Prevalence and factors associated with polypharmacy: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Polypharmacy is commonly associated with adverse health outcomes. There are currently no meta-analyses of the prevalence of polypharmacy or factors associated with polypharmacy. We aimed to estimate the pooled prevalence of polypharmacy and factors associated with polypharmacy in a systematic review and meta-analysis. METHODS: MEDLINE, EMBASE, and Cochrane databases were searched for studies with no restrictions on date. We included observational studies that reported on the prevalence of polypharmacy among individuals over age 19. Two reviewers extracted study characteristics including polypharmacy definitions, study design, setting, geography, and participant demographics. The risk of bias was assessed using the Newcastle-Ottawa Scales. The main outcome was the prevalence of polypharmacy and factors associated with polypharmacy prevalence. The pooled prevalence estimates of polypharmacy with 95% confidence intervals were determined using random effects meta-analysis. Subgroup analyses were undertaken to evaluate factors associated with polypharmacy such as polypharmacy definitions, study setting, study design and geography. Meta-regression was conducted to assess the associations between polypharmacy prevalence and study year. RESULTS: 106 full-text articles were identified. The pooled estimated prevalence of polypharmacy in the 54 studies reporting on polypharmacy in all medication classes was 37% (95% CI: 31-43%). Differences in polypharmacy prevalence were reported for studies using different numerical thresholds, study setting, and publication year. Sex, study geography, study design and geographical location 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. Clinicians should be aware of populations who have an increased likelihood of experiencing polypharmacy and efforts should be made to review the appropriateness of prescribed medications and occurrence of adverse effects potentially associated with polypharmacy. CONCLUSIONS AND IMPLICATIONS: Clinicians should be aware of the common occurrence of polypharmacy and undertake efforts to minimize inappropriate polypharmacy whenever possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.052 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".