40.1 47.1 47.5 26.5 41.7
37.3–43.0 42.6–51.7 44.0–51.1 20.9–32.9 39.8–43.7
71.9 19.7 28.0 52.9 45.3
68.2–75.2 16.5–23.2 20.5–37.0 41.1–64.3 42.6–48.0
61.4 19 27.4 51.0 38.8
57.6–65.0 16.5–21.8 20.7–35.3 41.5–60.5 36.4–41.3
66.4 19.3 27.7 51.9 41.8
63.7–69.0 17.2–21.6 22.3–33.8 44.1–59.5 39.9–43.8
14.3 40.6 52.1 49.6 32.7 45.6
8.2–23.8 36.1–45.3 48.4–55.9 40.2–58.9 22.6–44.8 42.9–48.3
11.8 33.5 45.7 32.5 31.5 38.7
7.6–17.9 30.1–37.0 42.2–49.3 25.5–40.3 22.4–42.3 36.3–41.1
12.9 36.8 48.6 40.1 32.2 41.8
9.1–17.9 34.0–39.7 45.9–51.4 33.7–46.9 25.0–40.3 39.9–43.8
64.7 62.0 48.4 9.0 31.6 89.7 25.6 37.0 45.4
34.9–86.3 56.6–67.1 29.3–68.0 4.6–16.8 20.7–44.9 85.0–93.0 19.6–32.7 33.2–41.0 42.7–48.2
26.6 47.0 39.6 1.8 26.2 74.3 28.7 35.8 38.6
22.7–31.0 42.7–51.2 25.8–55.3 0.3–10.4 14.7–42.1 67.4–80.1 20.9–38.0 31.3–40.6 36.2–41.0
27.7 52.6 44.1 6.4 29.3 82.9 26.8 36.5 41.7
23.7–32.1 49.1–56.1 31.8–57.2 3.4–11.5 21.2–39.0 78.9–86.3 22.0–32.2 33.4–39.7 39.8–43.7
33.6 48.2 32.3 54.2 48.0 40.2 47.9 54.7 45.2
27.1–40.8 40.4–56.2 25.2–40.4 43.6–64.4 40.4–55.7 34.6–46.1 39.7–56.2 45.9–63.2 42.6–48.0
28.3 41.6 21.5 33.8 41.9 39.4 42.1 43.7 38.8
22.1–35.4 34.6–49.0 16.6–27.4 26.3–42.3 36.2–47.8 34.1–45.1 34.8–49.8 36.5–51.2 36.4–41.2
31.1 44.9 26.4 43.9 44.2 39.8 44.6 48.8 41.7
25.7–37.1 39.2–50.7 21.2–32.4 37.4–50.7 39.3–49.2 35.5–44.3 38.6–50.7 43.0–54.6 39.8–43.7
p
n
0.001 1875 4460 1482 7817 0.001 4718 998 1358 722 7796 0.001 4743 1000 1469 614 7817 0.001 2990 3856 508 425 7779 0.001 346 2445 3984 640 355 7770 0.001 1073 1778 114 264 200 616 748 2832 7625 0.001 1051 1015 658 491 1379 1288 623 694 7817
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Table 2. Self-reported risk behaviours of sexually active respondents who also self-reported that they had used condoms during last sexual encounter by sex, South Africa 2008.
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Variable Rate of alcohol use in past 12 months Never Once a month or less Two to four times a month Two to three times a week Four or more times a week Total Sex after drinking Always Sometimes Never Total HIV risk perception Low HIV risk High HIV risk Total Number of sex partners in the last 12 months One Two Three or more Total Number of regular partners One Two or more Total Number of non-regular partners One Two or more Total Sexual partners: commercial sex workers None One to less than 30 Total Recent sex partner had other sexual partners Yes No Total Duration of current relationship Less than a year More than a year Total Age of first sex Early sexual debut (9–15 years) Late sexual debut > 15years Total Knowledge of HIV status Yes No Total
Males (3351)
Females (4466)
Total (7817)
%
95% CI
%
95% CI
%
95% CI
45.6 49.7 43.3 37.8 37.2 44.2
36.4–55.1 43.5–55.9 37.9–48.9 29.4–47.1 27.8–47.7 40.6–47.9
30.3 40.8 26.2 23.5 29.0 32.7
23.0–38.6 33.8–48.1 19.2–34.8 13.9–36.9 12.3–54.5 28.5–37.3
37.0 46.1 38.6 34.5 34.9 40.1
31.0–43.4 41.1–51.2 34.1–43.2 27.5–42.2 25.8–45.1 37.0–43.2
33.3 46.2 43.5 44.3
20.9–48.6 40.7–51.9 38.8–48.3 40.6–48.0
23.9 31.0 33.9 32.8
8.8–50.5 24.0–38.9 28.8–39.4 28.5–37.4
31.3 41.7 39.6 40.2
20.8–44.2 36.9–46.5 36.0–43.3 37.1–43.3
48.8 43.9 45.2
44.0–53.8 40.8–47.1 42.5–48.0
40.9 37.0 38.4
37.1–44.8 34.0–40.0 36.0–40.9
43.9 40.4 41.6
40.8–47.1 38.1–42.8 39.6–43.5
40.4 73.1 60.8 44.9
37.6–43.3 65.2–79.7 51.3–69.5 42.2–47.7
38.1 65.4 59.3 38.9
35.7–40.6 47.9–79.6 38.0–77.7 36.5–41.4
39.1 71.5 60.6 41.7
37.1–41.1 64.2–77.7 51.9–68.7 39.7–43.6
43.7 72.7 45.3
40.9–46.4 62.2–81.2 42.6–48.0
38.6 59.8 38.6
36.2–41.0 32.2–82.3 36.2–41.1
40.8 71.7 41.7
38.9–42.8 61.8–79.9 39.8–43.7
44.9 56.7 45.4
42.1–47.7 45.6–67.1 42.6–48.2
38.6 56.8 38.7
36.2–41.0 33.5–77.5 36.3–41.1
41.4 56.7 41.8
39.4–43.4 46.4–66.5 39.8–43.7
45.3 68.7 45.5
42.5–48.1 45.0–85.5 42.7–48.3
38.9 29.8 38.8
36.4–41.4 14.6–51.4 36.4–41.3
41.8 40.7 41.9
39.8–43.8 30.6–64.0 39.9–43.9
64.2 38.8 42.4
56.5–71.2 35.6–42.1 39.4–45.4
49.0 31.9 36.2
43.7–54.4 29.0–34.9 33.6–38.9
54.3 35.4 39.2
49.7–58.7 33.1–37.8 37.1–41.4
74.3 39.4 45.1
67.9–79.8 36.5–42.3 42.4–47.9
69.4 36.0 38.8
62.9–75.2 33.6–38.5 36.4–41.2
72.5 37.5 41.7
67.9–76.6 35.5–39.6 39.7–43.7
49.7 46.5 47.0
43.2–56.2 43.4–49.7 44.3–49.8
40.5 39.6 39.7
34.3–47.0 37.0–42.3 37.3–42.2
45.5 42.6 43.0
40.9–50.2 40.5–44.8 41.0–45.0
42.2 34.4 41.8
38.4–46.1 22.6–48.4 38.1–45.6
42.2 29.2 41.7
39.3–45.2 17.2–45.1 38.8–44.8
42.2 31.7 41.8
39.7–44.8 22.8–42.1 39.2–44.3
p
n
