Spatio-Temporal Patterns of Tuberculosis in Makassar, South Sulawesi Indonesia
DOI:
https://doi.org/10.55489/njcm.161020255644Keywords:
Tuberculosis, Spatial Analysis, Hotspot analysis, Spatio-temporalAbstract
Background: Tuberculosis (TB) remains a major global health challenge, with Indonesia ranking among the top three countries with the highest TB burden in 2019. Information about the distribution of the tuberculosis (TB) incidence rate over time and space is necessary for the effective control of the disease. This study aimed to examine the spatio-temporal trends of tuberculosis (TB) incidence rates in Makassar, South Sulawesi.
Methods: This Ecological study utilized aggregated TB cases data in Makassar City, from the Indonesian National Tuberculosis Information System (SITB). between January-December 2022 (3977 patients). Kulldorff’s space-time scan statistic, implemented using SaTScan, was applied to identify clusters of TB. In addition, Anselin’s Local Moran’s I, conducted in GeoDa, was utilized to further characterize tuberculosis hotspots and cold spots.
Results: The greatest Tuberculosis incidence rate was recorded in middle west area in Makassar during 2022. Kulldorf’s space-time scan statistic identified the most probable cluster in 60 villages in the mid-western region of Makassar from July to December 2022, with a relative risk (RR) of 1.50 (p-value <0.001) and secondary cluster (16 villages) was identified in the southern region of Makassar, with a RR of 1.41 (p = 0.0015). Some high-trend TB statistically significant clusters were found in the same places.
Conclusions: The TB cluster was located at Middle west Makassar. Prioritizing these clusters for resource allocation could lead to more successful control and prevention of TB. Future studies should examine socioeconomic and environmental determinants to better explain TB clustering and guide comprehensive interventions.
References
World health Organization. Global Tuberculosis Report 2020. Geneva: World health Organization; 2020. Available from: https://www.who.int/publications/i/item/9789240013131 [Accessed on Sep 4, 2025]
Chakaya J, Petersen E, Nantanda R, Mungai BN, Migliori GB, Amanullah F, et al. The WHO Global Tuberculosis 2021 Report - not so good news and turning the tide back to End TB. Int J Infect Dis. 2022 Nov;124:S26-9. DOI: https://doi.org/10.1016/j.ijid.2022.03.011 PMid:35321845 PMCid:PMC8934249
Kiani B, Raouf Rahmati A, Bergquist R, Hashtarkhani S, Firouraghi N, Bagheri N, et al. Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018. BMC Public Health. 2021 Jun 7;21:1093. DOI: https://doi.org/10.1186/s12889-021-11157-1 PMid:34098917 PMCid:PMC8186231
Harimurti P. Tuberculosis in Indonesia : Epidemic Projections and Opportunities to Accelerate Control : Findings from an Optima TB Analysis [Internet]. Washington, DC: World Bank Group; 2020. Available from: https://policycommons.net/artifacts/1250687/tuberculosis-in-indonesia/1811042/
Harta Wedari NLP, Pranata IWA, Budayanti NNS, Sukrama IDM. Tuberculosis cases comparison in developed country (Australia) and developing country (Indonesia): a comprehensive review from clinical, epidemiological, and microbiological aspects. Intisari Sains Medis. 2021 Jun 22;12(2):421-426. DOI: https://doi.org/10.15562/ism.v12i2.1034
Kustanto A. The role of socioeconomic and environmental factors on the number of tuberculosis cases in Indonesia. J Ekon Pembang. 2020 Dec 5;18(2):129-146. DOI: https://doi.org/10.29259/jep.v18i2.12553
Alisjahbana B, Koesoemadinata RC, Hadisoemarto PF, Lestari BW, Hartati S, Chaidir L, et al. Are neighbourhoods of tuberculosis cases a high-risk population for active intervention? A protocol for tuberculosis active case finding. Hasnain SE, editor. PLOS ONE. 2021 Aug 13;16(8):1-9. DOI: https://doi.org/10.1371/journal.pone.0256043 PMid:34388190 PMCid:PMC8362935
Amelia AR, Haeruddin, Batara AS, Darlis I. Spatial Analysis of Pulmonary Tuberculosis Transmission Based on Social, Economic, Cultural Interactions in the Slum Areas the City of Makassar. Media Kesehat Masy Indones. 2020;16(4):530-541.
Makassar City Health Office. Data on the Number of Tuberculosis Cases in Makassar City. Makassar: Makassar City Health Office; 2022.
Stang, Mallongi A, Dwinata I, Sumarni. Risk factors of Lung Tuberculosis Occurrence in the working area of Kaluku Bodoa Health Center Makassar city. Medico-Leg Update. 2020;20(3):714-718.
