Modeled Burden and Trends of Infertility Among People Aged 15–49 Years in the Original BRICS Countries: Comparisons with G7 and Global Estimates, 1990–2023, with Projections to 2050

Reproductive health is a cornerstone of sustainable human development and serves not only as a key indicator of population health, but also as a determinant of long-term economic, social, and demographic stability. 1 Globally, reproductive health challenges have intensified over recent decades. Since 1950, the global total fertility rate (TFR) has declined continuously, and by 2021 more than half of all countries and regions had fallen below the replacement level (TFR < 2.1). 2 Although this trend is largely driven by broader demographic, socioeconomic, and reproductive behavior changes, infertility represents a distinct reproductive health challenge at the individual and couple levels that may coexist with population-level changes in fertility patterns. According to the World Health Organization (WHO), infertility affects approximately one in six individuals worldwide during their lifetime. 3 Although this estimate reflects lifetime prevalence rather than age-specific burden, it underscores the broad global impact of infertility and its substantial public health and socioeconomic consequences. 4 Beyond its clinical dimensions, infertility profoundly affects individual well-being, family stability, and broader socioeconomic development. 5 , 6 Previous studies have shown that the burden of infertility is increasing globally, with particularly pronounced growth observed in middle and high-middle Socio-demographic Index (SDI) regions. 7 As major emerging economies largely situated within these SDI strata, the original BRICS countries (Brazil, Russia, India, China, and South Africa) represent a critical comparative setting in which these rising trends may be especially evident, yet remain insufficiently explored in existing global analyses. Although BRICS represents an economic grouping rather than an epidemiological category, these countries share rapid socioeconomic and demographic transitions relevant to reproductive health, providing a policy-relevant comparative framework for assessing collective burden while retaining substantial country-level heterogeneity. Collectively, BRICS countries account for over 40% of the global population, representing approximately 3.2–3.3 billion people based on 2023 estimates. 8 Alongside rapid socioeconomic growth, these countries are simultaneously exposed to a complex convergence of pressures, including population expansion, environmental pollution, lifestyle transitions, and the growing prevalence of metabolic diseases. 9 Within this context, infertility has emerged as a shared and increasingly prominent public health challenge. This burden is further compounded by structural shifts in fertility intentions and persistent inequities in access to reproductive healthcare resources, 10 posing significant risks to family well-being and demographic sustainability. 11 Importantly, infertility burden varies markedly across BRICS countries, reflecting substantial heterogeneity in socioeconomic systems, cultural norms, and health policy environments. Together, these shared transitions and within-group heterogeneity make the original BRICS countries a meaningful framework for comparative assessment of infertility burden. However, such complexity and within-group heterogeneity may be obscured in global or SDI-based analyses, highlighting the need for a focused investigation of infertility burden in BRICS countries. Given the dominant population sizes of China and India, aggregated BRICS estimates should be interpreted alongside country-specific results; the original five BRICS countries were retained to ensure a consistent historical framework across the 1990–2023 study period. The G7 countries (the United Kingdom, France, the United States, Germany, Italy, Japan, and Canada) were selected as a comparator group because they represent highly developed economies with different demographic profiles and healthcare systems, providing a reference for evaluating infertility burden across contrasting development trajectories. Because global estimates include both BRICS and G7 countries, these comparisons should be interpreted as descriptive benchmarks rather than statistically independent comparisons. The Global Burden of Disease (GBD) Study synthesizes epidemiological data using standardized analytical frameworks, providing a robust and authoritative platform for cross-national comparisons and long-term trend analysis. 12 Importantly, for epidemiological purposes, the GBD defines infertility as the inability to conceive after five years of unprotected intercourse among couples who desire a child. Although this differs from the commonly used clinical definition based on 12 months of unsuccessful attempts to conceive, it enables standardized estimation of population-level infertility burden and facilitates long-term and cross-national comparisons. However, existing GBD-based research on infertility has largely focused on global aggregates, single-country assessments, or gender-specific analyses. Consequently, comprehensive evaluations tailored specifically to the unique demographic and health transitions of BRICS countries remain scarce. Without such a targeted synthesis, the spatiotemporal dynamics of infertility and its underlying drivers across these emerging economies cannot be fully understood. To address this gap, this study provides a focused assessment of infertility burden in the original BRICS countries using GBD 2023 estimates. We systematically evaluate spatiotemporal trends in age-standardized prevalence rate (ASPR) and disability-adjusted life years (DALYs) attributable to infertility among reproductive-aged individuals (15–49 years) in BRICS countries from 1990 to 2023. We apply a suite of analytical approaches—including Joinpoint regression, age-period-cohort modeling, decomposition analysis, and descriptive analysis of cause-specific ranks—to characterize temporal patterns and identify key macroscopic contributors to observed changes. In addition, Bayesian modeling is used to project infertility burden from 2024 to 2050. Findings for BRICS countries are benchmarked against those of G7 countries and global averages, enabling a comparative perspective across different development trajectories. By doing so, this study aims to provide an evidence-based foundation for optimizing reproductive health resource allocation, informing context-specific public health strategies, and supporting the localized implementation of the United Nations Sustainable Development Goals (SDGs 3.7 and 3.8).

This study used data from the Global Burden of Disease, Injuries, and Risk Factors Study (GBD) 2023 to systematically assess the burden of infertility in BRICS countries (Brazil, Russia, India, China, and South Africa) from 1990 to 2023. The evaluated metrics included prevalence, absolute numbers of DALYs, and age-standardized rates (ASRs) per 100,000 population, all reported with 95% uncertainty intervals (UIs). Analyses were stratified by sex and 5-year age groups within the reproductive age range (15–49 years). 12 We applied Joinpoint regression, age-period-cohort (APC) modeling, decomposition analysis, and descriptive analysis of cause-specific ranks to characterize temporal trends and their underlying drivers. In addition, Bayesian models were used to project infertility burden from 2024 to 2050, with estimates compared against global levels and those of G7 countries. Through this comprehensive analytical framework, the study aims to clarify the spatiotemporal evolution of infertility burden in BRICS countries and to provide an evidence base for public health policy development. Data for this study were obtained from the GBD 2023 Results Tool developed by the Institute for Health Metrics and Evaluation (IHME) ( https://vizhub.healthdata.org/gbd-results/ ). Data extraction was performed in November 2025. Detailed extraction parameters are provided in Table S1 . All data were generated in accordance with the standardized methodological framework of the GBD study. 13 Detailed descriptions of data sources, case definitions, and analytical methods are available in the official GBD methodology publications. 12 This study adopts the GBD definition of infertility. In the GBD impairment analysis, infertility is defined as the inability to conceive a child, regardless of cause, after five years of unprotected sexual intercourse (without the use of contraceptives) among couples who desire to have a child. Estimates include both female and male infertility. This definition differs from the clinical definition of infertility—commonly defined as the failure to conceive after 12 months or more of unprotected intercourse. 14 However, for epidemiological purposes, the GBD adopts a demographic definition, which is more suitable for assessing long-term trends and facilitating cross-national comparisons. 7 By applying a standardized definition and harmonized modeling framework, this approach enables consistent estimation of population-level patterns and trends across countries and over time, although heterogeneity may remain. The primary metrics used in this study were prevalence and DALYs. Prevalence reflects the proportion of the population living with infertility during a specified period. DALYs represent a composite measure of disease burden, calculated as the sum of years of life lost (YLLs) and years lived with disability (YLDs), which capture health loss due to premature mortality and reduced quality of life, respectively. One DALY corresponds to the loss of one year of full health. 