n.s. 602 1149 1015 397 247 3410 n.s. 103 1200 2082 3385 n.s. 2134 5637 7771 0.001 7066 375 262 7703 0.001 7549 196 7745 0.001 7315 161 7476 0.001 7314 59 7373 0.001 1037 5184 6221 0.001 922 6806 7728 n.s. 895 6284 7179 n.s. 4421 224 4645
Table 3. Univariate regression analyses showing associations between various sociodemographic and behavioural variables and condom use at last sexual encounter by sex. Males
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Variable
p Value
96% CI
0.044 0.037 0.083
0.001 0.001 0.001
0.160–0.337 0.213–0.361 0.220–0.556
6.057 0.715
0.001 0.001
22.535–46.841 2.976–5.836
0.406 0.503 0.621
n.s. n.s. 0.01
0.923–2.586 0.989–3.069 1.316–3.872
0.013 0.116
0.001 0.001
0.081–0.131 0.262–0.736
0.714 0.044 0.023 1.308
n.s. 0.001 0.001 0.001
0.324–3.908 0.250–0.423 0.029–0.129 3.278–8.616
0.212 0.354 0.224
0.001 0.02 n.s.
1.454–2.292 1.081–2.512 0.475–1.399
0.048
0.001
0.235–0.427
0.073
0.001
0.177–0.476
0.514
0.001
1.980–4.041
0.038
0.001
0.162–0.312
0.345 0.371 0.284 0.282
n.s. 0.05 n.s. n.s.
0.841–2.253 1.035–2.540 0.805–1.95 0.552–1.719
1.34
n.s.
0.979–7.147
Unadjusted odds ratio (n = 4466) 0.161 0.281 0.136 (n = 4455) 25.033 8.883 (n = 4466) 2.199 3.418 2.929 (n = 4444) 0.158 0.656 (n = 4287) 0.41 0.594 0.021 3.255 (n = 4444) 1.905 1.087 1.041 (n = 4405) 0.352 (n = 4426) 0.422 (n = 3415) 2.057 (n = 4413) 0.248 (n = 1379) 1.411 2.239 1.157 1.33 (n = 4221) 0.668
SE
p Value
96% CI
0.034 0.043 0.04
0.001 0.001 0.001
0.106–0.244 0.208–0.380 0.076–0.24
6.249 2.128
0.001 0.001
15.338–40.858 5.551–14.213
0.376 0.64 0.517
0.001 0.001 0.001
1.572–3.077 2.367–4.935 2.072–4.141
0.018 0.139
0.001 0.05
0.126–0.198 0.433–0.994
0.054 0.073 0.02 0.609
0.001 0.001 0.001 0.001
0.317–0.530 0.467–0.757 0.003–0.132 2.255–4.701
0.196 0.207 0.261
0.001 n.s. n.s.
1.557–2.330 0.749–1.578 0.636–1.704
0.101
0.001
0.200–0.619
0.246
n.s.
0.134–1.327
0.261
0.001
1.603–2.639
0.038
0.001
0.184–0.335
0.531 0.841 0.452 0.832
n.s. 0.05 n.s. n.s.
0.674–2.956 1.071–4.682 0.537–2.492 0.389–4.540
0.314
n.s.
0.266–1.680
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(n = 3351) 0.233 0.278 0.349 (n = 3341) 32.489 4.168 (n = 3351) 1.545 1.742 2.258 (n = 3335) 0.103 0.439 (n = 3138) 1.124 0.325 0.061 5.314 (n = 3326) 1.826 1.648 0.815 (n = 3298) 0.317 (n = 3319) 0.291 (n = 2806) 2.829 (n = 3315) 0.225 (n = 2031) 1.377 1.622 1.255 0.974 (n = 3152) 2.646
SE
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Race White vs. Black African Coloured vs. Black African Indian vs. Black African Age (Years) 15–24 vs. 50 + 25–49 vs. 50 + Locality Urban formal vs. rural formal Urban informal vs. rural formal Rural informal vs. rural formal Marital status Married vs. never married Widower/divorced/separated vs. never married Employment Housewife/homeworker vs. unemployed Pensioner vs. unemployed Employed vs. unemployed Student learner vs. unemployed Education No schooling vs. tertiary Primary vs. tertiary Secondary vs. tertiary Number of sexual partners One vs. two or more Regular partners One vs. two or more Recent partner had other partners Yes vs. no Duration of current relationship More than a year vs. less than a year Alcohol use No alcohol vs. two to three times a week Once a month or less vs. two to three times a week Two to four times a month vs. two to three times a week Four or more times a week vs. two to three times a week Number of sexual partners (sexual workers) Yes vs. no
Unadjusted odds ratio
Females
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Table 4. Multivariate regression analyses showing associations between various sociodemographic and behavioural variables and condom use at last sexual encounter by sex. Males (n = 2509)
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Variable
Adjusted odds ratios
Age (years) 15–24 vs. 50 + 25–49 vs. 50 + Race White vs. Black African Coloured vs. Black African Indian vs. Black African Locality type Urban formal vs rural formal Urban informal vs rural formal Rural informal vs rural formal Marital status Married vs. never married Widower/divorced/ separated vs. never married Highest education qualification No schooling vs. tertiary Grades 3–9 vs. tertiary Grades 10–12 vs. tertiary Employment Housewife/homeworker vs. unemployed Pensioner vs. unemployed Employed vs. unemployed Student learner vs. unemployed Number of sex partners in the last 12
Females (n = 3177)
SE
p Value
95% CI
Adjusted odds ratios
SE
p Value
95% CI
6.959 2.622
2.341 0.618
0.001 0.001
3.596–3.467 1.650–4.164
7.935 6.869
2.887 2.120
0.001 0.001
3.88–16.204 3.749–12.587
0.276 0.309 0.616
0.074 0.068 0.183
0.001 0.001 n.s.