Nguipdop-Djomo P, Rodrigues LC, Smith PG, Abubakar I, Mangtani P. Drug misuse, tobacco smoking, alcohol and other social determinants of tuberculosis in UK-born adults in England: a community-based case-control study. Sci Rep. 2020;10(1):1-10s. DOI: https://doi.org/10.1038/s41598-020-62667-8 PMid:32221405 PMCid:PMC7101386
Shartova N, Korennoy F, Makhazova S. Spatial patterns of tuberculosis in Russia in the context of social determinants. Spat Spatio-Temporal Epidemiol. 2023;45:1-12. DOI: https://doi.org/10.1016/j.sste.2023.100580 PMid:37301595
Saputra FF, Wahyuni CU, Isfandiari MA. Spatial Modeling of Environmental-Based Risk Factors of Tuberculosis in Bali Province: An Ecological Study. J Berk Epidemiol. 2020;8(1):26-34. DOI: https://doi.org/10.20473/jbe.V8I12020.26-34
Auld SC, Shah N, Cohen T, Martinson N, Gandhi NR. Where is tuberculosis transmission happening? Insights from the literature, new tools to study transmission and implications for the elimination of tuberculosis. Respirology. 2018;23(9):807-817. DOI: https://doi.org/10.1111/resp.13333 PMid:29869818 PMCid:PMC6281783
Zetola NM, Moonan PK, Click ES, Oeltmann JE, Basotli J, Wen X, et al. Population-Based Geospatial and Molecular Epidemiologic Study of Tuberculosis Transmission Dynamics, Botswana, 2012-2016. Emerg Infect Dis. 2021;27(3):835-844. DOI: https://doi.org/10.3201/eid2703.203840 PMid:33622470 PMCid:PMC7920683
Li H, Ge M, Zhang M. Spatio-temporal distribution of tuberculosis and the effects of environmental factors in China. BMC Infect Dis. 2022;22(1):1-13. DOI: https://doi.org/10.1186/s12879-022-07539-4 PMid:35733132 PMCid:PMC9215012
Rao H, Shi X, Zhang X. Using the Kulldorff's scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009-2016. BMC Infect Dis. 2017;17(1):1-11. DOI: https://doi.org/10.1186/s12879-017-2643-y PMid:28826399 PMCid:PMC5563899
Kulldorff M. SaTScanTM v10 Software for the Spatial and Space-Time Scan Statistics. Boston USA: Information Management Services Inc; 2013.
Anselin L. Local Indicators of Spatial Association LISA. Geogr Anal. 1995;27(2):93-115. DOI: https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Chirenda J, Gwitira I, Warren RM, Sampson SL, Murwira A, Masimirembwa C, et al. Spatial distribution of Mycobacterium Tuberculosis in metropolitan Harare, Zimbabwe. PloS One. 2020;15(4):e0231637. DOI: https://doi.org/10.1371/journal.pone.0231637 PMid:32315335 PMCid:PMC7173793
Zainuddin AA, Soma AS, Kasim MF, Ramadany S, Djaharuddin I. Spatial Distribution of Drug-Resistant Tuberculosisin Makassar City, South Sulawesi Province, Indonesia. J Kesehat Masy. 2022;18(2):296-302. DOI: https://doi.org/10.15294/kemas.v18i2.36436
Rahman SA, Salmah AU, Dwinata I, Mallongi A. Risk Prediction Model of Lung Tuberculosis Using Spatial Approach in the Coastal Area of Makassar City. Indian J Public Health Res Dev. 2019;10(1):1220. DOI: https://doi.org/10.5958/0976-5506.2019.00222.5
BPS. Indonesia Statistics 2022. Jakarta: Statistics Indonesia; 2022.
Zille AI, Werneck GL, Luiz RR, Conde MB. Social determinants of pulmonary tuberculosis in Brazil: an ecological study. BMC Pulm Med. 2019;19(1):87. DOI: https://doi.org/10.1186/s12890-019-0855-1 PMid:31068155 PMCid:PMC6507035
Chinpong K, Thavornwattana K, Armatrmontree P, Chienwichai P, Lawpoolsri S, Silachamroon U, et al. Spatiotemporal Epidemiology of Tuberculosis in Thailand from 2011 to 2020. Biology. 2022;11(5):1-14. DOI: https://doi.org/10.3390/biology11050755 PMid:35625483 PMCid:PMC9138531
Im C, Kim Y. Spatial pattern of tuberculosis (TB) and related socio-environmental factors in South Korea, 2008-2016. PloS One. 2021 Aug;16(8):1-14. DOI: https://doi.org/10.1371/journal.pone.0255727 PMid:34352032 PMCid:PMC8341643
Gwitira I, Karumazondo N, Shekede MD, Sandy C, Siziba N, Chirenda J. Spatial patterns of pulmonary tuberculosis (TB) cases in Zimbabwe from 2015 to 2018. PloS One. 2021;16(4):1-15. DOI: https://doi.org/10.1371/journal.pone.0249523 PMid:33831058 PMCid:PMC8031317
Berra TZ, Queiroz AAR de, Yamamura M, Arroyo LH, Garcia MC da C, Popolin MP, et al. Spatial risk of tuberculosis mortality and social vulnerability in Northeast Brazil. Rev Soc Bras Med Trop. 2017;50(5):693-697. DOI: https://doi.org/10.1590/0037-8682-0160-2017 PMid:29160520
Nanque AR, Ramos ACV, Moura HSD, Berra TZ, Tavares RBV, Monroe AA, et al. Spatial and temporal analysis of tuberculosis incidence in Guinea-Bissau, 2018 to 2020. Rev Bras Enferm. 2023;76(4):1-8. DOI: https://doi.org/10.1590/0034-7167-2022-0481
Chen X, Emam M, Zhang L, Rifhat R, Zhang L, Zheng Y. Analysis of spatial characteristics and geographic weighted regression of tuberculosis prevalence in Kashgar, China. Prev Med Rep. 2023 Oct 1;35:102362. DOI: https://doi.org/10.1016/j.pmedr.2023.102362 PMid:37584062 PMCid:PMC10424202
Moraga P. Geospatial health data: modeling and visualization with R-INLA and Shiny. Boca Raton: CRC Press/Taylor & Francis Group; 2020. 27-31 p. (Chapman & Hall/CRC biostatistics series). DOI: https://doi.org/10.1201/9780429341823-3
Moraga P, Dean C, Inoue J, Morawiecki P, Noureen SR, Wang F. Bayesian spatial modelling of geostatistical data using INLA and SPDE methods: A case study predicting malaria risk in Mozambique. Spat Spatio-Temporal Epidemiol. 2021;39:100440. DOI: https://doi.org/10.1016/j.sste.2021.100440 PMid:34774255
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