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Partner concurrency and HIV infection risk in South Africa

Open AccessPublished:March 05, 2016DOI:https://doi.org/10.1016/j.ijid.2016.03.001

      Highlights

      • Partner concurrency is defined as one's partner having other sexual partners.
      • The association between partner concurrency and HIV was assessed in a representative sample of South Africans.
      • At a cross-racial level, partner concurrency was associated with HIV prevalence.
      • At an individual level, partner concurrency was associated with HIV infection in black women.

      Summary

      Background

      The relationship between concurrent sexual partnerships and HIV risk is not fully understood. Evidence on the relationship between partner concurrency (one's sexual partner has another partner) and individual HIV risk is limited. In this study, the relationship between reported sexual partner concurrency and the risk of HIV infection was explored among South Africans.

      Methods

      Data from the third South African national HIV survey were used. In this survey, performed in 2008, questionnaires and HIV tests were administered to a nationally representative sample of 15 031 persons. Bivariate analysis and multiple logistic regression were used to evaluate the relationship between partner concurrency and HIV serostatus. Spearman's correlation was used to test the association between the prevalence of HIV and partner concurrency by race in women.

      Results

      The relationship between HIV prevalence and partner concurrency varied by race. At a cross-racial level there was a positive association between HIV prevalence and partner concurrency for women (rho = 0.95, p = 0.05). Among coloured, white, and Indian persons, HIV prevalence and partner concurrency rates were too low to allow further statistical testing. In the bivariate analysis, black African women who reported partner concurrency had a higher prevalence of HIV (36% (95% confidence interval (CI) 29.7–42.0) vs. 23% (95% CI 19.6–26.1), p < 0.001). After controlling for demographic, social, biological, and behavioural variables, the association remained statistically significant (adjusted odds ratio (aOR) 1.4, p = 0.04). The association was stronger among 15–29-year-old black African women (aOR 1.8, p = 0.03) than among women aged 30 years and older (aOR 1.3, p = 0.36).

      Conclusions

      These results suggest that partner concurrency may increase the HIV infection risk for black South African women, and in particular, for younger women.

      Keywords

      1. Introduction

      The role of concurrent sexual partnerships in the spread of HIV has not been established definitively. Defined as two or more partnerships with overlapping dates, concurrent sexual partnerships may be a characteristic of one's own sexual behaviour (individual concurrency) or one's partner's sexual behaviour (partner concurrency – PC). Theoretically, for the individual, the HIV infection risk associated with having concurrent partners oneself is the same as with having multiple partners sequentially (i.e., whether these partners are serial or concurrent is irrelevant – ceteris paribus). The important individual-level risk factor relating to concurrency is PC, which increases one's partner's risk of getting HIV and therefore the risk of acquiring HIV from one's partner. Partner concurrency creates the potential for exponential amplification of one's sexual network (Figure 1).
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      This has been shown in modelling studies
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      The role of sexual partnership networks in the epidemiology of gonorrhea.
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      Characteristics of persons with syphilis in areas of persisting syphilis in the United States: sustained transmission associated with concurrent partnerships.
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      Chlamydia transmission: concurrency, reproduction number, and the epidemic trajectory.
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      genital herpes,
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      Partner-concurrency associated with herpes simplex virus 2 infection in young South Africans.
      and bacterial vaginosis
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      • et al.
      Reproductive tract infections in rural women from the highlands, jungle, and coastal regions of Peru.
      ). In contrast, the results of studies testing the association between HIV and PC have been conflicting: a positive association has been found in some studies,
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      Sexual mixing patterns and sex-differentials in teenage exposure to HIV infection in rural Zimbabwe.
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      The association of HIV serodiscordance and partnership concurrency in Likoma Island (Malawi).
      • Nattrass N.
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      Poverty, sexual behaviour, gender and HIV infection among young black men and women in Cape Town, South Africa.
      • Helleringer S.
      • Mkandawire J.
      • Kohler H.P.
      A new approach to measuring partnership concurrency and its association with HIV risk in couples.
      but not in others.
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      • McGrath N.
      • Newell M.L.
      Effect of concurrent sexual partnerships on rate of new HIV infections in a high-prevalence, rural South African population: a cohort study.
      • Lagarde E.
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      • Carael M.
      • Laourou M.
      • Ferry B.
      • Akam E.
      • et al.
      Study Group on Heterogeneity of HIV Epidemics in African Cities
      Concurrent sexual partnerships and HIV prevalence in five urban communities of sub-Saharan Africa.
      None has involved nationally representative samples.
      Figure thumbnail gr1
      Figure 1The effect of concurrent partnering on A's risk of HIV acquisition is determined by whether A has two partners simultaneously – respondent concurrency (RC) – or A's partner has two partners simultaneously – partner concurrency (PC). (a) In the RC scenario, the risk of infection to A is determined by the number of partners that A has. Only if one of A's partners has other partners (dashed lines) will concurrency increase A's risk of HIV. (b) In the PC scenario, A's risk of HIV is increased by the fact that A's partner has another partner. If one of these partners has other partners then this will further enhance the risk.
      One possible reason why the relationship between HIV and PC has not been studied in nationally representative samples in countries with generalized HIV epidemics is the fact that most surveys do not ask questions relating to PC. South Africa has conducted four national AIDS indicator surveys. In only one of these was a question on PC included. This 2008 dataset was used to evaluate, at an individual level, the relationship between HIV serostatus and PC, controlling for a wide range of potential confounders.