0.163–0.468 0.201–0.475 0.344–1.103
0.191 0.237 0.161
0.056 0.046 0.057
0.001 0.001 0.001
0.107–0.341 0.162–0.348 0.081–0.321
1.369 1.087
0.378 0.334
n.s. n.s.
0.796–2.353 0.595–1.987
1.569 1.719
0.364 0.429
0.05 0.05
0.995–2.474 1.054–2.805
1.488
0.472
n.s.
0.799–2.774
1.612
0.391
0.05
1.002–2.595
0.298 1.088
0.055 0.351
0.001 n.s.
0.208–0.427 0.577–2.050
0.269 1.210
0.047 0.427
0.001 n.s.
0.191–0.378 0.605–2.418
1.516 1.881 1.845
0.269 0.594 0.681
0.05 0.05 n.s.
1.071–2.146 1.011–3.497 0.894–3.808
1.451 1.321 1.894
0.235 0.376 0.575
0.05 n.s. 0.05
1.057–1.993 0.755–2.311 1.043–3.437
0.466
0.448
n.s.
0.071–3.072
1.456
0.263
0.05
1.022–2.075
0.605 0.271 1.426
0.120 0.143 n.s.
0.01 0.01 n.s.
0.410–0.893 0.096–0.762 0.691–2.945
1.297 0.421 1.674
0.239 0.454 0.425
n.s. n.s. 0.05
0.903–1.863 0.050–3.506 1.017–2.755
months 1.344
0.298
n.s.
0.869–2.077
0.932
0.366
n.s.
0.431–2.014
n.s.
0.05
0.205–0.851
0.711
0.526
n.s.
0.166–3.039
n.s.
n.s.
0.892–2.211
1.053
0.163
n.s.
0.776–1.427
n.s.
0.000
0.277–0.690
0.488
0.114
0.05
0.308–0.771
Regular partners One vs. two or more 0.418 Recent sex partner had other sexual partners 1.405 Duration of current relationship More than a year vs. less than 0.437 a year
partner and among females in new relationships of less than a year. Both these findings are consistent with those from other previous local and international studies (Chimbindi et al., 2010; Hendriksen et al., 2007; Yotebieng, Halperin, Mitchell, & Adimoru, 2009) and several Demographic and Health Surveys (e.g., Uganda Bureau of Statistics (UBOS) & ICF International Inc., 2012; Zimbabwe National Statistics Agency (ZIMSTAT) & ICF International, 2012; although cf. Central Statistical Agency (Ethiopia) and ICF International, 2012). Indeed,
lower condom use generally appears to be the trademark of stable and durable relationships, including marriage, thus putting married couples at risk of HIV infection, especially if one partner is engaged in multiple sexual relationships (see Dhalla & Poole, 2009; Hargreaves et al., 2009; Yotebieng et al., 2009). Although this study was conducted using a nationally representative sample, there were several limitations. First, condom use was only measured for the last sex act but not also for consistency of use as is required.
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AIDS Care Second, the survey data are based on self-reporting which can be biased mainly due to social desirability. Third, the study was correlational, and consequently, causality cannot be inferred between the variables under study. In spite of these shortcomings, we believe that the findings are generally both valid and reliable and can be replicated in a similar survey. In conclusion, although condom use at last sexual encounter has improved tremendously in South Africa, this study demonstrates that further promotion of condom use is still needed, especially among population subgroups with lower rates of condom use. It is, therefore, important that HIV-prevention programme planners in South Africa develop appropriate health promotion strategies to encourage condom use among these subgroups. Acknowledgements The authors would like to thank the three anonymous reviewers for their helpful comments as well as Ms Meredith Evans for careful proofreading and editing through the manuscript.
Funding This research was supported by the President’s Emergency Plan for AIDS Relief (PEPFAR) through CDC under the terms of Cooperative Agreement Number [grant number U2GPS000570-05].
References APA. (n.d.). HIV/AIDS & socioeconomic status. Retrieved September 7, 2013, from http://www.apa.org/pi/ses/resources/ publications/factsheet-hiv-aids.aspx Baker, D. P., Leon, J., & Collins, J. M. (2011). Facts, attitudes, and health reasoning about HIV and AIDS: Explaining the education effect on condom use among adults in subSaharan Africa. AIDS & Behaviour, 15, 1319–1327. doi:10.1007/s10461-010-9717-9 Beksinska, M. E., Smit, J. A., & Mantell, J. E. (2012). Progress and challenges to male and female condom use in South Africa. Sexual Health, 9, 51–58. doi:10.1071/SH11011 Central Statistical Agency (Ethiopia) & ICF International. (2012). Ethiopia demographic and health survey 2011. Addis Ababa and Calverton, MD: Author. Chimbindi, N. Z., McGrath, N., Herbst, K., Tint, K. S., & Newell, M. L. (2010). Socio-demographic determinants of condom use among sexually active young adults in rural KwaZulu-Natal, South Africa. The Open AIDS Journal, 4, 88–95. doi:10.2174/1874613601004010088 Davidoff-Gore, A., Luke, N., & Wawire, S. (2011). Dimensions of poverty and inconsistent condom use among youth in urban Kenya. AIDS Care, 23, 1282–1290. doi:10.1080/ 09540121.2011.555744 Dhalla, S., & Poole, G. (2009). Determinants of condom use: Results of the Canadian community health survey. Canadian Journal of Public Health, 100, 299–303. Exavery, A., Mubyazi, G. M., Rugemalila, J., Mushi, A. K., Massaga, J. J., Malebo, H. M., … Malecela, M. N. (2012). Acceptability of condom promotion and distribution
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among 10–19 year-old adolescents in Mpwapwa and Mbeya rural districts, Tanzania. BMC Public Health, 12, 569. doi:10.1186/1471-2458-12-569 Fishbein, M. (2000). The role of theory in HIV prevention. AIDS Care, 12, 273–278. doi:10.1080/09540120050042918 Hargreaves, J. R., Morison, L. A., Kim, J. C., Busza, J., Phetla, G., Porter, J. D., … Pronyk, P. M. (2009). Characteristics of sexual partnerships, not just of individuals, are associated with condom use and recent HIV infection in rural South Africa. AIDS Care, 21, 1058–1070. doi:10.1080/ 09540120802657480 Hendriksen, E. S., Pettifor, A., Lee, S., Coates, T. J., & Rees, H. V. (2007). Predictors of condom use among young adults in South Africa: The reproductive health and HIV research unit national youth survey. American Journal of Public Health, 97, 1241–1247. doi:10.2105/ AJPH.2006.086009 Lane, T. (2004). In South Africa, Wives HIV prevention beliefs affect condom use with spouse. International Family Planning Perspectives, 30, 150–151. Leclerc-Madlala, S., Simbayi, L. C., & Cloete, A. (2009). The sociocultural aspects of HIV/AIDS in South Africa. (Chapter 2). In P. Rohleder, L. Swartz, S. Kalichman, & L. Simbayi (Eds.), HIV/AIDS in South Africa 25 years on: Psychosocial perspectives, (pp. 13–25). New York, NY: Springer. Rahamefy, O. H., Rivard, M., Ravaoarinoro, M., Ranaivoharisoa, L., Rasamindrakotroka, A. J., & Morisset, R. (2008). Sexual behaviour and condom use among university students in Madagascar. Journal of Social Aspects of HIV/AIDS, 5(1), 28–35. doi:10.1080/17290376.2008.97 24899 SANAC. (2011). The HIV & AIDS, TB and STI strategic plan for South Africa 2012–2016. Pretoria: South African