15 , 16 As infertility does not cause premature mortality (YLLs = 0), its entire disease burden is attributable to YLDs; therefore, DALYs equal YLDs. Estimates were reported as both absolute numbers (crude case counts) and rates (cases per 100,000 population). To account for differences in population age structure, ASRs were calculated using the GBD standard population. All estimates are presented with 95% uncertainty intervals (UIs). UIs were generated through a standardized process based on 1000 posterior draws, with the 2.5 th and 97.5 th percentiles defining the bounds. This approach incorporates uncertainty arising from multiple sources, including data inputs, model structure, and parameter estimation. 15 Secondary analyses, including EAPC, Joinpoint regression, APC modeling, decomposition analysis, and projections, were conducted using GBD point estimates; the 1000 posterior draws were not fully propagated through these analyses. To facilitate group-level comparisons, country-level absolute numbers were summed for BRICS and G7, whereas rate-based metrics were calculated using population-weighted averages: ASR(G,t) = [Σ ASR(c,t) × P(c,t)] / [Σ P(c,t)], where G denotes BRICS or G7, c represents a constituent country, ASR(c,t) is the country-specific age-standardized rate, and P(c,t) is the corresponding population. These group-level estimates were then compared with the corresponding global values. The estimated annual percentage change (EAPC) quantifies the average annual rate of change in a health metric over time, reflecting both the direction and magnitude of temporal trends. We calculated the EAPC for ASRs and the number of infertility cases to characterize trends during the study period. EAPC estimation was based on a linear regression model fitted on the natural logarithmic scale: Y = α + βX + ε, where Y represents the natural logarithm of the rate or number, and X denotes calendar year. The EAPC was then derived as: EAPC = 100 × [exp(β) − 1]. The 95% confidence interval (CI) for the EAPC was obtained from the fitted regression model. For descriptive purposes, an increasing trend was defined when the lower bound of the 95% CI was greater than 0, whereas a decreasing trend was defined when the upper bound of the 95% CI was less than 0. Trends were considered stable when the 95% CI included 0. The EAPC was used to provide a general description of overall temporal trends under a log-linear assumption and to facilitate comparison with previous GBD-based studies. Joinpoint regression analysis was performed using the Joinpoint Regression Software (version 5.1.0). This method identifies potential changes in temporal trends and statistically significant joinpoints and partitions long-term trends into distinct linear segments for separate evaluation. Rates were log-transformed before analysis, and standard errors of the estimates were provided during model fitting. A grid search algorithm was used to identify candidate joinpoints, and the optimal number and locations of joinpoints were determined using permutation tests (overall significance level = 0.05; 4499 permutations). The maximum number of permitted joinpoints was set to five. An uncorrelated error structure was specified. Analyses were conducted separately by sex and location. Unlike EAPC, which assumes a constant log-linear trend over the entire study period, average annual percentage change (AAPC) accounts for changes in trend segments identified by Joinpoint regression and provides an overall summary of non-linear temporal patterns. The analysis generated estimates of the annual percentage change (APC) and AAPC, both reported with 95% CIs. The APC reflects the direction and magnitude of change within each segment identified by joinpoints, whereas the AAPC summarizes the overall trend across the entire study period. Interpretation of trend direction (increasing or decreasing) was based on the 95% confidence intervals, with the same criteria applied across all estimates. 17 To characterize age-, period-, and cohort-related temporal patterns in infertility burden, this study employed an age-period-cohort (APC) analysis. The model estimates age, period, and cohort effects under specific identification constraints rather than providing completely independent or causal effects. The analysis was conducted using the online APC analysis tool developed by the US National Cancer Institute (NCI) (available at: https://analysistools.cancer.gov/apc/ ). The model assumed a Poisson distribution and was fitted using the weighted least squares approach, allowing for extra-Poisson variation to account for potential overdispersion. Statistical significance of the estimable APC functions was evaluated using Wald χ 2 tests under the identification constraints of the APC framework. Data were structured into 5-year age groups and 5-year calendar periods, encompassing seven age groups (15–49 years), six periods (1994–2023), and twelve birth cohorts (1949–2008). Annual infertility counts and corresponding population denominators were summed within each 5-year period before being entered into the NCI APC Web Tool. Because the overall study period from 1990 to 2023 comprised 34 years and could not be divided into complete 5-year intervals, the APC analysis was restricted to 1994–2023, yielding six consecutive 5-year periods. The period 2004–2008 and the birth cohort 1974–1978 were automatically determined as the reference categories using the default settings of the NCI APC Web Tool. Model outputs included net drift (overall annual percentage change), local drifts (age-specific annual percentage changes), longitudinal age curves, period rate ratios, and cohort rate ratios. Together, these parameters characterize modeled age-, period-, and cohort-related patterns under the identification constraints inherent in the APC framework. 17 To quantify the contributions of different factors to changes in the infertility burden, this study employed the Das Gupta decomposition method to examine the effects of population aging, population growth, and epidemiological change on infertility-related DALYs between 1990 and 2023. The equations and detailed implementation of the decomposition analysis are provided in Supplementary Methods . This method decomposes the total change in DALYs into three components within the decomposition framework: the aging effect (attributable to shifts in age-group composition), the population effect (attributable to changes in total population size), and the epidemiological effect (attributable to changes in age-specific prevalence rates). Absolute contributions were used to quantify the magnitude and direction of each component’s contribution to the overall change in DALYs, whereas relative contributions represented the proportion of each component’s contribution relative to the total observed change. When the total change was negative or close to zero, relative contributions exceeding 100% or negative values could occur because of offsetting effects among components. Using data from the GBD 2023 study, we extracted cause-specific prevalence estimates for female and male infertility according to the GBD cause hierarchy (eight causes for females, four for males). Temporal changes in the relative rankings of these causes from 1990 to 2023 were examined and visualized using heatmaps. This approach serves as a descriptive visualization of changes in cause-specific distributions, employing color-gradient mapping to identify overall patterns in cause composition rather than performing independent quantitative attribution calculations. 12 To project the burden of infertility from 2024 to 2050, this study applied a Bayesian age-period-cohort (BAPC) model within the Integrated Nested Laplace Approximation (INLA) framework. The model was fitted using age-specific infertility case counts and corresponding population estimates derived from the GBD 2023 study for seven 5-year age groups (15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 years), combined with population projections from the United Nations (available at: https://population.un.org ). The BAPC model assumes that observed case counts follow a Poisson distribution with a log link function, with population size incorporated as an offset. A second-order random walk prior was specified for smoothing the age-, period-, and cohort-specific rate parameters, using the default BAPC log-Gamma priors for the precision parameters (hyperparameters: 1 and 0.00005); an independent and identically distributed overdispersion term was assigned a log-Gamma prior with hyperparameters 1 and 0.005. Marginal posterior distributions were estimated using INLA. 18 Model estimation was performed using the BAPC and INLA packages in R (version 4.4.1). Because formal temporal validation was not performed in the present analysis, these projections should be interpreted as exploratory scenario-based estimates based on historical trends under current epidemiological trajectories rather than precise forecasts. All statistical analyses, data processing, and visualizations were performed using R software (version 4.4.1; R Foundation for Statistical Computing, Vienna, Austria). The analyses were performed using publicly available R packages, including BAPC and INLA. All statistical tests were two-sided, and a P value < 0.05 was considered statistically significant.

