We made use of pooled data from the current Demographic and Health Surveys (DHS) conducted from January 1, 2010 and December 31, 2018 in 28 countries in SSA (see Figure 1). DHS is a nationwide survey collected every five-year period across low and middle-income countries. DHS focused on maternal and child health by interviewing women of reproductive age (15 – 49 years) and men between 15 and 64 years. DHS surveys followed the same standard procedures – sampling, questionnaire development and data collection. However, data cleaning, coding and analysis were done in this study for cross – country comparison. The survey employed a stratified two stage sampling technique. The initial stage involves the selection of points or clusters (enumeration areas [EAs]) followed by a systematic sampling of households listed in each cluster or EA. For this study, the women file of the DHS data was used. All the participants were women in their reproductive age (15–49), who were usual members of the selected households and/or visitors who slept in the household on the night before the survey. In this study, only women in unions who had complete information on all the variables of interest were included (N= 195,307). We relied on the “Strengthening the Reporting of Observational Studies in Epidemiology” (STROBE) statement writing the manuscript.
Definition of variables
Outcome variable
The outcome variable was HIV testing. It was derived from the question “have you ever tested for HIV?” and the response was coded as “1=Yes and 0=No”.
Explanatory variables
Thirteen explanatory variables were considered in our study including the key explanatory variable (women’s decision-making on healthcare). It was derived from the question “Who usually makes decisions about healthcare for yourself: you, your (husband/partner), you and your (husband/partner) jointly, or someone else?” The responses were categorised as respondent alone, respondent and husband/partner, husband/partner alone, someone else and other. These were recoded into respondent/woman alone=1, respondent and husband/partner=2, husband/partner alone=3 and someone else and other=4.
Besides, 12 additional variables were included in the study. These are; country, age, educational level, marital status, religion, wealth status, place of residence, parity, occupation, and exposure to mass media (radio, television and newspaper). Apart from country of origin which was predetermined based on the geographical scope of the study, the selection of the rest of the variables were based on their association with HIV testing and counselling in previous studies [6, 7, 8, 20-25]. Marriage was recoded into ‘married (1)’, ‘cohabiting (2)’. Occupation was captured as ‘not working (0)’, ‘managerial (1)’, ‘clerical (2)’, ‘sales (3)’, ‘agricultural (4)’, ‘household/domestic (5)’, ‘services (6)’, and ‘manual (7)’. We recoded parity (birth order) as ‘zero birth’(0) ‘one birth (1)’, ‘two births (2)’, ‘three births (3)’, and four or more births (4)’. Lastly, religion was recoded as ‘Traditionalist (1)’ ‘Christianity (2)’, ‘Islam (3)’, ‘No religion (4)’ and ‘Other(5)’.
Statistical analyses
The data was analysed with STATA version 14.2 for Mac OS. The analysis was done in three steps. The first step was the computation of the prevalence of HIV testing in SSA (see Figure 1). The second step was a cross-tabulation by which we calculated the prevalence and proportions of HIV testing across the socio-demographic characteristics (see Table 1). Then, we conducted a bivariate logistic regression (Model I) and multivariate regression (Model II) analyses to assess the predictors of HIV testing among women in SSA (see Table 2). All frequency distributions were weighted while the survey command (svy) in STATA was used to adjust for the complex sampling structure of the data in the regression analyses. There was multicollinearity between knowing a place for HIV testing and HIV testing uptake. Due to this it was taken out of the analysis. After it was taken out, there was no evidence of multicollinearity among the remaining variables (Mean VIF=1.35, Maximum VIF=1.70, Minimum VIF=1.05). All results of the logistic regression analyses were presented as Crude Odds Ratios (CORs) and Adjusted Odds Ratios (AORs) at 95% confidence intervals (CIs).