National AIDS Council. Shisana, O., Rehle, T., Simbayi, L. C., Parker, W., Zuma, K., Bhana, A., … Pillay, V. (2005). South African national HIV prevalence, incidence, behaviour and communication survey 2005. Cape Town: HSRC Press. Shisana, O., Rehle, T., Simbayi, L. C., Zuma, K., Jooste, S., Pillay-van-Wyk, V., … The SABSSM Implementation Team. (2009). South African national HIV prevalence, incidence, behaviour and communication survey, 2008: A turning tide among teenagers? Cape Town: HSRC Press. Shisana, O., & Simbayi, L. (2002). Nelson Mandela/HSRC study of HIV/AIDS: South African national HIV prevalence, behavioural risks and mass media household survey 2002. Cape Town: HSRC Press. Uganda Bureau of Statistics (UBOS) & ICF International Inc. (2012). Uganda demographic and health survey 2011. Kampala and Calverton, MD: Author. UNAIDS. (2013). UNAIDS report on the global AIDS epidemic 2013. Geneva: Author. UNAIDS/WHO Working Group on Global HIV/AIDS and STI Surveillance. (2013). Surveillance of the HIV/AIDS epidemic: A comprehensivepackage. Geneva: Author. Yotebieng, M., Halpern, C. T., Mitchell, E.M.H., & Adimora, A. (2009). Correlates of condom use among sexually experienced secondary school male students in Nairobi, Kenya. Journal of Social Aspects of HIV/AIDS, 6, 9–16. doi:10.1080/17290376.2009.9724924 Zimbabwe National Statistics Agency (ZIMSTAT) & ICF International. (2012). Zimbabwe demographic and health survey 2010–2011. Calverton, MD: Author.
AIDS Care: Psychological and Socio-medical Aspects of AIDS/HIV Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/caic20
Covariates of condom use in South Africa: findings from a national population-based survey in 2008 ab
c
c
a
Leickness C. Simbayi , Gladys Matseke , Njeri Wabiri , Nolusindiso Ncitakalo , Mercy d
e
f
Banyini , Cily Tabane & Dynah Tshebetshebe a
HIV/AIDS, STIs & TB, Human Sciences Research Council of South Africa, Cape Town, South Africa b
Department of Psychiatry & Mental Health, University of Cape Town, Cape Town, South Africa c
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HIV/AIDS, STIs & TB, Human Sciences Research Council of South Africa, Pretoria, South Africa d
Department of Psychology, University of Venda, Thohoyandou, South Africa
e
Department of Social Work, University of the Witwatersrand, Johannesburg, South Africa
f
Energy Policy and Planning Branch, Department of Energy, Pretoria, South Africa Published online: 31 Mar 2014.
To cite this article: Leickness C. Simbayi, Gladys Matseke, Njeri Wabiri, Nolusindiso Ncitakalo, Mercy Banyini, Cily Tabane & Dynah Tshebetshebe (2014) Covariates of condom use in South Africa: findings from a national populationbased survey in 2008, AIDS Care: Psychological and Socio-medical Aspects of AIDS/HIV, 26:10, 1263-1269, DOI: 10.1080/09540121.2014.902419 To link to this article: http://dx.doi.org/10.1080/09540121.2014.902419
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AIDS Care, 2014 Vol. 26, No. 10, 1263–1269, http://dx.doi.org/10.1080/09540121.2014.902419
Covariates of condom use in South Africa: findings from a national population-based survey in 2008 Leickness C. Simbayia,b*, Gladys Matsekec, Njeri Wabiric, Nolusindiso Ncitakaloa, Mercy Banyinid, Cily Tabanee and Dynah Tshebetshebef a
HIV/AIDS, STIs & TB, Human Sciences Research Council of South Africa, Cape Town, South Africa; bDepartment of Psychiatry & Mental Health, University of Cape Town, Cape Town, South Africa; cHIV/AIDS, STIs & TB, Human Sciences Research Council of South Africa, Pretoria, South Africa; dDepartment of Psychology, University of Venda, Thohoyandou, South Africa; eDepartment of Social Work, University of the Witwatersrand, Johannesburg, South Africa; fEnergy Policy and Planning Branch, Department of Energy, Pretoria, South Africa
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(Received 22 February 2013; accepted 4 March 2014) Condom use has increased significantly over the past decade among all adult age groups in South Africa, and it is widely believed to have played a major role in the recent significant decline in HIV incidence in the country, especially among young people. This study investigated the demographic, behavioural and psychosocial correlates of condom use at last sex among a national random probability sample of sexually experienced respondents aged 15 years and older (n = 7817, 42.9% males and 57.1% females) using data from the 2008 South African national HIV population-based household survey. Multivariate logistic regression analyses revealed that for both sexes, being aged 15–24 years and 25–49 years old, Black African, never married and unemployed were significantly associated with condom use at last sex. In addition, for males, condom use was associated with having had two or more sexual partners, whereas for females it was associated with living in urban formal, urban informal and rural informal areas, and having been in a current relationship for less than a year. Based on these findings, it was concluded that there is a need to further promote condom use especially among the subgroups of people with lower rates of condom use in order to reduce their risk of HIV infection. Keywords: condom use; covariates; HIV prevention; South Africa
Introduction The use of condoms both correctly and consistently reduces the risk of STI/HIV transmission by over 90% (UNAIDS, 2013). South Africa, the country with the largest burden of HIV/AIDS in the world with an estimated 5.6 million of its citizens living with HIV/ AIDS (UNAIDS, 2013), boasts one of the largest condom distribution programmes across the globe (SANAC, 2011). Consequently, it is not surprising that the rates of condom use have increased tremendously over the past decade, especially among the youth (Beksinska, Smit, & Mantell, 2012; UNAIDS, 2013). Importantly, condom use is believed to be partly responsible for the recent decline in HIV incidence in the country, especially among youth aged 15–24 years (UNAIDS, 2013). Although there have been many studies on condom use in South Africa, many of them are now dated (e.g., Hendriksen, Pettifor, Lee, Coates, & Rees, 2007; Lane, 2004; Shisana & Simbayi, 2002; Shisana et al., 2005), while more recent studies are of relatively smaller scale (e.g., Hargreaves et al., 2009) or limited in geographic scope (e.g., Chimbindi, McGrath, Herbst, *Corresponding author. Email: [email protected] © 2014 Taylor & Francis
Tint, & Newell, 2010). This paper presents findings from an investigation into demographic, behavioural and psychosocial correlates of condom use at last sex among people aged 15 years and older nationally in South Africa who were sexually active during the previous 12 months prior to the 2008 national population-based survey.
