Overuse of medications in low- and middle-income countries: a scoping review
Bibliographic record
Abstract
Objective: To identify and summarize the evidence about the extent of overuse of medications in low- and middle-income countries, its drivers, consequences and potential solutions. Methods: We conducted a scoping review by searching the databases PubMed®, Embase®, APA PsycINFO® and Global Index Medicus using a combination of MeSH terms and free text words around overuse of medications and overtreatment. We included studies in any language published before 25 October 2021 that reported on the extent of overuse, its drivers, consequences and solutions. Findings: We screened 3489 unique records and included 367 studies reporting on over 5.1 million prescriptions across 80 low- and middle-income countries - with studies from 58.6% (17/29) of all low-, 62.0% (31/50) of all lower-middle- and 60.0% (33/55) of all upper-middle-income countries. Of the included studies, 307 (83.7%) reported on the extent of overuse of medications, with estimates ranging from 7.3% to 98.2% (interquartile range: 30.2-64.5). Commonly overused classes included antimicrobials, psychotropic drugs, proton pump inhibitors and antihypertensive drugs. Drivers included limited knowledge of harms of overuse, polypharmacy, poor regulation and financial influences. Consequences were patient harm and cost. Only 11.4% (42/367) of studies evaluated solutions, which included regulatory reforms, educational, deprescribing and audit-feedback initiatives. Conclusion: Growing evidence suggests overuse of medications is widespread within low- and middle-income countries, across multiple drug classes, with few data of solutions from randomized trials. Opportunities exist to build collaborations to rigorously develop and evaluate potential solutions to reduce overuse of medications.
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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 | 0.013 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".