Overview and factors associated with pregnancies and abortions occurring in sex workers in Benin

Background: Behavioural and structural factors related to sex work, place female sex workers (FSWs) at high risk of maternal mortality and morbidity (MMM), with a large portion due to unintended pregnancies and abortions. In the African context where MMM is the highest in the world, understanding the frequency and determinants of pregnancy and abortion among FSWs is important in order to meet their sexual and reproductive health needs. Methods: Data from two Beninese cross-sectional surveys among FSWs aged 18+ (2013, N = 450; 2016, N = 504) were merged. We first performed exploratory univariate analyses to identify factors associated with pregnancy and abortion (p < 0.20) using Generalized Estimating Equations with Poisson regression and robust variance. Multivariate analyses first included all variables identified in the univariate models and backward selection (p ≤ 0.05) was used to generate the final models. Results: Median age was 39 years (N = 866). The proportion of FSWs reporting at least one pregnancy during sex work practice was 16.4%, of whom 42.3% had more than one. Most pregnancies ended with an abortion (67.6%). In multivariate analyses, younger age, longer duration in sex work, previous HIV testing, having a boyfriend and not using condoms with him were significantly (p < 0.05) associated with more pregnancies. Conclusion: One FSW out of five had at least one pregnancy during her sex work practice. Most of those pregnancies, regardless of their origin, ended with an abortion. Improving access to various forms of contraception and safe abortion is the key to reducing unintended pregnancies and consequently, MMM among FSWs in Benin. We used data from two cross-sectional surveys conducted in 2013 and 2016 that recruited, respectively, 450 and 504 FSWs from numerous sex work sites across the country. The primary objective of these surveys was to describe the overall context of sex work in 11 cities or towns located in seven departments of Benin (Fig. 1) and its evolution over this three-year period, when we implemented an human immunodeficiency viruses (HIV) prevention and reproductive health intervention program aimed at FSWs. Map of Benin. Blue-colored areas represent the departments and cities of the project. Figure built using an empty map frame freely and openly available at http://www.carte-du-monde.net/pays-1007-carte-benin-vierge.html and modified using Microsoft® Word for Office 365 MSO (16.0.12624.20348) 64-bit, version 2003 Before the two data collection periods, a local team mapped the different sex work sites in Benin. This mapping allowed an exhaustive census of all important sex work sites in the country and enumerated the FSW population (details given elsewhere) [25]. Then, we used cluster sampling to select a representative sample of sex work sites in the intervention localities (Fig. ​(Fig.1).1). In a second phase, trained and experienced investigators visited each selected site. All FSWs (defined as women aged ≥18 years and selling sex for money or goods at the time of the study) present at each site were enrolled after having provided informed consent. This process was done in 2013 and 2016 until the projected sample size of at least 450 FSWs was reached for each year. Following the recruitment period, investigators administered a quantitative reproductive health questionnaire during face-to-face interviews with each participant. The same questionnaire was used for both cross-sectional surveys. The two outcomes of interest in the present study were the occurrence of at least one pregnancy and that of at least one abortion since the moment each participant started engaging in sex work. We explored three types of independent variables during our model selection process: 1) Socio-demographic characteristics (age, region, country of origin, religion, education, marital status, having a boyfriend, cohabitation with a sexual partner, the numbers of dependent individuals and the number of biological children); 2) Sexual behaviours (age at sexual debut, age at first sex work experience, number of years involved in sex work, number of clients during the last working day, number of clients during the last 7 days and money received for the last sexual relation); and 3) Information about the use of SRH prevention services and contraception methods (using at least once SRH prevention services during sex work practice, participating as peer educator in HIV and sexually transmitted infections (STI) preventions activities, being tested for HIV at least once during lifetime, currently using hormonal contraception, condom use with clients and boyfriends in the last 7 days). We evaluated the impact of merging databases from both surveys (2013 and 2016) as means to enhance the statistical power of our analysis and identified participants that may have contributed information to both surveys, in order to exclude one of their contributions or to consider repeated measures in the data analysis. Because no nominal information was disclosed in both surveys, we used aggregate socio-demographic characteristics to identify potential participants contributing information in both surveys. We explored eight different combinations of six variables stable across time (i.e. month and year of birth, country of origin, religion, education level, age at sexual debut and age at first sex work experience). Following merger, we carried out descriptive statistics using proportions for discrete variables and means with standard deviations for continuous variables. We then compared the population characteristics between both cross-sectional surveys. Ultimately, we used univariate and multivariate Poisson regression models to identify factors associated with our two outcomes of interest. We estimated adjusted prevalence ratios (aPR) and their 95% confidence intervals (95%CI) with generalized estimating equations (GEE) using a robust variance estimator to decrease the potential impact of a correlation matrix incorrectly specified, and a clustering effect related to the FSWs recruited at the same prostitution site. We also adjusted all the models for survey year (2013 or 2016) to account for potential variations in behavioural characteristics between both surveys. We used a two-step model selection process to choose our independent variables. First, variables associated with the occurrence of at least one pregnancy with p-values < 0.2 in the univariate analysis were automatically included in the multivariate model. Then, we removed the least associated variables until all p-values were ≤ 0.05. Similar analyses were carried out for the occurrence of abortion among women reporting at least one pregnancy during their sex work practice. We performed all the analyses using SAS 9.4 (SAS Institute, Cary, NC, USA). To diminish the potential impact of sensitive questions, the interviewers were trained on ethical issues. Each participant provided written informed consent prior to the interview and no nominal information was reported on the questionnaire. The study was approved by the ethics committee of the CHU de Québec – Université Laval (Québec, Canada) and by the National Health Research Ethics Committee in Benin.