Methodology Secondary data analysis was performed using data from the third cross-sectional national HIV population-based household survey conducted in 2008 (see Shisana et al., 2009 for more details). A complex (multistage stratified cluster) national probability sample of respondents was employed with a design effect of two. The second-generation HIV surveillance approach was used to allow for the linking of HIV sero-status and behaviour (UNAIDS/WHO Working Group on Global HIV/AIDS and STI Surveillance, 2013). Appropriate ethical approval was obtained from the Human Sciences Research Council’s (HSRC) Research Ethics Committee (REC 2/23/10/07). Weighted data for respondents aged 15 years and older were analysed using STATA Version 10 to account for
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the complex multilevel sampling design. Data were disaggregated by sex because of different dynamics involved when making decisions on condom use (Leclerc-Madlala, Simbayi, & Cloete, 2009). Analyses first used the chisquared test statistics to determine the relationship between condom use and various demographic, behavioural and psychosocial factors. Then, both univariate and multivariate logistic regression methods were conducted to determine significance of selected variables using a p-value of < 0.05.
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Results Table 1 presents the distribution of condom use by demographic characteristics of 7817 respondents who reported that they had had sexual intercourse (defined as penetrative vaginal or anal sex) during the previous 12 months (out of 13,839 respondents and 9635 of whom reported having ever had sexual intercourse). The majority of the respondents was female (57.1%), aged 25–49 years (57.1%), Black African (60.5%), married (49.6%) and lived in urban formal areas (60.7%). The largest proportion of the respondents had attained Grades 10–12 education (51.3%), while 37.1% were employed. Overall, 41.7% of the sample reported condom use during last sexual encounter (“Did you use a condom the last time you had sex?” Yes/No). A further examination of Table 1 shows that reported levels of condom use significantly varied by age, race, locality type, marital status, education, employment status and province (all p-values < 0.001). Table 2 presents the prevalence of condom use in relation to different risk behaviours. It shows that condom use was higher or highest for both sexes among those that reported that they had had two sexual partners (73.1% vs. 65.4% for males and females, respectively) and three or more sexual partners (60.8% vs. 59.3%) during the past 12 months, had two or more regular (72.7% vs. 59.8%) and non-regular (56.7% vs. 56.8%) sexual partners, had a number of sexual partners who were commercial sex workers (68.7% vs. 29.8%), had recent partners who had other sexual partners (64.2% vs. 49.0%) and had a current relationship that was less than a year old (74.3% vs. 69.4%). Table 3 shows the results of both univariate logistic regression analyses that identify some individual significant correlates of condom use separately for the two sexes. When significant variables were entered into a multivariate regression analysis (see Table 4), the following factors were found to be significantly associated with condom use for both sexes: being youth aged 15–24 years and adult 25–49 years, Black African, never married, less educated and unemployed. For males only, having had two or more regular sexual partners was found to be significantly associated with condom use,
whereas living in urban formal, urban informal and rural informal areas and having had been in a current relationship for less than a year were significant for females only. Discussion This study found that several demographic and behavioural factors, but not psychosocial ones, were significantly associated with condom use. This is partly in line with predictions by the Integrated Model of behaviour change by Fishbein (2000). First, youth and adults of both sexes reported significantly higher levels of condom use when compared to the elderly. These findings are consistent with those from previous studies (e.g., Hargreaves et al., 2009; Rahamefy et al., 2008). This likely reflects the national successes of a large-scale condom promotion and distribution system as well as HIV communication programmes that have encouraged younger people to internalise risk (SANAC, 2011). Consequently, younger people are more likely to practice safer sex by using condoms. Second, large differences in the rates of condom use were found between Black Africans and the other race groups of both sexes as well as among females living in urban formal, urban informal and rural informal areas compared to those from