Spatio-temporal analysis of tuberculosis incidence in North Aceh District, Indonesia 2019-2021

Submitted: 28 August 2022
Accepted: 30 October 2022
Published: 29 November 2022
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Tuberculosis (TB) infection continues to present as a leading cause of morbidity and mortality in North Aceh District, Aceh Province, Indonesia. Local TB spatial risk factors have been investigated but space-time clusters of TB in the district have not yet been the subject of study. To that end, research was undertaken to detect clusters of TB incidence during 2019-2021 in this district. First, the office of each of the 27 sub-districts wasgeocoded by collecting data of their geographical coordinates. Then, a retrospective space-time scan statistics analysis based on population data and annual TB incidence was performed using SaTScan TM v9.4.4. The Poisson model was used to identify the areas at high risk of TB and the clusters found were ranked by their likelihood ratio (LLR), with the significance level set at 0.05.There were 2,266 TB cases reported in North Aceh District and the annualized average incidence was 122.91 per 100,000 population. The SaTScan analysis identified that there were three most like clusters and ten secondary clusters, while Morans’Ishowed that there was spatial autocorrelation of TB in the district. The sub-district of GeureudongPase was consistently the location of most likely clusters. The indicators showed that there were significant differences between TB data before the COVID-19 pandemic and those found during the study period. These findings may assist health authorities to improve the TB preventive strategies and develop public health interventions, with special reference to the areas where the clusters were found.

