Abstract
South Africa has the highest number of HIV infected individuals in the world. The primary objective of the current study was to describe temporal changes in HIV incidence rates using the data from 9,948 women who enrolled in one of the six HIV prevention trials conducted in KwaZulu-Natal, South Africa (2002-2016). Characteristics of the study population were presented and compared across the four study periods: 2002-2004, 2005-2008, 2009-2011 and 2012-2016. Despite the significant progress in prevention programs, infection rates increased from 6.2 to 9.3 per 100 person-year over the 15 years. These rates were as high as 14 per 100 person-year among women younger than 20 years age. Being single/not cohabiting, using injectable contraceptives, having less than two children and diagnosed with STI(s) (at baseline) were consistently associated with increased risk of HIV infection in all four consecutive cohorts. These four factors were collectively associated with 71%, 75%, 80% and 88% of the HIV seroconversions in four study periods. As the research continues to find ways of controlling the spread of the infections, quantifying the temporal trends in risk factors and their population-level impacts on HIV infection may have significant implications. This information may assist in developing effective counselling and education programs by targeting the sexually active single women and delivering more realistic messages.
Keywords: HIV, South Africa, temporal trends, population level impact
INTRODUCTION
South Africa has the highest number of HIV infected individuals in the world (UNAIDS, 2016). Besides low socioeconomic conditions, a number of sexual behaviours have been linked to the increasing number of infections in the region (Ramjee, Moonsamy, Abbai & Wand, 2016; Balkus et al. 2016;Wand et al. 2017). As the number of infections continues to spread, there is no cure for HIV and prevention remains the most effective and cost-effective strategy to control the epidemic. Over the years, HIV prevention efforts intensified and primarily focused on developing biomedical interventions in African populations (Abdool Karim et al.,2011;McCormack et al.,2010;Baeten et al.,2016). There has also been significant progress in expanding culturally and socially appropriate HIV/AIDS awareness programs (Koker et al., 2010;Birdsall et al. 2004;Abdool Karim, Churchyard, Abdool Karim Q & Lawn, 2009; Hansoti et al. 2018).
As the research continues to find ways of controlling the spread of the infections, describing the temporal trends in HIV incidence rates can be crucial to evaluate the effectiveness of the prevention programs. The primary objective of the current study was to describe temporal changes in HIV incidence rates among a cohort of women who enrolled in biomedical intervention trials conducted in KwaZulu-Natal, South Africa. Given the significant developments in HIV prevention programs in the region, it is important to investigate the temporal changes in HIV incidence and identify those at high risk of infection. In an additional analysis, we also reported the temporal trends in a combination of the risk factors on HIV infections after accounting for their potential correlations in multivariable analysis setting.
METHODS
We used a combined data from 9,948 sexually active women at reproductive age (16-49 years) who enrolled in prevention trials during the period of 2002-2016 in KwaZulu-Natal, South Africa (Abdool Karim et al. 2011; McCormack et al. 2010;Marrazzo et al. 2015; Padian et al. 2007; Skoler-Karpoff et al. 2008;Baeten et al., 2016). All trials had the same eligibility criteria and used similar testing and diagnostic testing procedures for HIV and other sexually transmitted infections (STIs). Since none of the study interventions has been shown to be ineffective, we considered all study arms as a single cohort.
Measurements
The primary outcome of this study was HIV incidence. We included in the analysis: age, marital-status, total number of sex partners in past three months, age at sexual debut, condom used at last sex, method of contraceptive, parity, knowledge of partner’s other partner and circumcision, and baseline sexually transmitted infections (STIs) positivity. Since some of these trials were conducted in overlapping years, we used the individual-level data and allocated the study participants into one of the four consecutive study periods based on their enrolment and first infection dates (and/or end of study). We calculated total years of follow-up for each woman. Next, we ensured that participant’s enrolment and last visit dates (or date of HIV positivity) occurred within the same cohort that woman is allocated. Each woman was followed up at least 24 months from the date of enrolment to HIV incidence or end of study (or loss to follow-up), whichever occurred first.
