Effectiveness of interventions for improving timely diagnosis of breast and cervical cancers in low-income and middle-income countries: a systematic review
Bibliographic record
Abstract
OBJECTIVES: To systematically synthesise available evidence on the nature and effectiveness of interventions for improving timely diagnosis of breast and cervical cancers in low and middle-income countries (LMICs). DESIGN: A systematic review of published evidence. The review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses. DATA SOURCES: A comprehensive search of published literature was conducted. In addition, relevant grey literature sources and bibliographical references of included studies were searched for potentially eligible evidence. STUDY SELECTION: Studies published between January 2010 and November 2020 were eligible for inclusion. To be eligible, studies had to report on interventions/strategies targeted at women, the general public or healthcare workers, aimed at improving the timely diagnosis of breast and/or cervical cancers in LMIC settings. DATA EXTRACTION AND SYNTHESIS: Literature search, screening, study selection, data extraction and quality appraisal were conducted by two independent reviewers. Evidence was synthesised and reported using a global taxonomy framework for early cancer diagnosis. RESULTS: From the total of 10 593 records identified, 21 studies conducted across 20 LMICs were included in this review. Most of the included studies (16/21) focused primarily on interventions addressing breast cancers; two focused on cervical cancer while the rest examined multiple cancer types. Reported interventions targeted healthcare workers (12); women and adolescent girls (7) and both women and healthcare workers (3). Eight studies reported on interventions addressing access delays; seven focused on interventions addressing diagnostic delays; two reported on interventions targeted at addressing both access and diagnostic delays, and four studies assessed interventions addressing access, diagnostic and treatment delays. While most interventions were demonstrated to be feasible and effective, many of the reported outcome measures are of limited clinical relevance to diagnostic timeliness. CONCLUSIONS: Though limited, evidence suggests that interventions aimed at addressing barriers to timely diagnosis of breast and cervical cancer are feasible in resource-limited contexts. Future interventions need to address clinically relevant measures to better assess efficacy of interventions. PROSPERO REGISTRATION NUMBER: CRD42020177232.
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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 | 0.023 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".