Abstract: Modeling of Prediction Tuberculosis Prevalence Based Geographically Weighted Poisson Regression
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Open Access | Fully Refereed | Peer Reviewed

ISSN: 2319-7064

Downloads: 118

Research Paper | Medical Surgical | Indonesia | Volume 5 Issue 7, July 2016

Modeling of Prediction Tuberculosis Prevalence Based Geographically Weighted Poisson Regression

Suprajitno, Sri Mugianti, Wiwin Martiningsih, Wagiyo

Tuberculosis is an infectious disease was cause of death. Tuberculosis patient were continues each year. In epidemiology, the environment and the human were two factors of tuberculosis prevalence, particularly individuals at risk of infection. Basically, tuberculosis prevalence able to predicted with statistically to determine of the two main factors in epidemiology. The purpose of this research was to produce a formula prediction model based GWPR (Geographically Weighted Poisson Regression). Methods Research design was descriptive. The research sample as much as 111 Tb patients were selected by simple random sampling, which all patients were recorded in Dinas Kesehatan Kota and Kabupaten Blitar on 2015, January ? May. Inclusion criteria were Non MDR (multi drug resistance) and not being hospitalized. Environmental factors were predictors is spacious house, spacious living room, spacious bedrooms, number of bedroom windows, spacious bedroom window, living room temperature, humidity living room, and the amount of sunlight entering the homes of people Tb. The human factor was a patient body weight. Result, where X1 = weight, X2 = area of homes of people, X3 = spacious living room patients, X4 = spacious bedrooms patients, X5 = number of bedroom window sufferers, X6 = spacious bedroom window sufferers, X7 = the temperature of the patient's living room, X8 = humidity living room patients, and X9 = the amount of light entering the living room. Analysis Formula produce a proportion of 0.07% and a predictor effect by 27%. Discuss Before using a formula to predict tuberculosis prevalence needed to measurements variables and population that can be predicted precisely match the desired time

Keywords: Tuberculosis, Prediction, GWPR

Edition: Volume 5 Issue 7, July 2016

Pages: 1477 - 1482

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How to Cite this Article?

Suprajitno, Sri Mugianti, Wiwin Martiningsih, Wagiyo, "Modeling of Prediction Tuberculosis Prevalence Based Geographically Weighted Poisson Regression", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=ART2016488, Volume 5 Issue 7, July 2016, 1477 - 1482

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