Abstract
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.
Subject
Health Policy,Geography, Planning and Development,Health (social science),Medicine (miscellaneous)
Reference30 articles.
1. 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.
2. 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.
3. 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
4. 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.
5. 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.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献