Statistical analysis
We used descriptive statistics (percentages) to characterise the study population by study years. Cox proportional models were used to investigate the associations between sociodemographic, sexual behaviours and HIV incidence by the study periods. We also the calculated the population-level impacts (PAR%) of the risk factors if they were statistically significant in multivariate models (Wand & Ramjee 2011) (Appendix). All analyses conducted using Stata 14.0 (College Station, TX USA).
RESULTS
Characteristics of the study population were presented and compared across the four study periods. Study participants were increasingly younger, more likely to be single, and more likely to have less than two children. Our data also showed significant temporal changes in the contraceptive use, with injectables increasing from 39% to 76% (p<0.001). The proportion of women who reported two or more sexual partners also increased over time from 7% to 20% (p-value<0.001).
Temporal trends in HIV incidence
Overall, 15-year HIV incidence rate was 7.00 (95%CI:6.44,7.43). Figure 1 presents a three-dimensional “heat-map” of the HIV incidence rates by age groups across the four study periods. In the latest two cohorts, HIV incidence rates were as high as 12 to 14 per 100-person year among women younger than 30 years of age, HIV incidence rates increased uniformly over time and peaked in 2012-2016; while older women had the lowest HIV incidence rates during the 15-year.
Figure 1:
Age-specific HIV incidence rates by four cohorts
Temporal trends in predictors HIV and their population-level impacts
Table 2 presents the adjusted hazard ratios and population-level impacts of the HIV-related risk factors considered in this study for each study period separately. Compared to the older women, younger women were significantly more likely to acquire HIV in all four cohorts. Those younger than 20 years old were at the highest risk of infections with adjusted hazard ratios (aHR):3.12 (2002-2004), aHR:4.21 (2005-2008), aHR:4.00 (2009-2011) and aHR: 6.32 (2012-2016). Besides younger age, being single (aHRs ranged:1.79 to 2.82), using injectables (aHRs ranged:1.29 to 1.70), having less than two children (aHRs ranged:1.36 to 2.04) and STI positivity (aHRs ranged:1.54 to 2.26) were all significantly associated with increased risk of HIV in all four cohorts. Multiple sex partners in past two months had significant impact on HIV seroconversions among women who were in the two most recent cohorts (aHR:1.39,95%CI:1.04,1.85;aHR:1.60,95%CI:1.13,2.27 respectively). Women’s knowledge about their sexual partners has also increased overtime and was significantly associated with increased risk of HIV infections in the two most recent cohorts.
Table 2:
Associations between characteristics and HIV incidence by study years
| Characteristics | 2002-2004 | 2005-2008 | 2009-2011 | 2012-2016 | ||||
|---|---|---|---|---|---|---|---|---|
| aHR (95% CI) | p-value | aHR (95% CI) | p-value | aHR (95% CI) | p-value | aHR (95% CI) | p-value | |
| Age | ||||||||
| <20 | 3.12 (2.05, 4.76) | <0.001 | 4.21 (2.05, 8.62) | <0.001 | 4.00 (1.82, 8.72) | 0.001 | 6.32 (2.47, 16.13) | <0.001 |
| 20-24 | 2.92 (2.04, 4.18) | <0.001 | 2.65 (1.40, 5.00) | 0.003 | 3.12 (1.57, 6.17) | 0.001 | 4.52 (1.85, 11.04) | 0.001 |
