American Journal of Theoretical and Applied Statistics

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Count Regression Models with Application to Caries Experience for Children Attending Lady Northey Dental Clinic in Nairobi

Received: 20 May 2017    Accepted: 31 May 2017    Published: 09 June 2017
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Abstract

Count regression models were developed to model data with integer outcome variables. These models can be employed to examine occurrence and frequency of occurrence. Four common types of count regression models are applied to caries data among children aged between three and six years attending Lady Northey Dental clinic between September, 2014 and November 2014. These models include Poisson, Negative Binomial (NB), Zero Inflated Poisson (ZIP) and Zero Inflated Negative Binomial (ZINB). The simplest count regression model, Poisson, was fitted first before considering other complex models. However, it did not perform better than its improved counterparts. The NB model proved to be the the simplest model that fits the data well according to Akaike Information Criterion (AIC), and was therefore employed to determine the important predictors of caries experience among the children. Model comparison was performed on the four models by use of AIC. Deviance values for various NB models were compared and the model with the least deviance value was considered to give a subset of best predictors of Early Childhood Caries (ECC). These predictors included age, gender, brushing frequency, feeding habit biscuits, feeding habit jam and highest education of the mother.

DOI 10.11648/j.ajtas.20170604.12
Published in American Journal of Theoretical and Applied Statistics (Volume 6, Issue 4, July 2017)
Page(s) 176-181
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

Count Regression Models, Model Selection, AIC

References
[1] Agresti, A., & Kateri, M. (2011). Categorical data analysis. Springer.
[2] Chepkwony, F. C. (2015). Oral health status and treatment needs among 3-6 year old children attending lady northey dental clinic, nairobi city county. Unpublished doctoral dissertation, University of Nairobi.
[3] Coxe, S., West, S. G., & Aiken, L. S. (2009). The analysis of count data: A gentle introduction to poisson regression and its alternatives. Journal of personality assessment, 91 (2), 121–136.
[4] Greene, W. (2008). Functional forms for the negative binomial model for count data. Economics Letters, 99 (3), 585–590.
[5] Lee, A. H., Wang, K., Scott, J. A., Yau, K. K., & McLachlan, G. J. (2006). Multilevel zero-inflated poisson regression modelling of correlated count data with excess zeros. Statistical methods in medical research, 15 (1), 47–61.
[6] Marthaler, T. (2004). Changes in dental caries 1953–2003. Caries research, 38 (3), 173–181.
[7] Mwalili, S. M., Lesaffre, E., & Declerck, D. (2008). The zero-inflated negative binomial regression model with correction for misclassification: an example in caries research. Statistical Methods in Medical Research, 17 (2), 123–139.
[8] Ngatia, E., Imungi, J., Muita, J. G., et al. (2001). Dietary patterns and dental caries in nursery school children in nairobi, kenya. East African medical journal, (12), 673–677.
[9] Njoroge, N., Kemoli, A., & Gatheche, L. (2010). Prevalence and pattern of early childhood caries among 3-5 year olds in kiambaa, kenya. East African medical journal, 87 (3), 134–137.
[10] Osiro, K., Macigo, & Dienya. (2011). Knowledge, perception and practice of atraumatic restoration treatment among dentists in nairobi.
[11] Pearl, J. (2015). Detecting latent heterogeneity. Sociological Methods & Research, 0049124115600597.
[12] Sonfield, A., Hasstedt, K., Kavanaugh, M. L., & Anderson, R. (2013). The social and economic benefits of womenâs ability to determine whether and when to have children. New York: Guttmacher Institute.
[13] Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A., & Smith, G. M. (2009). Zero-truncated and zero-inflated models for count data. In Mixed effects models and extensions in ecology with r (pp. 261–293). Springer.
Author Information
  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

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    Agnes Njambi Wanjau, Samuel Musili Mwalili. (2017). Count Regression Models with Application to Caries Experience for Children Attending Lady Northey Dental Clinic in Nairobi. American Journal of Theoretical and Applied Statistics, 6(4), 176-181. https://doi.org/10.11648/j.ajtas.20170604.12

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    Agnes Njambi Wanjau; Samuel Musili Mwalili. Count Regression Models with Application to Caries Experience for Children Attending Lady Northey Dental Clinic in Nairobi. Am. J. Theor. Appl. Stat. 2017, 6(4), 176-181. doi: 10.11648/j.ajtas.20170604.12

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    AMA Style

    Agnes Njambi Wanjau, Samuel Musili Mwalili. Count Regression Models with Application to Caries Experience for Children Attending Lady Northey Dental Clinic in Nairobi. Am J Theor Appl Stat. 2017;6(4):176-181. doi: 10.11648/j.ajtas.20170604.12

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  • @article{10.11648/j.ajtas.20170604.12,
      author = {Agnes Njambi Wanjau and Samuel Musili Mwalili},
      title = {Count Regression Models with Application to Caries Experience for Children Attending Lady Northey Dental Clinic in Nairobi},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {6},
      number = {4},
      pages = {176-181},
      doi = {10.11648/j.ajtas.20170604.12},
      url = {https://doi.org/10.11648/j.ajtas.20170604.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtas.20170604.12},
      abstract = {Count regression models were developed to model data with integer outcome variables. These models can be employed to examine occurrence and frequency of occurrence. Four common types of count regression models are applied to caries data among children aged between three and six years attending Lady Northey Dental clinic between September, 2014 and November 2014. These models include Poisson, Negative Binomial (NB), Zero Inflated Poisson (ZIP) and Zero Inflated Negative Binomial (ZINB). The simplest count regression model, Poisson, was fitted first before considering other complex models. However, it did not perform better than its improved counterparts. The NB model proved to be the the simplest model that fits the data well according to Akaike Information Criterion (AIC), and was therefore employed to determine the important predictors of caries experience among the children. Model comparison was performed on the four models by use of AIC. Deviance values for various NB models were compared and the model with the least deviance value was considered to give a subset of best predictors of Early Childhood Caries (ECC). These predictors included age, gender, brushing frequency, feeding habit biscuits, feeding habit jam and highest education of the mother.},
     year = {2017}
    }
    

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    AU  - Agnes Njambi Wanjau
    AU  - Samuel Musili Mwalili
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    AB  - Count regression models were developed to model data with integer outcome variables. These models can be employed to examine occurrence and frequency of occurrence. Four common types of count regression models are applied to caries data among children aged between three and six years attending Lady Northey Dental clinic between September, 2014 and November 2014. These models include Poisson, Negative Binomial (NB), Zero Inflated Poisson (ZIP) and Zero Inflated Negative Binomial (ZINB). The simplest count regression model, Poisson, was fitted first before considering other complex models. However, it did not perform better than its improved counterparts. The NB model proved to be the the simplest model that fits the data well according to Akaike Information Criterion (AIC), and was therefore employed to determine the important predictors of caries experience among the children. Model comparison was performed on the four models by use of AIC. Deviance values for various NB models were compared and the model with the least deviance value was considered to give a subset of best predictors of Early Childhood Caries (ECC). These predictors included age, gender, brushing frequency, feeding habit biscuits, feeding habit jam and highest education of the mother.
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