| dc.description.abstract | Generalized linear models (GLMs) form a class of fixed effects regression models for several types of dependent variable, whether continuous, dichotomous or counts.
Common GLMs include linear regression, Logistic regression and Poison regression. These models have typically been used a lot in modeling of data arising from a heterogeneous population under the assumption of independence. However, in applied science and in real life situations in general, one is confronted with collection of correlated data (Mark Aerts et al, 2005). This generic term embraces a multitude of data structures, such as multivariate observations, clustered data, repeated measurements, longitudinal data, and spatially correlated data. Generalized Linear Mixed Models (GLMMs), also called Generalized Linear Mixed Effects Models (GLMEMs) are able to handle extra ordinary range of complications in regression- type analyses. They are often used to handle correlations as it arises in longitudinal and other clustered data.
In this paper we describe use of GLMMs to explain different factors and their influence on an individual morbidity in Kenya. We use maximum likelihood (ML) as the main estimation method. We also use Restricted Maximum Likelihood (REML) estimation when we relax the assumption of equal cluster sizes, to help in estimating intra and inter block weights. We shall assume normality of the random effects.
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The (2005/6) Kenya Integrated Household Budget Survey findings pointed out that there has been a worsening health situation in Kenya. The result further outlined that the number of
individuals having environmental health related disease had increases' as compared to the
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previous studies. The higher prevalence of individual morbidity was* associated with social,
economic and demographic factors. Reducing environmental health related diseases to a population remains one of the thorny issues that face developing countries, among them Kenya. Therefore, there is an urgent need to explain the way forward in addressing and guiding policy towards this noble goal. In our results, we deduced that gender increases the log-odds of a individual getting a disease, while people who are living in good housing conditions reduce the log-odds of individual experiencing morbidity. Main source of drinking water was also significant in explaining individual morbidity in Kenya. The human waste disposal method was also significant in explaining morbidity among individuals. This study can however be extended to incorporate income level of individuals. Individuals with low level of income are believed to be more likely to experience environmental health related diseases than individuals with higher levels of income. | |
