Age-standardised incidence rate and epidemiology of colorectal cancer in Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Colorectal cancer (CRC) is the second-leading cause of cancer deaths globally, with low-income and middle-income countries (LMICs) disproportionately affected. Estimates of CRC rates in LMIC are scarce. We aimed to (1) estimate sex-specific incidence of CRC, (2) estimate temporal trend and (3) determine regional variations of CRC rates on the African continent. DESIGN: Systematic review and meta-analysis METHODS: PubMed (MEDLINE), OVID (MEDLINE), Scopus and Cochrane Library databases were systematically searched from inception to 12 December 2020. We included population-based studies that reported the incidence or prevalence estimates of CRC in Africa. Studies not conducted in humans or did not directly report the rates of CRC were excluded. Random effects model was used to pool the estimates. The methodological quality of studies was assessed with the Newcastle-Ottawa Scale. OUTCOME MEASURES: Overall and sex-specific annual age-standardised incidence rates (ASIR) of CRC per 100 000 population. RESULTS: The meta-analysis included 14 studies consisting of 3365 individuals with CRC (mean age, 58 years, 53% male). The overall ASIR of CRC in Africa per 100 000 population was 5.25 (95% CI 4.08 to 6.75). The rates were slightly higher in males (4.76) than in females (4.18), but not significantly different. Subgroup analysis indicated greater point estimates in North Africa (8.66) compared with sub-Saharan Africa (5.91); and higher estimates in Eastern (8.29) and Northern (8.66) Africa compared with Western (3.55) and Southern (3.57) Africa, but not statistically significant. The overall trend in ASIR has remained constant at nearly 5 per 100 000 population for the last 6 decades. CONCLUSION: CRC estimates in Africa are heterogeneous and could be underestimated. High-quality data collection systems such as population-based cancer registries may facilitate accurate estimation of country-specific rates and provide critical information which would be lucrative to the consideration of resources needed for screening, early detection, treatment and improving overall patient outcomes.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".