Lifetime prevalence and adherence rate of cervical cancer screening among women living with HIV: a systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Women living with HIV (WLWH) are more likely to develop cervical cancer. Screening and available healthcare can effectively reduce its incidence and mortality rates. We aimed to summarize the lifetime prevalence and adherence rate of cervical cancer screening among WLWH across low- and middle-income countries (LMICs), and high-income countries (HICs). METHODS: We systematically searched PubMed, Web of Science and Embase for studies published between database inception and 2 September 2022, without language or geographical restrictions. Those reporting the lifetime prevalence and/or adherence rate of cervical cancer screening among WLWH were included. Pooled estimates across LMICs and HICs were obtained using DerSimonian-Laird random-effects models. When the number of eligible studies was greater than 10, we further conducted stratified analyses by the World Health Organization (WHO) region, setting (rural vs. urban), investigation year, screening method, type of cervical cancer screening programme, age and education level. RESULTS: Among the 63 included articles, 26 provided data on lifetime prevalence, 24 on adherence rate and 13 on both. The pooled lifetime prevalence in LMICs was 30.2% (95% confidence interval [CI]: 21.0-41.3), compared to 92.4% in HICs (95% CI: 89.6-94.6). The pooled adherence rate was 20.1% in LMICs (95% CI: 16.4-24.3) and 59.5% in HICs (95% CI: 51.2-67.2). DISCUSSION: There was a large gap in cervical cancer screening among WLWH between LMICs and HICs. Further analysis found that those in LMICs had higher lifetime prevalence in subgroups with urban settings, with older age and with higher education levels; and those in HICs had higher adherence in subgroups with younger age and with higher education levels. CONCLUSIONS: Cervical cancer screening among WLWH falls considerably short of the WHO's goal. There should be continuous efforts to further increase screening among these women, especially those residing in the rural areas of LMICs and with lower education levels.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.039 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".