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Background: Health insurance coverage is one of the several measures being implemented to reduce the inequity in access to quality health services among vulnerable groups. Although women’s empowerment has been viewed as a cost-effective strategy for the reduction of maternal and child morbidity and mortality, as it enables women to tackle the barriers to accessing healthcare, its association with health insurance usage has been barely investigated. Our study aims at examining the prevalence of health insurance utilisation and its association with women empowerment as well as other socio-demographic factors among Rwandan women. Methods: We used Rwanda Demographic and Health Survey (RDHS) 2020 data of 14,634 women aged 15–49 years, who were selected using multistage sampling. Health insurance utilisation, the outcome variable was a binary response (yes/no), while women empowerment was assessed by four composite indicators; exposure to mass media, decision making, economic and sexual empowerment. We conducted multivariable logistic regression to explore its association with socio-demographic factors, using SPSS (version 25). Results: Out of the 14,634 women, 12,095 (82.6%) (95% CI 82.0–83.2) had health insurance, and the majority (77.2%) were covered by mutual/community organization insurance. Women empowerment indicators had a negative association with health insurance utilisation; low (AOR = 0.85, 95% CI 0.73–0.98) and high (AOR = 0.66, 95% CI 0.52–0.85) exposure to mass media, high decision making (AOR = 0.78, 95% CI 0.68–0.91) and high economic empowerment (AOR = 0.63, 95% CI 0.51–0.78). Other socio-demographic factors found significant include; educational level, wealth index, and household size which had a negative association, but residence and region with a positive association. Conclusions: A high proportion of Rwandan women had health insurance, but it was negatively associated with women’s empowerment. Therefore, tailoring mass-media material considering the specific knowledge gaps to addressing misinformation, as well as addressing regional imbalance by improving women’s access to health facilities/services are key in increasing coverage of health insurance among women in Rwanda.
We used the 2019–20 Rwanda Demographic Survey (RDHS) for this analysis, which was a cross-sectional study and employed a two-stage sample design, with the first stage involving cluster selection consisting of enumeration areas (EAs) [14]. The second stage involved systematic sampling of households in all the selected EAs leading to a total of 13,005 households [14]. In particular, the data used in this analysis were from the household and the woman’s questionnaires. During this survey, the data collection period was from November 2019 to July 2020, taking longer than expected due to the COVID-19 pandemic restrictions [14]. Women aged 15–49 years who were either permanent residents of the selected households or visitors who had stayed in the household the night before the survey were eligible to be interviewed. Out of the total 13,005 households that were selected for the survey, 12,951 were occupied and 12,949 were successfully interviewed leading to a 99.9% response rate [14]. This analysis included all women interviewed during the survey, and of the selected households, 14,675 women aged 15–49 were eligible to be interviewed but 14,634 women were successfully interviewed leading to a 99.7% response rate [14]. The study outcome variable was the usage of health insurance. This was defined if a respondent had any type of insurance that covers the whole or a part of the risk incurred from medical expenses, and was a binary variable directly coded yes or no [14]. Four indices were created to measure the empowerment of women: exposure to media, decision making, economic empowerment, and sexual empowerment. Women’s empowerment indices were measured as composite scores [5, 27]. Exposure to media was considered as the women’s ability to have the opportunity to read a newspaper or a magazine, listen to the radio and watch TV. Responses were re-coded (1 if the woman was exposed to newspapers, radio or TV and 0 if the woman was not). We then created an index, by adding all the scores for each woman, with the total score ranging from 0 to 3, after which we finally categorized the scores into four groups [5]. A total score of 0 meant no access to any of the three media, while scores of 1(low), 2(medium) and 3(high) implied exposure to one, two, and three media channels respectively [5, 27]. Decision-making included women’s ability to be involved in making decisions regarding; their own health; large household purchases; visits to their family and control over family earnings [5]. We re-coded the responses to have two categories (1 = woman involved in decision making alone or with a partner, 0 = woman not involved in decision making). We then added all the scores to form an index score ranging from 0 to 4, and we finally categorized the score into four groups. The highest score was four which meant that the woman was involved in the decision-making for the four used indicators. Medium decision-making ability meant that women were