Prevalence of Polypharmacy in Chinese Community‐Dwelling Older Adults and Forecast by 2035: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
PURPOSE: We aimed to derive a pooled polypharmacy prevalence estimate and forecast the total polypharmacy cases by 2035 in Chinese community-dwelling older adults. METHODS: We searched for studies in three databases (CNKI, Scopus and PubMed). We selected studies according to pre-defined inclusion and exclusion criteria. We assessed study quality using a modified Newcastle-Ottawa Scale. Polypharmacy in our study was defined as the concurrent use of at least five different medications. Pooled prevalence was estimated overall and by regions, time periods, and other important factors. We fitted Bayesian random-effects logit models to synthesize single studies and reported a 95% uncertainty interval (95UI). RESULTS: A total of 25 studies were finally included. The pooled polypharmacy prevalence was estimated to be 31.04% (95UI: 18.16 ~ 47.66). The pooled prevalence was highest in the east region (37.98%, 95UI: 21.92 ~ 57.69), followed by the middle region (33.53%, 95UI: 4.89 ~ 84.46) and the west region (25.85%, 95UI: 8.78 ~ 50.74). The pooled prevalence was 31.10% (95UI: 15.54 ~ 52.72) in the latest 5 years (2017-2021) and 30.88% (95UI: 11.53 ~ 60.56) in the beyond latest 5 years (2005-2016). The per cent change annualized in the forecasted total polypharmacy cases from 2022 to 2035 was estimated to be 3.69%, with the highest total cases forecasted to be 131.7 million (95UI: 77.1 ~ 202.2) in 2035. CONCLUSIONS: Our study suggests that polypharmacy is notably prevalent in Chinese community-dwelling older adults, highlighting the need for the development and delivery of community-based interventions targeted at 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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".