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Citations

Aditama W, Sitepu FY, Depari E, 2020. Having Contact History with Tb Active Cases and Malnutrition as Risk Factors of TB Incidence: A Cross-Sectional Study in North Sumatera, Indonesia. Malaysian J Public Health Med 20:192–98. DOI: https://doi.org/10.37268/mjphm/vol.20/no.1/art.482
Aditama W, Sitepu FY, Rahmat Saputra R, 2019. Relationship between Physical Condition of House Environment and the Incidence of Pulmonary Tuberculosis, Aceh, Indonesia. Int J Sci Healthc Res 4:227–31.
ArcGIS Pro., 2022. How Spatial Autocorrelation (Global Moran’s I) Works. Accessed: July 1, 2022. Available from: https://pro.arcgis.com/en/pro-app/2.8/tool-reference/spatial-statistics/h-how-spatial-autocorrelation-moran-s-i-spatial-st.htm
Caren GJ, Iskandar D, Pitaloka DEA, Abdulah R, Suwantika AA, 2022. COVID-19 Pandemic disruption on the management of tuberculosis treatment in Indonesia. J Multidiscip Healthc15:175–83. DOI: https://doi.org/10.2147/JMDH.S341130
Chan G, Triasih R, Nababan B, du Cros P, Wilks N, Main S, Huang GKL, Lin D, Graham SM, Majumdar SS, Bakker M, Khan A, Khan FA, Dwihardiani B. 2021. Adapting active case-finding for TB during the COVID-19 pandemic in Yogyakarta, Indonesia. Public Health Action 11:41–49. DOI: https://doi.org/10.5588/pha.20.0071
Dismer AM, Charles M, Dear N, Louis-Jean JM, Barthelemy N, Richard M, Morose W, Fitter DL, 2021. Identification of TB Space-Time Clusters and Hotspots in Ouest Department, Haiti, 2011-2016. Public Health Action 11:101–7. DOI: https://doi.org/10.5588/pha.20.0085
Endy TP, Nisalak A, Chunsuttiwat S, Libraty DH, Green S, Rothman AL, Vaughn DW, Ennis FA, 2002. Spatial and Temporal Circulation of Dengue Virus Serotypes : A Prospective Study of Primary School Children in Kamphaeng Phet, Thailand. Am J Epidemiol 156:52–59. DOI: https://doi.org/10.1093/aje/kwf006
GeoDa, 2020. An introduction to spatial data analysis. GeoDa Center. Accessed: April 20, 2022. Available from: https://geodacenter.github.io/workbook/5a_global_auto/lab5a.html
GeoDa, 2022. Download GeoDa Software.GeoDa Center. Accessed: April 20, 2022. Available from: https://geodacenter.github.io/download.html
Gwitira I, Karumazondo N, Shekede MD, Sandy C, Siziba N, Chirenda J. 2021. Spatial patterns of pulmonary tuberculosis (TB) cases in Zimbabwe from 2015 to 2018. PLoS ONE 16:1–15. DOI: https://doi.org/10.1371/journal.pone.0249523
Im C, Kim Y, 2021. Spatial pattern of tuberculosis (TB) and related socio-environmental factors in South Korea, 2008-2016. PLoS ONE 16:2008–16. DOI: https://doi.org/10.1371/journal.pone.0255727
Kulldorf M, 2005. SaTScan.SaTScanTM - Software for the Spatial, temporal, and space-time scan statistics. Available from: https://www.satscan.org/
Laghari M, Sulaiman SAS, Khan AH, Talpur BA, Bhatti Z, Memon N, 2019. Contact screening and risk factors for TB among the household contact of children with active TB: A way to find source case and new TB cases. BMC Public Health 19:1–10. DOI: https://doi.org/10.1186/s12889-019-7597-0
Lestari T, Kamaludin K, Lowbridge C, Kenangalem E, Poespoprodjo JR, Graham SM, Ralph AL, 2022. Impacts of tuberculosis services strengthening and the COVID-19 pandemic on case detection and treatment outcomes in Mimika District, Papua, Indonesia: 2014–2021. PLOS Global Public Health 2:e0001114. DOI: https://doi.org/10.1371/journal.pgph.0001114
McAllister S, Wiem Lestari B, Sujatmiko B, Siregar A, Sihaloho ED, Fathania D, Dewi NF, Koesoemadinata RC, Hill PC, Alisjahbana B, 2017. Feasibility of two active case finding approaches for detection of tuberculosis in Bandung City, Indonesia. Public Health Action 7:242–46. DOI: https://doi.org/10.5588/pha.17.0026
Ministry of Health of Republic of Indonesia, 2020. National strategy for combating tuberculosis in Indonesia 2020-2024.
North Aceh Bureau of Statistics, 2020a. Geureduong Pase Sub-District in Figure 2019. Vol. 59.
North Aceh Bureau of Statistics, 2020b. North Aceh District in Figures. North Aceh.
Noykhovich E, Mookherji S, Roess A, 2019. The risk of tuberculosis among populations living in slum settings: a systematic review and meta-analysis. J Urban Health 96:262–75. DOI: https://doi.org/10.1007/s11524-018-0319-6
Provincial Health Office of Aceh, 2021. Health profile of Aceh province.
Richie R, 2022. Spatial autocorrelation with GeoDa. Mobile statistik. Accessed: July 1, 2022. Available from: https://www.mobilestatistik.com/autokorelasi-spasial-dengan-geoda/
Ross JM, Xie Y, Wang Y, Collins JK, Horst C, Doody JB, Lindstedt P, Ledesma JR, Shapiro AE, Hay PSI, Kyu HH, Flaxman AD, 2021. Estimating the population at high risk for tuberculosis through household exposure in high-incidence countries: a model-based analysis. eClin Med 42:101206. DOI: https://doi.org/10.1016/j.eclinm.2021.101206
Statistics How to, 2022. Moran’s I: Definition, examples. Statistics How to. Accessed: July 1, 2022. Available from: https://www.statisticshowto.com/morans-i/
The World Bank, 2022a. Incidence of Tuberculosis (per 100,000 People) - Indonesia. World Health Organization, Global Tuberculosis Report. Accessed: June 30, 2022 (https://data.worldbank.org/indicator/SH.TBS.INCD?locations=ID).
The World Bank, 2022b. Tuberculosis Case Detection Rate (%, All Forms) - Indonesia.World Health Organization, Global Tuberculosis Report. Accessed: June 30, 2022 (https://data.worldbank.org/indicator/SH.TBS.DTEC.ZS?locations=ID).
Utomo B, Chan CK, Mertaniasih NM, Soedarsono S, Fauziyah S, Sucipto TH, Aquaresta F, Eljatin DS, Adnyana IMDM, 2022. Comparison Epidemiology between Tuberculosis and COVID-19 in East Java Province, Indonesia: An Analysis of Regional Surveillance Data in 2020. Trop Med Infect Dis 7:1–16. DOI: https://doi.org/10.3390/tropicalmed7060083
Wang Q, Guo L, Wang J, Zhang L, Zhu W, Yuan Y, Li J, 2019. Spatial Distribution of Tuberculosis and Its Socioeconomic Influencing Factors in Mainland China 2013–2016. Trop Med Int Health 24:1104–13. DOI: https://doi.org/10.1111/tmi.13289
WangT, Xue F, Chen Y, Ma Y, Liu Y, 2012. The spatial epidemiology of tuberculosis in Linyi. BMC Public Health 12:885. DOI: https://doi.org/10.1186/1471-2458-12-885
Yue Y, Sun J, Liu X, Ren D, Liu Q, Xiao X, Lu L, 2018. Spatial Analysis of dengue fever and exploration of its environmental and socio-economic risk factors using ordinary least squares: a case study in five districts of Guangzhou City. Int J Infect Dis 75:39–48. DOI: https://doi.org/10.1016/j.ijid.2018.07.023
Zulfikar Z, Sitepu FY, Depari E, Debataradja B, 2020. Space time clusters of dengue fever in Medan Municipality, North Sumatera, Indonesia. Malaysian J Public Health Med 20:37–42. DOI: https://doi.org/10.37268/mjphm/vol.20/no.2/art.543

How to Cite

Fahdhienie, F., & Sitepu, F. Y. (2022). Spatio-temporal analysis of tuberculosis incidence in North Aceh District, Indonesia 2019-2021. Geospatial Health, 17(2). https://doi.org/10.4081/gh.2022.1148

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