Extreme Heterogeneity in Global Prevalence Meta-Analyses: Evaluating Current Practices and Exploring Bayesian Alternatives - an Umbrella Review

Introduction: Global prevalence meta-analyses often exhibit extreme heterogeneity (I² > 90%), yet criteria designed for clinical trials, where homogeneity is desirable, continue to be applied without recognizing that in prevalence studies, variability reflects real differences between populations. Objective: To document the magnitude of heterogeneity in global prevalence meta-analyses, evaluate the methodological strategies employed for its exploration and management, and explore through illustrative application how Bayesian methods—rarely employed in prevalence meta-analyses—compare with standard frequentist approaches. Methods: Umbrella review conducted according to PRIOR guidelines. Systematic search in SCOPUS for systematic reviews with global/worldwide prevalence meta-analyses published between 2015-2025. Data were extracted on I², statistical models, subgroup analyses, sensitivity analyses, meta-regression, and prediction intervals. Three meta-analyses were randomly selected for illustrative Bayesian re-analysis using hierarchical models with weakly informative priors, and the results were compared with those from frequentist approaches. Results: Of 53 included meta-analyses, 52 (98.1%) presented I²≥75%, 47 (88.7%) I²≥90%, and 34 (64.2%) I²>99%. Management strategies showed a decreasing implementation rate: subgroup analyses (96.2%), sensitivity analyses (64.2%), meta-regression (34.0%), and prediction intervals (5.8%). Among studies with I²≥75%, 63.5% provided explicit justification for proceeding with pooling. The illustrative Bayesian analysis of three randomly selected studies demonstrated excellent concordance with frequentist estimates (differences