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Determinants of Environmental Health Related Diseases in Kenya with Generalized Linear Mixed Models: Analysis of Kenya Integrated Household Budget Survey
American Journal of Theoretical and Applied Statistics
Volume 5, Issue 4, July 2016, Pages: 162-172
Received: Feb. 29, 2016; Accepted: Mar. 11, 2016; Published: Jun. 4, 2016
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Jemimah Wangui Muraya, Department of Statistics and Computer Science, Moi University, Eldoret, Kenya
Beatrice Karanja Kimani, Department of Statistics and Computer Science, Moi University, Eldoret, Kenya
John Mwangi Ndiritu, School of Mathematics, University of Nairobi, Nairobi, Kenya
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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) are able to handle extraordinary range of complications in regression-type analyses. They are often used to handle correlations that arise in longitudinal and other clustered data. This study sought to fit GLMMs to Kenya integrated household data collected in 2005/6 to explain different factors and their influence on an individual morbidity in Kenya. The cluster variable was used to introduce the random effect in this data. From the analysis, it was deduced that gender increases the log-odds of an individual getting a disease, while people who are living in good housing conditions reduces the log-odds of an individual experiencing morbidity. Main source of drinking water and the human waste disposal method were significant in explaining individual morbidity in Kenya. This study can however be extended to incorporate other factors such as 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.
Generalized Linear Mixed Effects Model GLMEM, Maximum Likelihood ML, Restricted Maximum Likelihood REML, Marginal Quasi Likelihood MQL, Demographic and Health Surveys DHS, Deviance Information Criteria DIC, Akaike Information Criteria AIC
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Jemimah Wangui Muraya, Beatrice Karanja Kimani, John Mwangi Ndiritu, Determinants of Environmental Health Related Diseases in Kenya with Generalized Linear Mixed Models: Analysis of Kenya Integrated Household Budget Survey, American Journal of Theoretical and Applied Statistics. Vol. 5, No. 4, 2016, pp. 162-172. doi: 10.11648/j.ajtas.20160504.11
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cande V Ananth, Robert W Platt (2004) Re-examining the effects of gestational age, fetal grow and maternal smoking on neonatal mortality
Alan Agresti (2002), Categorical Data Analysis. 12, pg 491-537
Katrien Antonio, Jan Beirlant (2006) Acturial Statistics With Generalized Linear Mixed Models
Daowen Zhang (2004) Generalized Linear Mixed Models with Varying Coefficients for Longitudinal data
Artazcoz L, Benach J, Borrel C, Cortes I 2004 Unemployment and mental health: understanding the interactions among gender, family roles and social class. American Journal of Public Health 94: 82–88
Wolfinger, R., and M. O’ Connell. 1993. “Generalized Linear Mixed Models: A Pseudo-Likelihood Approach. Journal of Statistical Computation and Simulation 48: 233-43.
Gene A. Pennello, Susan S. Devesa, and Mitchell H. Gail (1999) using Mixed effects model to Estimate Geographic Variation in Cancer Rates
Lei Nei (2005) Convergence rate of MLE in Generalized Linear and Non Linear Mixed-effect Models: Theory and Applications
D. I Ohlssen, L. D Sharples, and Spiegel halter (2000) Flexible random effects models using Bayesian semi-parametric models: application to institutional comparisons
Petra Bukovand Thomas Lumley (2007). Longitudinal Data Analysis for Generalized Linear Models with Follow-up Dependent on Outcome-Related Variables. The Cana-dian Journal of Statistics, Vol. 35, No. 4, pp. 485-500
James A. Hanleyon (2002) Statistical Analysis of Correlated Data Using Generalized Estimating Equations: An Orientation.
Maxwell, S. E. & Delaney, H. D. (2004). Designing Experiments and Analyzing Data: A Model Comparison Perspective, Second Edition. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
Hayes, W. L. (1973). Statistics for the Social Sciences. New York: Holt, Rinehart, & Winston
American Journal of Theoretical and Applied Statistics 2015; 4 (3): 170-177177
Mohammed O. M. Mohammed. Statistical methods for analyzing complex survey data: An application to morbidity in ethiopia. 2013.
G Rasch. On general laws and the meaning of measurements in psychology in Neyman J, ed. Proceedings of the 4th Berkeley symposium on mathematical statistics and probability, vol 4. Berkeley, 1961.
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