| 25-29 | 2.24 (1.48, 3.37) | <0.001 | 2.23 (1.12, 4.46) | 0.023 | 3.20 (1.55, 6.56) | 0.002 | 2.79 (1.11, 6.97) | 0.027 |
| 30-34 | 1.48 (0.93, 2.34) | 0.095 | 1.25 (0.54, 2.84) | 0.596 | 2.20 (0.93, 5.17) | 0.073 | 1.85 (0.68, 5.01) | 0.227 |
| 35+ | 1 | 1 | ||||||
| Education | ||||||||
| <secondary | 1 | 1 | 1 | 1 | ||||
| None/primary | 1.02 (0.80, 1.20) | 0.852 | 1.17 (0.91, 1.51) | 0.216 | 1.10 (0.82, 1.45) | 0.564 | 1.38 (1.03, 1.85) | 0.030 |
| Age at sexual debut | ||||||||
| 20+ years | 1 | 1 | - | - | - | |||
| 15-19 years | 1.00 (0.63, 1.57) | 0.990 | 2.37 (1.15, 4.89) | 0.020 | - | - | - | |
| <15 years | 1.73 (0.90, 3.33) | 0.102 | 2.23 (0.73, 6.81) | 0.160 | - | - | - | |
| Condom used in last | ||||||||
| Yes | 1 | 1 | 1 | 1 | ||||
| No | 0.66 (0.52, 0.85) | 0.001 | 1.00 (0.75, 1.35) | 0.987 | 1.01 (0.69, 1.48) | 0.958 | 1.10 (0.78, 1.55) | 0.590 |
| Contraceptive | ||||||||
| Others1 | 1 | |||||||
| Injectables | 1.29 (1.02, 1.63) | 0.032 | 1.45 (1.04, 2.04) | 0.027 | 1.50 (1.05, 2.12) | 0.025 | 1.70 (1.11, 2.49) | 0.014 |
| Oral pills | 0.65 (1,04, 1.81) | 0.022 | 0.57 (0.26, 1.28) | 0.291 | 0.50 (0.25, 1.10) | 0.100 | 0.80 (0.35, 1.86) | 0.602 |
| Marital status | ||||||||
| Married/cohabiting | 1 | 1 | 1 | 1 | ||||
| Single/not cohabiting | 2.82 (1.82, 4.38) | <0.001 | 2.80 (2.02, 3.85) | <0.001 | 1.79 (1.30, 2.47) | <0.001 | 1.95 (1.10, 3.51) | 0.025 |
| Parity | ||||||||
| 2+ | 1 | 1 | 1 | 1 | ||||
| <2 children | 2.04 (1.61, 2.60) | <0.001 | 1.54 (1.00, 2.39) | 0.052 | 1.36 (0.96, 1.93) | 0.085 | 1.91 (1.37, 2.68) | <0.001 |
| Number of sex partners | ||||||||
| <2 | 1 | 1 | 1 | 1 | ||||
| 2+ partners | 0.82 (0.36, 1.86) | 0.635 | 2.00 (0.87, 4.58) | 0.101 | 1.39 (1.04, 1.85) | 0.026 | 1.60 (1.13, 2.27) | 0.009 |
| Partner-has-another partners | ||||||||
| No | 1 | 1 | 1 | 1 | ||||
| Yes | 1.27 (0.69, 2.36) | 0.443 | 0.85 (0.26, 2.80) | 0.794 | 2.50 (1.42, 4.42) | 0.002 | 2.31 (1,46, 3.65) | <0.001 |
| Don’t know | 1.46 (0.82, 2.59) | 0.196 | 0.79 (0.28, 2.20) | 0.647 | 2.11 (1.37, 3.25) | 0.001 | 2.11 (1.48, 3.00) | <0.001 |
| Partner’s circumcision status | ||||||||
| Yes | 1 | 1 | 1 | 1 | ||||
| No | 0.90 (0.64, 1.22) | 0.450 | 0.80 (0.50, 1.24) | 0.299 | 1.16 (0.80, 1.70) | 0.435 | 1.05 (0.75, 1.46) | 0.771 |
| Don’t know | 0.65 (0.39, 1.10) | 0.110 | 0.83 (0.48, 1.44) | 0.512 | 1.94 (1.20, 3.17) | 0.008 | 1.75 (1.12, 2.76) | 0.015 |
| Any STI | ||||||||
| No | 1 | 1 | 1 | 1 | ||||
| Yes | 1.54 (1.10, 2.19)) | 0.016 | 1.87 (1.35, 2.60) | <0.001 | 2.24 (1.65, 3.05) | <0.001 | 2.26 (1.53, 3.34) | <0.001 |
Including male/female condoms, IUD
Table 3 presents the population-level impacts of the significant predictors of HIV incidence for each study period. Marital status was identified as the influential factor for HIV seroconversions in all study periods with PAR%:51%,56%,62% and 65% respectively; while using injectables was the second most important characteristics with PAR%:10%,14%, 25% and 33% overtime. Population-level impacts of baseline STI positivity has also increased from 11% to 21%. Population attributable risk for parity had the highest impact in 2002-2004 with 33%. These four-factors collectively associated with 71%, 75%, 80% and 88% of the HIV seroconversions in four study periods.
