Utilization of cervical cancer screening and its associated factors among women living with HIV in East Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Women living with HIV (WLHIV), mostly in East Africa, are six times more likely to develop cervical cancer and are a priority population for secondary prevention. Numerous primary studies report inconsistent, widely varying rates of cervical cancer screening (CCS) among WLHIV in the region. This systematic review and meta-analysis aimed to synthesize data from various primary studies to provide a conclusive estimate of CCS uptake among WLHIV in East Africa and to identify factors associated with screening utilization. METHODS: This review followed PRISMA 2020 guidelines and was registered with PROSPERO. Databases searched included Google Scholar, PubMed, EMBASE, Scopus, Hinari, ScienceDirect, and other manual sources for studies published between January 2015 and April 2025. The Newcastle-Ottawa Scale was used for quality assessment. Data were analyzed using STATA 17, employing a random-effects model due to high heterogeneity. Cochran's Q test (χ²) and Higgins I² statistics were used to identify heterogeneity. Publication bias was assessed by funnel plots and Egger's test. Associations were reported as pooled adjusted odds ratios (aOR), with significance set at p < 0.05. RESULTS: This meta-analysis pooled effect estimates from 46 studies with a total of 98,028 participants. The pooled CCS uptake among WLHIV in East Africa was 34.48% (95% CI: 30.61, 38.36) with high heterogeneity. Those who had knowledge (aOR = 2.96, 95% CI: 2.60, 3.38) and information (aOR = 3.95, 95% CI: 2.15, 7.28), with a family history of cervical cancer (aOR = 2.18, 95% CI: 1.20, 3.96), high perceived vulnerability (aOR = 2.82, 95% CI: 1.65, 4.84), and low perceived barriers (aOR = 1.96, 95% CI: 1.05, 3.65) were significantly associated with CCS uptake among WLHIV in East Africa. CONCLUSION: The percentage of WLHIV in East Africa who obtain CCS is far below the WHO's 70% target. The utilization of CCS was significantly associated with knowledge, information, family history of cervical cancer, perceived vulnerability, and barriers. Increased screening rates can be achieved by interventions that target raising awareness, providing information, reducing barriers, and focusing on women with a family history of cervical cancer.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.010 | 0.009 |
| 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".