Background: Menstr ual hygiene (MH) remains a critical public health challenge in sub-Saharan Africa, yet the role of household structure, particularly polygamy, in shaping MH outcomes has received limited scholarly attention. Objective: This study estimates the impact of polygamous household structures on menstrual hygiene practices among females aged 15-49 in Ghana, addressing a significant gap in the literature on intrahousehold determinants of reproductive health. Methods: Using nationally representative data from the 2022 Ghana Demographic and Health Survey (N=5,548), we employed multiple causal inference methods including Propensity Score Matching (PSM), Inverse Probability Weighting (IPW), and Emily Oster’s coef ficient stability test to estimate the ef fect of polygamy on MH. Spatial analysis mapped regional variations in polygamy prevalence and MH outcomes across Ghana’s 16 regions. Results: Menstrual hygiene practices were consistently lower among females in polygamous households across all analytical methods. A simple regression analysis revealed a 19.2 percentage point reduction (β = –0.192, p < 0.01), while the fully controlled model showed a 16.1 percentage point decrease (β = –0.161, p < 0.01). PSM yielded an Average Treatment Effect on the Treated (ATET) of 15.5 percentage points (p < 0.001), and IPW demonstrated a 12.4 percentage point reduction (p < 0.001). Emily Oster’s robustness checks indicate that the findings are unlikely to be driven by omitted variables. The δ value of 3.17 suggests that unobserved factors would need to be more than three times as influential as observed controls to eliminate the estimated effect. Conclusions: This study provides robust evidence that polygamous household structures significantly impair MH among Ghanaian females through resource dilution and gendered power dynamics. The findings inform targeted policy interventions for improving reproductive health equity in contexts where polygamy is prevalent. Limitations include the cross-sectional design, which restricts causal interpretation over time, and potential bias from self-reported data.
ACHEAMPONG et al. (Fri,) studied this question.