      2. Methods

      2.1 Data

      The 2008 South African National HIV Prevalence, HIV Incidence, Behaviour and Communication Survey (SABSSM) was the third national HIV survey in South Africa.
      • Shisana O.
      South African National HIV Prevalence, Incidence, Behaviour and Communication Survey, 2008: a turning tide among teenagers?.
      It used a three-stage, stratified sampling approach. The sampling frames were based on a master sample consisting of 1000 enumerator areas (EA) used by Statistics South Africa for the 2001 census. These 1000 EAs constituted the primary sampling units. EA selection was stratified by province and locality type. These were identified as urban formal, urban informal, rural formal, and rural informal. Race was included as a third stratification variable (based on the predominant race group in the selected EA) in the formal urban areas. EAs that were dominated by white, Indian, or coloured race groups were oversampled to ensure that the minimum required sample size in these smaller race groups was obtained (‘coloured’ is a commonly used and socially acceptable term in South Africa for individuals of mixed race).
      • Kenyon C.
      • Buyze J.
      • Colebunders R.
      HIV prevalence by race co-varies closely with concurrency and number of sex partners in South Africa.
      • Mah T.L.
      Prevalence and correlates of concurrent sexual partnerships among young people in South Africa.
      Respondents were asked to self-identify with one of four racial categories: black, coloured, white, or Indian.
      Households constituted the secondary sampling units. The eligible individuals selected for the survey within each household represented the third sampling unit. Once weighted to account for the complex sampling design and HIV testing non-response, the survey produced a sample representative of the population in South Africa for sex, age, race, locality type, and province. Questionnaires were used to collect demographic, behavioural, and social data. Anonymous HIV testing was done on dried blood spot specimens. The midpoint for data collection was September 2008. The response rate was poorer than for the Demographic and Health Surveys conducted elsewhere in Southern Africa, but similar to other surveys conducted in South Africa.
      Out of 23 360 eligible individuals, 20 829 completed the interviews (89.1%). Of the 23 360 eligible individuals, 15 031 were tested for HIV; 1231 of the remainder were absent from the household at testing and 7109 declined HIV testing. An analysis found no difference in the HIV-related risk characteristics between survey participants who agreed and those who did not agree to HIV testing.
      • Shisana O.
      South African National HIV Prevalence, Incidence, Behaviour and Communication Survey, 2008: a turning tide among teenagers?.
      HIV testing rates were higher for women than men and for blacks and coloureds than for whites and Indians.
      • Shisana O.
      South African National HIV Prevalence, Incidence, Behaviour and Communication Survey, 2008: a turning tide among teenagers?.
      The survey adhered to international best practice ethical standards and the study protocol was approved by the South African Human Sciences Research Council Research Ethics Committee (REC 2/23/10/07). Further details of the survey design and HIV testing algorithm used are available in the full survey report.
      • Shisana O.
      South African National HIV Prevalence, Incidence, Behaviour and Communication Survey, 2008: a turning tide among teenagers?.
      The sample was restricted to respondents aged 15 years or older who reported sex in the past 12 months and agreed to an HIV test. Respondents with missing data on key variables were excluded from the analyses. All variables other than PC (2.15%), lifetime sex partners (3.78%), and age at first sex (0.73%) had less than 0.5% missing observations. Logistic regression analysis showed that individuals with missing data on the key dependent variable (PC) were not statistically different from those with data on HIV serostatus, age, geotype, education, relationship status, lifetime sex partners, or age of debut (results available upon request). Those who consumed alcohol 2–4 times per month were slightly less likely to have data missing on PC than those who did not drink (adjusted odds ratio (aOR) 0.6, 95% confidence interval (CI) 0.3–1.0). For an analysis of non-response bias please see the full SABSSM survey report.
      • Shisana O.
      South African National HIV Prevalence, Incidence, Behaviour and Communication Survey, 2008: a turning tide among teenagers?.

