Spatial Autocorrelation of Tuberculosis and Demographic, Health Services, Environment, and Economic Factors in West Java in 2024 - Cinansa Muthia Dewani; Indang Trihandini; Jihan Ramadhany Ginting Manik (Sekolah Tinggi Agama Islam Kuningan, 2026)
Background: Tuberculosis (TB) remains a major public health problem in Indonesia, with West Java reporting 229,683 cases in 2024. The geographic clustering distribution of TB cases requires spatial analysis to identify transmission patterns and determinants. Objective: This study aimed to analyze spatial autocorrelation of TB incidence and its relationships with demographic, health service, environmental, and economic factors in West Java in 2024. Method: Quantitative design with an ecological approach across 27 districts/cities in West Java using data from the West Java Health Profile and Statistics Agency 2025. Spatial autocorrelation analysis employed Global Moran's I and univariate–bivariate LISA with a Queen Contiguity weighting matrix. Variables included TB incidence, population size, population density, health facility ratio, adequate sanitation, non-earth floors, and poor population. Analysis used GeoDa 1.22.0.21 with α = 0.05 and 999 permutations. Result: TB incidence showed significant global spatial autocorrelation (Moran's I = 0.3514, p = 0.001). Univariate LISA identified High-High clusters in the Bogor–Bekasi–Karawang metropolitan corridor and Low-Low clusters in Ciamis–Tasikmalaya–Majalengka. Bivariate autocorrelation revealed significant positive relationships with health facility ratio (I= 0.3207, p = 0.005), population size (I = 0.2449, p = 0.014), and population density (I = 0.2088, p = 0.044). Negative autocorrelation with poor population (I = −0.2950, p = 0.006) indicated an urban paradox. Conclusion: TB incidence distribution demonstrates significant geographic clustering with spatial heterogeneity. Demographic and health service factors show positive correlations, while economic factors exhibit an urban paradox. Intervention priorities should focus on metropolitan High-High clusters with spatial data integration and cross-sectoral collaboration.