Survival Regression Model Allowing for Exposure-Mediator Interaction: Analysis of Kenya Demographic Health Survey (KDHS) 2014 Data

Abstract

Although infant and under-five mortality rates have decreased over the past decade, Kenya, like many other African nations, did not achieve the Millennium Development Goal (MDG) 4 target. To accelerate progress towards reaching Sustainable Development Goal 3 by 2030, it is essential to understand the factors influencing under-five child mortality (UFCM), taking into account all potential confounding variables and effect modifiers. The child mortality rate serves as a key health indicator for any country. This study used data from the 2014 Kenya Demographic and Health Survey (KHDS). This paper introduces the concept of decomposing the total effect of an independent variable into three components: a direct effect, an indirect effect, and an interactive effect. We attempt to account for the direct effect of an independent variable on the outcome, then proceed to check the effect of the presence of a possible mediator and, furthermore, the possible interactions between an exposure variable and a mediator variable. The outcome variable was UFCM, and the study was to determine the effect of mother’s education on UFCM, in the presence of mediators such as mother’s income and the effect via the interaction between mother’s education and maternal income was estimated. To capture the effect of mediation and interactions in the context of survival analysis, an Aalen additive model, including a product term for the exposure and mediator term, was developed. The methods were further illustrated with practical approach to KDHS data. KDHS has data on a broad scope of risk factors for UFCM. Computations for all data sets was implemented using the freely available R-software package. This analysis suggests that while a significant portion of the impact of maternal education on UFCM is mediated by increasing maternal income, it is the interaction between maternal education and maternal income that leads to a reduction in UFCM. Interventions targeting an increase in income among mothers with no education level would yield a greater reduction in UFCM than interventions targeting mothers with a higher education level. The total effect was ascribed to an interaction between the mother’s education level and maternal income, and part of it is attributable to pure indirect effect, and a given proportion is attributable to pure direct effect. The majority of the total effect (70%)is attributed to the interaction between change from no education level to primary education level and maternal income. 22% is associated with the pure direct effect, while 8% is linked to the pure indirect effect. Interventions with a given increase in income among those with no education level would yield a greater reduction in UFCM than interventions targeting mothers with a higher education level.