Physical and mental health impact of perimenopause, menopause and post menopause in a diverse global population (MARIE Project- Global Chapter WP 2a): cross-sectional quantitative data from a mixed-methods study

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Thirteen countries (UK, Singapore, Oman, Rwanda, Nigeria, Ghana, Tanzania, Sri Lanka, India, Pakistan, Nepal, Malaysia, and Brazil) were selected to participate for this study based on existing collaborative networks within the MARIE programme, ensuring representation across diverse geographic regions, health systems, and socio-economic contexts as the sample. This approach enabled the inclusion of both low- and middle-income countries (LMICs) and a high-income comparator setting. The inclusion of the UK, Oman and Singapore as high-income country provided a comparative benchmark to contextualise findings from LMIC settings, allowing examination of cross-country variation in symptom burden, healthcare access, and clinical patterns across differing resource environments. Data were collected using structured, self-administered electronic questionnaires distributed through clinical services and community-based pathways. The survey captured demographic, clinical, and symptom-related information using validated instruments. No qualitative interviews are reported in this analysis. This manuscript reports the quantitative component of a broader mixed-methods study within the MARIE programme. The present analysis focuses exclusively on cross-sectional quantitative data to characterise symptom burden and associated health domains across participating countries. Given the exploratory nature of the study, multiple outcome domains were analysed to characterise patterns of association. No formal adjustment for multiple comparisons was undertaken, and findings should be interpreted accordingly. Outcome measures reflect broader health domains associated with midlife and the menopausal transition, rather than menopause-specific symptoms alone. The primary aim of MARIE WP2a was to characterise the physical and mental health impacts of perimenopause, menopause, and post-menopause across diverse populations. Secondary aims included exploratory assessment of symptom patterns in a subset of participants with repeated observations; these analyses were not part of the primary cross-sectional design. This paper focuses on the quantitative portion of the study and reported using the STROBE guidelines. Participants were recruited through clinical services and community-based pathways, including digital dissemination platforms. A harmonised study protocol was implemented across all participating countries to ensure consistency in recruitment, data collection, and measurement. Country-specific ethics approvals were secured prior to commencing the study: UK-Health Research Authority and Health and Care Research Wales Approval (22/EE/0158); Nigeria (RUIH/CS/66/VOL 16/VER 3/388/2023/121), Ghana (ethical approval from Nar-Bita-Hospital), Rwanda (No.207/CMHS IRB/2025), Tanzania (Ref No. AC429/526/01/90), India (ICMR-RMRC/IHEC-2023/175), Sri Lanka (Ref. No: 2025.02.532), Pakistan (PRIME/IRB/2023-1044), Nepal (Ref 11/081/82), Malaysia (NMRR ID 23-03581-PO8), Singapore (Singhealth CIRB approval (2024/2126)), Oman (Reference no: MREC#3547), and Brazil (CAAE 77391024.3.0000.5404). Consent to participate was obtained from all participants. All cis women and transgender women (individuals assigned male at birth) experiencing perimenopause, menopause and post-menopause ( Table 1 ) that provided informed consent was included in the study. Menopausal populations were recruited through gynaecology and family planning clinics within primary and secondary care settings, community settings and social media from the UK (UK), Oman, Singapore, India, Sri Lanka, Pakistan, Nepal, Malaysia, Tanzania, Rwanda, Nigeria, Ghana, and Brazil. Table 1 Case definitions for different menopausal stages. Stage Definition Diagnostic criteria Hormonal changes Typical age range Duration References Perimenopause Stage leading up to menopause with endocrine, biological, and clinical changes signalling the end of reproductive years. - Menstrual cycle irregularity (≥7-day change in cycle length) - Vasomotor symptoms without other causes Fluctuating ovarian function; variable FSH and oestradiol levels 40–55 years 2–8 years before final menstrual period WHO (1996) 31 ; NICE NG23 (2015) 32 ; NAMS (2022) 33 Menopause Permanent cessation of menstruation due to loss of ovarian follicular activity. - 12 consecutive months of amenorrhoea - Elevated FSH (>30–40 IU/L) and low oestradiol (biochemical confirmation optional) - Includes natural and induced menopause Persistent ovarian failure; high FSH, low oestradiol Median age ∼51 years (varies globally) Point in time marking end of reproductive capability WHO (1996) 31 ; NICE NG23 (2015) 32 ; NAMS (2022) 33 Post-menopause Life stage following menopause characterised by prolonged hypoestrogenism and long-term health implications. - Begins 12 months after the final menstrual period - Persistent ovarian failure confirmed biochemically Sustained low oestradiol and high FSH Any age following menopause Remainder of life after menopause WHO (1996) 31 ; NICE NG23 (2015) 32 ; NAMS (2022) 33 Case definitions for different menopausal stages. Menstrual cycle irregularity (≥7-day change in cycle length) Vasomotor symptoms without other causes 12 consecutive months of amenorrhoea Elevated FSH (>30–40 IU/L) and low oestradiol (biochemical confirmation optional) Includes natural and induced menopause Begins 12 months after the final menstrual period Persistent ovarian failure confirmed biochemically Case definitions for different menopausal stages are outlined in Table 1 . Continuous variables were summarised as means ± standard deviations (SD), while categorical variables were presented as frequencies and percentages. In a subset of participants with repeated observations, paired t-tests were conducted as part of sensitivity analyses to assess consistency of findings across time points. Linear regression models were established with scores from various scales as outcome variables to measure the severity of menopausal symptoms. To address missing covariate data, Multiple Imputation by Chained Equations (MICE) was utilised to generate 10 complete imputed datasets. 34 , 35 Statistical analyses were performed on each dataset independently, and the results were pooled using Rubin’s rules. 35 Covariate selection was conducted using a bidirectional stepwise regression procedure based on the Bayesian Information Criterion (BIC). 36 To ensure the retention of critical study design variables, a “lower bound” model was defined, forcing the inclusion of Menopausal Stage and Country. The selection process proceeded from this base model, allowing for the addition or removal of covariates within the scope of the full model. This procedure was applied independently to each of the 10 imputed datasets. For variables that showed inconsistent selection results across datasets, the Wald tests were employed to determine their inclusion in the final multivariable models. 37 Country-specific funnel plots were generated using estimates obtained from separate country-level linear models fitted independently within each participating country. These plots were used descriptively to visualise the magnitude and pattern of between-country heterogeneity in outcome distributions and were not derived from the random-effects estimates of Model 1. Three complementary modelling approaches were employed: Model 1 (mixed-effects) to account for clustering and country-level variability; Model 2 (heterogeneity-focused) to better capture non-uniform cross-country patterns; and Model 3 (sensitivity analysis) to assess robustness within the subset of participants with repeated observations. Country groupings were derived as an exploratory, data-driven approach to summarise patterns of heterogeneity and should not be interpreted as fixed or generalisable classifications; findings from these groupings are therefore presented descriptively. The primary analysis is based on cross-sectional baseline data; analyses involving repeated observations are included as sensitivity analyses in a subset of participants and do not alter the primary cross-sectional design of the study. The analytical framework was designed to distinguish average population–level associations from descriptive patterns of cross-country heterogeneity. Accordingly, different models were used to address complementary rather than competing analytical objectives. Model 1 was intended to estimate average associations between menopausal stage, participant characteristics, and health outcomes while accounting for country-level clustering. Model 2 was intended to descriptively characterise heterogeneity patterns observed across countries. Model 3 was implemented solely as a sensitivity analysis to assess robustness in participants with repeated observations. Based on the data, one of the following three models is used. Model 1: We fitted a linear mixed-effects model with random intercepts for countries to account for clustering (center effects). Random effects were assumed to be normally distributed. Given that symptom trajectories across menopausal stages may vary by country, we included random slopes for Menopausal Stage (using “Perimenopause” as the reference category), thereby modelling the interaction between menopausal stage and country. Likelihood Ratio Tests (LRT) were conducted to assess the statistical significance of these random slopes. Model 2: To better capture and visualise inter-country heterogeneity, acknowledging that score distributions across countries may not conform to the normal distribution, we developed Model 2. First, linear models were fitted for each country individually. Based on the distribution of scale scores, countries with similar patterns were categorised into distinct groups. Model 2 was then established using these groups. We designated the results from Model 2 as the primary findings of this study due to its superior ability to demonstrate heterogeneity. Because country groupings were derived post-hoc from observed score distributions, these classifications should not be interpreted as causal, stable, or externally generalisable population strata. Their purpose is solely to provide an interpretable descriptive summary of heterogeneity patterns across participating countries. Model 3 (Sensitivity Analysis): A sensitivity analysis was conducted on the subset of participants with two observations. To assess the robustness of the single-observation results and to check for potential temporal differences, we expanded Model 1 by adding a random intercept for each participant. This model accounted for within-individual correlations (personal effects) in addition to country-level effects. The full mathematical specifications for Models 1, 2, and 3 are detailed in the Supplementary Materials . To explore the complex interplay between menopausal symptoms, we employed a Gaussian Graphical Model (GGM). 38 We employed a stepwise model selection procedure based on the Extended Bayesian Information Criterion (EBIC) to obtain an optimal sparse network structure. In the network, nodes represented symptoms, and edges represented partial correlation coefficients controlling for all other nodes. We computed centrality indices to identify core symptoms: Strength (the sum of absolute edge weights connected to a node, reflecting direct associations), Closeness (the inverse of the sum of shortest distances to all other nodes, indicating how quickly a symptom can affect the entire network), and Betweenness (the number of shortest paths passing through a node, representing its role as a bridge connecting different parts of the network). We also calculated Node Predictability (R 2 ) to assess the variance of each node explained by its neighbours. To ensure the reliability of the results, we performed stability analysis using 1000 non-parametric bootstrap samples. This included calculating the correlation stability coefficient (CS-coefficient) for centrality indices and estimating the 95% confidence intervals for edge weights. Network analysis using Gaussian Graphical Models was conducted to explore relationships between symptom domains. All observed relationships should be interpreted as descriptive associations, given the cross-sectional design. Results are presented in relation to the primary objective of characterising cross-country variation across key health domains. Subgroup analyses by menopausal stage and country were conducted to explore heterogeneity. Sensitivity analyses were performed on participants with repeated observations to assess robustness of findings. Any additional analyses introduced during peer review are explicitly identified as post-hoc analyses in the Methods section. The funders had no involvement in study design, data collection, data analyses, data interpretation, or the writing of the report.

