PEMODELAN JUMLAH KASUS PENYAKIT KUSTA DI PROVINSI SULAWESI TENGGARA MENGGUNAKAN METODE REGRESI POISSON INVERSE GAUSSIAN : PEMODELAN JUMLAH KASUS PENYAKIT KUSTA DI PROVINSI SULAWESI TENGGARA MENGGUNAKAN METODE REGRESI POISSON INVERSE GAUSSIAN - Moh Yamin Darsyah; M Nurul Ramadhan (Litbang PWM Jawa Tengah, 2022)
Leprosy data is census data so in its modeling, it can use Poisson regression. Leprosy data also has the potential to experience overdispersion so that in overcoming the case of overdispersion, several models are formed which are a combination of the Poisson distribution with several distributions both discrete and continuous (mixed Poisson distribution). One of them is the distribution of Poisson Inverse Gaussian (PIG) which is a mixed Poisson distribution with random effects that have the Gaussian inverse distribution. In the application of a Poisson Inverse Gaussian regression method, factors are affecting the occurrence of leprosy in Southeast Sulawesi. As for the factors namely the population density, the residents who experience health complaints and ever ambulatory care, the use of clean water, the Clean and Healthy Behavior (PHBS) Households, the health facilities, the health workers, and the economic growth from the BPS website and the Profile of the Health Office of Southeast Sulawesi Province in the PDF appendix. The results of data analysis and discussion, the Poisson Inverse Gaussian regression model are obtained with a significant variable to the model that is the percentage of population density and the percentage of clean water use. With the addition of 1 ratio to the percentage of population density will be proportional to the increase in the average number of leprosy in Southeast Sulawesi Province by 1.00177. And with the addition of 1 ratio on the percentage of the use of clean water, it would be comparable to the average increase in the number of leprosy in southeast Sulawesi province by 1.01205