Integrating Factor Analysis with the Cox Proportional Hazards Model for Neonatal Mortality Analysis Using KDHS 2022 Data
Berit Heddy Atieno *
The Catholic University of Eastern Africa, Nairobi, Kenya.
Hellen Waititu
The Catholic University of Eastern Africa, Nairobi, Kenya.
Leah Chege
The Catholic University of Eastern Africa, Nairobi, Kenya.
*Author to whom correspondence should be addressed.
Abstract
Aims/Objectives: To integrate Exploratory Factor Analysis (EFA) with Cox proportional hazards regression to reduce multicollinearity among correlated covariates and identify neonatal mortality risk factors in Kenya.
Study Design: Secondary analysis of a nationally representative cross-sectional demographic health survey, using a retrospective cohort (survival analysis) design.
Place and Duration of Study: The Catholic University of Eastern Africa, using data from the 2022 Kenya Demographic and Health Survey (KDHS); analysis conducted between April 2026 and August 2026.
Methodology: Data from 77,089 live births recorded in the 2022 KDHS were analyzed. Latent dimensions from correlated variables relating to socioeconomic status and fertility history were extracted. Resulting factor scores, together with selected observed covariates, were incorporated into a stratified Cox proportional hazards model. Five alternative model specifications were compared using Akaike and Bayesian Information Criteria, pseudo-R2, concordance index (C- index), events per variable, and generalized variance inflation factors.
Results: Child sex, birth order, preceding birth interval, maternal age at childbirth, pregnancy losses, multiple births, socioeconomic status, and fertility context were significant predictors of neonatal mortality. Female neonates had 30.5% lower hazard of death than males (HR = 0.695; 95% CI: 0.610–0.793), while multiple births had nearly six times the hazard of singletons (HR = 5.795; 95% CI: 4.849–6.925). The EFA-Cox framework required only 13 parameters versus 48 in the conventional model, while improving explained variation (28.7% vs. 14.3%) and discrimination (C-index: 0.787 vs. 0.712) and reducing multicollinearity. Embedding prior child mortality within a latent fertility-context factor, rather than including it directly, retained prognostic value while reducing predictive bias.
Conclusion: The EFA-Cox framework offers a concise, stable, and interpretable approach for modeling neonatal mortality and identifying its determinants in complex demographic survey data.
Keywords: Exploratory factor analysis, cox regression, neonatal mortality