Menopause in low and middle-income countries: a scoping review of knowledge, symptoms and management
Bibliographic record
Abstract
OBJECTIVE: This study aimed to systematically map available evidence on menopause-related knowledge, symptoms and management in low and middle-income countries (LMICs) and identify critical gaps to inform research and policy. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Review (PRISMA-ScR) guidelines, the MEDLINE, EMBASE, CINAHL and Scopus databases were searched for studies published between 2000 and 2024. Eligible studies were those that reported on menopause-related knowledge, symptom prevalence or management in LMICs. Data were extracted and synthesised descriptively and thematically. RESULTS: From 10,758 records, 252 studies from 41 LMICs were included. Most were cross-sectional (85%) and relied on non-probability sampling (62%), with only 4% being nationally representative. Menopause classification methods were often inconsistent, with only 17.5% of studies using the Stages of Reproductive Aging Workshop (STRAW) +10 or World Health Organization (WHO) criteria. National-level data on age at menopause are lacking across all LMICs. The prevalences of vasomotor symptoms, joint pain and sexual concerns were comparable with high-income countries. However, data on the severity and burden of symptoms were scarce. Knowledge about menopause and menopausal hormone therapy (MHT) was poor, especially in low-income and rural settings. Cultural stigma, misconceptions and healthcare provider knowledge gaps contributed to low uptake of evidence-based menopause care. MHT use was consistently low, with women predominantly using traditional remedies. Provider hesitancy, lack of training and structural health system barriers were key limitations in service delivery. CONCLUSIONS: Despite a growing population of postmenopausal women, menopause remains a neglected health issue across LMICs. There is an urgent need to integrate menopause into reproductive and non-communicable disease policies, invest in provider education and ensure equitable access to evidence-based menopause care, including MHT, for women in LMICs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.032 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".