A total of 5228 participants from 13 countries were included in the analysis ( Fig. 1 ). The median age was 55 years (IQR: 50–61). The distribution of menopausal stages was 18.6% (n = 972) perimenopause, 32.2% (n = 1684) menopause, and 49.2% (n = 2572) post-menopause. Clinically significant characteristics included a history of hysterectomy (12.2%), current or previous use of hormone replacement therapy (HRT, 21.7%), and a diagnosis of endometriosis (6.7%). Detailed sociodemographic and clinical characteristics stratified by country are presented in Table 2 . Fig. 1 Flow chart of participants included in the study. Table 2 Characteristics of study participants. Characteristic Total (n = 5228) Brazil (n = 262) Ghana (n = 540) India (n = 324) Malaysia (n = 443) Nepal (n = 331) Nigeria (n = 615) Oman (n = 274) Pakistan (n = 485) Rwanda (n = 171) Singapore (n = 160) Sri Lanka (n = 537) Tanzania (n = 270) UK (n = 816) Age Mean (±sd)/Median (Q1-Q3) 56.53 (±8.99)/55 (50–61) 57.37 (±10.30)/58 (51–64) 61.04 (±10.06)/60 (53–67) 56.20 (±5.47)/56 (54–59) 57.99 (±7.55)/57 (53–62) 56.61 (±9.09)/55 (50–62) 54.69 (±8.89)/54 (49–60) 56.23 (±7.77)/55 (52–61) 55.64 (±8.37)/55 (50–60) 59.36 (±9.81)/59 (53–66) 56.27 (±8.73)/54 (50–61) 58.16 (±8.81)/57 (53–63) 61.24 (±11.44)/60 (53–68) 51.30 (±5.78)/52 (48–55) Education (n = 5224) (n = 262) (n = 540) (n = 324) (n = 443) (n = 331) (n = 615) (n = 274) (n = 485) (n = 171) (n = 160) (n = 537) (n = 270) (n = 812) No formal educational qualifications 1091 (20.9%) 17 (6.5%) 64 (11.9%) 34 (10.5%) 89 (20.1%) 158 (47.7%) 50 (8.1%) 28 (10.2%) 384 (79.2%) 124 (72.5%) 12 (7.5%) 88 (16.4%) 38 (14.1%) 5 (0.6%) GCSE, O’Level, standard grade or equivalent 1250 (23.9%) 24 (9.2%) 178 (33.0%) 128 (39.5%) 178 (40.2%) 80 (24.2%) 104 (16.9%) 30 (10.9%) 49 (10.1%) 33 (19.3%) 54 (33.8%) 147 (27.4%) 126 (46.7%) 119 (14.7%) A-level, higher grade or equivalent 1068 (20.4%) 61 (23.3%) 102 (18.9%) 87 (26.9%) 60 (13.5%) 59 (17.8%) 83 (13.5%) 93 (33.9%) 22 (4.5%) 6 (3.5%) 18 (11.2%) 241 (44.9%) 74 (27.4%) 162 (20.0%) Undergraduate (e.g., BA or BSc) or equivalent 1183 (22.6%) 78 (29.8%) 182 (33.7%) 53 (16.4%) 68 (15.3%) 25 (7.6%) 288 (46.8%) 61 (22.3%) 12 (2.5%) 6 (3.5%) 44 (27.5%) 46 (8.6%) 27 (10.0%) 293 (36.1%) Postgraduate (e.g., MA or PhD) or equivalent 632 (12.1%) 82 (31.3%) 14 (2.6%) 22 (6.8%) 48 (10.8%) 9 (2.7%) 90 (14.6%) 62 (22.6%) 18 (3.7%) 2 (1.2%) 32 (20.0%) 15 (2.8%) 5 (1.9%) 233 (28.7%) Menopausal stage (n = 5228) (n = 262) (n = 540) (n = 324) (n = 443) (n = 331) (n = 615) (n = 274) (n = 485) (n = 171) (n = 160) (n = 537) (n = 270) (n = 816) Perimenopause 972 (18.6%) 23 (8.8%) 24 (4.4%) 60 (18.5%) 48 (10.8%) 46 (13.9%) 160 (26.0%) 34 (12.4%) 100 (20.6%) 14 (8.2%) 38 (23.8%) 71 (13.2%) 26 (9.6%) 328 (40.2%) Menopause 1684 (32.2%) 126 (48.1%) 301 (55.7%) 33 (10.2%) 234 (52.8%) 87 (26.3%) 205 (33.3%) 50 (18.2%) 20 (4.1%) 80 (46.8%) 69 (43.1%) 103 (19.2%) 77 (28.5%) 299 (36.6%) Post-menopause 2572 (49.2%) 113 (43.1%) 215 (39.8%) 231 (71.3%) 161 (36.3%) 198 (59.8%) 250 (40.7%) 190 (69.3%) 365 (75.3%) 77 (45.0%) 53 (33.1%) 363 (67.6%) 167 (61.9%) 189 (23.2%) Diagnosed with Endometriosis by a doctor (n = 5209) (n = 262) (n = 540) (n = 324) (n = 443) (n = 331) (n = 615) (n = 274) (n = 485) (n = 171) (n = 160) (n = 537) (n = 270) (n = 797) No 4858 (93.3%) 231 (88.2%) 537 (99.4%) 295 (91.0%) 394 (88.9%) 325 (98.2%) 558 (90.7%) 259 (94.5%) 483 (99.6%) 160 (93.6%) 146 (91.2%) 512 (95.3%) 248 (91.9%) 710 (89.1%) Yes 351 (6.7%) 31 (11.8%) 3 (0.6%) 29 (9.0%) 49 (11.1%) 6 (1.8%) 57 (9.3%) 15 (5.5%) 2 (0.4%) 11 (6.4%) 14 (8.8%) 25 (4.7%) 22 (8.1%) 87 (10.9%) Taking any GNRH analogue (n = 5211) (n = 262) (n = 540) (n = 324) (n = 443) (n = 331) (n = 615) (n = 274) (n = 485) (n = 171) (n = 160) (n = 537) (n = 270) (n = 799) No 5087 (97.6%) 249 (95.0%) 536 (99.3%) 312 (96.3%) 433 (97.7%) 330 (99.7%) 573 (93.2%) 272 (99.3%) 483 (99.6%) 167 (97.7%) 156 (97.5%) 529 (98.5%) 255 (94.4%) 792 (99.1%) Yes 124 (2.4%) 13 (5.0%) 4 (0.7%) 12 (3.7%) 10 (2.3%) 1 (0.3%) 42 (6.8%) 2 (0.7%) 2 (0.4%) 4 (2.3%) 4 (2.5%) 8 (1.5%) 15 (5.6%) 7 (0.9%) Diagnosed with premature ovarian insufficiency (n = 5209) (n = 262) (n = 540) (n = 324) (n = 443) (n = 331) (n = 615) (n = 274) (n = 485) (n = 171) (n = 160) (n = 537) (n = 270) (n = 797) No 5081 (97.5%) 244 (93.1%) 537 (99.4%) 314 (96.9%) 437 (98.6%) 328 (99.1%) 592 (96.3%) 268 (97.8%) 483 (99.6%) 168 (98.2%) 157 (98.1%) 529 (98.5%) 250 (92.6%) 774 (97.1%) Yes 128 (2.5%) 18 (6.9%) 3 (0.6%) 10 (3.1%) 6 (1.4%) 3 (0.9%) 23 (3.7%) 6 (2.2%) 2 (0.4%) 3 (1.8%) 3 (1.9%) 8 (1.5%) 20 (7.4%) 23 (2.9%) Had a hysterectomy (n = 5211) (n = 262) (n = 540) (n = 324) (n = 443) (n = 331) (n = 615) (n = 274) (n = 485) (n = 171) (n = 160) (n = 537) (n = 270) (n = 799) No 4576 (87.8%) 207 (79.0%) 484 (89.6%) 304 (93.8%) 380 (85.8%) 279 (84.3%) 548 (89.1%) 236 (86.1%) 453 (93.4%) 150 (87.7%) 103 (64.4%) 476 (88.6%) 246 (91.1%) 710 (88.9%) Yes 635 (12.2%) 55 (21.0%) 56 (10.4%) 20 (6.2%) 63 (14.2%) 52 (15.7%) 67 (10.9%) 38 (13.9%) 32 (6.6%) 21 (12.3%) 57 (35.6%) 61 (11.4%) 24 (8.9%) 89 (11.1%) How long have you worked prior to menopause (n = 5204) (n = 262) (n = 540) (n = 324) (n = 443) (n = 331) (n = 615) (n = 274) (n = 485) (n = 171) (n = 160) (n = 537) (n = 270) (n = 792) Less than 1 year 924 (17.8%) 50 (19.1%) 53 (9.8%) 4 (1.2%) 46 (10.4%) 44 (13.3%) 73 (11.9%) 87 (31.8%) 401 (82.7%) 8 (4.7%) 12 (7.5%) 112 (20.9%) 19 (7.0%) 15 (1.9%) 1–5 years 385 (7.4%) 29 (11.1%) 42 (7.8%) 36 (11.1%) 23 (5.2%) 9 (2.7%) 104 (16.9%) 10 (3.6%) 9 (1.9%) 13 (7.6%) 9 (5.6%) 33 (6.1%) 53 (19.6%) 15 (1.9%) 6–10 years 563 (10.8%) 22 (8.4%) 108 (20.0%) 142 (43.8%) 23 (5.2%) 10 (3.0%) 100 (16.3%) 7 (2.6%) 26 (5.4%) 10 (5.8%) 7 (4.4%) 52 (9.7%) 29 (10.7%) 27 (3.4%) 11–15 years 444 (8.5%) 8 (3.1%) 64 (11.9%) 78 (24.1%) 23 (5.2%) 12 (3.6%) 84 (13.7%) 26 (9.5%) 18 (3.7%) 10 (5.8%) 10 (6.2%) 53 (9.9%) 35 (13.0%) 23 (2.9%) 16–20 years 638 (12.3%) 24 (9.2%) 103 (19.1%) 48 (14.8%) 36 (8.1%) 51 (15.4%) 87 (14.1%) 61 (22.3%) 18 (3.7%) 24 (14.0%) 17 (10.6%) 73 (13.6%) 22 (8.1%) 74 (9.3%) 21 years or more 2250 (43.2%) 129 (49.2%) 170 (31.5%) 16 (4.9%) 292 (65.9%) 205 (61.9%) 167 (27.2%) 83 (30.3%) 13 (2.7%) 106 (62.0%) 105 (65.6%) 214 (39.9%) 112 (41.5%) 638 (80.6%) How many hours per week do you work since menopause began (n = 5201) (n = 262) (n = 540) (n = 324) (n = 443) (n = 331) (n = 615) (n = 274) (n = 485) (n = 171) (n = 160) (n = 537) (n = 270) (n = 789) Less than 20 h per week 1474 (28.3%) 50 (19.1%) 158 (29.3%) 19 (5.9%) 74 (16.7%) 81 (24.5%) 156 (25.4%) 109 (39.8%) 398 (82.1%) 97 (56.7%) 17 (10.6%) 170 (31.7%) 48 (17.8%) 97 (12.3%) 20–39 h per week 1303 (25.1%) 76 (29.0%) 117 (21.7%) 56 (17.3%) 70 (15.8%) 54 (16.3%) 172 (28.0%) 17 (6.2%) 28 (5.8%) 46 (26.9%) 9 (5.6%) 79 (14.7%) 57 (21.1%) 522 (66.2%) 40–59 h per week 1588 (30.5%) 117 (44.7%) 209 (38.7%) 166 (51.2%) 220 (49.7%) 76 (23.0%) 172 (28.0%) 50 (18.2%) 52 (10.7%) 23 (13.5%) 126 (78.8%) 134 (25.0%) 84 (31.1%) 159 (20.2%) 60–79 h per week 489 (9.4%) 9 (3.4%) 38 (7.0%) 75 (23.1%) 28 (6.3%) 66 (19.9%) 74 (12.0%) 43 (15.7%) 5 (1.0%) 2 (1.2%) 4 (2.5%) 95 (17.7%) 42 (15.6%) 8 (1.0%) 80 h or more per week 347 (6.7%) 10 (3.8%) 18 (3.3%) 8 (2.5%) 51 (11.5%) 54 (16.3%) 41 (6.7%) 55 (20.1%) 2 (0.4%) 3 (1.8%) 4 (2.5%) 59 (11.0%) 39 (14.4%) 3 (0.4%) How long have you worked in your current specialty or profession pre-menopause (n = 5191) (n = 262) (n = 540) (n = 324) (n = 443) (n = 331) (n = 615) (n = 274) (n = 485) (n = 171) (n = 160) (n = 537) (n = 270) (n = 779) Less than 1 year 1098 (21.2%) 29 (11.1%) 56 (10.4%) 17 (5.2%) 62 (14.0%) 52 (15.7%) 79 (12.8%) 96 (35.0%) 424 (87.4%) 11 (6.4%) 12 (7.5%) 164 (30.5%) 28 (10.4%) 68 (8.7%) 1–5 years 735 (14.2%) 14 (5.3%) 69 (12.8%) 106 (32.7%) 28 (6.3%) 28 (8.5%) 131 (21.3%) 10 (3.6%) 25 (5.2%) 17 (9.9%) 13 (8.1%) 74 (13.8%) 61 (22.6%) 159 (20.4%) 6–10 years 711 (13.7%) 17 (6.5%) 103 (19.1%) 151 (46.6%) 24 (5.4%) 28 (8.5%) 122 (19.8%) 11 (4.0%) 18 (3.7%) 13 (7.6%) 12 (7.5%) 53 (9.9%) 43 (15.9%) 116 (14.9%) 11–15 years 501 (9.7%) 31 (11.8%) 63 (11.7%) 37 (11.4%) 25 (5.6%) 29 (8.8%) 82 (13.3%) 31 (11.3%) 7 (1.4%) 13 (7.6%) 15 (9.4%) 56 (10.4%) 33 (12.2%) 79 (10.1%) 16–20 years 611 (11.8%) 31 (11.8%) 93 (17.2%) 4 (1.2%) 51 (11.5%) 36 (10.9%) 100 (16.3%) 59 (21.5%) 5 (1.0%) 19 (11.1%) 20 (12.5%) 78 (14.5%) 21 (7.8%) 94 (12.1%) 21 years or more 1535 (29.6%) 140 (53.4%) 156 (28.9%) 9 (2.8%) 253 (57.1%) 158 (47.7%) 101 (16.4%) 67 (24.5%) 6 (1.2%) 98 (57.3%) 88 (55.0%) 112 (20.9%) 84 (31.1%) 263 (33.8%) Suffer from any long-term conditions (n = 5208) (n = 262) (n = 540) (n = 324) (n = 443) (n = 331) (n = 615) (n = 274) (n = 485) (n = 171) (n = 160) (n = 537) (n = 270) (n = 796) No 3196 (61.4%) 132 (50.4%) 340 (63.0%) 295 (91.0%) 299 (67.5%) 211 (63.7%) 436 (70.9%) 125 (45.6%) 280 (57.7%) 113 (66.1%) 89 (55.6%) 314 (58.5%) 201 (74.4%) 361 (45.4%) Prefer not to say 198 (3.8%) 4 (1.5%) 1 (0.2%) 18 (5.6%) 20 (4.5%) 17 (5.1%) 15 (2.4%) 73 (26.6%) 0 (0.0%) 4 (2.3%) 2 (1.2%) 15 (2.8%) 19 (7.0%) 10 (1.3%) Yes 1814 (34.8%) 126 (48.1%) 199 (36.9%) 11 (3.4%) 124 (28.0%) 103 (31.1%) 164 (26.7%) 76 (27.7%) 205 (42.3%) 54 (31.6%) 69 (43.1%) 208 (38.7%) 50 (18.5%) 425 (53.4%) Suffer from any disabilities (n = 5212) (n = 262) (n = 540) (n = 324) (n = 443) (n = 331) (n = 615) (n = 274) (n = 485) (n = 171) (n = 160) (n = 537) (n = 270) (n = 800) No 4740 (91.0%) 236 (90.1%) 527 (97.6%) 262 (80.9%) 483 (89.9%) 428 (96.6%) 558 (90.7%) 317 (95.8%) 201 (73.4%) 472 (97.3%) 162 (94.7%) 158 (98.8%) 251 (93.0%) 685 (85.7%) Prefer not to say 183 (3.5%) 3 (1.1%) 8 (1.5%) 55 (17.0%) 7 (1.3%) 5 (1.1%) 7 (1.1%) 10 (3.0%) 58 (21.2%) 1 (0.2%) 6 (3.5%) 1 (0.6%) 10 (3.7%) 12 (1.5%) Yes 288 (5.5%) 23 (8.8%) 5 (0.9%) 7 (2.2%) 47 (8.8%) 10 (2.3%) 50 (8.1%) 4 (1.2%) 15 (5.5%) 12 (2.5%) 3 (1.8%) 1 (0.6%) 9 (3.3%) 102 (12.8%) Current or previous use of Hormone replacement therapy (n = 5206) (n = 262) (n = 540) (n = 324) (n = 443) (n = 331) (n = 615) (n = 274) (n = 485) (n = 171) (n = 160) (n = 537) (n = 270) (n = 794) No 4077 (78.3%) 147 (56.1%) 536 (99.3%) 207 (63.9%) 387 (87.4%) 317 (95.8%) 549 (89.3%) 237 (86.5%) 483 (99.6%) 158 (92.4%) 128 (80.0%) 501 (93.3%) 256 (94.8%) 171 (21.5%) Yes 1129 (21.7%) 115 (43.9%) 4 (0.7%) 117 (36.1%) 56 (12.6%) 14 (4.2%) 66 (10.7%) 37 (13.5%) 2 (0.4%) 13 (7.6%) 32 (20.0%) 36 (6.7%) 14 (5.2%) 623 (78.5%) Flow chart of participants included in the study. Characteristics of study participants. Across all outcome domains, substantial heterogeneity in symptom burden was observed across countries and menopausal stages ( Fig. 2 ). A consistent pattern emerged in which the UK and Rwanda clustered among the highest-burden settings across multiple psychological, somatic, and overall symptom measures, whereas Nepal, Sri Lanka, Malaysia, and Singapore tended to cluster at the lower end of the distribution. This cross-country gradient was evident across anxiety, depression, somatic symptoms, vasomotor symptoms, and overall menopausal burden (MRS), indicating a broad, multidimensional pattern rather than outcome-specific variation. Fig. 2 Heatmap of Menopausal Symptom Scores by Country and Stage. This figure displays the distribution of mean scores for 12 symptom scales across 13 countries (rows) and three menopausal stages (columns: Perimenopause, Menopause, Post-menopause). Cell values indicate the mean scale score for each subgroup. The colour gradient represents the symptom severity, with darker blue indicating higher mean scores (more severe symptoms) and lighter colours indicating lower mean scores. The range of scores varies by scale, as indicated by the legend bars on the right of each panel. Heatmap of Menopausal Symptom Scores by Country and Stage. This figure displays the distribution of mean scores for 12 symptom scales across 13 countries (rows) and three menopausal stages (columns: Perimenopause, Menopause, Post-menopause). Cell values indicate the mean scale score for each subgroup. The colour gradient represents the symptom severity, with darker blue indicating higher mean scores (more severe symptoms) and lighter colours indicating lower mean scores. The range of scores varies by scale, as indicated by the legend bars on the right of each panel. Across all models, several consistent cross-cutting correlates of symptom burden were identified. Higher educational attainment was consistently associated with lower symptom severity across psychological, disability, and quality-of-life outcomes. In contrast, markers of clinical complexity, including endometriosis, premature ovarian insufficiency, use of GnRH analogues, long-term conditions, and disability, were strongly associated with higher symptom burden across nearly all domains. Occupational history also demonstrated consistent associations, with shorter professional tenure pre-menopause linked to higher psychological, somatic, and disability scores. These patterns were consistent across outcome domains, supporting the presence of shared underlying drivers of menopausal burden. The funnel plot ( Supplementary Fig. S1A ) visually highlights substantial global heterogeneity in anxiety levels during the perimenopause stage. Quantifying these disparities ( Supplementary Table S4 ), the UK and Rwanda exhibited significantly higher anxiety levels (Coefficient = 2.76, 95% CI: 1.92–3.61) than countries with medium scores (Intercept = 7.83, 7.15–8.51), while Malaysia, Singapore, and Nepal showed significantly lower scores (Coefficient = −3.55, −4.33 to −2.76). Furthermore, higher educational attainment was protective against anxiety; for instance, participants with a postgraduate degree reported lower anxiety compared to those with standard secondary education (Coefficient = −1.18, −1.55 to −0.80). Similarly, having had a hysterectomy was associated with lower anxiety levels (Coefficient = −0.69, −1.01 to −0.37). In contrast, factors associated with increased anxiety included current or previous use of HRT (Coefficient = 1.33, 1.00–1.65), a diagnosis of endometriosis (Coefficient = 1.11, 0.66–1.56), the use of GnRH analogues (Coefficient = 1.32, 0.57–2.06), and suffering from disabilities (Coefficient = 0.95, 0.38–1.51). Additionally, shorter duration of work in the current specialty pre-menopause was linked to higher anxiety compared to those with over 21 years of experience (e.g., 1–5 years: Coefficient = 1.55, 1.21–1.89). The funnel plot ( Supplementary Fig. S2A ) reveals marked global variation in depression during the perimenopause stage. Model 2 estimates ( Supplementary Table S6 ) confirm this heterogeneity: Tanzania, Ghana, and Rwanda exhibited significantly elevated depression scores (Coefficient = 1.33, 95% CI: 0.49–2.18) compared to countries with medium scores (Intercept = 7.20, 6.58–7.81), whereas Malaysia, Singapore, and Nepal clustered with significantly lower symptom burdens (Coefficient = −3.19, −3.98 to −2.40). In the pooled analysis, higher educational attainment served as a protective factor; specifically, participants holding a postgraduate degree reported lower depression compared to those with standard secondary education (Coefficient = −1.64, −2.00 to −1.28). A history of hysterectomy was also associated with reduced depression levels (Coefficient = −0.74, −1.04 to −0.44). Conversely, elevated depression scores were linked to current or previous HRT use (Coefficient = 0.77, 0.47–1.07), suffering from long-term conditions (Coefficient = 0.33, 0.12–0.55), and living with disabilities (Coefficient = 1.25, 0.81–1.69). Furthermore, limited professional tenure in the current specialty pre-menopause was associated with higher depression relative to those with over 21 years of experience (e.g., 1–5 years: Coefficient = 1.15, 0.82–1.48). Visual inspection of the funnel plot ( Supplementary Fig. S3A ) indicates global variation in anxiety measured by the GCS scale during the perimenopause stage. Quantifying these disparities ( Supplementary Table S8 ), the UK and Rwanda exhibited significantly elevated anxiety levels (Coefficient = 2.08, 95% CI: 1.00–3.16) compared to countries with medium scores (Intercept = 4.37, 3.52–5.22), whereas Sri Lanka and Nepal clustered with significantly lower symptom burdens (Coefficient = −0.69, −2.26 to −0.11). In the pooled analysis, the post-menopause stage was associated with lower anxiety levels compared to perimenopause (Coefficient = −0.69, −1.29 to −0.08). Conversely, factors linked to increased anxiety included older age (Coefficient = 0.02, 0.01–0.03), current or previous use of HRT (Coefficient = 0.87, 0.59–1.14), a diagnosis of endometriosis (Coefficient = 0.84, 0.46–1.22), the use of GnRH analogues (Coefficient = 1.55, 0.91–2.19), and living with disabilities (Coefficient = 1.37, 0.99–1.75). Furthermore, shorter or medium-term professional tenure in the current specialty pre-menopause was associated with higher anxiety relative to those with over 21 years of experience (e.g., 6–10 years: Coefficient = 0.81, 0.52–1.11). Visual inspection of the funnel plot ( Supplementary Fig. S4A ) suggests substantial global variation in baseline depression measured by the GCS scale during the perimenopause stage. Quantifying these disparities ( Supplementary Table S10 , Model 2), the UK and Rwanda exhibited significantly elevated depression levels (Coefficient = 2.18, 95% CI: 1.24–3.13) compared to countries with medium scores (Intercept = 4.78, 4.27–5.28), whereas