Table 1 and Table 2 and Figure 1 present trends in infertility prevalence and DALYs across BRICS countries, G7 countries, and the global population from 1990 to 2023. The corresponding annual ASPR and ASDR estimates with 95% UIs are provided in Tables S2 and S3 , respectively. Overall, the infertility burden in BRICS countries remained substantial in 2023. The estimated number of prevalent infertility cases was 106177.32 thousand (65073.57– 176539.25), representing a 70.02% increase compared with 1990 and accounting for 51.40% of the global total. Of these cases, female infertility accounted for 78112.33 thousand cases (47487.26–130311.34), whereas male infertility accounted for 28064.99 thousand cases (15451.5–47854.65). Figure 1 Trends in infertility prevalence and DALYs for global, G7, and BRICS from 1990 to 2023. A two-part graph compares infertility measures from 1990 to 2023 across locations. Left chart: Age-standardized prevalence rate per 100,000 people, ranging from 2500 to 6500. Trends: Russian Federation rises from 6464 to 6706; China rises early to 6400, stays near 6500; BRICS stable at 5100-5200, then rises to 6134; Global rises from 4288 to 5160; G7 lowest, rises from 2711 to 3369; Brazil declines to 2290, then rises to 3840; India declines to 3516, then rises to 6189; South Africa drops to 3363, rises to 5822. Right chart: Age-standardized DALY rate per 100,000 people, ranging from 15 to 35. Trends: Russian Federation rises from 37 to 39; China rises to 35; BRICS stable at 28-29, then rises to 34-35; Global rises from 24 to 29; G7 lowest, rises from 16 to 20; Brazil declines to 13-14, then rises to 21-22; India declines to 21, then rises to 34-35; South Africa drops to 24-25, rises to 33. A multi-line graph showing infertility prevalence and DALY rates by location from 1990 to 2023. Table 1 Trends in Infertility Prevalence for Global, G7, and BRICS from 1990 to 2023 Prevalence (95% UI) Location Absolute Number, Thousands Age-Standardized Rate, per 100,000 People 1990 2023 EAPC 1990–2023 1990 2023 AAPC 1990–2023 EAPC 1990–2023 Global 111361.96 (73801.65 to 177825.66) 206571.9 (133273.33 to 332523.9) 1.92 (1.86 to 1.98) 4287.7 (1949.36 to 8419.77) 5159.74 (2206.46 to 10483.99) 0.59 (0.51 to 0.67) 0.62 (0.5 to 0.75) Female 79473.93 (53070.71 to 125757.12) 149182.5 (95579.15 to 242381.89) 1.97 (1.91 to 2.03) 6256.08 (2760.81 to 12435.15) 7562.42 (3221.49 to 15545.48) 0.61 (0.51 to 0.71) 0.64 (0.52 to 0.77) Male 31888.03 (18723.21 to 50573.07) 57389.4 (33389.42 to 91405.89) 1.8 (1.72 to 1.87) 2383.25 (1005.55 to 4799.44) 2825.88 (1132.02 to 5811.19) 0.54 (0.45 to 0.62) 0.55 (0.42 to 0.68) G7 9589.25 (6273.45 to 15219.14) 11793.47 (7337.88 to 19187.87) 0.71 (0.57 to 0.85) 2711.12 (1325.46 to 5472.24) 3368.72 (1504.3 to 7140.89) 0.68 (0.54 to 0.82) 0.78 (0.62 to 0.95) Female 6525.31 (4280.71 to 10517.06) 8104.36 (5095.93 to 13408.17) 0.74 (0.62 to 0.87) 3705.94 (1863.59 to 7423.95) 4638.88 (2133.94 to 9847.04) 0.71 (0.56 to 0.86) 0.81 (0.65 to 0.96) Male 3063.94 (1759.25 to 5135.73) 3689.1 (2071.95 to 6254.11) 0.64 (0.47 to 0.82) 1725.34 (691.08 to 3618.92) 2117.54 (808.23 to 4605.49) 0.64 (0.52 to 0.76) 0.74 (0.55 to 0.94) BRICS 62448.79 (37717.83 to 104490.59) 106177.32 (65073.57 to 176539.25) 1.71 (1.63 to 1.79) 5191.76 (1982.97 to 10839.25) 6134.24 (2414.87 to 12808.56) 0.51 (0.42 to 0.59) 0.67 (0.53 to 0.81) Female 45315.81 (26996.73 to 75311.23) 78112.33 (47487.26 to 130311.34) 1.78 (1.69 to 1.86) 7850.57 (2895.98 to 16654.38) 9275.58 (3582.96 to 19401.49) 0.52 (0.43 to 0.61) 0.68 (0.53 to 0.83) Male 17132.98 (9273.37 to 28635.97) 28064.99 (15451.5 to 47854.65) 1.53 (1.46 to 1.6) 2712.11 (999.5 to 5821.72) 3167.62 (1215.85 to 6662.05) 0.45 (0.32 to 0.59) 0.57 (0.44 to 0.7) Brazil 1916.53 (1198.12 to 3172.32) 4124.41 (2454.48 to 6915.81) 3.25 (2.75 to 3.75) 2622.3 (1100.92 to 5342.87) 3602.03 (1355.79 to 7732.56) 0.96 (0.81 to 1.10) 1.76 (1.34 to 2.18) Female 1306.69 (821.14 to 2194.86) 2855.21 (1630.42 to 5060.2) 3.31 (2.81 to 3.82) 3549.89 (1481.92 to 7332.93) 4859.06 (1688.06 to 10796.04) 0.94 (0.72 to 1.15) 1.78 (1.36 to 2.21) Male 609.84 (347.82 to 1005.84) 1269.2 (714.27 to 2101.16) 3.1 (2.61 to 3.59) 1661.89 (645.3 to 3465.52) 2282.67 (893.97 to 4787.48) 0.99 (0.79 to 1.19) 1.7 (1.3 to 2.09) Russian Federation 5241.89 (3073.41 to 8957.32) 4915.51 (2943.95 to 8321.32) −0.05 (−0.11 to 0) 6463.66 (2501.15 to 13338.28) 6706.45 (2603.55 to 13795.25) 0.12 (0.10 to 0.13) 0.14 (0.12 to 0.15) Female 3442.45 (1988.89 to 5801.38) 3367.64 (1971.18 to 5705.58) 0.06 (0.01 to 0.11) 8530.96 (3187.97 to 17754.28) 8983.18 (3384.57 to 18899.39) 0.16 (0.14 to 0.18) 0.2 (0.18 to 0.22) Male 1799.44 (1005.84 to 3031.98) 1547.87 (876.37 to 2538.17) −0.29 (−0.37 to −0.22) 4408.46 (1654.08 to 9358.91) 4380.36 (1655.15 to 9331.32) −0.02 (−0.03 to −0.01) −0.01 (−0.03 to 0) India 15948.73 (10294.27 to 25895.95) 49230 (32185.84 to 78677.04) 4.23 (3.8 to 4.66) 3990.39 (1586.12 to 8433.07) 6150.55 (2541.42 to 12641.15) 1.36 (1.21 to 1.52) 2.05 (1.59 to 2.51) Female 11780 (7580.95 to 18876.83) 36422.66 (24183.78 to 58860.3) 4.26 (3.82 to 4.7) 6194.12 (2379.43 to 13218.73) 9400.2 (3777.02 to 19258.07) 1.32 (1.15 to 1.50) 2.02 (1.55 to 2.5) Male 4168.73 (2354.3 to 6898.21) 12807.34 (7447.04 to 20774.03) 4.13 (3.72 to 4.55) 1963.94 (744.17 to 4226.57) 3088.8 (1224.29 to 6413.97) 1.40 (1.25 to 1.56) 2.02 (1.58 to 2.46) China 38316.09 (22524.73 to 64816.12) 45724.33 (26177.85 to 79090.43) 0.31 (0.08 to 0.55) 6083.43 (2267.77 to 12667.17) 6501.09 (2428.29 to 13805.73) 0.20 (0.16 to 0.24) 0.1 (0.06 to 0.14) Female 27974.4 (16115.44 to 47116.2) 33763.33 (18704.5 to 57887.91) 0.38 (0.14 to 0.63) 9291 (3341.12 to 19727.23) 9969.05 (3717.82 to 21220.45) 0.20 (0.16 to 0.24) 0.11 (0.08 to 0.14) Male 10341.69 (5449.23 to 17343.08) 11961 (6149.9 to 21597.13) 0.12 (−0.09 to 0.33) 3124.49 (1132.1 to 6720.94) 3311.35 (1224.72 to 7050.38) 0.17 (0.13 to 0.21) 0.01 (−0.04 to 0.06) South Africa 1025.56 (627.3 to 1648.88) 2183.07 (1311.45 to 3534.65) 1.55 (0.89 to 2.22) 5788.37 (2272.95 to 12162.84) 5786.69 (2228.83 to 12168.56) 0.26 (−1.22 to 1.76) −0.63 (−1.29 to 0.03) Female 812.28 (490.3 to 1321.95) 1703.5 (997.39 to 2797.35) 1.44 (0.77 to 2.11) 9060.21 (3394.57 to 19135.05) 8998.97 (3376.77 to 19011.92) 0.23 (−1.26 to 1.74) −0.63 (−1.29 to 0.03) Male 213.28 (116.18 to 356.87) 479.58 (263.91 to 844.17) 1.96 (1.31 to 2.62) 2399.73 (873.53 to 5209.63) 2543.29 (958.88 to 5422.08) 0.61 (−0.29 to 1.51) −0.31 (−0.97 to 0.35) Table 2 Trends in Infertility DALYs for Global, G7, and BRICS from 1990 to 2023 DALYs (95% UI) Location Absolute Number, Thousands Age-Standardized Rate, per 100,000 People 1990 2023 EAPC 1990–2023 1990 2023 AAPC 1990–2023 EAPC 1990–2023 Global 632.15 (258.83 to 1498.37) 1169.39 (466.38 to 2789.87) 1.9 (1.84 to 1.97) 24.17 (7.55 to 63.14) 29.21 (8.6 to 76.24) 0.60 (0.52 to 0.69) 0.62 (0.5 to 0.75) Female 445.49 (180.46 to 1062.62) 834.1 (340.5 to 2049.6) 1.95 (1.89 to 2.01) 34.83 (10.38 to 92.43) 42.28 (12.22 to 110.68) 0.62 (0.51 to 0.72) 0.64 (0.52 to 0.77) Male 186.66 (69.16 to 449.5) 335.28 (123.47 to 793.47) 1.78 (1.71 to 1.86) 13.84 (4.09 to 35.97) 16.51 (4.63 to 42.86) 0.55 (0.45 to 0.65) 0.56 (0.43 to 0.68) G7 57.29 (22.25 to 136.28) 69.72 (26.53 to 171.62) 0.67 (0.53 to 0.82) 16.22 (5.11 to 41.48) 20.04 (5.88 to 52.2) 0.66 (0.54 to 0.79) 0.76 (0.59 to 0.93) Female 38.27 (14.74 to 91.5) 46.95 (18.15 to 114.76) 0.7 (0.57 to 0.83) 21.77 (7.04 to 56.58) 27.04 (8.15 to 71.27) 0.68 (0.55 to 0.82) 0.78 (0.62 to 0.94) Male 19.02 (7.04 to 45.94) 22.77 (8.25 to 54.71) 0.62 (0.43 to 0.8) 10.73 (2.91 to 28.35) 13.14 (3.38 to 35.36) 0.63 (0.52 to 0.74) 0.73 (0.53 to 0.93) BRICS 346.83 (128.15 to 844.28) 592.41 (222.15 to 1437.86) 1.71 (1.63 to 1.79) 28.65 (7.54 to 78.64) 34.33 (9.49 to 92.44) 0.54 (0.45 to 0.63) 0.69 (0.56 to 0.82) Female 249.28 (92.86 to 624.26) 431.41 (163.25 to 1069.36) 1.77 (1.69 to 1.86) 42.92 (10.75 to 120.54) 51.37 (13.48 to 138.46) 0.55 (0.45 to 0.64) 0.7 (0.56 to 0.84) Male 97.55 (34.29 to 243.31) 161 (56.73 to 394.7) 1.54 (1.47 to 1.62) 15.34 (3.94 to 41.5) 18.24 (4.84 to 48.28) 0.51 (0.37 to 0.64) 0.6 (0.48 to 0.73) Brazil 11.47 (4.4 to 28.39) 23.68 (9.27 to 59.62) 3.14 (2.65 to 3.64) 15.55 (4.59 to 41.51) 20.74 (5.65 to 58.12) 0.89 (0.76 to 1.01) 1.69 (1.28 to 2.1) Female 7.68 (3.05 to 19.05) 16.11 (6.24 to 41.82) 3.21 (2.71 to 3.72) 20.69 (5.92 to 57.22) 27.51 (7.13 to 78.85) 0.87 (0.69 to 1.04) 1.72 (1.3 to 2.14) Male 3.79 (1.42 to 9.09) 7.56 (2.83 to 18.4) 2.99 (2.51 to 3.47) 10.23 (2.85 to 27.24) 13.64 (3.71 to 37.45) 0.93 (0.68 to 1.18) 1.62 (1.24 to 2.01) Russian Federation 30.15 (11.15 to 73.11) 27.97 (10.37 to 68.83) −0.09 (−0.14 to −0.03) 37.32 (10.28 to 100.99) 38.63 (10.54 to 104.79) 0.11 (0.10 to 0.12) 0.13 (0.11 to 0.14) Female 19.47 (7.07 to 46.98) 18.83 (6.87 to 45.64) 0.03 (−0.02 to 0.08) 48.42 (12.55 to 133.38) 50.83 (12.95 to 138.75) 0.15 (0.13 to 0.17) 0.18 (0.16 to 0.2) Male 10.68 (3.79 to 26.19) 9.14 (3.28 to 22.55) −0.31 (−0.38 to −0.24) 26.29 (6.84 to 71.66) 26.18 (6.82 to 71.53) −0.02 (−0.03 to 0.00) −0.01 (−0.02 to 0) India 93.67 (36.24 to 223.57) 282.56 (109.99 to 665.96) 4.07 (3.67 to 4.47) 23.12 (6.49 to 60.59) 35.24 (10.19 to 91.52) 1.32(1.20 to 1.45) 1.93 (1.5 to 2.37) Female 68.17 (27.1 to 158) 206.5 (83.07 to 496.81) 4.11 (3.7 to 4.52) 35.36 (9.45 to 93.79) 53.2 (14.72 to 138.88) 1.29 (1.15 to 1.44) 1.92 (1.47 to 2.37) Male 25.5 (9.61 to 59.64) 76.06 (27.58 to 176.43) 3.97 (3.59 to 4.35) 11.85 (3.08 to 31.34) 18.31 (4.94 to 47.82) 1.34 (1.20 to 1.49) 1.89 (1.49 to 2.3) China 205.68 (74.16 to 505.14) 245.83 (87.68 to 614.65) 0.3 (0.06 to 0.54) 32.54 (8.2 to 91.41) 35.12 (9.19 to 98.16) 0.23 (0.19 to 0.27) 0.11 (0.07 to 0.15) Female 149.36 (53.89 to 389.36) 180.41 (63.29 to 462.3) 0.37 (0.12 to 0.62) 49.45 (11.86 to 142.83) 53.51 (13.15 to 148.61) 0.22 (0.18 to 0.27) 0.12 (0.08 to 0.15) Male 56.32 (19.01 to 145.18) 65.43 (22.06 to 170.34) 0.12 (−0.1 to 0.33) 16.95 (4.29 to 46.3) 18.23 (4.75 to 48.55) 0.22 (0.15 to 0.28) 0.03 (−0.03 to 0.09) South Africa 5.87 (2.19 to 14.07) 12.36 (4.85 to 28.81) 1.51 (0.85 to 2.18) 32.76 (8.92 to 90.94) 32.76 (8.86 to 90.35) 0.39 (−0.56 to 1.36) −0.65 (−1.31 to 0.02) Female 4.6 (1.76 to 10.88) 9.56 (3.79 to 22.78) 1.4 (0.73 to 2.07) 50.79 (13.2 to 140.77) 50.48 (13.31 to 139.64) 0.37 (−0.60 to 1.35) −0.65 (−1.3 to 0.01) Male 1.26 (0.45 to 3.2) 2.8 (0.99 to 6.97) 1.9 (1.24 to 2.56) 14.06 (3.67 to 38.34) 14.87 (3.91 to 40.34) 0.60 (−0.27 to 1.48) −0.35 (−1 to 0.32) Trends in infertility prevalence and DALYs for global, G7, and BRICS from 1990 to 2023. Trends in Infertility Prevalence for Global, G7, and BRICS from 1990 to 2023 Trends in Infertility DALYs for Global, G7, and BRICS from 1990 to 2023 The ASPR in BRICS countries increased from 5191.76 per 100,000 population (1,982.97–10,839.25) in 1990 to 6134.24 per 100,000 (2,414.87–12,808.56) in 2023. The corresponding point estimate was higher than those for G7 countries and the global average, although the uncertainty intervals substantially overlapped. The AAPC in ASPR for BRICS was 0.51 (95% CI: 0.42–0.59), which was lower than those for the global average and G7 countries. Across BRICS countries, the burden of female infertility consistently exceeded that of male infertility. In 2023, the estimated ASPR and ASDR in females were approximately three times those in males. Marked heterogeneity was observed across individual BRICS countries. In 2023, Russia had the highest estimated ASPR, followed closely by China. However, ASPR trends in both countries were relatively stable over time. In contrast, India exhibited the fastest increase in ASPR (AAPC = 1.36; 95% CI: 1.21–1.52), with a faster increase than those observed globally, in G7 countries, and in BRICS overall. Although Brazil showed the lowest ASPR in 2023, it nonetheless experienced a relatively rapid increase over time (AAPC = 0.96; 95% CI: 0.81–1.10). Joinpoint regression analysis showed that ASPRs and ASDRs of infertility followed broadly similar patterns in the global population, BRICS countries, and G7 countries from 1990 to 2023, with an overall fluctuating upward trend. A pronounced increase was observed across all three groups during 2010–2014, with the most marked rise occurring in BRICS countries, where the APC reached 5.02 (4.10–5.95). Within BRICS countries, Brazil experienced a sharp increase in ASPR between 2000 and 2004, reaching a peak with an APC of 12.73, followed by a period of relative stabilization. Russia and China both exhibited modest but persistent increase in ASPR over the study period, with AAPC of 0.12 (0.10–0.13) and 0.20 (0.16–0.24), respectively. In India, ASPR increased rapidly during 2011 to 2014 (APC: 15.88), after which the rate of increase moderated between 2014 and 2023. In contrast, South Africa showed fluctuations in ASPR trends, with a decline between 2009 and 2015 followed by a short-term increase from 2020 to 2023 (APC: 23.26). Figure 2 presents the net drift and local drift estimates of infertility prevalence across age groups in the global population, BRICS countries, and G7 countries from 1990 to 2023. Net drift represents the overall annual percentage change in age-standardized prevalence over the study period, whereas local drift reflects age-specific annual percentage changes relative to the net drift. Figure 2 Net drift rates of infertility by age group in global, G7, and BRICS from 1990 to 2023. The image A showing a multi-line graph titled Local Drifts with Net Drift Both. The horizontal axis label is Age with categories 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49. The vertical axis label is Percent per Year with range negative 3 to 3. Legend series: Global, G7, BRICS, Brazil, Russian Federation, India, China, South Africa. Multiple solid lines with point markers and vertical error bars are plotted, plus horizontal dashed reference lines for each series. The India series is the highest across most ages, peaking around age 25-29 to 30-34 near 3 percent per year, then declining toward about 2 percent per year by 45-49. The Russian Federation series is also high, around 2 to 2.5 percent per year through ages 20-24 to 35-39, then declines to near 1 percent per year by 45-49. The BRICS series stays around 1.5 to 2.5 percent per year, highest near 20-24, then declines toward about 1 percent per year by 45-49. The Global series trends downward from about 1 to 1.5 percent per year at 15-19 to about 0.4 percent per year by 45-49. The G7 series stays near about 0.7 to 1.1 percent per year with a slight decline by 45-49. South Africa is near 0 percent per year at younger ages and drops below 0 after 35-39, reaching the lowest value near negative 3 percent per year at 45-49. The image B showing a multi-line graph titled Local Drifts with Net