Table 3:
Individual and combined population level impacts (PAR%) of the risk factors
| 2002-2004 PAR% (95% CI) |
2005-2008 PAR% (95% CI) |
2009-2011 PAR% (95% CI) |
2012-2016 PAR% (95% CI) |
|
|---|---|---|---|---|
| Characteristics | ||||
| Not married | 51% (35%, 67% ) | 56% (50%, 62% ) | 62% (52%, 71% ) | 65% (52% , 83%) |
| Injectables | 10% (8%, 13% ) | 14% (9% , 18% ) | 25% (18%, 35% ) | 33% (24% , 45% ) |
| Any STI | 11% (8%, 14%) | 14% (11%, 17%) | 19% (16%, 23% ) | 21% (17%, 26% ) |
| Parity<2 | 33% (28%, 38% ) | 11% (8%, 15%) | 14% (9%, 20%) | 18% (14%, 24%) |
| Age at sexual debut | - | 49% (34%, 64%) | - | - |
| Number of sex partners 2+ | - | 13% (7%, 20%) | 7% (5%, 10%) | 12% (8%, 17%) |
| Partner has partner | - | - | 45% (38%, 54% ) | 43% (29%, 58%) |
| Don’t know | - | - | 37% (30%, 44%) | 36% (25%, 49%) |
| yes | - | - | 9% (6%, 12%) | 6% (4%, 9%) |
| Circumcision | - | - | 17% (10%, 27%) | 10% (6%, 18%) |
| No | - | - | 8% (4%, 14%) | 3% (1%, 7%) |
| Don’t know | - | - | 10% (4%, 12%) | 8% (6%, 10%) |
| No education | - | - | - | 4% (2%, 6%) |
| Combined risk factors 1 | ||||
| Not married | - | - | 76% | 79% |
| + not know | ||||
| if partner has another partner | ||||
| Combined risk factors 2 | ||||
| Not married | ||||
| + injectables | 71% (57%, 82% ) | 75% | 80% | 88% |
| + STI | ||||
| + Parity<2 | ||||
| Combined risk factors 3 | ||||
| Not married | - | - | 86% | 93% |
| + injectables | ||||
| + STI | ||||
| + Parity<2 | ||||
| + not know | ||||
| if partner has another partner | ||||
| + not know | ||||
| partner’s circumcision status |
DISCUSSION
Monitoring changes in HIV infections is crucial to evaluate the impact of the prevention efforts on the spread of the infections. In this study, we estimated temporal trends in HIV incidence rates among a cohort of women who resided in KwaZulu-Natal, South Africa. Despite the significant progress in prevention programs, infection rates increased from 6.2 to 9.3 per 100 person-year over the 15 years (p=0.0014). Our results are consistent with the most recent systematic review which reported the highest HIV incidence rates in KwaZulu-Natal, (Birdthistle et al. 2019). While, methodologically different estimates from the national surveys reported low rates (Shisana et al. 2005; Shisana et al. 2014;Zuma et al.2016).
In our study population, besides younger age, being single/not cohabiting, using injectable contraceptives, having less than two children and diagnosed with STI(s) (at baseline) were consistently associated with increased risk of HIV infection in all four consecutive cohorts. In particular, marital/cohabitation status was identified as the most influential factor on HIV infection rates. More than 50% of the infections were associated with single/not cohabiting women which was previously reported (Jewkes et al.,2004;Street et al., 2015). Considering the other factors, namely using injectable contraceptives, having less than two children and baseline STI positivity collectively had the highest impact in the last cohort, compared to the earliest cohort with PAR%:88% and 71% respectively. Although the most recent clinical trial found no association between hormonal contraceptives such as injectables and HIV infection, the current recommendations of World Health Organisation (WHO) reinforces “correct and consistent” condom use for high-risk women who prefer to use non-barrier contraceptives (ECHO 2019; WHO 2019). Our study also highlighted the impact of women’s knowledge in relation to their partners (i.e. not knowing if their partner has another partner and not knowing partner’s circumcision status), which was increased substantially overtime. More than 75% of the infections in last two cohorts were collectively associated with women’s marital/cohabitation status and lack of knowledge if their partner had another partner. Considering the injectable contraceptives, null/primiparity, STI diagnosis and not being aware of partner’s circumcision status changed the combined impact modestly by increasing PAR%s by 13% and 18% in 2009-2011 and 2012-2016 respectively. These relatively small changes were due to the overlapping nature of the factors considered in this study.