Singapore and Nepal clustered with significantly lower symptom burdens (Coefficient = −1.53, −2.49 to −0.57). In the pooled analysis, higher educational attainment was a strong protective factor; specifically, participants holding a postgraduate degree reported lower depression compared to those with standard secondary education (Coefficient = −0.74, −1.03 to −0.45). The post-menopause stage was also associated with reduced depression levels relative to perimenopause (Coefficient = −0.76, −1.44 to −0.08). Conversely, factors linked to increased depression scores included current or previous use of HRT (Coefficient = 0.87, 0.62–1.13), a diagnosis of endometriosis (Coefficient = 0.96, 0.64–1.29), suffering from long-term conditions (Coefficient = 0.38, 0.21–0.56), and living with disabilities (Coefficient = 1.24, 0.88–1.60). Furthermore, shorter or medium-term professional tenure in the current specialty pre-menopause was associated with higher depression relative to those with over 21 years of experience (e.g., 6–10 years: Coefficient = 0.74, 0.47–1.01). Visual inspection of the funnel plot ( Supplementary Fig. S5A ) highlights notable global heterogeneity in physiological symptoms during the perimenopause stage. Quantifying these disparities ( Supplementary Table S12 , Model 2), the UK and Rwanda exhibited significantly elevated physiological scores (Coefficient = 2.01, 95% CI: 0.58–3.45) compared to countries with medium scores (Intercept = 3.18, 2.18–4.18). While Ghana also showed elevated levels, the difference was not statistically significant (Coefficient = 1.29, −0.62 to 3.20). Several clinical and occupational factors were associated with increased symptom burden. Specifically, a diagnosis of endometriosis (Coefficient = 1.05, 0.59–1.50) and the use of GnRH analogues (Coefficient = 2.17, 1.43–2.92) were linked to higher physiological scores. Similarly, participants suffering from disabilities (Coefficient = 1.93, 1.46–2.39) or long-term conditions (Coefficient = 0.62, 0.39–0.84) reported more severe symptoms. Additionally, shorter or medium-term professional tenure in the current specialty pre-menopause was associated with higher scores relative to those with over 21 years of experience (e.g., 1–5 years: Coefficient = 1.31, 0.97–1.66). The funnel plot ( Supplementary Fig. S6A ) visually captures the global disparity in the baseline intensity of vasomotor symptoms, such as hot flushes and night sweats. Model 2 estimates ( Supplementary Table S14 ) confirm distinct geographic patterns: Ghana and Rwanda formed a high-burden cluster, exhibiting significantly elevated scores (Coefficient = 0.85, 95% CI: 0.29–1.41) compared to countries with medium scores (Intercept = 2.18, 1.96–2.40). Conversely, Nepal and Sri Lanka were associated with significantly reduced vasomotor complaints (Coefficient = −0.70, −1.18 to −0.21). In the pooled analysis, clinical history played a prominent role; specifically, a diagnosis of endometriosis (Coefficient = 0.41, 0.22–0.61) and the use of GnRH analogues (Coefficient = 1.38, 1.06–1.70) were strongly linked to increased symptom severity. Living with disabilities was also associated with higher scores (Coefficient = 0.67, 0.47–0.86). Interestingly, regarding occupational history, shorter professional tenure pre-menopause was associated with lower vasomotor scores compared to those with over 21 years of experience (e.g., <1 year: Coefficient = −0.44, −0.59 to −0.29). The funnel plot ( Supplementary Fig. S7A ) visually captures the substantial global variation in overall menopausal symptom burden as measured by the MRS. Model 2 estimates ( Supplementary Table S16 ) quantify distinct clusters: Brazil, the UK, Pakistan, and Rwanda exhibiting drastically elevated scores (Coefficient = 6.30, 95% CI: 3.61–8.98) compared to countries with medium scores (Intercept = 8.52, 6.00–11.03). Conversely, Nepal was associated with significantly reduced symptom burden (Coefficient = −5.02, −9.87 to −0.17). Clinical complexity played a dominant role. Specifically, the use of GnRH analogues (Coefficient = 4.06, 2.58–5.55) and suffering from disabilities (Coefficient = 4.02, 3.14–4.90) were the strongest predictors of higher MRS scores. Other significant contributors included a diagnosis of endometriosis (Coefficient = 2.56, 1.70–3.42), premature ovarian insufficiency (Coefficient = 2.10, 0.79–3.41), and current or previous HRT use (Coefficient = 1.29, 0.66–1.92). Older age and long-term conditions were also linked to increased scores. Interestingly, occupational history showed divergent associations: while shorter tenure in the current specialty pre-menopause was linked to higher symptom burden (e.g., 1–5 years: Coefficient = 2.67, 1.96–3.39), shorter work duration prior to menopause was associated with lower MRS scores compared to those with over 21 years of experience (e.g., <1 year: Coefficient = −3.00, −3.88 to −2.13) The funnel plot ( Supplementary Fig. S8A ) visually suggests some variation in disability scores measured by the QPDS, with Oman, India, and the UK appearing to have higher burdens compared to Nepal and Sri Lanka. However, Model 2 estimates ( Supplementary Table S18 ) indicate that these geographic disparities were not statistically significant: neither the potential “High-Score Group” (Coefficient = 6.30, 95% CI: −5.51 to 14.73) nor the “Low-Score Group” (Coefficient = 1.97, −8.38 to 12.31) showed a significant difference compared to countries with medium scores (Intercept = −4.94, −11.01 to 1.13). Higher educational attainment was a robust protective factor; specifically, participants with a postgraduate degree reported significantly lower disability scores compared to those with standard secondary education (Coefficient = −5.78, −7.40 to −4.16). Conversely, clinical complexity was the dominant driver of higher disability scores. The strongest predictors included suffering from disabilities (Coefficient = 14.22, 12.25–16.19) and the use of GnRH analogues (Coefficient = 10.34, 7.11–13.57). Other significant risk factors included a diagnosis of premature ovarian insufficiency (Coefficient = 6.13, 3.21–9.04), endometriosis (Coefficient = 3.84, 1.92–5.76), and older age. Additionally, shorter or medium-term professional tenure in the current specialty pre-menopause was linked to higher disability compared to those with over 21 years of experience (e.g., 6–10 years: Coefficient = 5.04, 3.54–6.53). The funnel plot ( Supplementary Fig. S9A ) visually suggests geographic variation in pain intensity, with the UK, Nigeria, Ghana, and Nepal appearing to exhibit higher burdens, while Oman, India, and Sri Lanka cluster with lower scores. However, Model 2 estimates ( Supplementary Table S20 ) indicate that these geographic disparities were not statistically significant: neither the potential “High-Score Group” (Coefficient = 6.30, 95% CI: −5.51 to 14.73) nor the “Low-Score Group” (Coefficient = −4.94, −11.01 to 1.13) showed a significant difference compared to countries with medium scores (Intercept = 2.04, 1.42–2.67). Also, clinical factors were the primary drivers of pain severity. Specifically, the use of GnRH analogues was strongly associated with higher pain scores (Coefficient = 1.32, 0.93–1.70). Older age was also positively associated with increased pain intensity (Coefficient = 0.02, 0.01–0.03). Furthermore, participants suffering from long-term conditions reported significantly higher scores (Coefficient = 0.56, 0.43–0.68). Living with disabilities was also a robust predictor of pain (Coefficient = 0.75, 0.49–1.01), with even those preferring not to disclose their disability status showing elevated levels (Coefficient = 0.37, 0.02–0.71). The funnel plot ( Supplementary Fig. S10A ) visually suggests a geographic divergence in insomnia severity, with the UK and Rwanda appearing to exhibit higher burdens, while Nigeria and Sri Lanka cluster with lower scores. However, Model 2 estimates ( Supplementary Table S22 ) indicate that these apparent geographic disparities were not statistically significant: neither the potential “High-Score Group” (Coefficient = 2.15, 95% CI: −0.76 to 5.06) nor the “Low-Score Group” (Coefficient = −0.31, −3.04 to 2.43) showed a significant difference compared to countries with medium scores (Intercept = −4.52, −16.81 to 7.77). Furthermore, clinical factors were robust predictors of sleep disruption. Specifically, suffering from disabilities was strongly associated with higher insomnia scores (Coefficient = 2.83, 2.14–3.51), as was a diagnosis of endometriosis (Coefficient = 1.99, 1.37–2.62). Current or previous use of HRT was also linked to increased insomnia severity (Coefficient = 1.64, 1.15–2.13). Regarding