Spatial Analysis of Pulmonary Tuberculosis Risk in Surakarta, Central Java, Indonesia
DOI:
https://doi.org/10.26911/jepublichealth.2024.09.03.12Abstract
Background: Pulmonary tuberculosis is an infectious disease, especially in developing countries. In 2022, the total number of tuberculosis cases in Indonesia was 677,464 cases. This study aims to conduct a spatial analysis of factors such as population size, population density, number of poor population, number of unemployed, healthy houses, and temperature to the number of pulmonary tuberculosis cases in the working area of health centers in Surakarta City in 2022.
Subjects and Method: Spatial analysis was carried out by descriptive analysis of distribution maps using QGIS, spatial cluster analysis using SaTScanTM, and autocorrelation analysis by Local Indicators of Spatial Autocorrelation (LISA) method using GeoDa. Secondary data was obtained from the public communication of the Central Statistics Agency of Surakarta and the Surakarta Health Profile in 2022. The analysis unit includes 17 working areas of health centers in Surakarta. The dependent variable is the TB cases. The independent variables were population density, number of poor people, number of unemployed, number of healthy houses, and temperature.
Results: The highest number of pulmonary TB cases were in Purwodiningratan Health Center and Sangkrah Health Center. There is the most likely cluster of pulmonary tuberculosis cases which is statistically significant in 2022 (Radius = 1.81 km; RR=17.65; p=0.003). Autocorrelation analysis showed E[I]=-0.06. There was a positive and significant spatial autocorrelation of the population (I=0.40; p=0.003), population density (I=0.33; p=0.002), number of poor people (I=0.40; p=0.002), number of unemployed (I=0.40; p=0.003), and temperature (I=0.25; p=0.009) of pulmonary TB cases in Surakarta.
Conclusion: Population number, population density, number of poor population, number of unemployed, and temperature have positive spatial autocorrelation with pulmonary TB.
Keywords:
Spatial analysis, SaTScan, GeoDa, pulmonary tuberculosisCorrespondence
Dinda Nabila Silva Diba. Master’s Program in Public Health, Universitas Sebelas Maret. Jl. Ir. Sutami 36A, Surakarta 57126, Central Java, Indonesia. Email: Dinda.silva.diba@student.uns.ac.id. Mobile: 082268893633.
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Published
2024-07-16
Issue
Vol. 9 No. 3 (2024)
Section
Articles
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