Cervical cancer screening uptake in Sub-Saharan Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
The objective of this study is to estimate the pooled uptake of cervical cancer screening and identify its predictors in Sub-Saharan Africa. Systematic review and meta-analysis. We searched PubMed, EMBASE, CINAHL, African Journals OnLine, Web of Science and Scopus electronic databases from January 2000 to 2019. All observational studies published in the English language that reported cervical cancer uptake and/or predictors in Sub-Saharan Africa were initially screened. We assessed methodological quality using the Newcastle-Ottawa Scale. An inverse variance-weighted random-effects model meta-analysis was performed to estimate the pooled uptake and odds ratio (OR) of predictors with a 95% confidence interval (CI). The I2 test statistic was used to check between-study heterogeneity, and the Egger's regression statistical test was used to check publication bias. We initially screened 3537 citations and subsequently 29 studies were selected for this review, which included a total of 36,374 women. The uptake of cervical cancer screening in Sub-Saharan Africa was 12.87% (95% CI: 10.20, 15.54; I2 = 98.5%). A meta-analysis of seven studies showed that knowledge about cervical cancer increased screening uptake by nearly five times (OR: 4.81; 95% CI: 3.06, 7.54). Other predictors of cervical screening uptake include educational level, age, Human Immune deficiency Virus (HIV) status, contraceptive use, perceived susceptibility and awareness about screening locations. Cervical screening uptake is low in Sub-Saharan Africa as a result of several factors. Health outreach and promotion programmes to target these identified predictors are required.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.045 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".