occupational history, shorter or medium-term professional tenure in the current specialty pre-menopause was associated with higher scores relative to those with over 21 years of experience (e.g., 1–5 years: Coefficient = 1.56, 1.04–2.08). Interestingly, the post-menopause stage was associated with lower insomnia scores compared to the perimenopause stage (Coefficient = −1.44, −2.79 to −0.08), suggesting a potential alleviation of sleep disturbances in the later stage. The funnel plot ( Supplementary Fig. S11A ) visually suggests a geographic divergence in burnout levels, with the UK, Brazil, and Singapore appearing to exhibit higher burdens, while Ghana and Sri Lanka cluster with lower scores. However, Model 2 estimates indicate that the elevated levels in the potential “High-Score Group” were not statistically significant compared to countries with medium scores (Coefficient = 0.16, 95% CI: −0.13 to 0.44). In contrast, Ghana and Sri Lanka formed a distinct cluster with significantly lower burnout levels (Coefficient = −0.81, −1.20 to −0.42). And, clinical and occupational factors were key drivers of burnout. Specifically, the use of GnRH analogues (Coefficient = 0.34, 0.20–0.48) and a diagnosis of premature ovarian insufficiency (Coefficient = 0.21, 0.07–0.34) were associated with higher scores. Current or previous HRT use was also linked to increased burnout (Coefficient = 0.18, 0.12–0.25). Regarding occupational history, shorter or medium-term professional tenure in the current specialty pre-menopause was associated with higher burnout relative to those with over 21 years of experience (e.g., 6–10 years: Coefficient = 0.21, 0.14–0.28). Additionally, participants suffering from disabilities (Coefficient = 0.35, 0.26–0.44) or long-term conditions reported significantly higher symptom burdens. The funnel plot ( Supplementary Fig. S12A ) visually suggests country variation in health-related quality of life (HrQoL), with the UK, India, and Sri Lanka appearing to exhibit higher scores, while Oman, Nepal, and Tanzania cluster with lower scores. However, Model 2 estimates ( Supplementary Table S26 ) reveal that the elevated levels in the potential “High-Score Group” were not statistically significant compared to countries with medium scores (Coefficient = 1.24, 95% CI: −0.10 to 2.58). In contrast, Oman, Nepal, and Tanzania formed a distinct cluster with significantly lower score (Coefficient = −2.16, −3.74 to −0.58). Besides, clinical complexity was the primary driver of poorer HrQoL. Specifically, suffering from disabilities was strongly associated with higher scores (Coefficient = 3.12, 2.66–3.58), as was the use of GnRH analogues (Coefficient = 1.62, 0.89–2.35). Participants suffering from long-term conditions also reported significantly higher scores (Coefficient = 1.61, 1.38–1.83). Additionally, a diagnosis of endometriosis was linked to increased scores (Coefficient = 0.71, 0.27–1.16). No significant associations were found for menopausal stages. A sensitivity analysis (Model 3) was conducted on the subset of participants (n = 2477) with two observations to account for potential temporal effects and within-individual correlations. No significant differences were observed between the two time points for the majority of scales. Slight decreases in scores at the second observation were noted only for physiological (GCS), vasomotor (GCS), and MRS scores. The consistency between Model 1 and Model 3 estimates confirms the robustness of our primary findings ( Supplementary Tables S4, S6, and S26 ). Network analysis revealed a tightly interconnected symptom structure, with anxiety and depression demonstrating the strongest association. Burnout emerged as the most central node within the network, bridging psychological and somatic domains, and demonstrating the highest centrality and predictability. In contrast, health-related quality of life showed weaker connectivity, suggesting greater influence from external factors ( Fig. 3 , Supplementary Figs. S13 and S14 ). Fig. 3 Symptom Network of Menopausal Symptoms. The network nodes represent the following constructs: Anxiety (measured by HADS-A), Depression (HADS-D), Somatic (GCS Physiological), Vasomotor (GCS Vasomotor), Disability (QPDS), Insomnia (ISI), Burnout (BAT), and HrQoL (HrQoL score). Nodes are colored according to their symptom scales: Psychological symptoms (Anxiety, Depression, Burnout) are in teal, Physiological symptoms (Insomnia, Somatic, Vasomotor, Disability) are in yellow, and Quality of Life (HrQoL) is in purple. Edges represent regularised partial correlation coefficients. The thickness of an edge indicates the magnitude of the association (thicker edges indicate stronger connections). Blue lines represent positive correlations, while red lines represent negative correlations. Symptom Network of Menopausal Symptoms. The network nodes represent the following constructs: Anxiety (measured by HADS-A), Depression (HADS-D), Somatic (GCS Physiological), Vasomotor (GCS Vasomotor), Disability (QPDS), Insomnia (ISI), Burnout (BAT), and HrQoL (HrQoL score). Nodes are colored according to their symptom scales: Psychological symptoms (Anxiety, Depression, Burnout) are in teal, Physiological symptoms (Insomnia, Somatic, Vasomotor, Disability) are in yellow, and Quality of Life (HrQoL) is in purple. Edges represent regularised partial correlation coefficients. The thickness of an edge indicates the magnitude of the association (thicker edges indicate stronger connections). Blue lines represent positive correlations, while red lines represent negative correlations. We employed a Gaussian Graphical Model (GGM) to explore the complex interrelationships between menopausal symptoms ( Fig. 3 ). The strongest positive association in the network was observed between Anxiety and Depression, highlighting their tight comorbidity. Burnout emerged as the most central node within the symptom ecosystem. As illustrated in the centrality analysis ( Supplementary Fig. S13 ), Burnout exhibited the highest values for Strength (direct connectivity), Closeness (global connectivity), and Betweenness (bridging role), serving as a hub connecting psychological symptoms (Anxiety, Insomnia) with somatic symptoms (Physiological). Node predictability analysis ( Supplementary Fig. S14 ) further corroborated this finding; Burnout had the highest predictability (R 2 = 0.594), indicating that nearly 60% of its variance could be explained by the state of other symptoms in the network. In contrast, Health-related Quality of Life (HrQoL) occupied a peripheral position with the lowest predictability (R 2 = 0.119), suggesting it is largely influenced by factors external to the symptom network. Stability analysis based on 1000 bootstrap samples confirmed that the estimated network structure and the central role of Burnout were robust ( Supplementary Tables S27 and S28 ). Taken together, these findings demonstrate that menopausal symptom burden is not uniformly distributed but reflects a complex interplay between geographic context, clinical complexity, and socioeconomic factors, with consistent patterns observed across multiple outcome domains.

In this large multi-country study of 5228 midlife participants across 13 countries, we observed a substantial and highly heterogeneous burden of menopausal symptoms, psychological (anxiety, depression, burnout), somatic (physiological, vasomotor), pain, sleep disturbance, disability and impaired quality of life (HrQoL). The distribution of symptom severity varied not only by menopausal stage (perimenopause, menopause, post-menopause) but more strikingly by geographic region. Notably, participants in the UK and Rwanda consistently exhibited among the highest levels of anxiety, depression, somatic, vasomotor and overall menopausal symptom burden (as measured by MRS), whereas several South and South-East Asian countries (e.g., Nepal, Sri Lanka, Malaysia, Singapore) tended to cluster at the lower end of the burden spectrum. Social and clinical factors modulated these burdens: higher educational attainment was strongly protective across multiple measures; by contrast, clinical complexity (such as diagnosis of endometriosis, use of GnRH analogues, living with disabilities or long-term conditions, history of premature ovarian insufficiency) was associated with markedly higher symptom and disability scores. Use of HRT (current or previous) was paradoxically associated with increased anxiety, depression, insomnia, and poorer somatic symptom burden in our pooled analysis. Occupational history also mattered: shorter professional tenure pre-menopause correlated with higher psychological, somatic and disability burden. Our observational, cross-sectional data contribute to a growing body of evidence that menopausal symptom burden varies widely across populations and regions. A recent systematic review and meta-analysis of 321 studies (482,067 middle-aged women) reported high global prevalence of menopausal symptoms, with joint and muscular discomfort most common (≈65%) and substantial variation according to menopausal stage, symptom type, and region. 