The national and sub-national patterns of age of menopause and its associated factors: An experience from Iran STEPS Survey 2021
Bibliographic record
Abstract
Abstract Background: Menopause as an important health issue needs special attention and planning to provide extensive services and interventional programs to manage the complications and adverse health consequences. Benefiting from a national and sub-national representative sample, the present study aimed to estimate the distribution of age of menopause and its associated factors in Iranian women. Methods: Present investigation was a part of the national survey of non-communicable disease risk factor surveillance (STEPS 2021) in Iran. Through a systematic random sampling, under safety considerations during the COVID-19 pandemic, of the 28,520 adults who were found to be in included in the survey, 27,874 participants completed the questionnaires (step 1), 27,745 individuals were anthropometrically measured (step 2), and 18,119 individuals completed laboratory evaluation (step 3). Present study included 13291 females who had non-missing menopause values. Menopause before the age of 45 years is considered "early menopause". Theovarian failure/surgical removal of the ovaries before the age of 40 years as; "premature ovarian insufficiency". The others group were; normal (40-50), and late (55 ≤) menopause. Results: In 2021, 28.58% (95% CI: 27.67, 29.48) of 25≤ year-old Iranian women were menopause. In adult menopause, the prevalence of early menopause was 4.55% (3.88,5.22) and 11.63% (10.64,12.63) had late menopause. Based on the sub-national distribution of age of menopause; early menopause was the dominant pattern in the most of eastern provinces. The low physical activity, significantly decreased the age-adjusted Odds ratio of menopause (p< 0.05). The current smoking and hookah consumption, as well as the history of continuous smoking, were significantly associated to the decreasing in age-adjusted OR of the age of menopause (p< 0.05). These significant association also detected in widow marriage status participants; women with public sector employment and who had more than 12 years of schooling (p< 0.05). Conclusion: The findings of present study could be used for promotion of health planning and interventions at national and sub-national levels. Special attentions need for early monitoring of women with a family history of early menopause, especially, who have life style risk factors. Consequently, special attention should be considered to probable factors confounding menopause age among Iranian women.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".