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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".