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Background  In sub-Saharan Africa, the use of modern contraception (MC) is a critical intervention aimed at reducing mortality rates associated with unintended, high-risk pregnancies. However, among Congolese women aged 15–49, the prevalence of MC use is low. Research suggests that women’s general participation in decision-making is important in increasing MC use. However, little is known about the specific role of women’s decision-making power over their own health care and how it relates to MC use. Thus, this study aimed to investigate the relationship between women’s decision-making power over their own health care and use of MC.    Methods  A cross-sectional secondary data analysis was conducted using the most recent data from the 2013–2014 Democratic Republic of the Congo (DRC) Demographic and Health Survey. Women who were considered in need of contraception based on their family planning preferences were included in the study population (N = 6422). Multivariate logistic regression was used to determine whether women’s decision-making power over their own health care was associated with the use of MC.    Results  Only one in ten women reported using a modern method of contraception. Logistic regression showed that women who made decisions alone regarding their own health care were more likely to use MC than women who had no say in these decisions, even after controlling for important covariates (OR 1.48; 95% CI 1.00, 2.17).    Conclusion  The results of this study lend further support that promoting women’s autonomy and right to independently make decisions regarding their own health may be important in increasing the use of MC in the DRC. However, in order to effectively empower women to negotiate for the use of MC, qualitative research is needed to further assess the relationship between decision-making power and MC use.

bmc women's health

Relationship between women’s decision-making power over their own health care and use of modern contraception in the Democratic Republic of the Congo: a cross-sectional secondary data analysis