Quantifying Vulnerabilities of Single Women and Sexually Transmitted Infections in South Africa (2002–2016): Is It Getting Better (or Worse)?

Abstract

Curable sexually transmitted infections (STIs) affect millions of people across the world. Besides unacceptably high HIV rates, South Africa also has the highest burden of STIs in the world. The aim of the study was to investigate temporal changes in STI incidence rates using the data from ~ 10,000 women who enrolled in several HIV prevention trials, KwaZulu-Natal, South Africa (2002–2016). We particularly focused on the changes in distribution of the most influential factors and their population-level impacts on STI incidence rates over time. Characteristics of the women were compared across the study periods: 2002–2004, 2005–2008, 2009–2011, and 2012–2016. Besides multivariable Cox regression models, population attributable risks were calculated for the significant factors. Despite the significant progress in prevention, testing, and treatment programs, infection rates increased substantially from 13.6 to 20.0 per 100 person-year over the study period. Our findings provided a compelling evidence for single/non-cohabiting South African women to be the most vulnerable population who consistently and substantially contributed to increasing STI rates during the 15 years of study duration (PAR%: 44%–47%). We also highlighted the impact of women’s lack of knowledge related to their partner, using injectable contraceptives, less parity, and baseline STI positivity which were increased substantially over time. Our findings suggest that a significant proportion of STIs could be prevented by targeting single/non-cohabiting. These results may provide guidance in developing more effective STI prevention programs by targeting women at highest risk of infections and delivering more realistic messages.

Appendix 1: Population Attributable Risk

We estimated population attributable risk (i.e. $PAR\%$) for the factors in the combined study population using a methodology which can handle complex and correlated nature of the several risk factors in a multivariable model setting (Wand & Ramjee, 2011) where $PAR\%$( 95% CIs) were estimated by combining the prevalence of an exposure and its strength with STI incidence (i.e. HRs). For a binary risk factor:

$$ {\text{PAR}} = \frac{{p({\text{HR}} - 1)}}{{p({\text{HR}} - 1) + 1}} = 1 - \frac{1}{{\sum\nolimits_{s = 1}^{2} {p_{s} {\text{HR}}_{s} } }} $$

(1)

${\text{HR}}$ is the hazard ratio, $p$ is the prevalence of the risk factor in the population and $s$ indexes the two strata determined by the value of the risk factor. In multi-factorial setting:

$$ {\text{PAR}} = \frac{{\sum\nolimits_{s = 1}^{S} {} p_{s} ({\text{HR}}_{s} - 1)}}{{1 + \sum\nolimits_{s = 1}^{S} {} p_{s} ({\text{HR}}_{s}^{{}} - 1) + 1}} = 1 - \frac{1}{{\sum\nolimits_{s = 1}^{S} {p_{s} {\text{HR}}_{s} } }} $$

(2)

where ${\text{HR}}_{s}$ and $p_{s}$, $s = 1,...,S$, are the hazard ratios and the prevalence of an exposure in the population for the $s$ th combination of the risk factors.

$$ {\text{PAR}} = \frac{{\sum\nolimits_{s = 1}^{S} {\sum\nolimits_{t = 1}^{T} {p_{st} {\text{HR}}_{1s} {\text{HR}}_{2t} - \sum\nolimits_{s = 1}^{S} {} \sum\nolimits_{t = 1}^{T} {p_{st} {\text{HR}}_{2t} } } } }}{{\sum\nolimits_{s = 1}^{S} {\sum\nolimits_{t - 1}^{T} {p_{st} {\text{HR}}_{1s} {\text{HR}}_{2t} } } }} = 1 - \frac{{\sum\nolimits_{t = 1}^{T} {p_{ \bullet t} {\text{HR}}_{st} } }}{{\sum\nolimits_{s = 1}^{S} {\sum\nolimits_{t = 1}^{T} {p_{st} {\text{HR}}_{1s} {\text{HR}}_{2t} } } }} $$

(3)

where $t$ denotes a stratum of unique combinations of levels of all background risk factors which are not modifiable and/or not under study, $t = 1,...,T,$ and $HR_{2t}$ is the hazard ratio in combination $t$ relative to the lowest risk level, where $HR_{2,1}$ = 1.