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Background. Renal failure is a leading cause of morbidity and mortality in many resource-constrained settings. In developing countries, little has been known about the prevalence and predisposing factors of renal failure using population-based data. The objective of this study was to examine the prevalence and associated factors of renal failure among women of reproductive age in Burundi. Methods. We used nationally representative cross-sectional data from the 2016-2017 Burundi Demographic and Health Survey (BDHS). Data on 17,269 women of reproductive age were included. The outcome variable was a renal failure as determined by the patient’s report. Percentage, chi-square test, and multivariable logistic regression model were used to analyze the data. The results from the logistic regression model were presented as adjusted odds ratio (AOR) and confidence interval (95% CI). The significance level was set at p<0.05. Results. The overall prevalence of renal failure was 5.0% (95% CI: 4.4%, 5.7%). Higher-Aged women were more likely to have a renal failure when compared with women aged 15-19 years. Rural dwellers were 1.65 times as likely to have a renal failure when compared with women in the urban residence (AOR = 1.65; 95% CI: 1.24, 2.20). Women who had secondary + education had a 39% reduction in the odds of renal failure when compared with women with no formal education (AOR = 0.61; 95% CI: 0.46, 0.81). Health insurance coverage accounted for a 23% reduction in the odds of renal failure when compared with women who were not covered by health insurance (AOR = 0.77; 95% CI: 0.63, 0.93). Women who had a terminated pregnancy were 1.50 times as likely to have a renal failure when compared with women with no history of terminated pregnancy (AOR = 1.50; 95% CI: 1.24, 1.82). Furthermore, women with a history of contraceptive use were 1.32 times as likely to have a renal failure when compared with women without a history of contraceptive use (AOR = 1.32; 95% CI: 1.11, 1.57). Conclusion. Lack of formal education, having no health insurance coverage, and ever used anything or tried to delay or avoid getting pregnant were the modifiable risk factors of renal failure. The nonmodifiable risk factors were old age, rural residence, certain geographical regions, and having a history of pregnancy termination. Understanding the risk factors of renal failure will help to instigate early screening, detection, and prompt treatment initiation. In addition, early detection of the risk factors can help to reduce the adverse health impact including maternal death.
We used cross-sectional nationally representative data extracted from the 2016-17 Burundi Demographic and Health Survey (BDHS). A sample of 17,269 women aged 15–49 years was included in this study. BDHS data was collected through a stratified multistage cluster sampling technique. The procedure for the stratification approach divides the population into groups by geographical region and crossed by place of residence–urban versus rural. A multilevel stratification approach is used to divide the population into first-level strata and to subdivide the first-level strata into second-level strata, and so forth. A major advantage is that the sampling design and data collection approach are similar across countries which makes the results of different settings comparable. Though from the onset, DHS was designed to expand on fertility, demographic, and family planning data collected in the World Fertility Surveys and Contraceptive Prevalence Surveys, it has become the prominent source of population surveillance for the monitoring of population health indices, particularly in resource-constrained settings. BDHS has great merits with national coverage of high-quality data to enhance the understanding of epidemiological research that estimates prevalence, trends, and inequalities and by communicating them to policymakers. BDHS data is available in the public domain and accessed at http://dhsprogram.com/data/available-datasets.cfm. The main outcome variable in this study was a renal failure as determined by the respondent's report. To derive this variable, BDHS asked the question: “Suffering from renal failure?” The respondents answered yes versus no. This was self-reported by the women based on their health condition. The independent variables include women's age, residential status, geographical region, education, religion, exposure to media, wealth quintiles, marital status, health insurance coverage, participation in the labor force, parity, source of drinking water, sanitation, ever had a terminated pregnancy, body mass index, ever used anything or tried to delay or avoid getting pregnant, anemia status, smoking/use tobacco product, and alcohol use. These variables were categorized as follows: women's age: 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49; residential status: urban versus rural; geographical region: Bubanza, Bujumbura Rural, Bururi, Cankuzo, Cibitoke, Gitega, Karusi, Kayanza, Kirundo, Makamba, Muramvya, Muyinga, Mwaro, Ngozi, Rutana, Ruyigi, Bujumbura Mairie, and Rumonge; education: no formal education, primary, and secondary/higher; religion: Christianity, Islam, and traditional/no religion; exposure to media: yes versus no; marital status: unmarried, currently married/living with a partner, and formerly married; health insurance coverage: covered versus not covered; participation in labor force: yes versus no; parity: nil, 1–3, and 4+; source of drinking water: improved versus unimproved; sanitation: improved versus unimproved; ever had a terminated pregnancy: yes versus no; body mass index: underweight, normal, overweight, and obese; ever used anything or tried to delay or avoid getting pregnant: yes versus no; anemia status: anemic versus not anemic; smoking/use tobacco product: yes versus no; alcohol use: yes versus no. The wealth indicator weights were determined by DHS using the principal component analysis (PCA) technique to assign the wealth indicator weights. Wealth indicator variable scores were allocated and standardized using household assets such as wall type, floor type, roof type, water supply, sanitation facilities, radio, electricity, television, refrigerator, cooking fuel, furniture, and the number of persons per room. The factor loadings and z-scores have then been determined. The indicator values were multiplied by the factor loadings for each household and summarized to generate the wealth index value of the household. To categorize the overall scores into wealth quintiles, the standardized z-score was used: poorest, poorer, middle, richest, and richest [24]. The factors examined in this study are based on previous studies related to renal failure [11, 12, 20, 21]. BDHS data is publicly available. We sought permission from MEASURE DHS/ICF International, and access to the data was granted after our intent for the request was assessed and approved. MEASURE DHS Program is consistent with the standards for ensuring the protection of respondents' privacy. ICF International ensures that the survey complies with the U.S. Department of Health and Human Services regulations for the respect of human subjects. No further approval was required as secondary data analysis was conducted in this study. More details about data and ethical standards are available at http://goo.gl/ny8T6X. The survey (‘svy') module was used to adjust for survey design. A variance inflation factor of 10 was used to determine multicollinearity known to cause major concerns in regression models [25, 26]. However, no variable was excluded from the model as they were not found to be interdependent. Percentage and multivariable logistic regression model were used to estimate the prevalence of renal failure and its associated factor, respectively [27]. The statistical significance was determined at p < 0.05. Stata Version 14 (StataCorp., College Station, TX, USA) was used for data analysis.
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