Accelerating Kenya’s progress to 2030: understanding the determinants of under-five mortality from 1990 to 2015
Bibliographic record
Abstract
INTRODUCTION: Despite recent gains, Kenya did not achieve its Millennium Development Goal (MDG) target for reducing under-five mortality. To accelerate progress to 2030, we must understand what impacted mortality throughout the MDG period. METHODS: Trends in the under-five mortality rate (U5MR) were analysed using data from nationally representative Demographic and Health Surveys (1989-2014). Comprehensive, mixed-methods analyses of health policies and systems, workforce and health financing were conducted using relevant surveys, government documents and key informant interviews with country experts. A hierarchical multivariable linear regression analysis was undertaken to better understand the proximal determinants of change in U5MR over the MDG period. RESULTS: U5MR declined by 50% from 1993 to 2014. However, mortality increased between 1990 and 2000, following the introduction of facility user fees and declining coverage of essential interventions. The MDGs, together with Kenya's political changes in 2003, ushered in a new era of policymaking with a strong focus on children under 5 years of age. External aid for child health quadrupled from 40 million in 2002 to 180 million in 2012, contributing to the dramatic improvement in U5MR throughout the latter half of the MDG period. Our multivariable analysis explained 44% of the decline in U5MR from 2003 to 2014, highlighting maternal literacy, household wealth, sexual and reproductive health and maternal and infant nutrition as important contributing factors. Children living in Nairobi had higher odds of child mortality relative to children living in other regions of Kenya. CONCLUSIONS: To attain the Sustainable Development Goal targets for child health, Kenya must uphold its current momentum. For equitable access to health services, user fees must not be reintroduced in public facilities. Support for maternal nutrition and reproductive health should be prioritised, and Kenya should acknowledge its changing demographics in order to effectively manage the escalating burden of poor health among the urban poor.
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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) | 0.001 | 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".