Progress and priorities for reproductive, maternal, newborn, and child health in Kenya: a Countdown to 2015 country case study
Bibliographic record
Abstract
BACKGROUND: Progress in reproductive, maternal, newborn, and child health (RMNCH) in Kenya has been inconsistent over the past two decades, despite the global push to foster accountability, reduce child mortality, and improve maternal health in an equitable manner. Although several cross-sectional assessments have been done, a systematic analysis of RMNCH in Kenya was needed to better understand the push and pull factors that govern intervention coverage and influence mortality trends. As such, we aimed to determine coverage and impact of key RMNCH interventions between 1990 and 2015. METHODS: We did a comprehensive, systematic assessment of RMNCH in Kenya from 1990 to 2015, using data from nationally representative Demographic Health Surveys done between 1989 and 2014. For comparison, we used modelled mortality estimates from the UN Inter-Agency Groups for Child and Maternal Mortality Estimation. We estimated time trends for key RMNCH indicators, as defined by Countdown to 2015, at both the national and the subnational level, and used linear regression methods to understand the determinants of change in intervention coverage during the past decade. Finally, we used the Lives Saved Tool (LiST) to model the effect of intervention scale-up by 2030. FINDINGS: After an increase in mortality between 1990 and 2003, there was a reversal in all mortality trends from 2003 onwards, although progress was not substantial enough for Kenya to achieve Millennium Development Goal targets 4 or 5. Between 1990 and 2015, maternal mortality declined at half the rate of under-5 mortality, and changes in neonatal mortality were even slower. National-level trends in intervention coverage have improved, although some geographical inequities remain, especially for counties comprising the northeastern, eastern, and northern Rift Valley regions. Disaggregation of intervention coverage by wealth quintile also revealed wide inequities for several health-systems-based interventions, such as skilled birth assistance. Multivariable analyses of predictors of change in family planning, skilled birth assistance, and full vaccination suggested that maternal literacy and family size are important drivers of positive change in key interventions across the continuum of care. LiST analyses clearly showed the importance of quality of care around birth for maternal and newborn survival. INTERPRETATION: Intensified and focused efforts are needed for Kenya to achieve the RMNCH targets for 2030. Kenya must build on its previous progress to further reduce mortality through the widespread implementation of key preventive and curative interventions, especially those pertaining to labour, delivery, and the first day of life. Deliberate targeting of the poor, least educated, and rural women, through the scale-up of community-level interventions, is needed to improve equity and accelerate progress. FUNDING: US Fund for UNICEF, Bill & Melinda Gates Foundation.
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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 | 0.002 | 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 | 0.001 | 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".