HPV self-sampling versus healthcare provider collection on the effect of cervical cancer screening uptake and costs in LMIC: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Cervical cancer is a major global health issue, with 89% of cases occurring in low- and middle-income countries (LMICs). Human papillomavirus (HPV) self-sampling tests have been suggested as an innovative way to improve cervical cancer screening uptake and reduce the burden of disease. The objective of this review was to examine the effect of HPV self-sampling on screening uptake compared to any healthcare provider sampling in LMICs. The secondary objective was to estimate the associated costs of the various screening methods. Method Studies were retrieved from PubMed, Embase, CINAHL, CENTRAL (by Cochrane), Web of Science, and ClinicalTrials.gov up until April 14, 2022, and a total of six trials were included in the review. Meta-analyses were performed mainly using the inverse variance method, by pooling effect estimates of the proportion of women who accepted the screening method offered. Subgroup analyses were done comparing low- and middle-income countries, as well as low- and high-risk bias studies. Heterogeneity of the data was assessed using I 2 . Cost data was collected for analysis from articles and correspondence with authors. Results We found a small but significant difference in screening uptake in our primary analysis: RR 1.11 (95% CI : 1.10–1.11; I 2 = 97%; 6 trials; 29,018 participants). Our sensitivity analysis, which excluded one trial that measured screening uptake differently than the other trials, resulted in a clearer effect in screening uptake: RR : 1.82 (95% CI : 1.67–1.99; I 2 = 42%; 5 trials; 9590 participants). Two trials reported costs; thus, it was not possible to make a direct comparison of costs. One found self-sampling more cost-effective than the provider-required visual inspection with acetic acid method, despite the test and running costs being higher for HPV self-sampling. Conclusion Our review indicates that self-sampling improves screening uptake, particularly in low-income countries; however, to this date, there remain few trials and associated cost data. We recommend further studies with proper cost data be conducted to guide the incorporation of HPV self-sampling into national cervical cancer screening guidelines in low- and middle-income countries. Systematic review registration PROSPERO CRD42020218504.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.050 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".