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Updated: Jan 11, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Survival probability and under-five mortality predictors in Western Kenya between 2015 and 2020
Harun Odhiambo Owuor1,2, Asito Stephen Amolo3, George Aol4
1Kenya Medical Research Institute Center for Global Health Research, PO BOX 1578-40100, Kisumu, Kenya. howuor@kemri.go.ke.
Background Information:
Despite gains in reducing under-five mortality in Kenya from 52 to 41.6 deaths per 1000 live births between 2015 and 2022, the rate remains notably higher than the Sustainable Development Goal (SDG) 3 target of 25 deaths per 1000 by 2030. The impact of interventions on risk factors and socio-structural inequities have varied across regions, indicating potential effects of unaddressed issues. There is a need to further understand the key drivers of under-5 mortality in given contexts to help improve intervention effectiveness.
Objective:
To estimate the survival probability and determine predictors of under-five mortality in Siaya County.
Methods:
The study utilized secondary data from the Siaya Health Demographic Surveillance System from 2015 to 2020; semi-annual population-based longitudinal surveillance, and data analyzed in cohort design. We performed both descriptive and multivariable analyses. We used the Cox regression model to estimate survival probability and predictors of under-five mortality. We employed an adjusted hazard ratio (AHR) to measure association. Significance was declared at p < 0.0.
Results:
There were 24,452 live births in the study period with 1,540 (6.3%) children dying before their fifth birthday. The cumulative survival probability among children under five was 92% with children of younger age being at increased risk of death. In Cox multivariable analysis, the risk of under-five mortality increased with decreasing maternal education (aHR 1.93; 95%CI1.19, 3.10) and number of Antenatal Clinic (ANC) visits (aHR 2.1; 95%CI 1.14, 1.62). Delivery at home/way to the hospital (aHR 1.51; 95% CI 1.29, 1.77); Maternal age less than 18 years (aHR 1.34; 95% CI 1.11, 1.62); no latrine (aHR 1.22; 95% CI 1.04, 1.43) and male gender were associated with reduced survival.
Conclusion:
Under-five mortality in Siaya was 63/1000 live births, which is higher than the national average of 41.6/1000 live births. Gender, maternal education, number of ANC visits, maternal age, delivery in hospital, and availability of toilets at home were the main predictors of under-five mortality. These observations reinforce the need to target multiple components at the community and institutional levels to reduce childhood mortality. Such interventions may target increasing uptake of preventive healthcare and education attainment.
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