Application of the Geographically Weighted Negative Binomial Regression (GWNBR) Method to Tuberculosis Cases in North Sumatra Province in 2024

Abstract

Tuberculosis is one of the leading causes of death worldwide. Approximately 1.2 million deaths occur annually due to tuberculosis. According to the World Health Organization (WHO), Indonesia is the second-largest tuberculosis country after India, with a 10% prevalence rate (WHO, 2024). According to Ministry of Health data, in 2024, North Sumatra was the province with the highest number of TB cases on Sumatra Island, with several cases above the national average, ranking third in Indonesia. The number of tuberculosis cases in North Sumatra is census data and is overdispersed, with spatial influences. Therefore, the method used is Geographically Weighted Negative Binomial Regression (GWNBR), which produces local parameters. The results show that GWNBR forms eight regional groups based on significant variables. Rainfall and per capita expenditure variables have a significant influence in all districts/cities, and the percentage of BCG immunizations and the percentage of smoking population have a significant influence in almost all regions. Meanwhile, health fund allocation only shows a significant influence in several districts/cities. The AIC value of the GWNBR is not smaller than the AIC value of the negative binomial regression. However, the GWNBR model can be used to examine the influence of independent variables on tuberculosis cases spatially in North Sumatra.