Drift Male. The horizontal axis label is Age with categories 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49. The vertical axis label is Percent per Year with range negative 2 to 3. Legend series match image A. India is the highest, rising from about 1.3 percent per year at 15-19 to near 3 percent per year around 25-29 to 30-34, then falling to about 0.5 to 1 percent per year by 45-49. BRICS remain above Global across most ages, around 1.2 to 1.4 percent per year at younger ages, then decline toward about 0 percent per year by 45-49. Global declines from roughly 1.1 percent per year at 15-19 to near 0 percent per year at 45-49. G7 stays near about 0.8 to 1.1 percent per year with a mild decline by 45-49. Brazil remains around 2 percent per year through the younger and middle age groups. China remains close to 0 percent per year. South Africa drops below 0 after about 35-39 and reaches the lowest point near negative 1.5 percent per year at 45-49. The image C showing a multi-line graph titled Local Drifts with Net Drift Female. The horizontal axis label is Age with categories 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49. The vertical axis label is Percent per Year with range negative 3 to 4. Legend series match image A. India is the highest, near 1.9 percent per year at 15-19, peaking around 3.2 percent per year near 25-29, then declining to about 1.7 percent per year by 45-49. BRICS remains above Global across most ages, around 1.4 to 1.9 percent per year through mid ages, then decline toward about 0.5 percent per year by 45-49. Global declines from about 1.4 percent per year at 15-19 to about 0.3 percent per year at 45-49. G7 stays near about 0.7 to 1.0 percent per year with a slight decline by 45-49. Brazil remains around 2 percent per year through younger and middle ages. China remains near 0 percent per year. South Africa is near 0 at younger ages and drops below 0 after about 35-39, reaching the lowest value near negative 3 percent per year at 45-49. A multi-line graph set showing local drifts with net drift of infertility across age groups by sex. Net drift rates of infertility by age group in global, G7, and BRICS from 1990 to 2023. Age-standardized infertility prevalence showed an overall increasing trend globally, in BRICS countries, and in G7 countries during 1990–2023. The net drift rate in BRICS countries was 0.94% per year (0.82–1.07), exceeding the corresponding rates observed in both G7 countries and the global population. Among BRICS countries, South Africa was the only country with a negative net drift rate (−0.67% per year; −1.01 to −0.32). This estimate, derived from the APC model, indicates a downward trend after accounting for age, period, and cohort effects. In contrast, infertility prevalence increased markedly in India and Brazil, with net drift rates substantially higher than those observed globally, in G7 countries, and in BRICS overall. At the age-group level, the most pronounced increase in infertility prevalence within BRICS was observed among Indian women aged 25–29 years, with a local drift rate of 3.21% per year (2.72–3.69). Conversely, the steepest decline occurred among South African women aged 45–49 years, with a local drift rate of −2.74% per year (−4.02 to −1.44). Figure 3 presents the age-period-cohort model of infertility for the global, G7, and BRICS from 1990 to 2023. Age effects consistently exhibited a unimodal pattern, indicating that the modeled age effect increased initially and subsequently declined with advancing age. The highest modeled ASPRs were observed in the 30–44-year age groups. Globally and in BRICS countries, the modeled ASPR peaked at 35–39 years, whereas in G7 countries the peak occurred at 40–44 years. Among BRICS countries, the highest ASPR for male infertility was estimated among Russian men aged 30–34 years, at 7598.17 (7559.42–7637.11). For female infertility, the highest ASPR was estimated in Chinese women aged 35–39 years, at 21,248.70 (21,077.98–21,420.80). Figure 3 Age-period-cohort model of infertility in global, G7, and BRICS from 1990 to 2023. The image features nine graphs examining age, period and cohort effects on infertility for both sexes globally and in specific regions: G7, BRICS, Brazil, Russia, India, China and South Africa. Age Effects: The x-axis shows age groups (15-19 to 45-49), with the y-axis indicating rates. Infertility peaks between ages 30-44, especially in BRICS countries, notably China. Period Effects: The x-axis covers periods (1994-1998 to 2019-2023), with the y-axis showing rate ratios. Cohort Effects: The x-axis spans cohorts (1949-1953 to 2004-2008), with the y-axis showing rate ratios. Multi-line graphs showing age, period and cohort effects on infertility by sex and region from 1990 to 2023. Age-period-cohort model of infertility in global, G7, and BRICS from 1990 to 2023. Regarding period effects, the 2004–2008 period served as the reference period (rate ratio [RR] = 1). The estimated period rate ratio for BRICS increased from 0.93 (0.90–0.96) in the 1994–1998 period to 1.17 (1.13–1.20) in the 2014–2018 period, followed by a slight decline during 2019–2023. Among BRICS countries, India demonstrated a continuous increase in the estimated period rate ratio, reaching its highest value in the 2019–2023 period (RR = 1.60, 1.45–1.77). In contrast, both Brazil and South Africa showed their highest estimated period rate ratios during 2004–2008, after which an overall downward trend was observed from 2009 to 2023. Notably, South Africa experienced a particularly marked decline, with the estimated period rate ratio decreasing to 0.56 (0.52–0.64) in 2014–2018 period, followed by a slight rebound in 2019–2023. Regarding cohort effects, the 1974–1978 birth cohort was used as the reference group (RR = 1). Overall, the estimated cohort rate ratio for BRICS exhibited an upward trend across successive birth cohorts. The highest value was observed in the 1999–2003 birth cohort, reaching 1.49 (1.40–1.59). Among BRICS countries, India and Brazil showed particularly rapid increases in modeled cohort relative rates. In India, the highest estimated cohort rate ratio was observed in the 1999–2003 birth cohort (RR = 1.92, 1.64–2.26). To assess the contributions of population growth, population aging, and epidemiological change to temporal trends in infertility burden from 1990 to 2023, a decomposition analysis was conducted ( Figure 4 ), with detailed quantitative contributions reported in Table S4 . The results indicated that population growth represented the largest contributing component of the increase in infertility-related DALYs in both BRICS countries and globally, followed by epidemiological change. In contrast, epidemiological change represented the largest contributing component of DALY changes in G7 countries. Figure 4 Decomposition analysis of infertility DALYs in global, G7, and BRICS from 1990 to 2023. Three horizontal bar charts labeled Both, Male and Female show DALYs changes across regions: Global, G7, BRICS, Brazil, Russia, India, China, South Africa. X-axis represents Absolute change in DALYs. Legend: Aging, Epidemiological change, Population, Total change. Both chart: Global reaches ~537,000 DALYs, BRICS ~246,000, India ~189,000, China ~40,000; others near 0. Male chart: Global ~149000, BRICS ~63,000, India ~51,000, China ~9000; others near 0. Female chart: Global ~389.000, BRICS ~182,000, India ~138,000, China ~31,000; others near 0. A stacked horizontal bar chart showing absolute change in infertility DALYs by component and sex. Decomposition analysis of infertility DALYs in global, G7, and BRICS from 1990 to 2023. Among BRICS countries, Russia was the only country to experience an overall decline in infertility-related DALYs during the study period (total change: −2.18 thousand DALYs). Brazil and India exhibited patterns similar to the overall BRICS trend, with population growth representing the largest contributing component of the increase in DALYs, followed by epidemiological change. In China, population aging accounted for the largest share of the increase in DALYs, whereas population growth contributed negatively, partially offsetting the overall increase. Notably, these components are derived from aggregated data and are not mutually independent; therefore, they should be interpreted as contributions within the decomposition framework rather than independent causal effects. To explore temporal patterns in the composition of cause-specific infertility burden from 1990 to 2023, a heatmap analysis was conducted ( Figures 5 and 6 ). According to the GBD 2023 framework, four causes were specified for male infertility: chlamydial infection, gonococcal infection, Klinefelter syndrome, and urogenital congenital anomalies. However, no GBD 2023 data were available for chlamydial or gonococcal infection in relation to male infertility; therefore, these causes could not be included as independent categories in the rankings. Figure 5 Heatmaps of cause-specific ranks for male infertility at the global level and in G7 and BRICS countries, 1990–2023. Two heatmaps show ranks: Male infertility, Klinefelter syndrome, Urogenital anomalies; cells numbered. Figure 6 Heatmaps of cause-specific ranks for female infertility at the global level and in G7 and BRICS countries, 1990–2023. Heatmaps titled ′1990 Female ASPR′ and ′2023 Female ASPR′ rank female health issues across regions: Global, G7, BRICS, Brazil, China, India, Russian Federation and South Africa. Rows represent causes like Female infertility, Polycystic ovarian syndrome and Urogenital congenital anomalies. Columns represent regions. Each cell contains a rank number from 1 to 9. Warmer colors indicate higher ranks, cooler colors lower ranks. In both years, Female infertility ranks 1st globally. In 1990, Polycystic ovarian syndrome ranks 2nd in some regions, while Other sexually transmitted infections are 2nd in others. By 2023, Polycystic ovarian syndrome moves above Other sexually transmitted infections in several regions. Urogenital congenital anomalies consistently rank 9th. Notable changes include Polycystic ovarian syndrome rising in Russia, India, and China. The heatmaps show stable and shifting patterns in female health issue rankings over time. Global female infertility causes ranked by heatmaps: G7, BRICS, Brazil, China, India, Russia, S. Africa, 1990-2023. Heatmaps of cause-specific ranks for male infertility at the global level and in G7 and BRICS countries, 1990–2023. Heatmaps of cause-specific ranks for female infertility at the global level and in G7 and BRICS countries, 1990–2023. In contrast, eight causes were identified for female infertility, including chlamydia infection, gonococcal infection, other sexually transmitted infections (STIs), maternal sepsis and other infections, polycystic ovarian syndrome (PCOS), endometriosis, Turner syndrome, and urogenital congenital anomalies. In addition, the categories of “female infertility” and “male infertility” are defined as the inability to conceive a child after five years of unprotected sex (without using contraceptives) that is not due to another cause of infertility (ie, this cause refers only to idiopathic cases). In 1990, idiopathic infertility ranked highest among the categorized conditions contributing to male infertility burden in BRICS countries, followed by Klinefelter syndrome, while urogenital congenital anomalies ranked lowest. This ranking pattern remained largely unchanged in 2023. For female infertility, the categories with the highest relative rankings globally in 1990 were idiopathic infertility, other STIs and PCOS. By 2023, PCOS ranked second globally. Within BRICS countries, however, other STIs consistently ranked among the top contributing categories to female infertility burden throughout the study period. This pattern was particularly evident in Brazil, Russia, and South Africa, where other STIs and chlamydia infection ranked second and third, respectively. Meanwhile, compared with 1990, the relative ranking of PCOS increased in Russia, India, and China. In this study, population projections for 2024–2050 were integrated with the BAPC model to generate exploratory projections of infertility ASPR and ASDR through 2050 ( Figures 7 and 8 ). Solid lines represent historical GBD estimates from 1990 to 2023, whereas dashed lines indicate modeled projections for 2024–2050 derived using the INLA framework and BAPC methodology. Figure 7 Projections of infertility prevalence trends in global, G7, and BRICS from 1990 to 2050. The image features line graphs showing infertility prevalence from 1990 to 2050 across regions and sexes. The x-axis is ′Period′ (1990-2050) and the y-axis is ′Age-standardized prevalence rate per 100,000′. Regions include Global, Russian Federation, G7, India, BRICS, China, Brazil and South Africa, categorized by Both, Male and Female. A dotted line at 2023 separates historical data from projections. Shaded bands indicate prediction intervals, with uncertainty growing towards 2050. Trends reveal a general rise in prevalence, with notable male-female differences. Global Female rates increase more steeply than Global Male. The graphs underscore growing uncertainty in future trends, highlighting demographic variability. Multiple line graphs showing infertility prevalence projections by region and sex from 1990 to 2050. Figure 8 Projections of infertility DALYs trends in global, G7, and BRICS from 1990 to 2050. Image displays 18 line graphs in a grid, each titled for different demographics: Global, Russian Federation, G7, India, BRICS, China, Brazil and South Africa, divided by gender. The x-axis shows periods from 1990 to 2050, while the y-axis represents age-standardized DALY rates per 100,000. A dotted line at 2023 separates historical data from projections. Global, G7, BRICS, China, Brazil and Russian Federation graphs show stable historical data with widening projections. India′s graph indicates a rise around 2020-2023 before projections. South Africa′s graphs have a broader y-axis scale, showing large fluctuations from negative to positive values, with a narrow band near zero expanding sharply after 2030 towards 2050. A grid of line graphs showing projected infertility disability adjusted life years by period and sex. Projections of infertility prevalence trends in global, G7, and BRICS from 1990 to 2050. Projections of infertility DALYs trends in global, G7, and BRICS from 1990 to 2050. The exploratory projections suggest that from 2024 to 2050, under current demographic and epidemiological trajectories, infertility ASPR and ASDR may continue increasing globally, while declining trends may be observed in BRICS and G7 countries. By 2050, the modeled ASPR projection for infertility in BRICS was approximately 5842.04 per 100,000 population. Although this value was projected to be lower than the global level, it remained numerically higher than the corresponding projection for G7 countries. At the country level within BRICS, Brazil and India showed projected declining trends, South Africa showed a projected upward trajectory, whereas China and Russia were projected to remain relatively stable over time.

