Ecologic Analysis of Correlates of Cervical Cancer Morbidity and Mortality in Sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: Cervical cancer is the fourth leading cause of death among women worldwide, with 85% of the burden falling on low- to middle- income countries. We studied the correlates of cervical cancer incidence and mortality, and case-fatality in Sub-Saharan Africa. METHODS: Country-level data on 16 putative cervical cancer correlates for 37 Sub-Saharan African countries were collected from publicly available data sources. We performed univariate and multiple (stepwise) linear regression analyses to identify correlates of cervical cancer incidence and mortality, and case-fatality. RESULTS: In univariate analyses, incidence and mortality rates were significantly correlated with contraceptive use, penile cancer incidence, and human immunodeficiency virus prevalence. Incidence rates were also correlated with literacy rates, whereas mortality rates were correlated with the proportion of rural population and screening coverage. Multiple regression analyses showed contraceptive use (P = 0.009) and penile cancer incidence (P = 0.004) as associated with cervical cancer incidence. Penile cancer incidence (P = 9.77 × 10-5) and number of medical doctors (P = 0.0433) were associated with mortality. The goodness of fit of the incidence and mortality models was moderate at best, explaining 49% and 37% of variability in the data, respectively. However, the case-fatality model had the best fit explaining most of the variation (adjusted R2 = 0.948; P = 6.822 × 10-16). CONCLUSIONS: To reduce the burden of cervical cancer in Sub-Saharan Africa, it would be important to design multimodal interventions that not only target screening and HPV vaccination, but also focus on cervical cancer correlates. IMPACT: Identifying contextual factors associated with cervical cancer in this region could inform targeted interventions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".