Global prevalence of polypharmacy among the COVID-19 patients: a comprehensive systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
Abstract Background Polypharmacy has traditionally been defined in various texts as the use of 5 or more chronic drugs, the use of inappropriate drugs, or drugs that are not clinically authorized. The aim of this study was to evaluate the prevalence of polypharmacy among the COVID-19 patients, and the side effects, by systematic review and meta-analysis. Methods This study was performed by systematic review method and in accordance with PRISMA 2020 criteria. The protocol in this work is registered in PROSPERO (CRD42021281552). Particular databases and repositories have been searched to identify and select relevant studies. The quality of articles was assessed based on the Newcastle–Ottawa Scale checklist. Heterogeneity of the studies was measured using the I 2 test. Results The results of meta-analysis showed that the prevalence of polypharmacy in 14 studies with a sample size of 189,870 patients with COVID-19 is 34.6% (95% CI: 29.6–40). Studies have shown that polypharmacy is associated with side effects, increased morbidity and mortality among patients with COVID-19. The results of meta-regression analysis reported that with increasing age of COVID-19 patients, the prevalence of polypharmacy increases ( p < 0.05). Discussion The most important strength of this study is the updated search to June 2022 and the use of all databases to increase the accuracy and sensitivity of the study. The most important limitation of this study is the lack of proper definition of polypharmacy in some studies and not mentioning the number of drugs used for patients in these studies. Conclusion Polypharmacy is seen in many patients with COVID-19. Since there is no definitive cure for COVID-19, the multiplicity of drugs used to treat this disease can affect the severity of the disease and its side effects as a result of drug interactions. This highlights the importance of controlling and managing prescription drugs for patients with COVID-19.
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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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".