Cervical cancer screening utilization and predictors among eligible women in Ethiopia: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Despite a remarkable progress in the reduction of global rate of maternal mortality, cervical cancer has been identified as the leading cause of maternal morbidity and mortality, particularly in sub-Saharan African countries. The uptake of cervical cancer screening service has been consistently shown to be effective in reducing the incidence rate and mortality from cervical cancer. Despite this, there are limited studies in Ethiopia that were conducted to assess the uptake of cervical cancer screening and its predictors, and these studies showed inconsistent and inconclusive findings. Therefore, this systematic review and meta-analysis was conducted to estimate the pooled cervical cancer screening utilization and its predictors among eligible women in Ethiopia. METHODS AND FINDINGS: Databases like PubMed, Web of Science, SCOPUS, CINAHL, Psychinfo, Google Scholar, Science Direct, and the Cochrane Library were systematically searched. All observational studies reporting cervical cancer screening utilization and/ or its predictors in Ethiopia were included. Two authors independently extracted all necessary data using a standardized data extraction format. Quality assessment criteria for prevalence studies were adapted from the Newcastle Ottawa quality assessment scale. The Cochrane Q test statistics and I2 test were used to assess the heterogeneity of studies. A random effects model of analysis was used to estimate the pooled prevalence of cervical cancer screening utilization and factors associated with it with the 95% confidence intervals (CIs). From 850 potentially relevant articles, twenty-five studies with a total of 18,067 eligible women were included in this study. The pooled national cervical cancer screening utilization was 14.79% (95% CI: 11.75, 17.83). The highest utilization of cervical cancer screening (18.59%) was observed in Southern Nations Nationalities and Peoples' region (SNNPR), and lowest was in Amhara region (13.62%). The sub-group analysis showed that the pooled cervical cancer screening was highest among HIV positive women (20.71%). This meta-analysis also showed that absence of women's formal education reduces cervical cancer screening utilization by 67% [POR = 0.33, 95% CI: 0.23, 0.46]. Women who had good knowledge towards cervical screening [POR = 3.01, 95%CI: 2.2.6, 4.00], perceived susceptibility to cervical cancer [POR = 4.9, 95% CI: 3.67, 6.54], severity to cervical cancer [POR = 6.57, 95% CI: 3.99, 10.8] and those with a history of sexually transmitted infections (STIs) [POR = 5.39, 95% CI: 1.41, 20.58] were more likely to utilize cervical cancer screening. Additionally, the major barriers of cervical cancer screening utilization were considering oneself as healthy (48.97%) and lack of information on cervical cancer screening (34.34%). CONCLUSIONS: This meta-analysis found that the percentage of cervical cancer screening among eligible women was much lower than the WHO recommendations. Only one in every seven women utilized cervical cancer screening in Ethiopia. There were significant variations in the cervical cancer screening based on geographical regions and characteristics of women. Educational status, knowledge towards cervical cancer screening, perceived susceptibility and severity to cervical cancer and history of STIs significantly increased the uptake of screening practice. Therefore, women empowerment, improving knowledge towards cervical cancer screening, enhancing perceived susceptibility and severity to cancer and identifying previous history of women are essential strategies to improve cervical cancer screening practice.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".