Data analysis
A meta-analysis was performed to synthesize community-based global prevalence of infertility. To account for differences in study participants and diagnosed infertility among the included studies, we used random-effects models to calculate pooled estimates and 95% CI. For studies reporting prevalence of primary and secondary infertility, we also performed random-effects models to calculate pooled prevalence and 95%CI between the two groups. Higgins’ statistic and Q-test were used to detect heterogeneity across studies. An I ² value greater than 50% or a p-value less than 0.05 indicated significant heterogeneity. We used funnel plot to assess potential publication bias, for which p <0.1 was regarded as significant.
Subgroup analyses were conducted to estimate the infertility prevalence for participants or studies with different characteristics. Subgroup analyses were performed by study region (Africa, Asia, North Americas, Oceania, and Europe), year of investigation (1940-1990, 1991-2000, 2001-2010, and 2011-2022), female age (before 35 years versus above 35 years), research type (cross-sectional study and cohort or prospective study), and risk of bias (high, medium and low). Unordered multi-categorical information was compared two by two using the Bonferroni method, and grade information was analyzed for correlation using chi-square trend test.
Univariate meta-regression analysis is used in the text to explore possible sources of heterogeneity (Table S4). The dependent variable was infertility prevalence and the independent variables were year of investigation (dummy variable: 1940-1990), female age (dummy variable: before 35 years), study region (dummy variable: Africa), research type (dummy variable: cohort/prospective study), risk of bias (dummy variable: high), or sample size (defined as a continuous variable). We used a random effects meta-regression model with a restricted maximum likelihood approach. The proportion of prevalence estimates explained by any meta-regression model was estimated by the R² statistic.
To assess the stability of the results, we performed sensitivity analysis using leave-one-out method to assess the dependence of the findings on any individual study.