GBD 2023 estimates indicate that the original BRICS countries experienced a large and heterogeneous modeled burden of infertility in 2023. Temporal patterns differed substantially by country, sex, and age group, while population growth represented the largest contributor to changes in absolute infertility-related DALYs within the decomposition framework. Projections to 2050 remain exploratory scenarios based on current epidemiological trajectories and require further validation. These findings support strengthened infertility surveillance, equitable reproductive healthcare, and country-specific investigation of potential contributing factors, but they do not establish causal relationships.

This study provides a comprehensive assessment of the burden of male and female infertility among reproductive-aged individuals in BRICS countries from 1990 to 2023 and projects potential future trends through 2050. Our results suggest that BRICS countries have consistently borne a substantial absolute infertility burden, accounting for more than half of the global estimated prevalent infertility cases in 2023. Over the study period, the ASPR in BRICS countries increased, with point estimates generally higher than those for the global and G7 populations. However, the AAPC in BRICS was lower than those observed globally and in G7 countries, indicating a slower overall rate of increase. A marked sex disparity was evident, with the estimated prevalence of female infertility approximately threefold higher than that of male infertility. Notable heterogeneity was observed across BRICS countries: India and Brazil experienced the most rapid increases in ASPR, Russia and China maintained high but relatively stable prevalence levels, whereas South Africa exhibited pronounced temporal fluctuations. Findings from decomposition analysis further indicate that population growth represented the largest contributing component of the increase in infertility-related DALYs in BRICS countries within the decomposition framework. Given the use of standardized GBD estimates, these findings are intended to characterize relative patterns and temporal trends in infertility burden rather than to provide clinically precise estimates of absolute prevalence. BRICS countries collectively account for more than half of the global infertility burden, highlighting their substantial current reproductive health burden. This pattern coincides with the convergence of multiple, interrelated factors characteristic of economies undergoing rapid transition. Given the ecological design of this study based on country-level aggregated data, these findings should be interpreted at the population level and do not imply individual-level or causal relationships. First, accelerated socioeconomic development has been accompanied by profound lifestyle shifts—including excess caloric intake and reduced physical activity—which may be associated with the rising prevalence of metabolic disorders at the population level. These trends may reflect an ongoing epidemiological transition in BRICS countries, from a predominance of infectious diseases toward noncommunicable diseases. 9 However, the burden of infectious diseases has not been fully alleviated. Descriptive analyses of cause-specific rankings in the present study showed that categories related to other STIs remained prominent in the rankings, while the relative ranking of PCOS, an endocrine-metabolic disorder, increased over time. Importantly, these findings should be interpreted cautiously, as they reflect descriptive rankings of GBD-defined categories rather than causal attribution; in particular, residual categories such as “other STIs” may be influenced by diagnostic and reporting practices. Second, rapid urbanization, industrialization, and population growth are likely to be associated with increased environmental exposures. 19 Many of the resulting chemical pollutants have been identified as endocrine-disrupting chemicals in prior studies and have been associated with impaired reproductive function and adverse fertility outcomes. 20 In addition, substantial regional and population-level disparities in the availability and accessibility of reproductive health services persist within BRICS countries. 21 , 22 Limited access to timely and high-quality reproductive care may hinder early diagnosis and standardized management of infertility, further exacerbating existing inequalities. These interpretations, particularly those related to potential biological, environmental, and health system mechanisms, should be considered in light of possible differences in case ascertainment and reporting practices across countries. The estimated burden of female infertility was approximately three times higher than that of male infertility. This pronounced disparity should be interpreted with caution, as it may reflect both underlying differences in infertility patterns and sex-specific variations in case ascertainment, diagnostic practices, and health-seeking behavior. Female reproductive health conditions are more frequently identified and captured in population health surveillance systems, 23 whereas male infertility remains under-investigated and under-diagnosed in many settings. Lower public awareness, reduced clinical evaluation, and limited societal attention to male infertility may contribute to underestimation of its population-level burden. 24 In addition, within the sociocultural contexts of many BRICS countries, reproductive responsibility is disproportionately placed on women, potentially increasing healthcare-seeking behavior and reporting of infertility-related conditions. 25 Decomposition analysis indicates that population growth constituted the largest contributing component of the observed increase in infertility-related DALYs in BRICS countries, whereas epidemiological change constituted the largest component in G7 countries. This contrast underscores fundamental differences between these two groups of economies. In BRICS countries, the absolute expansion of the reproductive-age population constituted a major component of the observed DALY increase within the decomposition framework, with epidemiological changes exerting a secondary influence. By contrast, in more developed regions, the observed patterns may be interpreted in the context of factors such as delayed childbearing, environmental exposures, and lifestyle-related risks. 26 China represents a particularly distinctive case within BRICS, where population aging emerged as the principal contributor to the increase in infertility-related DALYs. This pattern may reflect the long-term demographic consequences of sustained population policies, highlighting how historical policy decisions can shape contemporary reproductive health burdens. The highest ASPRs of infertility in BRICS countries were concentrated among individuals aged 30–44 years, peaking at 35–39 years, which corresponds closely to the core biological window of reproductive capacity. With advancing age, declines in female ovarian reserve and male sperm quality, together with an increased risk of reproductive system–related disorders, have been well documented in prior clinical studies and may partly explain the observed age-specific patterns in infertility prevalence. 