Schulz · Menopause (New York, N.Y.) 2026 · Cross-sectional population survey · n=15,322

Age at natural and surgical menopause and related factors: NHANES 2013-2023.

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Level 4 - case-series / case-control

Cross-sectional survey analysis of population data (NHANES 2013-2023)

PubMed 42413018 · doi:10.1097/GME.0000000000002848 · record verified 2026-08-26

What was done

Analyzed cross-sectional data from 15,322 women aged 15 years and older in the US National Health and Nutrition Examination Survey (NHANES) from 2013 to 2023. The authors calculated age at natural and surgical menopause using life table methods and survey-adjusted weighted means, and classified onset into premature, early, typical, or late categories. Multivariable Cox proportional hazards models assessed factors associated with the timing of natural and surgical menopause.

What was found

Life tables showed a median age of natural menopause of 50 years, with a survey-adjusted weighted mean age of 49.5 years (95% CI: 49.2-49.8). Among postmenopausal women, the prevalence was 4.6% for premature natural menopause and 10.0% for early natural menopause. Younger natural menopause onset was associated with less education, smoking, and mild depression. Younger age at surgical menopause was associated with obesity, hypertension, hypercholesterolemia, and arthritis. Increased odds of premature natural menopause were linked to less education, short sleep, diabetes, and mild depression (exact numerical effect sizes and confidence intervals for these associations were not provided in the abstract).

Why it matters

Provides contemporary, nationally representative benchmarks for the timing of natural and surgical menopause in US women. It highlights several modifiable behavioral and cardiometabolic factors associated with earlier ovarian aging.

Limits

The cross-sectional design cannot establish causality. Age at menopause was self-reported and subject to recall bias. Effect sizes (hazard ratios and odds ratios) for associated risk factors were omitted from the abstract.

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