Spatial Autocorrelation of Tuberculosis and Demographic, Health Services, Environment, and Economic Factors in West Java in 2024
DOI:
https://doi.org/10.59784/glosains.v7i2.686Keywords:
tuberculosis, spatial autocorrelation, LISA, moran'si, west java, risk factorsAbstract
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.
References
Asare-Baah, M., Luong, T. M., Kwarteng, E., Domotey, C., Arthur, N., Zoungrana, M., Mireku-Apah, S., Ganu, H., Omari, M. A., Sackey, A., Kwara, A., Afriyie-Mensah, J. S., & Séraphin, M. N. (2025). Geospatial analysis of shared risks for tuberculosis transmission in an urban cohort. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-23120-w
Ayenew, B., Belay, D. M., Gashaw, Y., Gimja, W., & Gardie, Y. (2024). WHO’s end of TB targets: unachievable by 2035 without addressing under nutrition, forced displacement, and homelessness: trend analysis from 2015 to 2022. BMC Public Health, 24(1). https://doi.org/10.1186/s12889-024-18400-5
Bai, W., & Ameyaw, E. K. (2024). Global, regional and national trends in tuberculosis incidence and main risk factors: a study using data from 2000 to 2021. BMC Public Health, 24(1). https://doi.org/10.1186/s12889-023-17495-6
Bakhsh, Y., Readhead, A., Flood, J., & Barry, P. (2023). Association of Area-Based Socioeconomic Measures with Tuberculosis Incidence in California. Journal of Immigrant and Minority Health, 25(3), 643–652. https://doi.org/10.1007/s10903-022-01424-7
Çelik, M., Döker, M. F., Kırlangıçoğlu, C., Ünsal, Gökçeoğlu, S., Ceylan, M. R., & Karabay, O. (2025). Comprehensive Spatial Investigation of Tuberculosis Dynamics and Affecting Factors in Şanlıurfa, Türkiye (2016–2023). GeoHealth, 9(7). https://doi.org/10.1029/2024GH001235
Chen, X., Emam, M., Zhang, L., Rifhat, R., Zhang, L., & Zheng, Y. (2023). Analysis of spatial characteristics and geographic weighted regression of tuberculosis prevalence in Kashgar, China. Preventive Medicine Reports, 35. https://doi.org/10.1016/j.pmedr.2023.102362
Dlangalala, T., Musekiwa, A., McKelly, D., Baloyi, E., & Mashamba-Thompson, T. P. (2024). Accessibility of TB diagnostic services at primary healthcare clinics in the eThekwini district, South Africa: A geospatial analysis. BMJ Open, 14(9). https://doi.org/10.1136/bmjopen-2023-082129
Jenkins, H. E., Ayuk, S., Puma, D., Brooks, M. B., Millones, A. K., Jimenez, J., Lecca, L., Galea, J. T., Becerra, M., Keshavjee, S., & Yuen, C. M. (2022). Geographic accessibility to health facilities predicts uptake of community-based tuberculosis screening in an urban setting. International Journal of Infectious Diseases, 120, 125–131. https://doi.org/10.1016/j.ijid.2022.04.031
Kemenkes RI. (2025, April 11). Gerakan Indonesia Akhiri TBC. https://kemkes.go.id/id/indonesias-movement-to-end-tb
Kunjok, D. M., Mwangi, J. G., Kairu-Wanyoike, S., Kinyua, J., & Mambo, S. (2025). Spatial epidemiology of tuberculosis diagnostic delays, healthcare access disparities, and socioeconomic inequities in Nairobi County, Kenya. PLOS ONE, 20(8 August). https://doi.org/10.1371/journal.pone.0329984
Kuupiel, D., Cheabu, B. S. N., Yeboah, P., Duah, J., Addae, J. K., Ako-Nnubeng, I. T., Osei, F. A., Ziblim, S. D., Mchunu, G. G., Pillay, J. D., & Bawontuo, V. (2023). Geographic availability of and physical accessibility to tuberculosis diagnostic tests in Ghana: a cross-sectional survey. BMC Health Services Research, 23(1). https://doi.org/10.1186/s12913-023-09755-3
Lee, J. Y., Kwon, N., Goo, G. yeon, & Cho, S. il. (2022). Inadequate housing and pulmonary tuberculosis: a systematic review. BMC Public Health, 22(1). https://doi.org/10.1186/s12889-022-12879-6
Lima, L. V. de, Pavinati, G., Palmieri, I. G. S., Bernardo, P. H. P., Santos, V. M. A. dos, Gomes, M. F., Silveira, J. T. P. da, Magalhães, F. B. de, Gil, N. L. de M., & Magnabosco, G. T. (2024). An ecological study mapping socioeconomic inequalities in tuberculosis incidence in a southern state of Brazil. Spatial and Spatio-Temporal Epidemiology, 51. https://doi.org/10.1016/j.sste.2024.100689
Litvinjenko, S., Magwood, O., Wu, S., & Wei, X. (2023). Burden of tuberculosis among vulnerable populations worldwide: an overview of systematic reviews. The Lancet Infectious Diseases, 23(12), 1395–1407. https://doi.org/10.1016/S1473-3099(23)00372-9
Liyew, A. M., Clements, A. C. A., Akalu, T. Y., Gilmour, B., & Alene, K. A. (2024). Ecological-level factors associated with tuberculosis incidence and mortality: A systematic review and meta-analysis. PLOS Global Public Health, 4(10). https://doi.org/10.1371/journal.pgph.0003425
Mahato, R. K., Htike, K. M., Koro, A. B., Yadav, R. K., Sharma, V., Kafle, A., & Ojha, S. C. (2025). Spatial autocorrelation with environmental factors related to tuberculosis prevalence in Nepal, 2020–2023. Infectious Diseases of Poverty, 14(1). https://doi.org/10.1186/s40249-025-01283-y
Phuoc Dao, T., Hong Thi Hoang, X., Ngoc Nguyen, D., Quang Huynh, N., Tat Pham, T., Thuy Nguyen, D., Binh Nguyen, H., Hoang Do, N., Viet Nguyen, H., Huy Dao, C., Viet Nguyen, N., & My Bui, H. (2022). A geospatial platform to support visualization, analysis, and prediction of tuberculosis notification in space and time. Frontiers in Public Health, 10.
