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Background: Global commitment to stop Human Immunodeficiency Virus (HIV) and ensure access to HIV treatment calls for women empowerment, as these efforts play major roles in mother-to-child transmission. We examined the association between women’s healthcare decision-making capacity and uptake of HIV testing in sub-Saharan Africa. Methods: We used data from the current Demographic and Health Surveys (DHS) of 28 countries in sub-Saharan Africa, conducted between January 1, 2010 and December 31, 2018. At the descriptive level, we calculated the prevalence of HIV testing in each of the countries. This was followed by the distribution of HIV testing across the socio-demographic characteristics of women. Finally, we used binary logistic regression assess the likelihood of HIV testing uptake by women’s health care decision-making capacity and socio-demographic characteristics. The results were presented as Crude Odds Ratios (COR) and Adjusted Odds Ratios (AOR) with their corresponding 95% confidence intervals signifying precision. Statistical significance was set at p-value < 0.05. Results: We found that prevalence of HIV testing uptake in the 28 sub-Saharan African countries was 64.4%, with Congo DR having the least (20.2%) and the highest occurred in Rwanda (97.4%). Women who took healthcare decisions alone [COR = 3.183, CI = 2.880–3.519] or with their partners [COR = 2.577, CI = 2.335–2.844] were more likely to test for HIV, compared to those whose healthcare decisions were taken by others, and this persisted after controlling for significant covariates: [AOR = 1.507, CI = 1.321–1.720] and [AOR = 1.518, CI = 1.334–1.728] respectively. Conclusion: Sub-Saharan African countries intending to improve HIV testing need to incorporate women’s healthcare decision-making capacity strategies. These strategies can include education and counselling. This is essential because our study indicates that the capacity of women to make healthcare decisions has an association with decision to test for their HIV status.
We used 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 Fig. 1). DHS is a nationwide survey collected every five-year period across low- and middle-income countries. DHS focuses 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 involved 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’s 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 in writing the manuscript. Prevalence of HIV testing among women in SSA The outcome variable was HIV testing uptake. It was derived from the question “have you ever tested for HIV?” and the responses were coded as “1=Yes and 0=No”. Thirteen explanatory variables were considered in our study, including the key explanatory variable (women’s decision-making on healthcare). Women’s decision-making on healthcare 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 other = 4 (family members and friends). Besides women’s decision-making on healthcare, 12 additional variables were included in the study. These are survey 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 survey country which was predetermined based on the geographical scope of the study, the selection of the rest of the variables was based on their association with HIV testing uptake in previous studies [6–8, 20–25]. Marriage was recoded into ‘married (1)’ and ‘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 ‘Traditional religion (1)’, ‘Christianity (2)’, ‘Islam (3)’, ‘No religion (4)’, and ‘Other religion (5)’ (e.g. Hinduism, Buddhism, Atheism, Juddaism, Taoism, Confucianism, Sikhism). 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 uptake in SSA (see Fig. Fig.1).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 multivariable regression (Model II) analyses to assess the predictors of HIV testing among women in SSA (see Table 2). All frequency distributions were weighted and 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). Socio-demographic characteristics and prevalence of HIV testing among women in SSA *P values are from chi-square test *Other religion (e.g. Hinduism, Buddhism, Atheism, Juddaism, Taoism, Confucianism, Sikhism) Logistic regression analysis on women’s healthcare decision-making capacity and HIV testing in SSA COR Crude Odds Ratio, AOR Adjusted Odds Ratio, CI Confidence Interval in square brackets, Ref Reference; *p < 0.05, **p < 0.01, ***p < 0.001 *Other religion (Hinduism, Buddhism, Atheism, Juddaism, Taoism, Confucianism, Sikhism)
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