Abstract
Abstract
Background
Under-five mortality remains a major public health challenge in Zambia despite substantial reductions over recent decades. Demographic and Health Survey (DHS) data are commonly analysed using Cox proportional hazards models; however, violations of the proportional hazards assumption and unobserved cluster-level heterogeneity may compromise inference. This study compared advanced survival modelling approaches to determine the most appropriate framework for analysing under-five mortality using pooled Zambia DHS data.
Methods
We conducted a pooled secondary analysis of the 2018 and 2024 Zambia Demographic and Health Survey child recode datasets. The analysis included 9,982 children, among whom 266 died before their fifth birthday. A survey-weighted Cox proportional hazards model was fitted as the reference model and assessed using Schoenfeld residual diagnostics. To address identified methodological limitations, an extended Cox model, a shared-frailty model, and a Bayesian survival model were subsequently fitted. Model performance, assumptions, and the consistency of adjusted hazard ratio estimates were compared across modelling approaches.
Results
The global Schoenfeld test indicated violation of the proportional hazards assumption (χ²=51.26, p=0.002). In the adjusted extended Cox model, male children had higher mortality risk than females (aHR 1.95, 95% CI 1.40–2.73), while children with gestation ≥9 months had lower mortality risk than those with shorter gestation (aHR 0.21, 95% CI 0.14–0.32). Average and large birth size were associated with lower mortality compared with small birth size (aHR 0.47, 95% CI 0.34–0.66 and aHR 0.57, 95% CI 0.39–0.84, respectively). Newborn health checks before discharge were associated with lower mortality (aHR 0.50, 95% CI 0.38–0.66), as was any antenatal care (aHR 0.25, 95% CI 0.09–0.75). A high-risk fertility pattern involving fourth or higher births with an interval ≥24 months was also associated with lower mortality (aHR 0.57, 95% CI 0.41–0.80). The shared-frailty model produced similar estimates, including increased mortality among male children (aHR 1.39, 95% CI 1.08–1.78) and twins or multiple births (aHR 2.74, 95% CI 1.36–5.53). Bayesian estimates were broadly consistent with the extended Cox model.
Conclusions
Advanced survival models provided a more robust analytical framework than the conventional Cox model for evaluating under-five mortality in nationally representative DHS data. While the extended Cox model accommodated time-varying effects, the shared-frailty model captured unobserved cluster-level heterogeneity, and the Bayesian model confirmed the stability of parameter estimates. Incorporating these complementary approaches can improve the validity of survival analyses and strengthen evidence for child survival research and policy in low- and middle-income settings.