      2.2 Variables

      The dependent variable was a dichotomous indicator of HIV serostatus.
      Determination of PC was based on a question that was directed to everyone who reported having had sex in the prior 12 months: “Do you think your most recent sexual partners have had other sexual partners in the past month?” Answers were coded as ‘yes’, ‘no’ or ‘don’t know.’
      Based on a literature review and available data, a wide range of factors were considered as potential confounders in the relationship between PC and HIV serostatus (Figure 2). The risk factors that could affect both HIV and concurrency prevalence could be classified into four conceptual groups: demographic, social, biological, and behavioural.
      Figure thumbnail gr2
      Figure 2Simplified conceptual framework using directed acyclic graphs to represent the relationship between partner concurrency and other risk factors for HIV transmission and acquisition conceptualized as proximate and distal mechanisms. Partner concurrency acts to increase the connectivity of the local sexual network, which will in turn facilitate the spread of HIV within this network. Long-term concurrent relationships can result in high network connectivity without requiring high numbers of lifetime sexual partners. A high number of partners, via an increased rate of partner change, can lead to enhanced HIV spread in the absence of concurrent partnering. As such, partner concurrency and lifetime partners are conceptually distinct mechanisms. The framework is simplified in a number of ways. It does not represent the multiple interactions that are possible between factors in each column, e.g., between partner concurrency and lifetime partners. It also represents only one of the three ways that concurrency could enhance STI transmission.
      With regard to demographic factors, potentially confounding demographic characteristics considered included current age (expressed in four categories – see Table 1
      • Mah T.L.
      Prevalence and correlates of concurrent sexual partnerships among young people in South Africa.
      • Maughan-Brown B.
      • Kenyon C.
      • Lurie M.N.
      Partner age differences and concurrency in South Africa: implications for HIV-infection risk among young women.
      • Carter M.W.
      • Kraft J.M.
      • Koppenhaver T.
      • Galavotti C.
      • Roels T.H.
      • Kilmarx P.H.
      • et al.
      “A bull cannot be contained in a single kraal”: concurrent sexual partnerships in Botswana.
      ), locality type,
      • Kenyon C.
      • Colebunders R.
      Correlates of concurrency among young people in Carletonville, South Africa.
      and race. Sixty-one persons were classified as ‘other races’ and these individuals were omitted from the analysis.
      Table 1Percent of sexually experienced women and men aged 15 years and above who are HIV-positive, with p-values for the Chi-square test, according to selected demographic, social, biological, and behavioural characteristics; SABSSM 2008
      p-Value: * <0.05, ** <0.005, *** <0.0005.
      WomenMen
      % HIV-positive
      Percentages are weighted percentages of all participants aged 15 years or older who reported having had sex in the prior 12 months.
      n% HIV-positive
      Percentages are weighted percentages of all participants aged 15 years or older who reported having had sex in the prior 12 months.
      n
      Age, years******
       15–2925.913219.2930
       30–3926.490022.1526
       40–4912.977910.5520
       ≥505.95505.7604
      Race******
       Black27.2225216.01457
       White0.73760.0318
       Coloured4.26421.9570
       Indian0.42800.3235
      Geotype******
       Urban formal16.420129.01652
       Urban informal36.152620.0309
       Rural formal24.972716.8406
       Tribal area20.728512.9213
      Education****
       Primary or less23.180116.5597
       Secondary23.2234411.71660
       Post secondary7.83876.4309
      Relationship status****
       Married9.717178.91232
       Single or not living together32.8136013.31033
       Divorced/widowed25.820217.7133
       Living together24.326019.1176
      Alcohol consumption*
       ≤1/month22.3315213.31664
       2–4/month11.82489.5556
       ≥5/month11.01329.5351
      Lifetime partners****
       0–110.913716.7558
       2–526.7193212.71243
       ≥637.415412.8640
      Partner concurrency****
       No10.9214011.11674
       Yes26.756618.3237
       Do not know37.475412.0349
      Circumcision
       Yes11.41053
       No12.01511
      Condom at first sex
       Yes18.89689.1801
       No22.1256913.41770
      First sex ≤15 years
       Yes23.838513.8408
       No20.8314311.72149
      SABSSM, The 2008 South African National HIV Prevalence, HIV Incidence, Behaviour and Communication Survey.
      a p-Value: * <0.05, ** <0.005, *** <0.0005.
      b Percentages are weighted percentages of all participants aged 15 years or older who reported having had sex in the prior 12 months.
      Social factors considered included the maximum level of education attained
      • Kenyon C.
      • Boulle A.
      • Badri M.
      • Asselman V.
      “I don’t use a condom (with my regular partner) because I know that I’m faithful, but with everyone else I do”: the cultural and socioeconomic determinants of sexual partner concurrency in young South Africans.
      and relationship/marital status. Marital status has been shown to be associated with the number of sexual partners and concurrency, as well as with HIV serostatus.
      • Kenyon C.
      • Colebunders R.
      Correlates of concurrency among young people in Carletonville, South Africa.
      • Kenyon C.
      • Boulle A.
      • Badri M.
