Mortality among 5 to 19-year-olds in rural Mali
Bibliographic record
Abstract
The unique healthcare needs of 5 to 9-year-olds and adolescents (10-19 years) in low- and middle-income countries have been largely neglected. We generated estimates of 5 to 9-year-old and adolescent mortality in rural Mali, a setting with high under-five mortality, and aimed to define associated individual and household risk factors. We analysed cross-sectional baseline household survey data from the ProCCM trial (NCT02694055) conducted in Bankass District, Mali collected in December 2016 and January 2017. Deaths in the preceding five years, household information, and women's birth histories were documented. Factors associated with 5 to 9-year-old and adolescent mortality were analysed using Cox regression. Our study population comprised 23,485 children aged 5 to 9-years-old and 17,910 adolescents from 7,720 households. The 5 to 9-year-old and adolescent mortality rates were 3.10 and 1.90 deaths per 1,000 person-years, respectively. Mortality rates were similar among males and females aged 5 to 9 years, but grew increasingly divergent in adolescence (1.69 and 2.17 per 1,000 person-years, respectively). Five to 9-year-olds in households with untreated water had a higher risk of death than those in households with treated water. Adolescents living in the poorest households had a higher risk of death than those in the wealthiest, and adolescents in households in which no women received schooling had a higher risk of death than those in which women had some schooling. The risk of mortality was especially acute among female adolescents compared to their male counterparts, with low access to education for women being a strong contributing factor.
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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 | 0.002 |
| 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 | 0.001 |
| 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 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".