Barriers to Cancer Care in Northern Tanzania: Patient and Health-System Predictors for Delayed Presentation
Bibliographic record
Abstract
PURPOSE Cancer is a growing problem in Africa, and delays in receiving timely cancer care often results in poorer outcomes. The purpose of this study was to identify the patient and health-system factors associated with delayed cancer care in adults living in the Northern Zone of Tanzania. PATIENTS AND METHODS Between July 2018 and July 2019, we surveyed adult patients presenting to an oncology clinic in Northern Tanzania. Delayed presentation was defined as 12 weeks or longer from initial symptoms to presentation for cancer care. Multivariate Poisson regression and adjusted relative risk (aRR) were used to identify factors predicting delayed presentation. RESULTS Among 244 adult patients with cancer who completed the survey, 78% (n = 191) had delayed presentation. Patient-related factors associated with delayed presentation included lower educational attainment ( P = .03), increased travel time ( P = .05), lack of cancer knowledge ( P < .05), and fear of cancer and cancer treatments ( P < .05) on multivariate analysis. On analysis of aRR, patients without private car and those with health insurance had higher risk of delayed presentation (aRR: 1.27; 95% CI, 1.02 to 1.32 and aRR: 1.15; 95% CI, 1.01 to 1.32). There was a strong association with increased number of visits before presentation at the cancer center and delayed presentation ( P = .0009). CONCLUSION Cancer awareness and prevention efforts targeting patients and community-level health care workers are key to reduce delays in cancer care in Northern Tanzania.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".