      • Asselman V.
      “I don’t use a condom (with my regular partner) because I know that I’m faithful, but with everyone else I do”: the cultural and socioeconomic determinants of sexual partner concurrency in young South Africans.
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      • Chege J.
      • Musonda R.
      • et al.
      Why do young women have a much higher prevalence of HIV than young men? A study in Kisumu, Kenya and Ndola, Zambia.
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      • Morison L.
      • Lagarde E.
      • et al.
      Study Group on Heterogeneity of HIV Epidemics in African Cities
      Ecological and individual level analysis of risk factors for HIV infection in four urban populations in sub-Saharan Africa with different levels of HIV infection.
      • Manhart L.E.
      • Aral S.O.
      • Holmes K.K.
      • Foxman B.
      Sex partner concurrency: measurement, prevalence, and correlates among urban 18–39-year-olds.
      In terms of behavioural factors, measures of lifetime number of sex partners,
      • Kenyon C.
      • Boulle A.
      • Badri M.
      • Asselman V.
      “I don’t use a condom (with my regular partner) because I know that I’m faithful, but with everyone else I do”: the cultural and socioeconomic determinants of sexual partner concurrency in young South Africans.
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      • Krasnoselskikh T.V.
      • Niccolai L.M.
      • Golovanov S.
      • Kozlov A.P.
      • Abdala N.
      Concurrent sexual partnerships and sexually transmitted diseases in Russia.
      condom use at first sex,
      • Kenyon C.
      • Colebunders R.
      Correlates of concurrency among young people in Carletonville, South Africa.
      • Senn T.E.
      • Carey M.P.
      • Vanable P.A.
      • Coury-Doniger P.
      • Urban M.
      Sexual partner concurrency among STI clinic patients with a steady partner: correlates and associations with condom use.
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      • Agot K.
      • Ndinya-Achola J.O.
      • et al.
      Determinants of consistent condom use vary by partner type among young men in Kisumu, Kenya: a multi-level data analysis.
      • Steffenson A.E.
      • Pettifor A.E.
      • Seage 3rd, G.R.
      • Rees H.V.
      • Cleary P.D.
      Concurrent sexual partnerships and human immunodeficiency virus risk among South African youth.
      frequency of alcohol consumption,
      • Kenyon C.
      • Boulle A.
      • Badri M.
      • Asselman V.
      “I don’t use a condom (with my regular partner) because I know that I’m faithful, but with everyone else I do”: the cultural and socioeconomic determinants of sexual partner concurrency in young South Africans.
      • Zhan W.
      • Krasnoselskikh T.V.
      • Niccolai L.M.
      • Golovanov S.
      • Kozlov A.P.
      • Abdala N.
      Concurrent sexual partnerships and sexually transmitted diseases in Russia.
      • Senn T.E.
      • Carey M.P.
      • Vanable P.A.
      • Coury-Doniger P.
      • Urban M.
      Sexual partner concurrency among STI clinic patients with a steady partner: correlates and associations with condom use.
      and age at first sex
      • Kenyon C.
      • Boulle A.
      • Badri M.
      • Asselman V.
      “I don’t use a condom (with my regular partner) because I know that I’m faithful, but with everyone else I do”: the cultural and socioeconomic determinants of sexual partner concurrency in young South Africans.
      • Manhart L.E.
      • Aral S.O.
      • Holmes K.K.
      • Foxman B.
      Sex partner concurrency: measurement, prevalence, and correlates among urban 18–39-year-olds.
      • Zhan W.
      • Krasnoselskikh T.V.
      • Niccolai L.M.
      • Golovanov S.
      • Kozlov A.P.
      • Abdala N.
      Concurrent sexual partnerships and sexually transmitted diseases in Russia.
      • Ludema C.
      • Doherty I.A.
      • White B.L.
      • Villar-Loubet O.
      • McLellan-Lemal E.
      • O’Daniels C.M.
      • et al.
      Characteristics of African American women and their partners with perceived concurrent partnerships in 4 rural counties in the southeastern US.
      • Grieb S.M.
      • Davey-Rothwell M.
      • Latkin C.A.
      Concurrent sexual partnerships among urban African American high-risk women with main sex partners.
      were created. Condom use at first sex and lifetime number of sex partners were used, rather than more recent measures of condom use and number of partners, as more temporally distal variables are less likely to be affected by an HIV diagnosis than more recent sexual behaviour.
      • Nattrass N.
      • Maughan-Brown B.
      • Seekings J.
      • Whiteside A.
      Poverty, sexual behaviour, gender and HIV infection among young black men and women in Cape Town, South Africa.
      With regard to biological factors, circumcision was considered as a potential confounding factor, as it is both a well-established attenuator in HIV transmission
      • Wamai R.G.
      • Morris B.J.
      • Bailis S.A.
      • Sokal D.
      • Klausner J.D.
      • Appleton R.
      • et al.
      Male circumcision for HIV prevention: current evidence and implementation in sub-Saharan Africa.
      and may be associated with PC. A study from South Africa found that knowledge of the benefits of circumcision in reducing HIV transmission was associated with risk compensation in women.
      • Maughan-Brown B.
      • Venkataramani A.S.
      Learning that circumcision is protective against HIV: risk compensation among men and women in Cape Town, South Africa.
      Risk compensation could, hypothetically, include high-risk practices such as concurrency.