rural formal areas. These findings were expected as Black Africans, who form the largest racial group nationally and also mostly live in the three locality types identified above, have a HIV prevalence that is 17 times higher than that found among both whites and Indians (Shisana et al., 2009). The lower rates of condom use by female respondents from rural formal areas are most probably due to the both low supply and low demand in rural formal areas (Exavery et al., 2012; SANAC, 2011). Third, the study found that lower socio-economic status (SES) as measured by both low education and unemployment was associated with higher condom use. These findings contradict previous research findings by Chimbindi et al. (2010) in rural KwaZulu-Natal in South Africa and in other countries (e.g., Baker, Leon, & Collins, 2011) which show the opposite relationships which were explained by the belief that poverty leads to risk behaviour such as early sexual debut and less frequent use of condoms which puts poor people at risk of HIV infection (APA, n.d.; Davidoff-Gore, Luke, & Wawire, 2011). However, as HIV prevalence is highest among those who are poor and unemployed with low education and predominantly Black African, it is, therefore, not surprising that the poor also tend to use condoms more in order to protect themselves. Fourth, condom use was found to be significantly higher among males who had more than one regular sex
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Table 1. Demographic characteristics of sexually active respondents who self-reported that they had used condoms during their last sexual encounter by sex, South Africa 2008. Males (3351)
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Variable Age 15–24 25–49 50 + Total Race Black African White Coloured Indian Total Locality type Urban formal Urban informal Rural informal Rural formal Total Marital status Single Married Living together/not married Widower/divorced/separated Total Highest education qualification No schooling Grades 3–9 Grades 10–12 Diploma with Grade 12 Bachelors and postgraduate Total Employment status Housewife/homemaker Unemployed Informal sector Pensioner Sick/disabled Student learner Self-employed Employed (formal) Total Province Western Cape Eastern Cape Northern Cape Free State KwaZulu-Natal Gauteng Mpumalanga Limpopo Total
Females (4466)
Total
%
95% CI
%
95% CI
%
95% CI
84.3 40.7 14.1 45.2
81.0–87.1 37.2–44.3 10.8–18.3 42.6–48.0
63.1 37.8 6.4 38.8
58.8–67.2 34.8–40.9 4.1–9.8 36.4–41.2
73.4 39.0 10.8 41.7
70.5–76.1 36.6–41.5 8.6–13.4 39.8–43.7
52.9 20.7 23.8 28.2 45.3
49.8–56.1 15.6–27.0 19.8–28.3 20.1–38.0 42.6–48.0
45.6 11.9 19.1 10.2 38.8
42.9–48.3 8.3–16.7 15.1–23.8 6.1–16.7 36.4–41.2
48.9 16.2 21.6 18.2 41.8
46.7–51.0 13.0–20.0 18.9–24.6 13.2–24.6 39.9–43.7
43.5 46.4 52.9 33.2 45.2
39.8–47.1 39.6–53.4 47.5–58.3 23.3–44.9 42.6–48.0
36.9 47.6 43.7 21.0 38.8
33.3–40.5 42.2–52.9 39.4–48.1 16.5–26.3 36.4–41.2
40.1 47.1 47.5 26.5 41.7
37.3–43.0 42.6–51.7 44.0–51.1 20.9–32.9 39.8–43.7
71.9 19.7 28.0 52.9 45.3
68.2–75.2 16.5–23.2 20.5–37.0 41.1–64.3 42.6–48.0
61.4 19 27.4 51.0 38.8
57.6–65.0 16.5–21.8 20.7–35.3 41.5–60.5 36.4–41.3
66.4 19.3 27.7 51.9 41.8
63.7–69.0 17.2–21.6 22.3–33.8 44.1–59.5 39.9–43.8
14.3 40.6 52.1 49.6 32.7 45.6
8.2–23.8 36.1–45.3 48.4–55.9 40.2–58.9 22.6–44.8 42.9–48.3
11.8 33.5 45.7 32.5 31.5 38.7
7.6–17.9 30.1–37.0 42.2–49.3 25.5–40.3 22.4–42.3 36.3–41.1
12.9 36.8 48.6 40.1 32.2 41.8
9.1–17.9 34.0–39.7 45.9–51.4 33.7–46.9 25.0–40.3 39.9–43.8
64.7 62.0 48.4 9.0 31.6 89.7 25.6 37.0 45.4
34.9–86.3 56.6–67.1 29.3–68.0 4.6–16.8 20.7–44.9 85.0–93.0 19.6–32.7 33.2–41.0 42.7–48.2
26.6 47.0 39.6 1.8 26.2 74.3 28.7 35.8 38.6
22.7–31.0 42.7–51.2 25.8–55.3 0.3–10.4 14.7–42.1 67.4–80.1 20.9–38.0 31.3–40.6 36.2–41.0
27.7 52.6 44.1 6.4 29.3 82.9 26.8 36.5 41.7
23.7–32.1 49.1–56.1 31.8–57.2 3.4–11.5 21.2–39.0 78.9–86.3 22.0–32.2 33.4–39.7 39.8–43.7
33.6 48.2 32.3 54.2 48.0 40.2 47.9 54.7 45.2
27.1–40.8 40.4–56.2 25.2–40.4 43.6–64.4 40.4–55.7 34.6–46.1 39.7–56.2 45.9–63.2 42.6–48.0
28.3 41.6 21.5 33.8 41.9 39.4 42.1 43.7 38.8
22.1–35.4 34.6–49.0 16.6–27.4 26.3–42.3 36.2–47.8 34.1–45.1 34.8–49.8 36.5–51.2 36.4–41.2
31.1 44.9 26.4 43.9 44.2 39.8 44.6 48.8 41.7
25.7–37.1 39.2–50.7 21.2–32.4 37.4–50.7 39.3–49.2 35.5–44.3 38.6–50.7 43.0–54.6 39.8–43.7
p
n
0.001 1875 4460 1482 7817 0.001 4718 998 1358 722 7796 0.001 4743 1000 1469 614 7817 0.001 2990 3856 508 425 7779 0.001 346 2445 3984 640 355 7770 0.001 1073 1778 114 264 200 616 748 2832 7625 0.001 1051 1015 658 491 1379 1288 623 694 7817
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Table 2. Self-reported risk behaviours of sexually active respondents who also self-reported that they had used condoms during last sexual encounter by sex, South Africa 2008.
