The burden of sleep disturbances and vasomotor symptoms on work productivity, activity impairment and healthcare resource use in perimenopausal and postmenopausal women
Bibliographic record
Abstract
OBJECTIVE: To explore relationships between sleep disturbances and vasomotor symptoms (VMS) with work productivity/activity impairment (WPAI) and healthcare visits among peri- and postmenopausal women. STUDY DESIGN: We analyzed data from peri- and postmenopausal women aged 40-65 years who participated in the National Health and Wellness 2019/2021 survey (in the US; N = 27,621) and 2017/2020 survey (in France, Germany, Italy, Spain, and the UK; N = 20,220). We used generalized linear regression to calculate adjusted estimated marginal means (EMMs) to assess differences between four subgroups based on self-reported sleep disturbances and VMS. MAIN OUTCOME MEASURES: Scores on the WPAI questionnaire (higher scores indicating worse outcomes); number of healthcare visits in the previous 6 months. RESULTS: Among postmenopausal women, those with symptoms had worse WPAI outcomes than those with neither type of symptom. The highest scores (worst outcomes) were seen for women with both symptoms: the EMMs for this group in the US survey, vs. those with neither symptom, were 11.9 % vs. 9.3 % for presenteeism, 12.8 % vs. 10.2 % for work productivity impairment, and 20.3 % vs. 16.2 % for activity impairment. Corresponding estimates for Europe were 17.4 % vs. 12.9 % for presenteeism, 18.8 % vs. 14.4 % for work productivity impairment, and 28.1 % vs. 20.9 % for activity impairment. Worse WPAI outcomes were not clearly observed in perimenopausal women with symptoms vs. those with no symptoms. However, both peri- and postmenopausal women with symptoms had more previous healthcare visits than those with neither symptom, especially those with sleep disturbances irrespective of co-occurring VMS. CONCLUSIONS: Sleep disturbances and VMS were associated with worse WPAI scores and more healthcare visits in postmenopausal women, indicating a need for effective management of these symptoms among this population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".