Determinants of Time-to-Under-Five Mortality in Ethiopia: Comparison of Parametric Shared Frailty Models

Determinants of Time-to-Under-Five Mortality in Ethiopia: Comparison of Parametric Shared Frailty Models

Abebe Argaw Wogi (Mizan Tepi University, Teppi, Ethiopia), Shibru Temesgen Wakweya (Addis Ababa University, Ethiopia) and Yohannes Yebabe Tesfay (Cree, Durham, NC, USA)
Copyright: © 2018 |Pages: 24
DOI: 10.4018/IJBCE.2018010101
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This article describes how under-five mortality rate is one of the critical indicators of development of a country. This rate tells of children's access to basic health interventions such as vaccinations, medical treatment and adequate nutrition. This article proposes to identify the determinants of time to under-five mortality in Ethiopia based on the 2014 data taken from the Ethiopian Mini Demographic and Health Survey of women of the age group15-49 years. In this survival quantitative analysis, this article considers relevant socioeconomic, demographic variables and environmental factors. Various parameters shared among frailty models are employed to identify the determinants of Time-To-Under-Five Mortality of Ethiopia. The selection of the best-fit survival model is done by applying the Akaike information criterion (AIC). The AIC prevailed that the Weibull-gamma multivariable-shared frailty model is relatively the best-fit model. The estimation result of the Weibull-gamma multivariable-shared frailty model predicted that the major factors identified for under-five mortality in Ethiopia were mothers' educational level, mothers' age at first birth, place of residence, household size, sex of child born, preceding birth interval, economic status of family, place of delivery, marital status of family, and source of drinking water. The result implied that vast work is expected from governmental and non-governmental bodies to reduce the under-five mortality in the country by considering the identified factors.
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2. The Problem

Many scholars using logistic regression and semi-parametric proportional hazard models have conducted studies to identify covariates of under-five mortality in Ethiopia. However, logistic regression modelling does not account the censoring observations. That is, logistic regression modelling does not hold for time-to-event data, and in demographic applications, nonparametric and semi-parametric models are often used to model transition data. In such applications, the model assume that all heterogeneity captured by theoretically relevant covariates (Trussell and Richards, 1985; Trussell and Rodriguez, 1990). In many situations, however, there are ample reasons to suspect omitted or unmeasured factors. That is, while some individuals will be more at risk of experiencing the event, it is unlikely the underlying reasons for this variability captured by the observed covariates. If there is unmeasured frailty, the hazard will not only be a function of the covariates but also of the frailty. To assess the true effects of the observed covariates under this circumstance, some have stressed the need to explicitly account for unobserved heterogeneity. Indeed, results from several empirical and simulation studies have shown that accounting for unobserved heterogeneity significantly improves overall model fitness (Aalen 1994; Baker and Melino 2000; Blossfeld and Hamerle 1992; Heckman et al., 1985; Lancaster 1990; Manda and Meyer 2005; Vaupel et al., 1979).

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