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.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 5, Issue 4) |
| DOI | 10.11648/j.ajtas.20160504.11 |
| 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. |
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Copyright © The Author(s), 2016. Published by Science Publishing Group |
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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APA Style
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
ACS 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
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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Download@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://article.sciencepublishinggroup.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}
}
TY - JOUR T1 - Determinants of Environmental Health Related Diseases in Kenya with Generalized Linear Mixed Models: Analysis of Kenya Integrated Household Budget Survey AU - Jemimah Wangui Muraya AU - Beatrice Karanja Kimani AU - John Mwangi Ndiritu Y1 - 2016/06/04 PY - 2016 N1 - https://doi.org/10.11648/j.ajtas.20160504.11 DO - 10.11648/j.ajtas.20160504.11 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 162 EP - 172 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20160504.11 AB - 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. VL - 5 IS - 4 ER -
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