POISSON REGRESSION MODELS TO ANALYZE FACTORS THAT INFLUENCE THE NUMBER OF TUBERCULOSIS CASES IN JAVA

  • Yekti Widyaningsih Statistics Study Program, Faculty of Mathematics and Natural Sciences, University of Indonesia, Indonesia http://orcid.org/0000-0002-1309-6916
  • Zalfa Alifah Budiawan Statistics Study Program, Faculty of Mathematics and Natural Sciences, University of Indonesia, Indonesia
DOI: https://doi.org/10.30598/barekengvol17iss3pp1761-1772
Keywords: Generalized Poisson Regression, Geographically weighted negative binomial regression, Geographically weighted generalized poisson regression, Negative binomial regression

Abstract

Tuberculosis is an infectious disease and one of the world's top 10 highest causes of mortality in Indonesia. Based on this fact, it is necessary to study what factors affect number of tuberculosis cases. The number of tuberculosis cases as dependent variable is a count data that generally analyzed using Poisson regression. However, equidispersion assumption must be met, so Generalized Poisson Regression and Negative Binomial Regression are applied if the assumption is not met. Spatial aspects can be considered so Geographically Weighted Generalized Poisson Regression and Geographically Weighted Negative Binomial Regression were also conducted. Four models were built to evaluate relationship between number of tuberculosis cases and factors affecting it in Java in 2020. The explanatory variables are population density, percentage of children receiving BCG immunization, percentage of poor people, percentage of eligible drinking water facilities, percentage of family cards with access to proper sanitation, percentage of public places meet health requirements, and percentage of food management places meet hygienic requirements. This study shows that the best model for modeling the data is GWNBR with 2 groups of significant explanatory variables. Seven explanatory variables are statistically significant in 88 districts and six explanatory variables statistically significant in 12 districts.

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Published
2023-09-30
How to Cite
[1]
Y. Widyaningsih and Z. Budiawan, “POISSON REGRESSION MODELS TO ANALYZE FACTORS THAT INFLUENCE THE NUMBER OF TUBERCULOSIS CASES IN JAVA”, BAREKENG: J. Math. & App., vol. 17, no. 3, pp. 1761-1772, Sep. 2023.
Issue
Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Section
Articles

Copyright (c) 2023 Zalfa Alifah Budiawan, Yekti Widyaningsih

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