Given the significant progress in HIV prevention programs in the region, it is important to investigate the temporal changes in HIV incidence rates. Therefore, quantifying the temporal trends in risk factors and their population-level impacts on HIV infection rates may have significant public health and policy implications.
Limitations:
We used data from the women who enrolled in HIV prevention trials. Therefore, they may be at higher risk of HIV. All the sexual behaviours would be subject to recall bias. Nevertheless, our combined data provided a unique opportunity to estimate HIV incidence rates across the four consecutive cohorts among women who resided in KwaZulu-Natal, South Africa where the epidemic is currently described as hyperendemic (Tanser et al., 2009).
Supplementary Material
Table 1:
Characteristics of the study populations by study years (n=9,948)
| Characteristics | 2002-2004 (n=2,925) |
2005-2008 (n=2,853) |
2009-2011 (n=2,083) |
2012-2016 (n=2,087) |
p-value |
|---|---|---|---|---|---|
| Age | <0.001 | ||||
| <20 | 12% | 10% | 9% | 9% | |
| 20-24 | 27% | 33% | 42% | 43% | |
| 25-29 | 16% | 19% | 26% | 28% | |
| 30-34 | 15% | 13% | 13% | 11% | |
| 35+ | 29% | 25% | 11% | 8% | |
| Education | <0.001 | ||||
| None | 63% | 55% | 50% | 47% | |
| Primary | 36% | 41% | 42% | 47% | |
| Secondary/higher | 1% | 3% | 8% | 6% | |
| Age at sexual debut | - | ||||
| 20+ years | 6% | 5% | - | - | |
| 15-19 years | 78% | 78% | - | - | |
| <15 years | 16% | 17% | - | - | |
| Condom used in last sex | <0.001 | ||||
| Yes | 53% | 60% | 81% | 79% | |
| Contraceptive | <0.001 | ||||
| Others1 | 36% | 28% | 11% | 5% | |
| Injectables | 39% | 40% | 69% | 76% | |
| Oral pills | 6% | 6% | 14% | 20% | |
| Marital status | <0.001 | ||||
| Married/cohabiting | 33% | 15% | 23% | 13% | |
| Single/not cohabiting | 67% | 85% | 77% | 87% | |
| Parity | <0.001 | ||||
| < 2 children | 47% | 58% | 61% | 63% | |
| 2+ children | 53% | 42% | 39% | 37% | |
| Number of sex partners | <0.001 | ||||
| <2 | 93% | 96% | 81% | 80% | |
| 2+ partners | 7% | 4% | 19% | 20% | |
| Partner-has-another partners | <0.001 | ||||
| No | 31% | 24% | 11% | 14% | |
| Yes | 24% | 18% | 30% | 22% | |
| Don’t know | 45% | 58% | 59% | 64% | |
| Partner’s circumcision status | <0.001 | ||||
| Yes | 53% | 48% | 60% | 57% | |
| No | 23% | 25% | 26% | 32% | |
| Don’t know | 24% | 27% | 14% | 11% | |
| Any STI | 0.239 | ||||
| No | 83% | 82% | 81% | 82% | |
| Yes | 17% | 18% | 19% | 18% |
Including male/female condoms, IUD
Acknowledgements
We gratefully acknowledge the women who participated in the studies. We also acknowledge funding and support for the various studies from the UK Department for International Development and the Medical Research Council (MDP Feasibility Study: Grant number G0100137); the Bill & Melinda Gates Foundation (MIRA: Grant number 21,082); and the Division of AIDS, NIH (HPTN 055: Grant U01 AI048008).
Footnotes
Conflict of interest
The author(s) declare that they have no competing interests.
Ethical approval
Ethical approval for the trials, including all study protocols and informed consent forms, were received from the University of KwaZulu-Natal Biomedical Research Ethics Committee and the South African Medical Research Council Ethics Committee as well as the various study-specific Institutional Review Boards.
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