Prevalence And Definitions of Polypharmacy: A Systematic Review And Meta-Analysis
Bibliographic record
Abstract
Abstract INTRODUCTION: Polypharmacy is common associated with several adverse health outcomes. There are currently no systematic reviews or meta-analyses on the prevalence of polypharmacy and associated factors. We aimed to identify population-based observational studies reporting on the prevalence of polypharmacy and factors associated with polypharmacy. METHODS: MEDLINE, EMBASE, and Cochrane databases with no restriction on date. Population-based observational studies with cross-sectional, case-control, or cohort designs using administrative databases or registries to define or measure polypharmacy among individuals over 19. Using a standardized form, two reviewers independently extracted study characteristics, a crude prevalence rate of polypharmacy and its standard error with 95% confidence intervals (CIs). The risk of bias and quality of studies was assessed using the Newcastle-Ottawa Scale. The main outcome was the prevalence of polypharmacy and factors associated with polypharmacy. Using a random-effects model, pooled prevalence estimates with 95% CI was reported. Subgroup analysis was performed if significant heterogeneity was explored. Meta-regression analysis was conducted to predict polypharmacy prevalence.RESULTS: 106 full-text articles were identifies using 21 unique terms with 138 descriptive definitions of polypharmacy. The pooled estimated prevalence polypharmacy in studies reporting all medication classes was 37% (95% CI: 31%-43%). Differences in polypharmacy prevalence were reported for studies using different numerical threshold and polypharmacy was also associated with study year in meta-regression. Sex, study geography, study design and study setting were not associated with differences in polypharmacy prevalence. DISCUSSION: Our review highlights that polypharmacy is common particularly among older adults and those in inpatient settings. A variety of definitions are used to define polypharmacy and differences in polypharmacy definitions may have implications for understanding the burden or polypharmacy and outcomes associated with polypharmacy. CONCLUSIONS AND IMPLICATIONS: Clinicians should be aware of the common occurrence of polypharmacy in all populations and undertake efforts to minimize inappropriate polypharmacy whenever possible.
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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 | 0.027 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.046 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".