39 Importantly, that review noted higher vasomotor symptom prevalence in lower-income settings compared to high-income ones. 39 Our findings add nuance: for example, while high-income countries like the UK carried a high burden, some lower- or middle-income countries (e.g., Nepal, Sri Lanka, parts of South Asia) reported lower symptom severity; this suggests that socioeconomic classification alone does not predict menopausal burden. Rather, local cultural, occupational, lifestyle, environmental, and healthcare-access factors likely play a role. The global prevalence of depression in menopausal women has been estimated at 35.6% (95% CI: 32.0–39.2%), with perimenopausal women showing similar rates to postmenopausal women. 40 Our pooled data show that psychological symptom burden is highly variable by country, for instance, elevated depression and anxiety in our UK and Rwanda cohorts, lower in Malaysia, Nepal, Singapore. This heterogeneity supports the need for culturally and regionally tailored research and interventions. Much of the literature focuses on vasomotor, urogenital, musculoskeletal symptoms, or global symptom prevalence, far fewer large-scale studies provide cross-national comparisons incorporating mental health, disability, occupational history, and multimorbidity. Our study thus fills an important gap, providing the first multi-country, multi-domain empirical evidence of how clinical complexity (e.g., endometriosis, long-term conditions, GnRH use, disability) and socioeconomic/occupational factors intersect to shape the menopause experience globally. These findings also echo broader concerns about the mental health burden in midlife globally. According to recent global burden studies, anxiety disorders and depressive disorders remain among the top causes of years lived with disability (YLD) worldwide. Given demographic trends increasing life expectancy, larger ageing female populations, the absolute number of midlife and post-reproductive people experiencing menopause worldwide will continue to grow. Our results highlight that the “menopause transition” remains a critical window of vulnerability for mental and physical ill-health, but this vulnerability is not uniform. Thus, from a population-science and public-health perspective, our data suggest that region, socioeconomic position (education), clinical complexity, and occupational history should all be considered in epidemiological estimates of menopause burden, in modelling future global health needs, and in planning resource allocation. Standard “one-size-fits-all” prevalence estimates may mask substantial inequality and leave high-burden subgroups under-recognised. As for the clinical impact of this work, the elevated levels of anxiety, depression, insomnia, burnout, pain and somatic symptoms in some countries and subgroups indicate that menopause is more than a transient endocrine change, for many, it is a critical period of vulnerability for mental and physical health, with potentially long-lasting implications. Psychological distress (anxiety, depression, burnout) can impair functioning, productivity, social relationships, and quality of life; when combined with somatic symptoms, pain, sleep disturbance and disability, this burdensome mix may increase risk of chronic disease, social and occupational exclusion, and reduced life satisfaction. Indeed, our network analysis suggests that burnout acts as a central hub, bridging psychological and somatic symptoms where untreated burnout could perpetuate a vicious cycle, exacerbating both mental and physical ill-health. However, this finding is exploratory and should not be interpreted as evidence of a causal mechanism. From a clinical vantage point, failing to recognise and treat this complex comorbidity risks leaving many people with unmet health needs, particularly those with endometriosis, premature ovarian insufficiency, disability, long-term conditions, or who use GnRH analogues. Without holistic care, these individuals may experience chronic pain, poor sleep, mental health decline, reduced quality of life, and possibly long-term disability. Substantial regional variation suggests that global estimates of menopausal burden must not only consider prevalence of vasomotor or urogenital symptoms, but also context-specific mental health, disability, comorbidity, social determinants, and healthcare access. Our counter-intuitive finding that HRT use was associated with higher symptom burden (in a cross-sectional analysis) underscores the risks of simplistic assumptions about HRT as a panacea, especially when underlying social, occupational, clinical or psychological complexities remain unaddressed. The observed association between HRT use and higher symptom burden is most plausibly explained by confounding by indication, whereby individuals experiencing more severe symptoms are more likely to initiate treatment. Reverse causality may also contribute, as symptom burden precedes treatment initiation. Additionally, substantial variation in HRT utilisation across countries and sampling contexts may influence these findings. These results should therefore be interpreted cautiously and do not imply a detrimental effect of HRT. Current clinical guidelines, including NICE, recommend HRT primarily for vasomotor symptom management rather than as a treatment for primary psychological conditions. The clinical implications of these findings point to a profound mismatch between the lived realities of menopausal individuals and the models of care currently used within many health systems. Contemporary guidance, including the updated NICE NG23 guideline (2024), rightly emphasises the need for personalised, shared decision-making, with systemic HRT positioned as the principal therapeutic option for vasomotor, psychological, and broader menopausal symptoms. Vaginal oestrogen remains first-line for genitourinary syndrome, while non-hormonal or complementary approaches may be offered to those who cannot, or choose not to, utilise hormone therapy. This framework reflects the principle that menopause care must be tailored to individual needs; however, our global data reveal that the clinical terrain is far more complex than existing guidelines recognise. For many individuals, particularly those with multimorbidity, disability, endometriosis, long-term conditions, or a history of GnRH analogue use, standard guideline-driven care may be necessary, but rarely sufficient. These findings suggest the potential value of more holistic and multidisciplinary models of care. Individuals with endometriosis, premature ovarian insufficiency, disability, or long-term conditions are among those who bear the highest symptom burden; yet these are precisely the groups who struggle most to fit neatly into standardised menopausal care pathways. For them, “HRT plus generic lifestyle advice” is an inadequate clinical response. Early identification and risk stratification built into routine consultations would allow clinicians to identify individuals who require specialist menopause services, psychological support, pain specialists, sleep support, physiotherapy, occupational therapy or disability services, either concurrently or sequentially. Importantly, the absence of such structures in many health systems means that even when HRT is prescribed appropriately, the broader determinants of symptom persistence remain unaddressed. Our findings also signal the need to rethink how HRT initiation, monitoring, follow-up and dose adjustment are implemented in practice. International guidance acknowledges the importance of formulation (e.g., transdermal oestrogen, micronised progesterone), timing, and risk–benefit balancing; however, guidelines still emphasise vasomotor and genitourinary outcomes more than psychological, functional, or occupational ones. Yet our data show that untreated insomnia, anxiety, depression and pain have equal, if not greater, influence on disability and quality of life. The consistently elevated anxiety, depression and somatic scores in the HRT-user subgroup imply that in routine settings, mental health review is too often absent from HRT follow-up. This is a crucial gap: without addressing the psychological component