Incidence Rate and Spatial analysis of new pulmonary tuberculosis cases in Yasothon Province 2022–2024
Keywords:
Incidence Rate, Spatial analysis, new pulmonary tuberculosis casesAbstract
Background and Objectives: The incidence of newly diagnosed and relapsed pulmonary tuberculosis (TB) cases has shown an increasing. The integration of Geographic Information Systems (GIS) into spatial analysis provides a valuable tool for identifying high-risk areas and supporting data driven decision making in TB prevention and control. This study aimed to investigate spatial factors associated with new pulmonary TB cases and to analyze the incidence of new pulmonary TB cases in Yasothon Province during the period 2022–2024.
Methods: A retrospective cohort analysis was conducted using data from 1,432 newly diagnosed pulmonary TB patients residing in Yasothon Province who were registered in the national TB reporting system between 1January 2022 and 31 December 2024. Personal, climatic, and environmental variables were analyzed. Spatial association between these variable and TB incidences were assessed using Getis-Ord Gi*, Global Moran’s I, and Anselin Local Moran’s I (LISA). The incidence of new pulmonary TB cases was calculated reported with 95% confidence intervals (95%CI).
Results: The overall incidence rate of new pulmonary TB in Yasothon Province from 2022–2024 was 19.1 per 100,000 population per year (95% CI: 18.05–20.14). Males had a significantly higher incidence rate (27.4 per 100,000; 95% CI: 25.64–29.18) compared to females (11.0 per 100,000; 95% CI: 9.90–12.14). Among males, the highest age-standardized rates (ASRs) were found in Nam Kham Yai Subdistrict (ASR= 57.1, 95% CI: 33.95–80.32), Nai Mueang (ASR=55.8, 95% CI: 40.89–70.77), and Fa Yat (ASR=44.3, 95% CI: 19.08–50.53). For females, the highest ASRs were in Fa Huan (ASR= 27.6, 95% CI: 10.30–44.87), Lao Hai (ASR=24.8, 95% CI: 0.45–49.18), and Pho Sai (ASR= 21.4, 95% CI: 7.76–34.99).
The Getis-Ord Gi* analysis identified significant high clustering areas of new pulmonary TB incidence in Si Than, Ku Chan, Khu Mueang, Phue Hi, and Kho Wang Subdistricts, indicating these areas had significantly higher incidence compared to their surrounding areas. Spatial autocorrelation analysis using Global Moran’s I and Anselin Local Moran’s I (LISA) revealed a statistically significant inverse spatial association between COVID-19 prevalence and TB incidence (Moran’s I = -0.080, p= 0.042), suggesting that areas with higher COVID-19 prevalence tended to have lower TB incidence. However, LISA also identified a high-high cluster in Ku Chan, indicating co-occurrence of high rates of both diseases in this subdistrict.
Moreover, the average PM2.5 concentration showed a statistically significant inverse spatial association with new TB incidence (Moran’s I = -0.108, p<0.001), though LISA indicated a high-high cluster in Ku Chan, implying that areas with high PM2.5 may also report higher TB incidence in certain locations.
Conclusion: This study found that both COVID-19 prevalence and PM2.5 levels were spatially associated with new pulmonary TB incidence. The average annual incidence rate of new TB cases in Yasothon Province (19.1 per 100,000) was lower than the national average, likely due to national data including both new and relapsed cases. The findings support policy-making efforts for targeted screening in high-risk areas and for controlling environmental risk factors. Proactive screening programs and deployment of mobile health services such as Mobile X-ray units are recommended to enhance access in high-incidence communities.
References
กรมควบคุมมลพิษ. (2566). รายงานสถานการณ์และการจัดการปัญหามลพิษทางอากาศและเสียงของประเทศไทย ปี 2566. ค้นเมื่อ 10 มิถุนายน 2568, จาก https://www.pcd.go.th/publication/33068/
กรมควบคุมโรค กระทรวงสาธารณสุข. (2565). แนวทางการควบคมวัณโรคประเทศไทย พ.ศ. 2564 (พิมพ์ครั้งที่ 2). กรุงเทพฯ: อักษรกราฟฟิคแอนด์ดีไซน์.
กรมควบคุมโรค กระทรวงสาธารณสุข. (2566). แนวทางการควบคมวัณโรคประเทศไทย พ.ศ. 2566. กรุงเทพฯ: อักษรกราฟฟิคแอนด์ดีไซน์.
กรมควบคุมโรค กระทรวงสาธารณสุข. (2567). สถานการณ์และการดำเนินงานวัณโรคของประเทศไทย ปี พ.ศ. 2562-2566. กรุงเทพฯ: อักษรกราฟฟิคแอนด์ดีไซน์.
กระทรวงมหาดไทย. (2567). จำนวนประชากรรายอายุ. ค้นเมื่อ 20 กุมภาพันธ์ 2568, จาก https://stat.bora.dopa.go.th/stat/statnew/statMONTH/statmonth/#/mainpage
กองวัณโรค กรมควบคุมโรค กระทรวงสาธารณสุข. (2566). แผนปฏิบัติการระดับชาติ ด้านการต่อต้านวัณโรค ระยะที่ 2 (พ.ศ. 2566-2570). กรุงเทพฯ: อักษรกราฟฟิคแอนด์ดีไซน์.
