Low survival in younger adults with Acute Myeloid Leukemia (AML) in Tanzania linked to high disease burden and socioeconomic factors
Bibliographic record
Abstract
BACKGROUND: Acute Myeloid Leukemia (AML) accounts for 20-25% of all leukemia diagnosed worldwide. According to Globoccan 2020, leukemia ranked the 15th most prevalent cancer with an estimated 474,519 new cases and 311,594 deaths annually. However, due to scarcity of well documented cancer registries, epidemiological and survival data of patients with AML is lacking in many African countries, including Tanzania. Therefore, the primary objective of this study was to determine the clinical features, laboratory characteristics and survival outcomes of AML patients treated with different regimens in Tanzania. METHODS: Data from all adult patients diagnosed with AML at Muhimbili National Hospital (MNH) between January 2018 and December 2023 were analyzed in this retrospective study. STATA version 16 was used for data analysis. The survival outcome was calculated using the Kaplan-Meier method. To evaluate the statistical significance of the results, a p-value cut-off of 0.05 was used. The Cox proportional hazards model was used to identify predictors of survival and to estimate the effect of covariates on the hazard of death. Kruskal Wallis was used to compare the median values for laboratory results and pairwise comparison of median laboratory values was done using the Dunn Test. RESULTS: A total of 245 patients were diagnosed with acute leukemia during the five-year period (2018-2023), of which 169 (68%) had AML. The mean age at diagnosis was 47.2 (SD ± 18.5) years. Majority of the patients were females (60.7%), and had de novo AML. Most of the patients presented with symptoms of anemia (98.2%) and fever (79.5%). The median survival time was 81 days with a one-year overall survival probability of 15.2%. Patients that resided outside of the Dar es Salaam region had a 74% increase hazard of mortality compared to those living within the Dar es Salaam region (aHR: 1.74, 95% CI: 1.15-2.64, p = 0.008). Patients receiving any form of chemotherapy had a 39% lower hazard of mortality compared to those who were on supportive treatment alone (aHR: 0.61, 95% CI: 0.40-0.93, p = 0.022). CONCLUSION: AML is the most common acute leukemia among adults in Tanzania, primarily affecting young females under 50 years. Resource constraints and limited treatment options result in poor outcomes, with a median survival of 81 days and one-year overall survival of 15.2%.
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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) | 0.002 | 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".