      2.3 Analysis

      The prevalence of HIV varies by up to 40-fold between different racial groups in South Africa.
      • Shisana O.
      South African National HIV Prevalence, Incidence, Behaviour and Communication Survey, 2008: a turning tide among teenagers?.
      These differences have been attributed to a wide array of individual and sexual network level risk factors.
      • Kenyon C.
      • Dlamini S.
      • Boulle A.
      • White R.G.
      • Badri M.
      A network-level explanation for the differences in HIV prevalence in South Africa's racial groups.
      The way these risk factors combine to produce such different HIV prevalences in the various racial groups may vary.
      • Kenyon C.
      • Dlamini S.
      • Boulle A.
      • White R.G.
      • Badri M.
      A network-level explanation for the differences in HIV prevalence in South Africa's racial groups.
      Because of this, and the fact that sexual partnering in South Africa has been found to exhibit a high degree of homophily by race,
      • Kenyon C.
      • Colebunders R.
      Birds of a feather: homophily and sexual network structure in sub-Saharan Africa.
      the HIV prevalence and partner concurrency were first assessed by race. Spearman's correlation was used to test the association between these two measures by race. The biological and social factors associated with HIV transmission differ by sex and thus the analyses were stratified by gender as well as race.
      Two-sample differences in proportion tests across groups were conducted to examine the relationship between PC and HIV status in a bivariate analysis. The prevalence of HIV and PC was found to be too low in the whites, Indians, and coloureds for meaningful multiple regression analyses with these subgroups. Multiple logistic regression analyses were therefore limited to the black subgroup. All models controlled for the potential confounding factors described above: age, geotype, education, relationship status, alcohol consumption, life-time sexual partners, circumcision status (for men), condom use, and age at first sex. The final models were run separately for women and men.
      A previous study in South Africa found that patterns of age-mixing and concurrency within partnerships varied among partnerships involving younger compared to older women. The findings suggest that PC could have a larger impact on HIV infection risk among younger women by connecting young women to sexual networks involving older individuals.
      • Maughan-Brown B.
      • Kenyon C.
      • Lurie M.N.
      Partner age differences and concurrency in South Africa: implications for HIV-infection risk among young women.
      Two extra models were therefore run to evaluate the relationship between PC and HIV stratified by age – those aged 15 to 29 years and those aged 30 years and above. All analyses were weighted to account for the complex survey design. The statistical analysis was conducted using Stata version 13.0 software (StataCorp, College Station, TX, USA).

      3. Results

      A total of 3550 women and 2580 men met the criteria for inclusion in the analysis. The median age was 39 years for women (interquartile range (IQR) 25–52) and 36 years for men (IQR 23–50) (Table 1).
      Table 2 presents data on HIV prevalence by selected characteristics for men and women. HIV was associated with being under 40 years old, black, living in urban informal settlements, having more lifetime partners, and PC. Individuals who were married or had post secondary education were less likely to be HIV-positive. Considerable variation in the prevalence of both HIV and PC was evident. Among women, the proportion reporting PC/not knowing if a recent partner had other partners was 24%/28% for blacks, 12%/14% for coloureds, 7%/3% for whites, and 7%/7% for Indians. Of these respective groups, 27%, 5%, 0.7%, and 0.4% were HIV-positive.
      Table 2HIV prevalence and partner concurrency among individuals (aged 15 years and older) who had sex in past 12 months by race and gender
      p-Value: * <0.05, ** <0.005, *** <0.0005. p-Values derived from two-sample differences in proportion test across groups, with ‘no’ partner currency the reference group in all.
      BlackWhiteColouredIndian
      Women

      (n = 2180)
      Men

      (n = 1423)
      Women

      (n = 370)
      Men

      (n = 313)
      Women

      (n = 632)
      Men

      (n = 566)
      Women

      (n = 278)
      Men

      (n = 235)
      HIV prevalence27%16%0.7%0.0%5%2%0.4%0.2%
      Partner concurrency
       No48%67%90%87%74%87%88%93%
       Yes24%15%7%5%12%7%5%2%
       Don’t know28%18%3%8%14%7%7%6%
      Bivariate analysisHIV-pos

      % (n)
      HIV-pos

      % (n)
      HIV-pos

      % (n)
      HIV-pos

      % (n)
      HIV-pos

      % (n)
      HIV-pos

      % (n)
      HIV-pos

      % (n)
      HIV-pos

      % (n)
      Partner concurrency
       No23% (1070)16% (940)0.8% (340)0.0% (276)3% (479)1.5% (481)0.0% (251)0.2% (210)
       Yes36% (482)***21% (219)0.0% (15)
      Insufficient data for analysis.
      0.0% (14)
      Insufficient data for analysis.
      8% (60)
      Insufficient data to account for the complex survey design in the bivariate analysis. The analysis only includes a survey weight.
      0.0% (40)
      Insufficient data for analysis.
      8% (9)
      Insufficient data for analysis.
      0.0% (6)
      Insufficient data for analysis.
       Don’t know28% (628)*15% (264)0.0% (15)
      Insufficient data for analysis.
      0.0% (23)
      Insufficient data for analysis.
      7% (93)
      Insufficient data to account for the complex survey design in the bivariate analysis. The analysis only includes a survey weight.
      2% (45)0.0% (18)
      Insufficient data for analysis.
      0.0% (19)
      Insufficient data for analysis.
      a p-Value: * <0.05, ** <0.005, *** <0.0005. p-Values derived from two-sample differences in proportion test across groups, with ‘no’ partner currency the reference group in all.
      b Insufficient data for analysis.
      c Insufficient data to account for the complex survey design in the bivariate analysis. The analysis only includes a survey weight.
      At a population level, there was a positive association between HIV and PC prevalence in women, with higher levels of partner concurrency associated with greater HIV prevalence (rho = 0.95, p = 0.051; Figure 3). Bivariate analysis revealed that black women who reported PC had a higher prevalence of HIV (36%; 95% CI 29.7–42.0) than those reporting no PC (23%, 95% CI 19.6–26.1, p < 0.001; Table 2). The association between PC and HIV prevalence was positive, but not statistically significant (p = 0.21) for black men. HIV prevalence was 20.8% (95% CI 14.8–28.4) for black men reporting PC compared to 16.1% (95% CI 12.9–19.8) among those who did not report PC. Partner concurrency and HIV prevalence rates were so low in the other race/gender subgroups that there was insufficient data available to conduct this analysis in these other groups. Multiple regression analyses were therefore restricted to the black men and women subgroups.
      Figure thumbnail gr3
      Figure 3Association between the percentage of women reporting partner concurrency and HIV prevalence in women for four racial groups in South Africa (Spearman's rho = 0.95, p = 0.051).

