American Journal of Theoretical and Applied Statistics

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Determinants of Environmental Health Related Diseases in Kenya with Generalized Linear Mixed Models: Analysis of Kenya Integrated Household Budget Survey

Received: 29 February 2016    Accepted: 11 March 2016    Published: 04 June 2016
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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) 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.

DOI 10.11648/j.ajtas.20160504.11
Published in American Journal of Theoretical and Applied Statistics (Volume 5, Issue 4, July 2016)
Page(s) 162-172
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

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

References
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[6] Wolfinger, R., and M. O’ Connell. 1993. “Generalized Linear Mixed Models: A Pseudo-Likelihood Approach. Journal of Statistical Computation and Simulation 48: 233-43.
[7] Gene A. Pennello, Susan S. Devesa, and Mitchell H. Gail (1999) using Mixed effects model to Estimate Geographic Variation in Cancer Rates
[8] Lei Nei (2005) Convergence rate of MLE in Generalized Linear and Non Linear Mixed-effect Models: Theory and Applications
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[10] 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
[11] James A. Hanleyon (2002) Statistical Analysis of Correlated Data Using Generalized Estimating Equations: An Orientation.
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[13] Hayes, W. L. (1973). Statistics for the Social Sciences. New York: Holt, Rinehart, & Winston
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Author Information
  • Department of Statistics and Computer Science, Moi University, Eldoret, Kenya

  • Department of Statistics and Computer Science, Moi University, Eldoret, Kenya

  • School of Mathematics, University of Nairobi, Nairobi, Kenya

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    Jemimah Wangui Muraya, Beatrice Karanja Kimani, John Mwangi Ndiritu. (2016). 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, 5(4), 162-172. https://doi.org/10.11648/j.ajtas.20160504.11

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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. Am. J. Theor. Appl. Stat. 2016, 5(4), 162-172. doi: 10.11648/j.ajtas.20160504.11

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    AMA Style

    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. Am J Theor Appl Stat. 2016;5(4):162-172. doi: 10.11648/j.ajtas.20160504.11

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  • @article{10.11648/j.ajtas.20160504.11,
      author = {Jemimah Wangui Muraya and Beatrice Karanja Kimani and John Mwangi Ndiritu},
      title = {Determinants of Environmental Health Related Diseases in Kenya with Generalized Linear Mixed Models: Analysis of Kenya Integrated Household Budget Survey},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {5},
      number = {4},
      pages = {162-172},
      doi = {10.11648/j.ajtas.20160504.11},
      url = {https://doi.org/10.11648/j.ajtas.20160504.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtas.20160504.11},
      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) 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.},
     year = {2016}
    }
    

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