Colorectal Cancer Epidemiology in Tanzania: Patterns in Relation to Dietary and Lifestyle Factors
Bibliographic record
Abstract
Background: Chronic noncommunicable diseases are increasingly captured as contributing to morbidity and mortality in low and middle income countries. Aim: This study aimed to investigate the epidemiology of colorectal cancer and the potential modifiable local risk factors in Tanzania. Methods: A cross sectional retrospective chart audit study was conducted to establish the pattern and distribution of colorectal cancer, The Food Frequency Questionnaire and the Step® survey tool were used to collect data. Descriptive statistics, χ2 tests, and regression analysis were used and augmented by data visualization to display risk variable differences. Results: Tanzania's colorectal cancer incidence has increased six times in the last decade in which major towns and cities of Dar es Salaam (20.2 per 100,000), Pwani (7.2 per 100,000), Kilimanjaro (4.4 per 100,000), Arusha (4.2 per 100,000), and Morogoro (3.6 per 100,000) had the highest percentage. This study reported that, almost 45% of the participants were hypertensive. Two major dietary patterns, namely “healthy” and “western”, existed among the study sample. Obesity was found in 25% of participants, whereas overweight was present in 28%; of note, the prevalence was higher in females (26.9%) than in males (23.6%) respectively. The prevalence of alcohol consumption was 21.5%, with a significantly lower rate of smoking (12.2%) noted within the study subjects. Both alcohol consumption and tobacco smoking were more common in men than women (22.7 vs. 20.6% and 24.5 vs. 3.2%, respectively). The prevalence of vigorous, moderate, and low physical activity for both sexes was 18.6%, 54.1% and 42.3%, respectively. Conclusion: Evidence from this study demonstrate that, like other NCDs CRC is increasing in Tanzania. Colon cancer is increasing at higher rate than rectal cancer seeming to align with change in lifestyle. Major towns and cities had the highest share of CRC patients. Diet, obesity, tobacco smoking, alcohol consumption, and sedentary behavior have potential role to play in the rising trend of CRC and other NCDs. We recommend a large longitudinal study with robust methodology which can establish cause and effect relationships between specific lifestyle behaviors and the incidence of colorectal 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".