Low-Value Surgical Procedures in Low- and Middle-Income Countries
Bibliographic record
Abstract
Importance: Overuse of surgical procedures is increasing around the world and harms both individuals and health care systems by using resources that could otherwise be allocated to addressing the underuse of effective health care interventions. In low- and middle-income countries (LMICs), there is some limited country-specific evidence showing that overuse of surgical procedures is increasing, at least for certain procedures. Objectives: To assess factors associated with, extent and consequences of, and potential solutions for low-value surgical procedures in LMICs. Evidence Review: We searched 4 electronic databases (PubMed, Embase, PsycINFO, and Global Index Medicus) for studies published from database inception until April 27, 2022, with no restrictions on date or language. A combination of MeSH terms and free-text words about the overuse of surgical procedures was used. Studies examining the problem of overuse of surgical procedures in LMICs were included and categorized by major focus: the extent of overuse, associated factors, consequences, and solutions. Findings: Of 4276 unique records identified, 133 studies across 63 countries were included, reporting on more than 9.1 million surgical procedures (median per study, 894 [IQR, 97-4259]) and with more than 11.4 million participants (median per study, 989 [IQR, 257-6857]). Fourteen studies (10.5%) were multinational. Of the 119 studies (89.5%) originating from single countries, 69 (58.0%) were from upper-middle-income countries and 30 (25.2%) were from East Asia and the Pacific. Of the 42 studies (31.6%) reporting extent of overuse of surgical procedures, most (36 [85.7%]) reported on unnecessary cesarean delivery, with estimated rates in LMICs ranging from 12% to 81%. Evidence on other surgical procedures was limited and included abdominal and percutaneous cardiovascular surgical procedures. Consequences of low-value surgical procedures included harms and costs, such as an estimated US $3.29 billion annual cost of unnecessary cesarean deliveries in China. Associated factors included private financing, and solutions included social media campaigns and multifaceted interventions such as audits, feedback, and reminders. Conclusions and Relevance: This systematic review found growing evidence of overuse of surgical procedures in LMICs, which may generate significant harm and waste of limited resources; the majority of studies reporting overuse were about unnecessary cesarean delivery. Therefore, a better understanding of the problems in other surgical procedures and a robust evaluation of solutions are needed.
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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 | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".