Pinto, P. F. P. S., Teixeira, C. S. S., Ichihara, M. Y., Rasella, D., Nery, J. S., Sena, S. O. L., Brickley, E. B., Barreto, M. L., Sanchez, M. N., & Pescarini, J. M. (2024). Incidence and risk factors of tuberculosis among 420 854 household contacts of patients with tuberculosis in the 100 Million Brazilian Cohort (2004–18): a cohort study. The Lancet Infectious Diseases, 24(1), 46–56. https://doi.org/10.1016/S1473-3099(23)00371-7
Pungartnik, P. C., de Souza Viana, P. V., dos Santos, J. P. C., Macedo, L. R., Berra, T. Z., & Paiva, N. S. (2025). Spatial analysis of drug resistant tuberculosis (DRTB) incidence and relationships with determinants in Rio de Janeiro state, 2010 to 2022. PLoS ONE, 20(5 May). https://doi.org/10.1371/journal.pone.0321553
Qiu, J., Li, X., Zhu, H., & Xiao, F. (2024). Spatial Epidemiology and Its Role in Prevention and Control of Swine Viral Disease. In Animals (Vol. 14, Number 19). https://doi.org/10.3390/ani14192814
Silva, M., Viana, C. M., Betco, I., Nogueira, P., Roquette, R., & Rocha, J. (2024). Spatiotemporal dynamics of epidemiology diseases: mobility based risk and short-term prediction modeling of COVID-19. Frontiers in Public Health, 12. https://doi.org/10.3389/fpubh.2024.1359167
Spies, R., Hong, H. N., Trieu, P. P., Lan, L. K., Lan, K., Hue, N. N., Huong, N. T. L., Thao, T. T. L. N., Quang, N. L., Anh, T. D. D., Vinh, T. V., Ha, D. T. M., Dat, P. T., Hai, N. P., Van, L. H., Thwaites, G. E., Thuong, N. T. T., Watson, J. A., & Walker, T. M. (2024). Spatial Analysis of Drug-Susceptible and Multidrug-Resistant Cases of Tuberculosis, Ho Chi Minh City, Vietnam, 2020-2023. Emerging Infectious Diseases, 30(3), 499–509. https://doi.org/10.3201/eid3003.231309
Teibo, T. K. A., Andrade, R. L. de P., Rosa, R. J., Tavares, R. B. V., Berra, T. Z., & Arcêncio, R. A. (2023). Geo-spatial high-risk clusters of Tuberculosis in the global general population: a systematic review. BMC Public Health, 23(1). https://doi.org/10.1186/s12889-023-16493-y
Wang, L., Li, Y., Chen, J., Zhang, D., Zhang, Z., & Li, X. (2025). Spatiotemporal distribution and detection of spatial clusters of tuberculosis in Hubei Province, China using FleXScan (2017–2023). BMC Public Health, 25(1). https://doi.org/10.1186/s12889-025-23620-4
WHO. (2025). Monitoring health for the SDGs, Sustainable Development Goals.
Wolde, H. F., Clements, A. C. A., Erena, A. K., Gelaw, S. K., Lamma, S. W., & Alene, K. A. (2025). Quantifying undetected tuberculosis in Ethiopia using a novel geospatial modelling approach. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-09171-z
Wu, H., Li, X., Wang, J., Jian, R., Hu, J., Hu, Y., Xu, Y., Xiao, J., Jin, A., & Chen, L. (2025). Spatio-Temporal Pattern and Socio-economic Influencing Factors of Tuberculosis Incidence in Guangdong Province: A Bayesian Spatiotemporal Analysis. Biomedical and Environmental Sciences, 38(7), 819–828. https://doi.org/10.3967/bes2025.071
Yu, S., Zhan, M., Li, K., Chen, Q., Liu, Q., Gavotte, L., Frutos, R., & Chen, T. (2024). Analysis of Tuberculosis Epidemiological Distribution Characteristics in Fujian Province, China, 2005-2021: Spatial- Temporal Analysis Study. JMIR Public Health and Surveillance, 10. https://doi.org/10.2196/49123
Zabroski, J., Peptenatu, D., Mahler, B., Soliman, A., Gruia, A. K., Grecu, A., Munteanu, I., & Bǎloi, A. (2025). Geospatial analysis of tuberculosis incidence in relation to socio-economic and environmental indicators in Romania. Frontiers in Public Health, 13. https://doi.org/10.3389/fpubh.2025.1711489
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