      3.1 Regression analyses

      Black women who reported PC had an unadjusted odds ratio of 1.9 (95% CI 1.4–2.6) for testing HIV-positive. After controlling for potential confounding factors (Table 3), black women who reported PC had greater odds of testing HIV-positive than those who did not think their recent partner had other partners (aOR 1.4, 95% CI 1.0–2.0, p < 0.1). Among black men, the association was weaker and not significant (aOR 1.2, 95% CI 0.7–2.0, p = 0.486). Stratification of the analyses for women by age revealed a stronger association aged 30 years and older.
      Table 3Multiple logistic regression models for the association between HIV status and reported partner concurrency among black individuals; SABSSM 2008—adjusted odds ratios (95% confidence intervals)
      p-Value: * <0.05, ** <0.005, *** <0.0005.
      Dependent variableBlack men

      (≥15 years)
      Black women

      (≥15 years)
      Black women

      (15–29 years)
      Black women

      (≥30 years)
      Model1234
      aOR (95% CI)aOR (95% CI)aOR (95% CI)aOR (95% CI)
      Partner concurrency
       NoRef.Ref.Ref.Ref.
       Yes1.2 (0.7–2.0)1.4 (1.0–2.0)*1.8 (1.1–2.9)*1.3 (0.8–2.0)
       Do not know1.0 (0.6–1.6)1.2 (0.9–1.7)1.4 (0.9–2.3)1.0 (0.7–1.6)
      Age, years
       15–29Ref.Ref.NANA
       30–392.9 (1.5–5.6)**1.2 (0.8–1.7)NARef.
       40–491.6 (0.8–3.5)0.6 (0.4–0.9)*NA2.9 (1.5–5.4)**
       ≥500.7 (0.3–1.6)0.3 (0.2–0.6)**NA1.6 (0.8–2.9)
      Geotype
       Urban formalRef.Ref.Ref.Ref.
       Urban informal1.0 (0.6–1.8)1.4 (0.9–2.0)1.2 (0.7–2.1)1.6 (1.0–2.7)
       Rural formal1.1 (0.6–1.8)1.0 (0.7–1.4)0.8 (0.5–1.4)1.1 (0.7–1.8)
       Tribal area1.0 (0.5–2.1)1.2 (0.8–2.0)1.1 (0.5–2.2)1.3 (0.7–2.5)
      Education
       Primary or lessRef.Ref.Ref.Ref.
       Secondary0.8 (0.5–1.4)0.8 (0.5–1.1)0.5 (0.3–0.9)*0.9 (0.6–1.4)
       Post secondary0.3 (0.1–0.9)*0.3 (0.2–0.7)**0.3 (0.1–0.8)*0.3 (0.1–0.9)*
      Relationship status
       MarriedRef.Ref.Ref.Ref.
       Single or not living together1.1 (0.5–2.1)2.5 (1.7–3.7)**1.2 (0.6–2.2)3.7 (2.3–5.8)**
       Divorced/widowed1.6 (0.6–4.0)2.2 (1.1–4.4)*1.0 (0.1–15.0)2.7 (1.4–5.2)**
       Living together1.4 (0.6–3.1)1.6 (1.0–2.8)1.0 (0.4–2.4)1.6 (0.8–3.2)
      Alcohol consumption
       ≤1/monthRef.Ref.Ref.Ref.
       2–4/month0.9 (0.5–1.8)1.1 (0.5–2.3)1.5 (0.6–3.7)0.8 (0.2–2.8)
       ≥5/month1.0 (0.5–2.1)0.8 (0.2–2.9)0.7 (0.1–7.2)0.9 (0.2–3.2)
      Lifetime partners
       0–1Ref.Ref.Ref.Ref.
       2–51.7 (0.9–3.2)2.4 (1.7–3.4)**2.2 (1.3–3.6)**2.6 (1.6–4.1)**
       ≥61.4 (0.7–2.8)4.1 (2.2–7.7)**4.8 (1.8–12.8)**3.8 (1.6–8.8)**
      Circumcision
       YesRef.NANANA
       No1.7 (1.1–2.7)*NANANA
      Condom at first sex
       YesRef.Ref.Ref.Ref.
       No1.2 (0.7–2.2)1.5 (1.0–2.2)*1.7 (1.1–2.7)*1.0 (0.5–2.0)
      First sex <16 years
       YesRef.Ref.Ref.Ref.
       No1.0 (0.6–1.8)1.3 (0.9–1.9)1.5 (0.8–2.5)1.0 (0.5–2.0)
      Number130920699121157
      SABSSM, The 2008 South African National HIV Prevalence, HIV Incidence, Behaviour and Communication Survey; aOR, adjusted odds ratio; Ref., reference group; NA, not applicable.
      a p-Value: * <0.05, ** <0.005, *** <0.0005.
      Three sensitivity analyses were conducted. Two hundred and eighty-five black women did not respond ‘yes’ to ever having had sex, but then reported having had sex in the past 12 months. As it was uncertain whether these individuals should be included in the analysis, the models were rerun without them as an initial sensitivity analysis. It was found that the exclusion of these individuals strengthened the association between PC and HIV in the black women (aOR 1.6, 95% CI 1.1–2.3, p = 0.019). The second and third sensitivity analyses attempted to minimize the misclassification of PC. The PC variable was based on a question that asked: Do you think your most recent sexual partners have had other sexual partners in the past month? A person might answer ‘yes’ to this question if they last had sex with this person months previously but they knew that this person had another sexual partner in the past month. This would not constitute PC. In the second sensitivity analysis, the analyses were repeated, limited to respondents whose current relationship had started at least 12 months prior to the survey and who had only reported one partner in this time period. Restricting the analysis in this way led to a strengthening of the association between PC and HIV among black women (all ages: aOR 1.7, 95% CI 1.1–2.4, p = 0.012; age under 30 years: aOR 2.2, 95% CI 1.3–3.8, p = 0.006). The third sensitivity analysis limited the analyses to those who reported that they had at least one sex partner in the past month. Once again this restriction led to a strengthening of the association between PC and HIV among black women (all ages: aOR 1.6, 95% CI 1.1–2.3, p = 0.022; age under 30 year: aOR 1.9, 95% CI 1.1–3.3, p = 0.031).

