Overdiagnosis and overuse of diagnostic and screening tests in low-income and middle-income countries: a scoping review
Bibliographic record
Abstract
OBJECTIVE: Overdiagnosis and overuse of healthcare services harm individuals, take resources that could be used to address underuse, and threaten the sustainability of health systems. These problems are attracting increasing attention in low-income and middle-income countries (LMICs). Unaware of any review of relevant evidence, we conducted a scoping review of the evidence around overdiagnosis and overuse of diagnostic and screening tests in LMICs. DESIGN: Scoping review. METHODS: We searched PubMed, Embase, PsycINFO, Global Index Medicus for relevant studies published until 24 May 2021, with no restrictions on date or language. We categorised included studies by major focus (overdiagnosis, overuse of tests, or both) and main themes (presence or estimates of extent; drivers; consequences and solutions). RESULTS: We identified 2763 unique records and included 162 articles reporting on 154 studies across 55 countries, involving over 2.8 million participants and/or requests for tests. Almost half the studies focused on overdiagnosis (70; 45.5%), one-third on overuse of tests (61; 39.6%) and one-fifth on both (23; 14.9%). Common overdiagnosed conditions included malaria (61; 39.6%) and thyroid cancer (25; 16.2%), estimated to be >70% in China. Overused tests included imaging (n=25 studies) such as CT and MRI; laboratory investigations (n=18) such as serological tests and tumour markers; and procedures (n=14) such as colonoscopy. Drivers included fear of conflict with patients and expanding disease definitions. Common consequences included unnecessary treatments such as antimalarials, and wasted resources, with costs of malaria overdiagnosis estimated at US$86 million in Sudan in 1 year alone. Only 9% of studies discussed solutions, which included addressing inappropriately lowered diagnostic thresholds and reforming test-ordering processes. CONCLUSIONS: Overdiagnosis and overuse of tests are widespread in LMICs and generate significant harm and waste. Better understanding of the problems and robust evaluation of solutions is needed, informed by a new global alliance of researchers and policy-makers.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".