A Spatial Epidemiological Study of Tuberculosis Incidence in Indonesia

Defaullo Andrean Ronaldino Benge; Inggit Fatika; Edhy Sutanta; Retno Widiastuti; Catur Iswahyudi; Noviana Pratiwi

doi:10.31764/jtam.v10i4.39940

A Spatial Epidemiological Study of Tuberculosis Incidence in Indonesia

Defaullo Andrean Ronaldino Benge, Inggit Fatika, Edhy Sutanta, Retno Widiastuti, Catur Iswahyudi, Noviana Pratiwi

Abstract

Tuberculosis[As1.1] (TB) remains a major public health challenge in Indonesia, with an uneven distribution of cases across provinces. While previous studies have examined spatial patterns of TB, limited attention has been given to simultaneously addressing spatial dependence and spatial heterogeneity in TB distribution. This study aims to analyse the spatial distribution of TB cases across 38 provinces in Indonesia in 2024 and to identify their determinants using an integrated spatial modelling framework. The study employs secondary provincial-level data consisting of demographic, socioeconomic, health, and environmental indicators. The analysis includes descriptive statistics, Global Moran’s I, and Local Indicators of Spatial Association (LISA) to detect spatial autocorrelation, followed by spatial regression models including Ordinary Least Squares (OLS), Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). To further capture spatial heterogeneity, Geographically Weighted Regression (GWR) was also applied. The results reveal substantial disparities in TB cases across provinces in Indonesia. The OLS model identifies population density and the number of community health centers (Puskesmas) as significant determinants of TB cases. Moran’s I results confirm significant positive spatial autocorrelation, indicating that provinces with high TB cases tend to cluster geographically. Spatial regression analysis shows that the SDM provides the best model performance, with the lowest AIC value (19.84) compared to OLS (36.82), SAR (37.84), and SEM (36.21), as well as a significant spillover effect of population density from neighboring provinces (coefficient = 0.55, p < 0.01), suggesting the presence of both direct and spillover spatial effects between provinces. In addition, GWR results demonstrate spatial heterogeneity, indicating that the influence of explanatory variables varies across regions. These findings highlight that TB cases in Indonesia are influenced by both spatial dependence and spatial heterogeneity. Therefore, TB control policies should adopt spatially targeted approaches that consider regional characteristics to improve the effectiveness of public health interventions.

Keywords

Tuberculosis; Spatial Analysis; Spatial Durbin Model; Geographically Weighted Regression; Indonesia.

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DOI: https://doi.org/10.31764/jtam.v10i4.39940