Type | Thesis or Dissertation - Doctor of Philosophy in Demography and Population Studies |
Title | Contextual determinants of infant and child mortality in Nigeria |
Author(s) | |
Publication (Day/Month/Year) | 2014 |
URL | http://wiredspace.wits.ac.za/bitstream/handle/10539/13423/PhD_thesis_Sunday_ADEDINI.pdf?sequence=2&isAllowed=y |
Abstract | Background: Despite modest improvements in child health outcomes during the 20th century, infant and child mortality rates remain unacceptably high in Nigeria. With about 1 in 6 children dying before the age of five, Nigeria, like many other countries in sub-Saharan Africa, is not on track to achieve the Millennium Development Goal 4 (MDG 4) (i.e. reducing childhood mortality by 2015). Nigeria’s under-five mortality rate is among the highest in the world. Addressing poor infant and child health outcomes requires scientific evidence on how best to tackle its determinants. Literature shows that knowledge about the determinants of child mortality at the individual level is insufficient to address the problem. This is because the characteristics of the household and community context where a child is born or raised tend to modify individual-level factors and therefore affect child survival. However, there are gaps in evidence on the effects of characteristics of the community contexts on child survival in Nigeria. Hence, this study examined the contextual determinants of infant and child mortality in Nigeria with a focus on individual, household and community-level characteristics. The study addressed three specific objectives: (1) to examine the levels and magnitudes of infant and child mortality in Nigeria; (2) to identify the individual, household, and communitylevel factors associated with infant and child mortality in Nigeria; and (3) to determine the extent to which the contextual factors account for regional variations in infant and child mortality in Nigeria. Methodology: The study utilized data from 2003 and 2008 Nigeria Demographic and Health Survey (NDHS). The target population for this study (women aged 15-49 years who had at least a live birth in the five years preceding the survey) were extracted from the whole 2003 and 2008 NDHS datasets. Out of the survey’s total sample size of 7620 women contained in 2003 dataset, analysis was restricted to the live born children of 3775 women amounting to 6028 live births within the five years before the survey. Similarly, from a total of 33,385 women contained in 2008 dataset, analysis was restricted to the live born children of 18,028 women who were 28,647 children delivered in the five years before 2008 survey. In order to achieve the objectives of this study, analysis was restricted to births in the five years before the survey. All analyses were completely child-based. That is, child was the unit of analysis. The dependent variables in this study are: (i) infant mortality – defined as the risks of dying during the first year of life; (ii) child mortality – defined as the risk of dying between ages 12 and 59 months; and (iii) under-five mortality – defined as the risks of dying between birth and the fifth birthday. All the outcome variables were measured as the duration of survival since birth in months. Guided by the reviewed literature and the conceptual framework, relevant independent variables were selected at the individual-, household- and community-levels. Three levels of analysis – univariate, bivariate and multivariate – were conducted. At the multivariate level, Cox proportional hazards regression analysis was employed because of its suitability for analysing time-to-event data and censored observations. In addition, using generalized linear latent and mixed models (GLLAMM) implementable in Stata, multilevel survival analysis was employed to consider the hierarchical structure of the DHS mortality data; and to identify contextual factors xxi influencing regional variations in infant and child mortality in Nigeria. Data were analyzed using Stata software (version 11.1). Indirect estimations were obtained using MortPak-Lite, Microsoft Excel, and Model Life Tables. |
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