Determinants of Levels and Trends in Child Mortality in Rural Northern Malawi
Bibliographic record
Abstract
Complementing existing national analyses of Malawi’s extraordinary success in reducing child mortality during the Millennium Development Goal period, the work presented here aimed to develop a detailed epidemiological understanding of local trends in child survival in rural Malawi by identifying the subgroups of children among whom, and the causes of death from which, gains in child survival have been most evident. Further, it aimed to do so by leveraging the strengths of a well-established health and demographic surveillance system in rural Karonga district (KHDSS) that generates timely, high-quality longitudinal data on individual children and their households. We characterized trends in age- and cause-specific child mortality in the KHDSS from 2005-2017 and quantified the respective contribution of each trend to the overall gain in child survival. We estimated a net 40.5-day increase in five-year life expectancy at birth over the study period, much of it attributable to improved survival at 1-11m of age (with no improvement in neonatal survival), to declining HIV/AIDS mortality in the context of scaled-up antiretroviral therapy, and to declining pneumonia and diarrhea mortality in the context of new vaccines. We examined whether the prevalence of stunting or the risk of child mortality associated with stunting reduced over time in the KHDSS, as might be expected given the large proportion of child mortality that has been attributed to stunting globally. Stunting increased over time in the KHDSS, perhaps due to improved survival of HIV-exposed children, and its associated mortality remained unchanged. It is possible that the causes and consequences of linear growth failure differ between HIV-exposed and HIV-unexposed children, with implications for the targeting of interventions as the proportion of HIV-exposed children grows. We also investigated sex-based differences in child mortality in rural Malawi, given the importance of gender equity to further accelerating progress in child survival, and we found that girls experience higher-than-expected mortality beyond the neonatal period in the KHDSS, but with little evidence of sociocultural conditions that favour boys, perhaps implicating sex differentials in the effectiveness of vaccines or other interventions with equal coverage. This work underscores the value of community-based mortality surveillance for the timely monitoring and understanding of local epidemiological trends and highlights several areas for future methodological and substantive research in Malawi and similar settings.
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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".