of the menopause experience, hormonal modulation alone cannot deliver the outcomes that clinical guidelines assume. A clinical model that prioritises symptoms over syndromes is no longer fit for purpose. Instead, menopause care must recognise the layered impacts of social determinants, disability status, and occupational context. Shorter professional tenure, an indicator of work insecurity and possibly higher psychosocial strain, was strongly associated with greater symptom burden across multiple scales. This suggests that for many, menopause unfolds within complex work, financial and caregiving environments. Clinical practice must therefore be sensitive to the broader realities in which symptoms occur; future guidelines should explicitly incorporate occupational assessment and tailored advice on work adaptations, flexible working, and access to employer-based support. Overall, our findings suggest the need to reconsider how menopause is conceptualised within clinical medicine. The dominant paradigm that menopause is a benign, self-limiting physiological transition requiring limited intervention unless symptoms are severe no longer reflects the empirical reality across countries, populations and clinical conditions. Instead, menopause should be conceptualised as a significant health transition with the potential for wide-ranging psychological, physical and functional consequences, particularly in individuals who carry intersecting burdens of clinical complexity and social vulnerability. This requires care models built not around symptoms in isolation, but around individuals in context. To achieve this, we recommend that existing guidelines be expanded to (1) embed routine screening for psychological distress, burnout, sleep disturbance, pain and disability; (2) develop clear risk-stratification frameworks for high-burden groups; (3) incorporate multidisciplinary referral pathways; (4) emphasise personalised HRT formulation and monitoring that includes mental and occupational health; (5) and commit to long-term follow-up aimed at improving quality of life, functioning, and emotional well-being. Without such changes, clinical care may continue to fall short for the populations who need it most ( Table 3 ). Table 3 Summary of the findings reveal and required clinical evaluation. Clinical domain Current guidance approach What our findings reveal Required clinical evolution Hormone therapy First-line for vasomotor and general symptoms; limited focus on monitoring beyond efficacy and safety HRT users show high mental and somatic burden in real-world settings Integrate structured follow-up for mental health, sleep, pain and disability alongside HRT review Mental health Not routinely assessed in menopause consultations Anxiety, depression and burnout are central and persistent burdens Mandatory screening for mental health and burnout; access to psychological support embedded in menopause care Complex clinical profiles Limited tailored pathways Endometriosis, POI, disability, long-term conditions and GnRH use predict severe burden Risk stratification and fast-track referral to specialist multidisciplinary clinics Occupational context Seldom considered Shorter work tenure and burnout strongly predict poor outcomes Incorporate occupational health assessment and workplace-focused interventions Multidisciplinary care Not widely implemented Symptom burdens interact across physical, psychological, and functional domains Establish multidisciplinary menopause services (gynaecology, mental health, pain, physiotherapy, occupational therapy) Long-term follow-up Not consistently provided Persistent burden across multiple domains Longitudinal care model focusing on functioning, quality of life, pain, sleep, and emotional well-being Summary of the findings reveal and required clinical evaluation. Whilst acknowledging that this analysis is observational and cross-sectional, and therefore cannot establish causal relationships, our findings may have implications for health policy, public health planning, and resource allocation. The observed variation in symptom burden across countries and clinical subgroups ( Table 4 ) highlights potential inequities that may warrant consideration in policy and service planning. In the context of demographic ageing, these patterns suggest that the overall burden of menopausal symptoms could increase, although this cannot be directly inferred from the present data. Table 4 Summary of recommendations. Policy area Implication from our findings Recommended action Awareness & education Low educational attainment was strongly associated with higher psychological and somatic burden. Many high-burden subgroups may lack awareness about menopause symptomatic complexity and management. Fund public health campaigns (national, regional) to raise awareness about menopause as a multifaceted health issue — not just vasomotor symptoms, but mental health, disability, comorbidity, occupational burden. Target under-served or lower-education populations. Access to comprehensive menopause care Standard care (e.g., primary care offering HRT) may not suffice for people with clinical complexity, multimorbidity, disabilities or high symptom burden. Develop and fund multidisciplinary menopause clinics and integrated care pathways that include mental health, pain management, occupational health, physiotherapy, social support, especially in regions with high burden (e.g., UK, Rwanda). Workforce & occupational health Shorter professional tenure (pre-menopause) predicted worse outcomes — perhaps reflecting stress, less job security, or lack of workplace support. Encourage workplace policies to support menopausal (perimenopausal/postmenopausal) employees — flexible working, occupational health assessments, support for disability, mental health, workload adjustments. Global health equity Our cross-country data reveal inequalities in burden; lower-resource settings may lack capacity to deliver HRT or holistic care, possibly increasing morbidity and disability. International agencies (e.g., global public health bodies, NGOs, women’s health organisations) should prioritise menopause care in global health agendas; fund capacity building, training, and delivery of context-appropriate menopause services. Clinical guideline & commissioning Existing guidelines tend to focus narrowly on vasomotor and genitourinary symptoms; many aspects (burnout, pain, disability, comorbidity) are unaddressed. Commission updates to national and international guidelines (e.g., by NICE, EMAS, BMS) to integrate holistic assessment and management of menopause. Ensure commissioning bodies fund appropriate services beyond HRT prescribing. Summary of recommendations. Additionally, the network analysis indicating relatively weak connectivity of health-related quality of life within the symptom structure suggests that quality of life may not be determined solely by symptom burden. This observation is consistent with the possibility that broader contextual factors, such as socioeconomic conditions, access to care, workplace environments, and comorbidities, may play a role, although these were not directly measured in this analysis. Taken together, these findings highlight the potential importance of considering menopause within a broader biopsychosocial and health systems context. Further research, particularly longitudinal and interventional studies, is needed to better understand these relationships and to inform appropriate clinical and policy responses. A strength of the current phase of the study is its unprecedented multi-country sample of more than 5000 midlife individuals, allowing robust cross-regional comparisons rarely achieved in menopause research. The integration of psychological, somatic, occupational, and clinical complexity markers provides a uniquely holistic understanding of menopausal burden that extends far beyond vasomotor symptoms. The network analysis offers an innovative systems-level perspective, identifying burnout as a central driver that has not been highlighted in previous global studies, in addition groundwork for the MARiE country-specific, culturally competent and evidence-based toolkits. Taken together, these findings underscore menopause as a multidimensional and context-dependent health transition shaped by biological, clinical, and structural factors. The implications of these findings are hypothesis-generating and should inform future research, policy dialogue, and health system planning rather than be interpreted as prescriptive recommendations. Several other limitations of this work must be acknowledged. The cross-sectional design limits causal inference, particularly in explaining counter-intuitive associations such as higher symptom burden among HRT users. Additionally, variation in sampling approaches across countries and limited detail on HRT regimens, comorbidity severity, and healthcare access may introduce residual confounding and restrict generalisability. 