กองวัณโรค กระทรวงสาธารณสุข. (2567). .ฐานข้อมูลวัณโรคของประเทศไทย (NTIP). ค้นเมื่อ 6 กันยายน 2567, จาก https://ntip-ddc.moph.go.th/uiform/Login.aspx
สำนักงานสาธารณสุขจังหวัดยโสธร. (2566). รายงานประจำปี 2566 สสจ.ยโสธร. ค้นเมื่อ 6 9 มิถุนายน 2567, จาก https://yasothon.moph.go.th/ssjyasothon/FrontEnd/read_pdf-reportyearly.php?id=21
Amsalu, E., Liu, M., Li, Q., Wang, X., Tao, L., Liu, X., et al. (2019). Spatial-temporal analysis of tuberculosis in the geriatric population of China: An analysis based on the Bayesian conditional autoregressive model. Archives of Gerontology and Geriatrics, 83, 328-337.
Berra, T. Z., Ramos, A. C. V., Alves, Y. M., Tavares, R. B. V., Tartaro, A. F., Nascimento, M. C. D., et al. (2022). Impact of COVID-19 on Tuberculosis Indicators in Brazil: A Time Series and Spatial Analysis Study. Tropical Medicine and Infectious Disease, 7(9), 247.
Boyle, P., & Parkin, D.M. (1991). Cancer registration: Principles and methods, Statistical methods for registries. IARC Scientific Publications, (95), 126-158.
Byrne, A. L., Marais, B. J., Mitnick, C. D., Lecca, L., & Marks, G. B. (2014). Tuberculosis and chronic respiratory disease: a systematic review. International Journal of Infectious Diseases, 23, 138-146.
Corbett, E. L., Churchyard, G. J., Clayton, T. C., Williams, B. G., Mulder, D., Hayes, R. J., et al. (2000). HIV infection and silicosis: The impact of two potent risk factors on the incidence of mycobacterial disease in South African miners. AIDS, 14(17), 2759-2768.
Cuboia, N., Reis-Pardal, J., Pfumo-Cuboia, I., Manhiça, I., Mutaquiha, C., Nitrogénio, L., et al. (2024). Spatial distribution and determinants of tuberculosis incidence in Mozambique: A nationwide Bayesian disease mapping study. Retrieved June 13, 2024, from https://www.sciencedirect.com/science/article/pii/S1877584523000692.
Environmental Systems Research Institute. (2023). How hot spot analysis (Getis-Ord Gi) works. Retrieved June 9, 2024, from https://pro.arcgis.com/en/pro-app/3.3/tool-reference/spatial-statistics/h-how-hot-spot-analysis-getis-ord-gispatialstati.htm#:~:text=The%20Hot%20Spot%20Analysis%20tool,the%20context%20of%20neighboring%20features.
Getahun, M., Beyene, D., Mollalign, H., Diriba, G., Tesfaye, E., Yenew, B., et al. (2024). Population structure and spatial distribution of Mycobacterium tuberculosis in Ethiopia. Retrieved June 9, 2024, from https://www.nature.com/articles/s41598-024-59435-3.
Guo, T., Tian, S., Xin, H., Du, J., Cao, X., Feng, B., et al. (2024). Impact of fine particulate matter on latent tuberculosis infection and activetuberculosis in older adults: A population-based multicentre cohort study. Retrieved August 19, 2024, from https://www.tandfonline.com/doi/full/10.1080/22221751.2024.2302852?scroll=top&needAccess=true#abstract.
Gurp, M., Rood, E., Fatima, R., Joshi, P., Verma, S. C., Khan, A. H., et al. (2020). Finding gaps in TB notifications: spatial analysis of geographical patterns of TB notifications, associations with TB program efforts and social determinants of TB risk in Bangladesh, Nepal and Pakistan. Retrieved June 19, 2024, from https://link.springer.com/article/10.1186/s12879-020-05207-z
He, W. C., Ju, K., Gao, Y. M., Zhang, P., Zhang, Y. X., Jiang, Y., et al. (2020). Spatial inequality, characteristics of internal migration, and pulmonary tuberculosis in China, 2011-2017: A spatial analysis. Retrieved August 19, 2024, from https://link.springer.com/article/10.1186/s40249-020-00778-0
Huang, G., Xu, Z., Bai, L., Liu, J., Yu, S., & Yao, H. (2024). Spatiotemporal analysis of tuberculosis in the Hunan Province, China, 2014-2022. Retrieved August 19, 2024, from https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2024.1426503/full
Hwang, S. S., Kang, S., Lee, J. Y., Lee, J. S., Kim, H. J., Han, S. K., et al. (2014). Impact of outdoor air pollution on the incidence of tuberculosis in the Seoul metropolitan area, South Korea. The Korean Journal of Internal Medicine, 29(2), 183-190.
