MODELLING THE NUMBER OF NEW PULMONARY TUBERCULOSIS CASES WITH GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION METHOD
DOI:
https://doi.org/10.29244/ijsa.v2i2.175Abstract
Tuberculosis (TB) is an infectious disease caused by Mycobacterium Tuberculosis. Untill now, TB is still one of the main problems in many countries, especially developing countries. Indonesia ranked second as the country with the highest TB cases in the world in 2015, where most cases were found in Java. This study was conducted to model the number of new pulmonary TB cases in Java by considering the spatial aspects using Geographically Weighted Negative Binomial Regression (GWNBR). GWNBR method was chosen because the data used in this study are overdispered. The result showed that the population density and percentage of healty homes were not significantly influential in each region. While the number of puskesmas, the percentage of smokers, the percentage of good PHBS, the percentage of diabetes mellitus, and the percentage of less IMT were significant in some regions. In general, the GWNBR model was better for modelling the number of new pulmonary TB cases than negative binomial regression and GWPR.
Downloads
References
Aditama, Y. (2005). Tuberkulosis dan Kemiskinan. Majalah Kesehatan Indonesia, 55(2) , 49-51.
Anselin, L. (1995). Local Indicators of Spatial Association-LISA. Geographical Analysis, 27 (2), 93-115.
Anselin, L. (1988). Spatial Econometric Methods and Model. Dordrecht: Kluwer Academic Publishers.
Badan Penelitian dan Pengembangan Kesehatan Indonesia. (2014). Hasil Sample Registration Survey. Jakarta: Balitbangkes.
Cameron, A., & Trivedi, P. (2013). Regression Analysis of Count Data (Second ed). Cambridge: Cambridge University Press.
Da Silva, A. & Rodrigues, T. (2013). Geographically Weighted Negative Binomial Regression: Incorporating Overdispersion. New York: Springer.
Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression. Inggris: University of Newcastle.
Gill, P. J. (2001). Generalized Linear Models: A Unified Approach. California: Sage Publication, Inc.
Hilbe, J. (2011). Negative Binomial Regression (2nd ed). New York: Cambridge University Press.
Indahwati, S. D. (2016). Analisis Faktor-Faktor yang Memengaruhi Jumlah Kasus Tuberkulosis di Surabaya Tahun 2014 Menggunakan Geographically Weighted Negative Binomial Regression. Jurnal Sains dan Seni ITS, 5(2), 193-197.
Kementerian Kesehatan RI. (2011). Strategi Nasional Pengendalian Tb di Indonesia Tahun 2010-2014. Jakarta: Kementerian Kesehatan RI.
McCullagh, P., & Nelder, J. (1989). Generalized Linear Models (Second ed). London: Chapman and Hall.
Miller, H. (2004). Tobler's First Law and Spatial Analysis. Annalys of the Association of American Geographers, 94(2), 284-289.
Myers, R. (1990). Classical and Modern Regression with Application Second Edition. New York: PWS-KENT.
Ramadhan, R. F. (2016). Pemodelan Data Kematian Bayi dengan Geographically Weighted Negative Binomial Regression. Media Statistika, 9(2), 95-106.
Suarni, H. (2009). Faktor Risiko yang Berhubungan dengan Kejadian Penderita Penyakit TB Paru BTA Positif di Kecamatan Pancoran Mas Kota Depok Bulan Oktober Tahun 2008-April Tahun 2009 [Skripsi] . Depok: FKM UI.
Widarjono, A. (2007). Ekonometrika Teori dan Aplikasi untuk Ekonomi dan Bisnis. Yogyakarta: Ekonosia FE UII.
Widoyono. (2008). Penyakit Tropis: Epidemiologi, Penularan, Pencegahan, dan Pemberantasannya. Jakarta: Erlangga.
World Health Organization. (2016). Global Tuberculosis Report. Jenewa: WHO.
