The descriptive epidemiology of age at colorectal cancer diagnosis in Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: The optimal age for introducing colorectal cancer (CRC) screening remains controversial especially in low-and middle-income countries, where evidence is limited, and the CRC incidence appears to be gradually increasing. In this systematic review, we aimed to summarise the age profile of CRC cases at diagnosis on the African continent to inform the most appropriate age for CRC screening initiation. Methods: Four electronic databases: MEDLINE, Embase, Scopus and Web of Science were searched using relevant subject headings and keywords from 1 January 2012 to 22 September 2023. The third volume of the African Cancer Registry Network (AFCRN) report was also included. Studies of African populations reporting the age profiles of representative CRC patient populations were eligible for inclusion. Random effects meta-analysis was used to pool mean age at CRC diagnosis, and the proportions of early-onset CRC (EOCRC); <40 and <50 years old. Age-specific incidence rates and proportions of EOCRC were calculated from the AFCRN report. The study was registered with PROSPERO, CRD42020187538. Findings: Eighty-seven (87) studies reporting on 72,204 CRC cases from 24 African countries were included. The pooled mean age at CRC diagnosis was 53.5 years, with significant regional variations observed. The pooled proportion of EOCRC cases diagnosed <40 years old was 21% (95% confidence interval (CI): 18%-25%; n = 44 studies) and 38% for those <50 years old (95% CI 35%-41%; n = 41 studies). Estimated proportions of EOCRC from published studies and the AFCRN report were comparable. Interpretation: Whereas the incidence of CRC is still low in Africa, there is younger age at diagnosis and an overall high proportion of EOCRC. Future CRC screening programs on the continent may need to consider a lower CRC screening age compared to that used in Western countries where EOCRC appears to be less common. Funding: This work was funded by the Faculty of Medicine, Health and Life Sciences (FMHLS), Queen's University Belfast and the Department for the Economy, Northern Ireland in support of the doctoral degree of N.M.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.019 | 0.003 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".