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.