Prevalence of premenstrual syndrome and premenstrual dysphoric disorder in India: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: The burden and impact of premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD) is not well characterised among Indian population. Therefore, we conducted this systematic review and meta-analysis to estimate the prevalence of PMS and PMDD among females of reproductive age group living in India. Methods: We searched PubMed, Cochrane Library, Scopus and IndMed for studies reporting the prevalence of PMS and/ or PMDD from any part of India, published from 2000 up to Aug 2020. We performed random-effects meta-analyses evaluated using I2 statistic, subgroup analyses, sensitivity analyses and assessed study quality. Estimated prevalence along with 95% confidence intervals (CIs) were reported for each outcome of interest. The quality of each study was evaluated using modified Newcastle Ottawa Scale (NOS). This review was conducted following the standard of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Meta-Analysis of Observational Studies in Epidemiology (MOOSE) guidelines. The protocol was registered prospectively in PROSPERO (CRD42020199787). Results: Our search identified 524 citations in total, of which 25 studies (22 reported PMS, and 11 reported PMDD) with 8542 participants were finally included. The pooled prevalence of PMS and PMDD were 43% (95% CI: 0.35-0.50) and 8% (95% CI: 0.60-0.10) respectively. The estimated prevalence of PMS in adolescence was higher and account to be 49.6% (95% CI: 0.40-0.59). The heterogeneity for all the estimates was very high and could be explained through several factors involved within and between studies. Conclusion: This study identified a substantially high prevalence of PMS and PMDD in India. To identify potentially related factors, more focused epidemiological research is warranted. However, noticing the fact of significant prevalence and its potential impact on the population, stakeholders and policymakers need to address this problem at the community and individual level.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".