Causes of death among older children and adolescents (5–19 years) in the Magu Health and Demographic Surveillance Study, Tanzania, 1995–2022
Bibliographic record
Abstract
BACKGROUND: Population data on mortality and causes of death among 5-19-year-olds are limited. OBJECTIVES: To assess levels, trends, and risk factors of cause-specific mortality and place at death among 5-19-year-olds in Tanzania (1995-2022). METHODS: Using longitudinal data from the Magu Health and Demographic Surveillance System in northwest Tanzania, we identified leading causes of death among 5-19-year-olds from verbal autopsy interviews, using physician review and a Bayesian probabilistic model (InSilicoVA). We analyzed trends in cause and place of death using three periods: 1995-2004, 2005-2014 and 2015-2022, and assessed risk factors in a Cox-proportional hazards model. We compared the results with children aged 1-4 years and global estimates for Tanzania. RESULTS: Between 1995 and 2022, communicable disease mortality decreased by 73%, similar to the 76% decline among 1-4-year-olds. This decline in communicable disease mortality drove all-cause mortality declines of 43% and 48% among 5-14- and 15-19-year-olds, respectively. Non-communicable diseases and injuries gained importance, with their relative share of all deaths increasing from 15% in 1995-2004 to 58% in 2015-2022. Mortality risks were significantly higher among boys (particularly for injuries), those residing in rural areas (for non-communicable diseases), and those from the poorest households (for communicable diseases). By 2015-2022, 48% of 5-14 and 42% of 15-19-year-olds died in health facilities, up from 25% in 1995-2002. CONCLUSIONS: Since 1995, the decline in communicable disease mortality drove a major all-cause mortality reduction among 5-19-year-olds. Further progress will depend on continued reduction in communicable disease mortality, particularly among the poorest, and effectively addressing non-communicable and injury mortality.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".