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Variable Rate of alcohol use in past 12 months Never Once a month or less Two to four times a month Two to three times a week Four or more times a week Total Sex after drinking Always Sometimes Never Total HIV risk perception Low HIV risk High HIV risk Total Number of sex partners in the last 12 months One Two Three or more Total Number of regular partners One Two or more Total Number of non-regular partners One Two or more Total Sexual partners: commercial sex workers None One to less than 30 Total Recent sex partner had other sexual partners Yes No Total Duration of current relationship Less than a year More than a year Total Age of first sex Early sexual debut (9–15 years) Late sexual debut > 15years Total Knowledge of HIV status Yes No Total
Males (3351)
Females (4466)
Total (7817)
%
95% CI
%
95% CI
%
95% CI
45.6 49.7 43.3 37.8 37.2 44.2
36.4–55.1 43.5–55.9 37.9–48.9 29.4–47.1 27.8–47.7 40.6–47.9
30.3 40.8 26.2 23.5 29.0 32.7
23.0–38.6 33.8–48.1 19.2–34.8 13.9–36.9 12.3–54.5 28.5–37.3
37.0 46.1 38.6 34.5 34.9 40.1
31.0–43.4 41.1–51.2 34.1–43.2 27.5–42.2 25.8–45.1 37.0–43.2
33.3 46.2 43.5 44.3
20.9–48.6 40.7–51.9 38.8–48.3 40.6–48.0
23.9 31.0 33.9 32.8
8.8–50.5 24.0–38.9 28.8–39.4 28.5–37.4
31.3 41.7 39.6 40.2
20.8–44.2 36.9–46.5 36.0–43.3 37.1–43.3
48.8 43.9 45.2
44.0–53.8 40.8–47.1 42.5–48.0
40.9 37.0 38.4
37.1–44.8 34.0–40.0 36.0–40.9
43.9 40.4 41.6
40.8–47.1 38.1–42.8 39.6–43.5
40.4 73.1 60.8 44.9
37.6–43.3 65.2–79.7 51.3–69.5 42.2–47.7
38.1 65.4 59.3 38.9
35.7–40.6 47.9–79.6 38.0–77.7 36.5–41.4
39.1 71.5 60.6 41.7
37.1–41.1 64.2–77.7 51.9–68.7 39.7–43.6
43.7 72.7 45.3
40.9–46.4 62.2–81.2 42.6–48.0
38.6 59.8 38.6
36.2–41.0 32.2–82.3 36.2–41.1
40.8 71.7 41.7
38.9–42.8 61.8–79.9 39.8–43.7
44.9 56.7 45.4
42.1–47.7 45.6–67.1 42.6–48.2
38.6 56.8 38.7
36.2–41.0 33.5–77.5 36.3–41.1
41.4 56.7 41.8
39.4–43.4 46.4–66.5 39.8–43.7
45.3 68.7 45.5
42.5–48.1 45.0–85.5 42.7–48.3
38.9 29.8 38.8
36.4–41.4 14.6–51.4 36.4–41.3
41.8 40.7 41.9
39.8–43.8 30.6–64.0 39.9–43.9
64.2 38.8 42.4
56.5–71.2 35.6–42.1 39.4–45.4
49.0 31.9 36.2
43.7–54.4 29.0–34.9 33.6–38.9
54.3 35.4 39.2
49.7–58.7 33.1–37.8 37.1–41.4
74.3 39.4 45.1
67.9–79.8 36.5–42.3 42.4–47.9
69.4 36.0 38.8
62.9–75.2 33.6–38.5 36.4–41.2
72.5 37.5 41.7
67.9–76.6 35.5–39.6 39.7–43.7
49.7 46.5 47.0
43.2–56.2 43.4–49.7 44.3–49.8
40.5 39.6 39.7
34.3–47.0 37.0–42.3 37.3–42.2
45.5 42.6 43.0
40.9–50.2 40.5–44.8 41.0–45.0
42.2 34.4 41.8
38.4–46.1 22.6–48.4 38.1–45.6
42.2 29.2 41.7
39.3–45.2 17.2–45.1 38.8–44.8
42.2 31.7 41.8
39.7–44.8 22.8–42.1 39.2–44.3
p
n
n.s. 602 1149 1015 397 247 3410 n.s. 103 1200 2082 3385 n.s. 2134 5637 7771 0.001 7066 375 262 7703 0.001 7549 196 7745 0.001 7315 161 7476 0.001 7314 59 7373 0.001 1037 5184 6221 0.001 922 6806 7728 n.s. 895 6284 7179 n.s. 4421 224 4645
Table 3. Univariate regression analyses showing associations between various sociodemographic and behavioural variables and condom use at last sexual encounter by sex. Males
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Variable
p Value
96% CI
0.044 0.037 0.083
0.001 0.001 0.001
0.160–0.337 0.213–0.361 0.220–0.556
6.057 0.715
0.001 0.001
22.535–46.841 2.976–5.836
0.406 0.503 0.621
n.s. n.s. 0.01
0.923–2.586 0.989–3.069 1.316–3.872
0.013 0.116
0.001 0.001
0.081–0.131 0.262–0.736
0.714 0.044 0.023 1.308
n.s. 0.001 0.001 0.001
0.324–3.908 0.250–0.423 0.029–0.129 3.278–8.616
0.212 0.354 0.224
0.001 0.02 n.s.
1.454–2.292 1.081–2.512 0.475–1.399
0.048
0.001
0.235–0.427
0.073
0.001
0.177–0.476
0.514
0.001
1.980–4.041
0.038
0.001
0.162–0.312
0.345 0.371 0.284 0.282
n.s. 0.05 n.s. n.s.
0.841–2.253 1.035–2.540 0.805–1.95 0.552–1.719
1.34
n.s.
0.979–7.147
Unadjusted odds ratio (n = 4466) 0.161 0.281 0.136 (n = 4455) 25.033 8.883 (n = 4466) 2.199 3.418 2.929 (n = 4444) 0.158 0.656 (n = 4287) 0.41 0.594 0.021 3.255 (n = 4444) 1.905 1.087 1.041 (n = 4405) 0.352 (n = 4426) 0.422 (n = 3415) 2.057 (n = 4413) 0.248 (n = 1379) 1.411 2.239 1.157 1.33 (n = 4221) 0.668
SE
p Value
96% CI
0.034 0.043 0.04
0.001 0.001 0.001
0.106–0.244 0.208–0.380 0.076–0.24
6.249 2.128
0.001 0.001
15.338–40.858 5.551–14.213
0.376 0.64 0.517
0.001 0.001 0.001
1.572–3.077 2.367–4.935 2.072–4.141
0.018 0.139
0.001 0.05
0.126–0.198 0.433–0.994
0.054 0.073 0.02 0.609
0.001 0.001 0.001 0.001
0.317–0.530 0.467–0.757 0.003–0.132 2.255–4.701
0.196 0.207 0.261
0.001 n.s. n.s.
1.557–2.330 0.749–1.578 0.636–1.704
0.101
0.001
0.200–0.619
0.246
n.s.
0.134–1.327
0.261
0.001
1.603–2.639
0.038
0.001
0.184–0.335
0.531 0.841 0.452 0.832
n.s. 0.05 n.s. n.s.
0.674–2.956 1.071–4.682 0.537–2.492 0.389–4.540
0.314
n.s.