21 With any multi-cultural study, there is a chance that some of the differences in findings are due to the ways in which menopause and its symptoms are understood culturally. Substantial variation in HRT utilisation across countries, including disproportionately high representation in some settings, may reflect sampling bias and differences in healthcare access, and should be considered when interpreting these findings. The inclusion of multiple outcome variables may increase the likelihood of type I error, and findings should therefore be interpreted as exploratory. Although menopausal status was clinically assessed prior to inclusion using standard practice classifications (perimenopause, menopause, post-menopause), more granular differentiation of menopause aetiology (e.g., premature ovarian insufficiency or early menopause) was not feasible within the current dataset, and future studies with larger, stratified samples are warranted to explore these subgroups in greater detail. In conclusion, our multi-country analysis reveals that the menopause transition and beyond can be a period of substantial mental and physical health burden characterised by wide geographic heterogeneity, strong influence of clinical complexity, and close interconnection between psychological, somatic, pain, sleep, disability and quality-of-life domains. The central role of burnout suggests a need to reframe menopause as a complex biopsychosocial health transition, not simply a hormonal or vasomotor phenomenon. To improve outcomes, clinical practice should move beyond narrow symptom treatment: we need holistic, individualised, multidisciplinary menopause care, risk stratification, and long-term follow-up. In parallel, health policy should recognise menopause as a public-health priority, invest in menopause services, address social determinants, and support equity in access to care. Ultimately, broadening the scope of menopause care from hot flushes and vaginal dryness to mental health, disability, pain, and functional well-being will advance health, equity and quality of life for millions of midlife and older people worldwide. Future research should prioritise longitudinal designs and culturally grounded approaches to better understand trajectories and inform integrated models of care.

GD developed the ELEMI program and the MARIE project. This was furthered by GD and PP. GD, KE, PP, JT, LS and HFK submitted and secured the ethics approval for the study in the UK. KM, VC, LS, KR, SH, KP, GD, PP, VT, RP and HFK collected data. JS, JQS, PP and GD conducted the data analysis. GD and VP accessed and verified the underlying data. GD wrote the first draft and was furthered by all other authors. VP edited and formatted all versions of the manuscript. All authors critically appraised, reviewed and commented on all versions of the manuscript. All authors read and approved the final manuscript. All authors consented to publish this manuscript.

The menopausal experience is marked by hormonal changes that produce a wide range of physical and psychological symptoms experienced by cis women, transgender and LGBTQ + populations. 1 , 2 , 3 As life expectancy rises and population’s age, more women are spending a significant portion of their lives in perimenopause, menopause or post-menopausal stages. 4 In 2020, an estimated 985 million women worldwide were aged 50 or above, a number projected to reach 1.65 billion by 2050. 5 , 6 This demographic shift brings increasing concern about menopause-related health issues and chronic conditions that can adversely affect older women’s quality of life. Despite the large and growing population affected, menopausal health has historically received insufficient attention in research, clinical practice, and public policy, especially in low-resource settings. Common symptoms include hot flushes, night sweats, low mood, sleep disturbances, vaginal dryness and discomfort during sex, urinary incontinence, and joint or muscle pain. 7 For some women these symptoms are mild, but for others they can be severe and long-lasting, significantly impairing daily activities and overall quality of life. 8 , 9 The decline in oestrogen at menopause has long-term health implications such as bone density loss, leading to higher osteoporosis and fracture risk about half of postmenopausal women will develop osteoporosis. 10 , 11 Cardiovascular health also worsens as the protective effect of oestrogen fades: women’s risk of heart disease, stroke, and metabolic conditions increases post-menopause. 12 , 13 Beyond the commonly reported physical changes, menopause can deeply affect mental health and emotional well-being. 14 Many women experience low mood, irritability, brain fog or memory lapses during the menopausal transition. 15 , 16 , 17 Rates of clinical depression and anxiety tend to rise in midlife women. A recent global meta-analysis found that one in three women in perimenopause or post-menopause suffers from depression, highlighting how common and serious the mental health impact can be. 3 The MARIE-UK WP2a study found a negative physical and mental health impact during the menopausal period, which was also similar to the UK’s Women’s Health Strategy publication consultation report. 18 A negative mental health impact can disrupt psychosocial functioning. Symptoms such as low mood, poor concentration and memory can undermine women’s confidence and productivity that can affect their ability to work effectively. Despite not being an illness per, menopause often requires support and management to safeguard women’s mental health and quality of life. Despite the substantial health burden associated with menopause, there remain large gaps in research and practice. Historically, women’s health agendas, especially in low- and middle-income countries (LMICs) have focused on reproductive-age issues like maternal health, while the needs of midlife and older women have been comparatively neglected. 19 There is a significant menopause care gap globally where healthcare providers often lack specialised training in menopause management, along with restricted access to good quality and consistent clinical pathways, and accurate or effective treatments such as hormone replacement therapy (HRT). 20 , 21 , 22 , 23 Consequently, awareness and support for menopausal health are especially lacking in many LMICs. 21 Ironically, this is where the need is greatest: by the end of this decade, an estimated 76% of post-menopausal women globally will be living in developing countries. Thus, the majority of the world’s menopausal women are in regions that currently have the least support systems in place, a glaring gap in global health equity. There is an urgent need for research that examines menopause across diverse populations and settings, to inform better health interventions and policies. Most existing menopause studies and clinical trials have been concentrated in western, high-income countries, and relatively few have explored the experiences of women in Asia, Africa, or Latin America. The broad geographic scope of the MARIE project covering South and South-East Asia, Africa, and South America allowed the study to capture a richly diverse sample of women. 24 , 25 , 26 , 27 , 28 , 29 , 30 There are also a lack of comparative data that could illuminate how cultural, environmental, and socioeconomic factors influence menopausal symptoms and coping strategies. Understanding these differences (and commonalities) is crucial for developing interventions that are effective and culturally sensitive. The MARIE Work Package 2a is designed to help fill these knowledge and practice gaps by providing a comprehensive investigation of menopausal health impacts across a wide range of global populations. This study adopts a comparative epidemiological approach to examine global variation in menopausal symptom burden and associated health domains. Rather than testing causal hypotheses, the analysis is descriptive and hypothesis-generating, aiming to characterise patterns across diverse populations. Given the multidimensional nature of menopause, the study includes psychological, somatic, and functional health domains, recognising that these outcomes are not solely attributable to menopause but may interact with the menopausal transition within broader clinical and social contexts.

We declare no competing interests.