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Variation in and risk factors for paediatric inpatient all-cause mortality in a low income setting: data from an
David Gathara1, Lucas Malla2,3, Philip Ayieko2
1Department of Public Health Research, KEMRI Wellcome Trust Research Programme, P.O. Box 43640 00100, Nairobi, Kenya. dgathara@kemri-wellcome.org.
Insights
Hospital mortality rates in Kenya vary significantly, highlighting the need for better health data. Simple clinical factors consistently predict mortality, suggesting a framework for improving care quality and health systems.
Area of Science:
- Global Health
- Health Informatics
- Paediatric Medicine
Background:
- Reliable hospital mortality data are crucial for health planning and resource allocation, particularly in low-income countries.
- Such data are often unavailable in low-income settings, including Kenya.
- This study addresses the data gap by analyzing paediatric mortality in Kenyan county hospitals.
Purpose of the Study:
- To describe paediatric inpatient mortality across different hospital contexts in Kenya.
- To explore the association between simple clinical characteristics and inpatient mortality.
- To assess the consistency of these associations with existing treatment guidelines.
Main Methods:
- Utilized data from 12 county hospitals' paediatric admissions (2-59 months) from September 2013 to March 2015.
- Employed regression models accounting for hospital identity and malaria prevalence.
- Applied multiple imputation for missing data with sensitivity analyses for missing not at random assumptions.
Main Results:
- The overall mortality rate was 6.2%, with a nearly 5-fold variation across hospitals (range 2.1%-11.0%).
- Hospital identity was significantly associated with mortality.
- Clinical features used in guidelines for illness severity consistently predicted mortality (AROC = 0.86).
Conclusions:
- All-cause mortality exhibits significant variability across hospitals and is linked to identifiable clinical risk factors.
- A standardized panel of clinical features could form a basic data framework for health information systems.
- This framework can support quality evaluations, outcome assessments, and health system strengthening initiatives.
Background:
Hospital mortality data can inform planning for health interventions and may help optimize resource allocation if they are reliable and appropriately interpreted. However such data are often not available in low income countries including Kenya.
Methods:
Data from the Clinical Information Network covering 12 county hospitals' paediatric admissions aged 2-59 months for the periods September 2013 to March 2015 were used to describe mortality across differing contexts and to explore whether simple clinical characteristics used to classify severity of illness in common treatment guidelines are consistently associated with inpatient mortality. Regression models accounting for hospital identity and malaria prevalence (low or high) were used. Multiple imputation for missing data was based on a missing at random assumption with sensitivity analyses based on pattern mixture missing not at random assumptions.
Results:
The overall cluster adjusted crude mortality rate across hospitals was 6 · 2% with an almost 5 fold variation across sites (95% CI 4 · 9 to 7 · 8; range 2 · 1% - 11 · 0%). Hospital identity was significantly associated with mortality. Clinical features included in guidelines for common diseases to assess severity of illness were consistently associated with mortality in multivariable analyses (AROC =0 · 86).
Conclusion:
All-cause mortality is highly variable across hospitals and associated with clinical risk factors identified in disease specific guidelines. A panel of these clinical features may provide a basic common data framework as part of improved health information systems to support evaluations of quality and outcomes of care at scale and inform health system strengthening efforts.