0.266–1.680
1267
(n = 3351) 0.233 0.278 0.349 (n = 3341) 32.489 4.168 (n = 3351) 1.545 1.742 2.258 (n = 3335) 0.103 0.439 (n = 3138) 1.124 0.325 0.061 5.314 (n = 3326) 1.826 1.648 0.815 (n = 3298) 0.317 (n = 3319) 0.291 (n = 2806) 2.829 (n = 3315) 0.225 (n = 2031) 1.377 1.622 1.255 0.974 (n = 3152) 2.646
SE
AIDS Care
Race White vs. Black African Coloured vs. Black African Indian vs. Black African Age (Years) 15–24 vs. 50 + 25–49 vs. 50 + Locality Urban formal vs. rural formal Urban informal vs. rural formal Rural informal vs. rural formal Marital status Married vs. never married Widower/divorced/separated vs. never married Employment Housewife/homeworker vs. unemployed Pensioner vs. unemployed Employed vs. unemployed Student learner vs. unemployed Education No schooling vs. tertiary Primary vs. tertiary Secondary vs. tertiary Number of sexual partners One vs. two or more Regular partners One vs. two or more Recent partner had other partners Yes vs. no Duration of current relationship More than a year vs. less than a year Alcohol use No alcohol vs. two to three times a week Once a month or less vs. two to three times a week Two to four times a month vs. two to three times a week Four or more times a week vs. two to three times a week Number of sexual partners (sexual workers) Yes vs. no
Unadjusted odds ratio
Females
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L.C. Simbayi et al.
Table 4. Multivariate regression analyses showing associations between various sociodemographic and behavioural variables and condom use at last sexual encounter by sex. Males (n = 2509)
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Variable
Adjusted odds ratios
Age (years) 15–24 vs. 50 + 25–49 vs. 50 + Race White vs. Black African Coloured vs. Black African Indian vs. Black African Locality type Urban formal vs rural formal Urban informal vs rural formal Rural informal vs rural formal Marital status Married vs. never married Widower/divorced/ separated vs. never married Highest education qualification No schooling vs. tertiary Grades 3–9 vs. tertiary Grades 10–12 vs. tertiary Employment Housewife/homeworker vs. unemployed Pensioner vs. unemployed Employed vs. unemployed Student learner vs. unemployed Number of sex partners in the last 12
Females (n = 3177)
SE
p Value
95% CI
Adjusted odds ratios
SE
p Value
95% CI
6.959 2.622
2.341 0.618
0.001 0.001
3.596–3.467 1.650–4.164
7.935 6.869
2.887 2.120
0.001 0.001
3.88–16.204 3.749–12.587
0.276 0.309 0.616
0.074 0.068 0.183
0.001 0.001 n.s.
0.163–0.468 0.201–0.475 0.344–1.103
0.191 0.237 0.161
0.056 0.046 0.057
0.001 0.001 0.001
0.107–0.341 0.162–0.348 0.081–0.321
1.369 1.087
0.378 0.334
n.s. n.s.
0.796–2.353 0.595–1.987
1.569 1.719
0.364 0.429
0.05 0.05
0.995–2.474 1.054–2.805
1.488
0.472
n.s.
0.799–2.774
1.612
0.391
0.05
1.002–2.595
0.298 1.088
0.055 0.351
0.001 n.s.
0.208–0.427 0.577–2.050
0.269 1.210
0.047 0.427
0.001 n.s.
0.191–0.378 0.605–2.418
1.516 1.881 1.845
0.269 0.594 0.681
0.05 0.05 n.s.
1.071–2.146 1.011–3.497 0.894–3.808
1.451 1.321 1.894
0.235 0.376 0.575
0.05 n.s. 0.05
1.057–1.993 0.755–2.311 1.043–3.437
0.466
0.448
n.s.
0.071–3.072
1.456
0.263
0.05
1.022–2.075
0.605 0.271 1.426
0.120 0.143 n.s.
0.01 0.01 n.s.
0.410–0.893 0.096–0.762 0.691–2.945
1.297 0.421 1.674
0.239 0.454 0.425
n.s. n.s. 0.05
0.903–1.863 0.050–3.506 1.017–2.755
months 1.344
0.298
n.s.
0.869–2.077
0.932
0.366
n.s.
0.431–2.014
n.s.
0.05
0.205–0.851
0.711
0.526
n.s.
0.166–3.039
n.s.
n.s.
0.892–2.211
1.053
0.163
n.s.
0.776–1.427
n.s.
0.000
0.277–0.690
0.488
0.114
0.05
0.308–0.771
Regular partners One vs. two or more 0.418 Recent sex partner had other sexual partners 1.405 Duration of current relationship More than a year vs. less than 0.437 a year
partner and among females in new relationships of less than a year. Both these findings are consistent with those from other previous local and international studies (Chimbindi et al., 2010; Hendriksen et al., 2007; Yotebieng, Halperin, Mitchell, & Adimoru, 2009) and several Demographic and Health Surveys (e.g., Uganda Bureau of Statistics (UBOS) & ICF International Inc., 2012; Zimbabwe National Statistics Agency (ZIMSTAT) & ICF International, 2012; although cf. Central Statistical Agency (Ethiopia) and ICF International, 2012). Indeed,
lower condom use generally appears to be the trademark of stable and durable relationships, including marriage, thus putting married couples at risk of HIV infection, especially if one partner is engaged in multiple sexual relationships (see Dhalla & Poole, 2009; Hargreaves et al., 2009; Yotebieng et al., 2009). Although this study was conducted using a nationally representative sample, there were several limitations. First, condom use was only measured for the last sex act but not also for consistency of use as is required.
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AIDS Care Second, the survey data are based on self-reporting which can be biased mainly due to social desirability. Third, the study was correlational, and consequently, causality cannot be inferred between the variables under study. In spite of these shortcomings, we believe that the findings are generally both valid and reliable and can be replicated in a similar survey. In conclusion, although condom use at last sexual encounter has improved tremendously in South Africa, this study demonstrates that further promotion of condom use is still needed, especially among population subgroups with lower rates of condom use. It is, therefore, important that HIV-prevention programme planners in South Africa develop appropriate health promotion strategies to encourage condom use among these subgroups. Acknowledgements The authors would like to thank the three anonymous reviewers for their helpful comments as well as Ms Meredith Evans for careful proofreading and editing through the manuscript.
Funding This research was supported by the President’s Emergency Plan for AIDS Relief (PEPFAR) through CDC under the terms of Cooperative Agreement Number [grant number U2GPS000570-05].
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