      4. Discussion

      This is the first study based on a nationally representative sample to explore whether an association exists between PC and HIV. A positive association was present in both women and men, but the relationship was only statistically significant for black African women. There are a number of possible explanations for the gender difference. Studies from Southern Africa using different quantitative and qualitative methodologies to measure concurrency have found that individual concurrency rates are considerably higher among men than women.
      • Nattrass N.
      • Maughan-Brown B.
      • Seekings J.
      • Whiteside A.
      Poverty, sexual behaviour, gender and HIV infection among young black men and women in Cape Town, South Africa.
      • Mah T.L.
      Prevalence and correlates of concurrent sexual partnerships among young people in South Africa.
      • Carter M.W.
      • Kraft J.M.
      • Koppenhaver T.
      • Galavotti C.
      • Roels T.H.
      • Kilmarx P.H.
      • et al.
      “A bull cannot be contained in a single kraal”: concurrent sexual partnerships in Botswana.
      • Kenyon C.
      • Dlamini S.
      • Boulle A.
      • White R.G.
      • Badri M.
      A network-level explanation for the differences in HIV prevalence in South Africa's racial groups.
      Male concurrency has also been shown to connect young women to sexual networks involving older individuals (with a higher HIV prevalence).
      • Maughan-Brown B.
      • Kenyon C.
      • Lurie M.N.
      Partner age differences and concurrency in South Africa: implications for HIV-infection risk among young women.
      As a consequence, PC increases both the sexual network that these women are connected to and the probability that they come into contact with an HIV-infected part of this network.
      • Maughan-Brown B.
      • Kenyon C.
      • Lurie M.N.
      Partner age differences and concurrency in South Africa: implications for HIV-infection risk among young women.
      Partner concurrency may thus represent a greater HIV risk factor for women than it does for men.
      • Maughan-Brown B.
      • Kenyon C.
      • Lurie M.N.
      Partner age differences and concurrency in South Africa: implications for HIV-infection risk among young women.
      Another potential explanation is that women may be more likely to ascertain partner concurrency than men. Ethnographic research on concurrency in Southern Africa
      • Leclerc-Madlala S.
      Cultural scripts for multiple and concurrent partnerships in southern Africa: why HIV prevention needs anthropology.
      describes how various norms and discourses promote the acceptance of concurrent sexual partnerships, but predominantly for men. In line with this body of work, several studies have found that, while not generally socially condoned, there is a greater acceptance of men having concurrent partners than there is of women.
      • Mah T.L.
      • Maughan-Brown B.
      Social and cultural contexts of concurrency in a township in Cape Town, South Africa.
      The greater disapproval of concurrency for women may motivate women to hide concurrency more than men and thus men may be less accurate than women when reporting their perception about their partner's behaviour.
      Previous research from South Africa has highlighted that younger women are linked via PC to sexual networks involving older individuals, which may increase HIV infection risk.
      • Maughan-Brown B.
      • Kenyon C.
      • Lurie M.N.
      Partner age differences and concurrency in South Africa: implications for HIV-infection risk among young women.
      The result that PC was a risk factor for HIV predominantly for women under the age of 30 years in the present study provides support to the hypothesis generated from the earlier research that the influence of concurrency on HIV risk may vary by age, with concurrency playing a larger role in HIV infection risk among young women.
      • Maughan-Brown B.
      • Kenyon C.
      • Lurie M.N.
      Partner age differences and concurrency in South Africa: implications for HIV-infection risk among young women.
      Modelling studies have revealed that the major way that PC would enhance HIV transmission is via creating more interconnected sexual networks.
      • Morris M.
      • Kretzschmar M.
      Concurrent partnerships and the spread of HIV.
      • Johnson L.F.
      • Dorrington R.E.
      • Bradshaw D.
      • Pillay-Van Wyk V.
      • Rehle T.M.
      Sexual behaviour patterns in South Africa and their association with the spread of HIV: insights from a mathematical model.
      As a denser network could lead to greater transmission of HIV, the finding of the present study that PC may increase the HIV risk for women may be part of the explanation for the evident association between HIV and PC prevalence by race. In other words, compatible with the enhanced-network connectivity hypothesis,
