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

Area of Science:

• Public Health
• Reproductive Health
• Epidemiology

Background:

• The COVID-19 pandemic has had a profound global impact on public health.
• Menstrual disorders are a significant concern for women's reproductive health.
• Understanding the prevalence of menstrual disorders during the pandemic is crucial.

Purpose of the Study:

• To estimate the pooled prevalence of menstrual disorders in women surviving SARS-CoV-2 infection.
• To address publication bias in meta-analyses of menstrual disorder prevalence.
• To apply a novel Robust Bayesian Model Averaging-Publication Selection Model Averaging (RoBMA-PSMA) method.

Main Methods:

• A meta-analysis of existing evidence was conducted.
• Data analysis utilized both classical meta-analysis techniques and the novel RoBMA-PSMA approach.
• Statistical analyses were performed using R software and JASP software.

Main Results:

• The RoBMA-PSMA method yielded pooled prevalence estimates for various menstrual disorders.
• Specific estimates included: amenorrhea (12%), intermenstrual bleeding (17%), menstrual cycle regularity changes (24%), menstrual duration changes (15%), menstrual volume changes (12%), and pain-related changes (17%).
• Overall menstrual disorder prevalence was estimated at 9% (95% CI: 5%-13%) using RoBMA-PSMA, with significant differences compared to classical methods.

Conclusions:

• The COVID-19 pandemic significantly impacted women's reproductive health, with 9%-24% experiencing menstrual disorders.
• Educational initiatives and support services are vital for women affected by pandemic-related menstrual health issues.
• The RoBMA-PSMA method offers a robust and user-friendly approach to address publication bias and heterogeneity in meta-analyses.