Cervical cancer screening uptake in Sub-Saharan Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Cervical cancer screening and prevention programs have been given considerable attention in high-income countries, while only receiving minimal effort in many African countries. This meta-analytic review aimed to estimate the pooled uptake of cervical cancer screening uptake and identify its predictors in Sub-Saharan Africa. Methods PubMed, EMBASE, CINAHL, African Journals Online, Web of Science and SCOPUS electronic databases were searched. All observational studies conducted in Sub-Saharan Africa and published in English language from January 2000 to 2019 were included. The Newcastle-Ottawa Scale was applied to examine methodological quality of the studies. Inverse variance-weighted random-effects model meta-analysis was done to estimate the pooled uptake and odds ratio of predictors with 95% confidence interval. I 2 test statistic was used to check between-study heterogeneity, and funnel plot and Egger’s regression statistical test were used to check publication bias. To examine the source of heterogeneity, subgroup analysis based on sample size, publication year and geographic distribution of the studies was carried out. Results Of 3,537 studies identified, 29 studies were included with 36,374 women. The uptake of cervical cancer screening in Sub-Saharan Africa was 12.87% (95% CI: 10.20, 15.54; I 2 = 98.5%). Meta-analysis of seven studies showed that knowledge about cervical cancer increased screening uptake by nearly 5-folds (OR: 4.81; 95% CI: 3.06, 7.54). Other predictors include educational status, age, HIV status, contraceptive use, perceived susceptibility, and awareness about screening locations. Conclusion Cervical screening uptake is low in Sub-Saharan Africa and influenced by several factors. Health outreach and promotion targeting identified predictors are needed to increase uptake of screening service in the region.s Protocol registration CRD42017079375
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.016 | 0.004 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".