involved in 2 or 3 indicators, low decision making meant that the woman was involved in only one indicator and no decision making implied that the woman was not involved in any decision making [5, 27, 28]. Economic empowerment entailed women’s owning of a house, land and the type of earning from her work [5, 27]. We re-coded the three indicators as 1-if the women owned a house or land (either alone or jointly with a partner) or received cash payment for their work and 0-if didn’t own a house, land or cash payment for work. An index was then created by summing the scores for each woman, with a total score ranging from 0 to 3, after which we categorized the score into four groups. The highest score of 3 implied that the woman owned a house, land, and earned cash for her work, while scores of 2, 1 and 0 meant medium, low and no economic empowerment, respectively. Sexual empowerment referred to the women’s ability to refuse sex and ask a partner to use condoms [5, 38]. Responses were coded (1 if the woman could refuse sex or ask for a condom and 0 if the woman could not) and sexually empowered women were those who were able to refuse sex or ask their partners to use condoms. We then created an index by adding the scores for each woman with a total score ranging from 0 to 2, after which we categorized the score into three groups. The highest score of 2 implied high sexual empowerment, while scores of 1 and 0 respectively meant low and no sexual empowerment. Decision-making and sexual empowerment had about 7233 missing responses, while economic empowerment had about 3908 missing values, and this was because some of these questions were asked during the domestic violence survey sessions, yet not all women in the RDHS were included in the domestic violence module of the survey. These missing observations were assumed to be zero [5], thus we risked overestimating low subcategories of these composite indices/variables. To ensure that this doesn’t affect our findings, we conducted a sensitivity analysis by considering only women sampled in the domestic violence model and excluded those with missing responses. However, this showed no significant difference from the original analysis and more details are included in the sensitivity analysis section of the results. Moreover, for background characteristics, we provided frequencies of these variables considering only women with valid responses. We included possible determinants of health insurance utilisation based on available literature and data [32–37]. Ten (10) variables were considered and of these, two were community-level factors that included; place of residence and region of residence. Three household-level factors included; household size, sex of household head and wealth index. Wealth index was calculated by RDHS from information on household asset ownership using Principal Component Analysis [14]. Five individual-level factors were also considered in the analysis, including; age, educational level, working status, marital status, and religion. None of the included variables was a potential mediator of the the main relationship of interest (that is, health insurance and women empowerment). We applied the DHS sample weights to account for the unequal probability sampling in different strata and ensure the representativeness of the study results [39, 40]. We used Statistical Package for Social Sciences (SPSS) software (version 25.0) with a complex samples package, incorporating the following variables in the analysis plan to account for the multistage sample design inherent in the RDHS dataset: individual sample weight, sample strata for sampling errors/design, and cluster number [14, 39]. Initially, we did descriptive statistics for both dependent and independent variables. Frequencies and proportions/percentages for categorical dependent and independent variables have been presented. Afterwards, bivariable logistic regression was done to assess the association of each independent variable (i.e. women empowerment indicators and various socio-demographic factors) with health insurance utilisation and crude odds ratio (COR), 95% confidence interval (CI) and p-values are presented. Independent variables found significant at the bivariable level with p-values less than 0.25 were then included in the multivariable model, including factors known to be associated with health insurance usage based on previous studies, regardless of their significance. The final model controlled for all the included factors, where we calculated and presented their respective adjusted odds ratios (AOR), 95% CI and p-values, at a statistical significance level of 0.05. Since questions of decision making and sexual empowerment were asked to only women selected for the domestic violence module, we conducted a sensitivity analysis where we considered only women with domestic violence module responses, excluding those with no (missing) such responses. All socio-demographic variables in the model were assessed for multi-collinearity, which was considered present if the variables had a variance inflation factor (VIF) greater than 10 [41]. However, none of the variables had a VIF above 3.
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