Trends in maternal mortality and stillbirths by county in health facility data, Kenya, 2011-2022
Bibliographic record
Abstract
BACKGROUND: Reports on maternal deaths and stillbirths in health facilities are a critical but underutilized source of information to monitor the quality of care. In addition, with increasing coverage of deliveries by health facilities, such data can improve population estimates of maternal mortality and stillbirth rates. Data quality concerns, however, have often deterred use of facility data. This study aims to assess subnational trends in institutional mortality and examine its utility for improving population-based estimates of mortality. METHODS: Data from the routine monthly reporting system of the Ministry of Health in Kenya were used to assess levels and trends in maternal mortality and stillbirth rates in 47 counties from 2011 to 2022. Data quality was assessed using multiple methods, including consistency of annual reporting of live births, stillbirths and maternal deaths by counties, plausibility of the ratio of reported stillbirths to maternal death, the county institutional mortality in comparison to delivery coverage, socioeconomic development and health system characteristics. The consistency between institutional and population estimates of mortality was assessed using different scenarios. RESULTS: Institutional live birth coverage increased from 64.0% in 2014 to 87.8% in 2022, ranging from 49 to 99% in counties. Kenya and 39 of its 47 counties experienced a decline in institutional maternal mortality ratio and stillbirth rate during the study period 2011-2022. The national institutional maternal mortality decline stagnated from 2018 and was 99 maternal deaths per 100,000 live births in 2022. Consistency of reported data by county was good over time but several indicators suggest that maternal death reporting was incomplete and more so in less-developed counties. Estimates of the population maternal mortality ratio, derived from the facility data, were much lower than global estimates or census results, while the stillbirth rates were consistent. CONCLUSION: The health facility data on maternal death and stillbirths are an important data source for monitoring national and subnational institutional maternal mortality and stillbirth rates and can also inform population estimates. Systematic sustained assessment of reporting completeness will be critical to achieve the full potential of facility data-derived mortality monitoring.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".