      • Morris M.
      • Kretzschmar M.
      Concurrent partnerships and the spread of HIV.
      a hypothesis generated from the present findings is that higher rates of concurrency among black African individuals may be one contributing factor to the higher rates of HIV among this population.
      There are limitations to this study. Accurate PC determination was contingent on respondent assessments of whether or not their partner engaged in concurrency. The accuracy of these assessments is uncertain. An analysis from the Likoma Network Study, where both members of linked couples were asked whether they and their partner had other partners, showed reasonable concordance between self- and partner-reported concurrency.
      • Helleringer S.
      • Mkandawire J.
      • Kohler H.P.
      A new approach to measuring partnership concurrency and its association with HIV risk in couples.
      However, that study took place on a small island in Lake Malawi, and it is unclear if these results are generalizable to South Africa. Furthermore, a study of persons attending an STI clinic in the USA suggested poor concordance.
      • Drumright L.N.
      • Gorbach P.M.
      • Holmes K.K.
      Do people really know their sex partners? Concurrency, knowledge of partner behavior, and sexually transmitted infections within partnerships.
      It was attempted to limit misclassification of PC in the present study by considering respondents who reported certainty in their assessment of whether their partner had concurrent partnerships separate from those who were uncertain about their partner's concurrency status. However, there is also the potential that this measure is influenced by social desirability, recall, and ‘projection’ biases.
      • Helleringer S.
      • Mkandawire J.
      • Kohler H.P.
      A new approach to measuring partnership concurrency and its association with HIV risk in couples.
      • Morris M.
      • Vu L.
      • Leslie-Cook A.
      • Akom E.
      • Stephen A.
      • Sherard D.
      Comparing estimates of multiple and concurrent partnerships across population based surveys: implications for combination HIV prevention.
      Another potential limitation is that PC was only measured for recent partners. Prevalent HIV infection can be influenced by behaviour over a decade or longer, and thus the omission of measures from previous partnerships could lead to a misclassification bias that would reduce the ability to detect an association between PC and HIV. In addition, the measure of PC used in the present study might have falsely misclassified respondents as having had a partner with other partners (PC) if the respondent reported that their most recent sexual partner had another partner in the past month, but the respondent's sexual partnership with this person ended more than a month before the survey. Finally, other unmeasured factors that may influence both concurrency and HIV risk could have confounded the results. This would be expected to occur if respondents reporting PC were also more (or less) likely to engage in other higher risk behaviours.
      • Helleringer S.
      • Mkandawire J.
      • Kohler H.P.
      A new approach to measuring partnership concurrency and its association with HIV risk in couples.
      • Mah T.L.
      Prevalence and correlates of concurrent sexual partnerships among young people in South Africa.
      • Kenyon C.
      • Boulle A.
      • Badri M.
      • Asselman V.
      “I don’t use a condom (with my regular partner) because I know that I’m faithful, but with everyone else I do”: the cultural and socioeconomic determinants of sexual partner concurrency in young South Africans.
      For instance, these could include more frequent sex, the practice of sexual acts with higher per-act risk of infection, less frequent use of barrier contraception, or attributes of chosen partners. It is possible that the variables used in this study to control for each of these was insufficiently nuanced to capture a real effect on increased HIV acquisition.
      In conclusion, these results suggest that PC is associated with HIV prevalence among black women in South Africa, particularly young black women. Concurrency may be a suitable target for behavioural interventions to prevent HIV infections in this context. Further research is required to determine the impact of concurrency on HIV infection risk in different sub-populations and to track changes in concurrency prevalence.

      Author contributions

      CRK and BMB conceptualized the study. They conducted the analyses together with AT. All authors contributed to writing the manuscript.

      Acknowledgements

      We would like to thank the Human Sciences Research Council for conducting this survey and making the dataset available to us.
      Conflict of interest: The authors declare that they have no conflicts of interest.

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