Uptake and Barriers to Cervical Cancer Screening among Human Immunodeficiency Virus-Positive Women in Sub Saharan Africa: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background Cervical cancer is a leading cause of disability and mortality among women in Africa. Despite significant correlation between HIV/AIDS and cervical cancer, unacceptably low coverage of uptake of cervical cancer screening among Human Immunodeficiency Virus-positive women in Sub-Saharan Africa. Individual primary studies are limited in explaining the patterns of uptake of cervical cancer screening. Hence, this review considers the uptake of cervical cancer screening and its barriers among Human Immunodeficiency Virus-positive women in Sub-Saharan Africa. Methods We systematically searched articles published up to December 31st, 2019 from databases of PubMed, Cochrane Library, POP LINE, Google Scholar, African Journals Online and JURN. Quality of included articles was assessed by using the Newcastle-Ottawa Scale and the coverage of uptake of cervical cancer screening was pooled after checking for heterogeneity and publication bias. The random effect model was used and sub-group analysis estimate was done by countries. Results Twenty-one studies comprised of 20,672 Human Immunodeficiency Virus-Positive women were included. Applying random effect model, the overall cervical cancer screening uptake among this group of women in Sub Saharan Africa was estimated to be 30% (95% CI: 19, 41, I2 = 100%). The main barriers to uptake of cervical screening to include: poor knowledge about cervical cancer and screening, low risk perception of cervical cancer, fear of test result and fear of screening as painful, lack of access to screening services, high cost of screening service, and poor partner attitude and acceptance of the service. The perception of an additional burden of having a cervical cancer diagnosis was found to be a unique barrier among this population of women. Conclusion The review revealed that cervical cancer screening uptake is low due to poor knowledge about cervical cancer and screening, low risk perception of cervical cancer, Fear of test result and fear of screening, lack of access to screening services, high cost of screening services and poor partner attitude and acceptance of the service. Besides the above, perception of an additional burden of having a cervical cancer was found to be a unique barrier for these group of population.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.004 | 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".