Work

Robust Bayesian Model Averaging Meta‐Analysis of Menstrual Disorders in COVID‐19 Survivors: A Methodological Meta‐Analysis Study

Year: 2025

Type: article

Study designs: Systematic Review, Meta-Analysis

Abstract: ABSTRACT Background and Aims The COVID‐19 pandemic has significantly affected public health worldwide. This study applies a novel Robust Bayesian Model Averaging‐Publication Selection Model Averaging method (RoBMA‐PSMA) to address publication bias for a single proportion and estimate the pooled prevalence of menstrual disorders in surviving women from SARS‐CoV‐2 through a meta‐analysis of existing evidence. Methods Data analysis was performed using both classical and novel RoBMA‐PSMA approaches for meta‐analysis. The R software 4.4.1 package “metafor” and JASP 0.18.3 software were used to conduct statistical analysis. Results The pooled prevalence estimates via conditional ROBMA‐PSMA, were: amenorrhea, 12% (95% CI: 3%–20%); intermenstrual bleeding, 17% (95% CI: 3%–31%); menstrual cycle regularity changes, 24% (95% CI: 9%–34%); menstrual duration changes, 15% (95% CI: 3%–32%); menstrual volume changes, 12% (95% CI: 2%–24%), pain related changes, 17% (95% CI: 3%–30%), and overall, 9% (95% CI: 5%–13%). Results of other classical methods, including random/fixed and trim and fill methods showed significantly different pooled effect sizes. Conclusion Using a Robust Bayesian methodology, we found that 9%–24% of women of reproductive age experienced menstrual disorders during the COVID‐19 pandemic, highlighting its significant impact on women's health. To address this, it is crucial to educate women about the pandemic's effects on menstrual health and provide support services, including counseling and access to specialized healthcare providers. The RoBMA‐PSMA approach can help researchers effectively tackle publication bias and heterogeneity, offering a straightforward, data‐driven method that requires minimal technical expertise, supported by a user‐friendly tool. Trial Registration: Prospective Register of Systematic Reviews (PROSPERO): CRD42024561216.

Source: Health Science Reports

DOI: 10.1002/hsr2.71626

Authors: Ghazaleh Falahati, Akbar Biglarian, Samira Behboudi‐Gandevani, Razieh Bidhendi‐Yarandi

Institutions: University of Social Welfare and Rehabilitation Sciences, Nord University

Language: English

Topic: Meta-analysis and systematic reviews

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,280