Trends in obesity among premenopausal and postmenopausal women in the United States between 1999 and 2018: results from the National Health and Nutrition Examination Survey
Bibliographic record
Abstract
OBJECTIVE: The objective of the present work is to: (1) describe the trends in obesity among premenopausal and postmenopausal women in the United States between 1999 and 2018, and (2) describe the effect of aging on body mass index in women, using novel BMI-for-age percentile curves. METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) collected between 1999 and 2018, including self-identified female participants older than 20 years, was used. Menopause status was self-reported, and body mass index (BMI, kg/m 2 ) was calculated based on measured height and weight. Mean BMI across year is described according to menopause status and race/ethnicity. BMI-for-age percentiles and curves were created to describe adult BMI in the context of age. RESULTS: Mean BMI among premenopausal women increased from 27.7 (7.1) kg/m 2 in 1999 to 30.2 (8.8) kg/m 2 in 2018. In postmenopausal women, mean BMI increased from 28.7 (6.2) kg/m 2 in 1999 to 29.7 (7.1) kg/m 2 in 2018. Among premenopausal women, BMI values in the 50th percentile range from 25.0 kg/m 2 at age 20 to 28.6 kg/m 2 at age 60. Among postmenopausal women, BMI values in the 50th percentile range from 27.1 kg/m 2 at age 41 to 28.3 kg/m 2 at age 60, and 26.5 kg/m 2 at age 80. CONCLUSIONS: The present findings describe an increase in BMI by both calendar year and chronological age during the years before menopause leading to higher BMI levels among postmenopausal women. These findings highlight the premenopausal period and the menopause transition as an important opportunity for obesity screening, identification, and prevention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".