27 , 28 At the same time, individuals in this age range often face greater reproductive pressure and are therefore more likely to seek medical care, which may partially increase the likelihood of infertility diagnosis. 29 Notably, the age-related increase in infertility prevalence is more gradual among males than females. This difference reflects fundamental sex-specific patterns of reproductive aging: female ovarian function typically undergoes an accelerated decline after approximately 35 years of age, 30 whereas male reproductive capacity declines more slowly, with spermatogenic function remaining relatively stable over a longer period. 31 Brazil exhibits a relatively low age-standardized prevalence rate of infertility but a rapid increase over time, as reflected by a high AAPC. Although Brazil’s Unified Health System (Sistema Único de Saúde, SUS) has substantially expanded population coverage, persistent challenges—including inadequate funding, regional disparities in resource allocation, and increasing privatization—continue to constrain access to comprehensive reproductive health services. 32 , 33 Although Russia exhibits the highest age-standardized prevalence rate of infertility among BRICS countries, its temporal growth has remained relatively modest. This pattern may be considered in the context of the country’s earlier demographic transition, population aging, and comparatively well-established systems for chronic disease prevention and management. 34 , 35 Notably, the burden of male infertility in Russia is particularly pronounced, especially among men aged 30–34 years. At the population level, this observation occurs in a setting characterized by historically high levels of alcohol consumption 36 and tobacco use. 37 These factors may represent potential hypotheses for further investigation, as individual-level studies have identified both behaviors as important risk factors for impaired male reproductive health. 38 , 39 However, the ecological nature of the present analysis does not permit attribution of the observed national burden patterns to these factors. Infertility prevalence in India has increased rapidly over recent decades, with the steepest rise observed among women aged 25–29 years. India is characterized by relatively early marriage and childbearing, with approximately 97% of women marrying before the age of 30 and a mean age at first birth of 21.3 years. 40 , 41 These demographic patterns may offer potential context for interpreting the observed temporal trends; however, this population-level analysis cannot establish whether they are associated with the observed changes in infertility prevalence. China exhibits a high age-standardized prevalence rate of infertility but a relatively modest rate of increase over time. Decomposition analysis suggests that population aging has been the largest contributing component to infertility-related DALYs, whereas changes in population size have exerted a comparatively offsetting effect. These findings are consistent with China’s current demographic profile, characterized by persistently low fertility rates 42 and rapid population aging. 43 China also shows the highest prevalence of female infertility among BRICS countries, with a particularly pronounced burden among women aged 35–39 years. This pattern may be considered alongside the broader context of demographic and health transitions, including delayed childbearing, 44 , 45 increasing maternal age following the implementation of second- and third-child policies, 46 , 47 and the rising prevalence of metabolic disorders such as obesity and diabetes. 48 , 49 The relative ranking of PCOS among GBD-defined categories increased over time. Consistent with these observations, recent studies indicate that the prevalence of PCOS in China has increased markedly over the past decade and is characterized by a more severe overall phenotype, accompanied by higher rates of obesity, hyperandrogenism, and infertility. 50 South Africa is among the countries with the highest prevalence of sexually transmitted infections (STIs) globally, a long-standing public health concern with important implications for reproductive health. 51 Notably, South Africa was the only BRICS country to exhibit a negative net drift in infertility ASPR, with the most pronounced decline observed among women aged 45–49 years. Several contextual factors may warrant consideration. South Africa has strengthened integrated STI prevention and control programs over recent decades. 52 , 53 The short-term increase in infertility ASPR observed between 2020 and 2023 coincided with the COVID-19 pandemic, a period associated with disruptions to reproductive healthcare services and STI control activities. 54 , 55 Changes in healthcare access, service utilization, and diagnostic practices may represent contextual factors relevant to the observed prevalence patterns. However, given the ecological nature of this study, the extent to which these factors contributed to the observed trends cannot be determined. The observed heterogeneity in infertility burden across BRICS countries underscores the importance of context-specific reproductive health strategies. Strengthening infertility surveillance, improving equitable access to reproductive healthcare, maintaining effective STI prevention and control, and expanding research on country-specific determinants may help address these heterogeneous burden patterns. Persistent sex disparities in infertility diagnosis and treatment further highlight the importance of couple-based fertility care and greater research attention to male infertility. Several limitations should be acknowledged. First, although the GBD framework integrates multiple data sources, the estimates used in this study are modeled rather than directly observed, and data quality and availability vary across countries and time periods, which may affect estimate precision and comparability. Changes in GBD input data and modeling procedures may also affect the stability of temporal estimates, with wide uncertainty intervals observed for some estimates. In addition, this population-level ecological analysis does not permit individual-level inference, and uncertainty from the underlying GBD estimates was not fully propagated through all secondary analyses. Comparisons among BRICS, G7, and global estimates are not statistically independent, and male–female comparisons may also be affected by differences in case ascertainment and modeling. Second, the analysis of cause-specific contributions is constrained by the GBD disease classification framework and may not capture all relevant conditions or underrecognized contributors to infertility burden. The APC analysis is also subject to the inherent age-period-cohort identification problem and should therefore be interpreted as modeled relative-rate patterns rather than uniquely identified causal effects. Third, the projections were not externally validated and depend on historical trends and model assumptions. Therefore, they should be interpreted as plausible future scenarios rather than precise forecasts.