Sex differences in multimorbidity and polypharmacy trends: A repeated cross-sectional study of older adults in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Multimorbidity is increasing among older adults, but the impact of these recent trends on the extent and complexity of polypharmacy and possible variation by sex remains unknown. We examined sex differences in multimorbidity, polypharmacy (5+ medications) and hyper-polypharmacy (10+ medications) in 2003 vs 2016, and the interactive associations between age, multimorbidity level, and time on polypharmacy measures. METHODS AND FINDINGS: We employed a repeated cross-sectional study design with linked health administrative databases for all persons aged ≥66 years eligible for health insurance in Ontario, Canada at the two index dates. Descriptive analyses and multivariable logistic regression models were conducted; models included interaction terms between age, multimorbidity level, and time period to estimate polypharmacy and hyper-polypharmacy probabilities, risk differences and risk ratios for 2016 vs 2003. Multimorbidity, polypharmacy and hyper-polypharmacy increased significantly over the 13 years. At both index dates prevalence estimates for all three were higher in women, but a greater absolute increase in polypharmacy over time was observed in men (6.6% [from 55.7% to 62.3%] vs 0.9% [64.2%-65.1%] for women) though absolute increases in multimorbidity were similar for men and women (6.9% [72.5%-79.4%] vs 6.2% [75.9%-82.1%], respectively). Model findings showed that polypharmacy decreased over time among women aged < 90 years (especially for younger ages and those with fewer conditions), whereas it increased among men at all ages and multimorbidity levels (with larger absolute increases typically at older ages and among those with 4 or fewer conditions). CONCLUSIONS: There are sex and age differences in the impact of increasing chronic disease burden on changes in measures of multiple medication use among older adults. Though the drivers and health consequences of these trends warrant further investigation, the findings support the heterogeneity and complexity in the evolving association between multimorbidity and polypharmacy measures in older populations.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".