Joob, B., & Wiwanitkit, V. (2019). Incidence of pulmonary tuberculosis and particulate matter 2.5 pollutant level: The association analysis for 2019 air pollution crisis, Bangkok Thailand. Biomedical and Biotechnology Research Journal, 3(2), 126-128.
Kumwichar, P., & Chongsuvivatwong, V. (2023). COVID-19 pneumonia and the subsequent risk of getting active pulmonary tuberculosis: a population-based dynamic cohort study using national insurance claims databases. Retrieved September 19, 2024, from https://www.thelancet.com/action/showPdf?pii=S2589-5370%2823%2900002-0
Li, H., Ge, M., & Zhang, M. (2022). Spatio-temporal distribution of tuberculosis and the effect of environmental factors in China. Retrieved August 19, 2024, from https://link.springer.com/article/10.1186/s12879-022-07539-4
Liu, F., Zhang, Z., Chen, H., & Nie, S. (2020). Associations of ambient air pollutants with regional pulmonary tuberculosis incidence in the central Chinese province of Hubei: a Bayesian spatial-temporal analysis. Retrieved August 19, 2024, from https://ehjournal.biomedcentral.com/articles/10.1186/s12940-020-00604-y?utm.
Luo, D., Wang, L., Zhang, M., Martinez, L., Chen, S., Zhang, Y., et al. (2024). Spatial spillover effect of environmental factors on the tuberculosis occurrence among the elderly: A surveillance analysis for nearly a dozen years in eastern China. BMC Public Health, 24(1), 209.
MacPherson, P., Khundi, M., Nliwasa, M., Choko, A. T., Phiri, V. K., Webb, E. L., et al. (2019). Disparities in access to diagnosis and care in Blantyre, Malawi, identified through enhanced tuberculosis surveillance and spatial analysis. Retrieved June 30, 2024, from https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-019-1260-6.
Madao, E. P., Hermawati, E., Putri, N. A. A., & Makful, M. R. (2024). Evaluating spatial analysis of tuberculosis prevalence to identify priority districts or municipalities that need policy attention in West Java. Retrieved June 9, 2024, from https://journal.ugm.ac.id/v3/BKM/article/view/12160.
Molemans, M., Kayaert, L., Olislagers, Q., Abrahams, S., Berkowitz, N., Mohr-Holland, E., et al. (2024). Neighbourhood factors and tuberculosis incidence in Cape Town: A negative binomial regression and spatial analysis. Tropical Medicine & International Health, 29(7), 599-611.
Pai, M., Behr, M. A., Dowdy, D., Dheda, K., Divangahi, M., Boehme, C. C., et al. (2016). Tuberculosis. Retrieved August 19, 2024, from https://www.nature.com/articles/nrdp201676
Pan, S., Chen, L., Xin, X., Li, S., Zhang, Y., Chen, Y., et al. (2024). Spatiotemporal analysis and seasonality of tuberculosis in Pudong New Area of Shanghai, China, 2014-2023. BMC Infectious Diseases, 24(1), 761.
Shimeles, E., Enquselassie, F., Aseffa, A., Tilahun, M., Mekonen, A., Wondimagegn, G., et al. (2019). Risk factors for tuberculosis: A case–control study in Addis Ababa, Ethiopia. Retrieved August 19, 2024, from https://journals.plos.org/plosone/article?id=10.1371/journal.pone.02 14235.
Torres, M., Carranza, C., Sarkar, S., Gonzalez, Y., Osornio Vargas, A., Black, K., et al. (2019). Urban airborne particle exposure impairs human lung and blood Mycobacterium tuberculosis immunity. Thorax, 74(7), 675-683.
Virojskulchai, T., Dumavibhat, N., Udol, K., Phatharodom, P., & Chansaengpetch, S. (2024). Pulmonary Tuberculosis in Healthcare Workers at a University Hospital: A Significant Burden of Subclinical Disease. The Bangkok Medical Journal, 20(2), 69-69.
Yang, J., Zhang, M., Chen, Y., Ma, L., Yadikaer, R., Lu, Y., et al. (2020). A study on the relationship between air pollution and pulmonary tuberculosis based on the general additive model in Wulumuqi, China. International Journal of Infectious Diseases, 96, 42-47.
Zaidi, S. M. A., Jamal, W. Z., Mergenthaler, C., Azeemi, K. S., Van Den Berge, N., Creswell, J., et al. (2023). A spatial analysis of TB cases and abnormal X-rays detected through active case-finding in Karachi, Pakistan. Retrieved August 19, 2024, from https://www.nature.com/articles/s41598-023-28529-9
Zhu, S., Xia, L., Wu, J., Chen, S., Chen, F., Zeng, F., et al. (2018). Ambient air pollutants are associated with newly diagnosed tuberculosis: A time-series study in Chengdu, China. The Science of the Total Environment, 631-632, 47–55.
Zyl Smit, R. N., Pai, M., Yew, W. W., Leung, C. C., Zumla, A., Bateman, E. D., et al. (2010). Global lung health: the colliding epidemics of tuberculosis, tobacco smoking, HIV and COPD. Retrieved August 19, 2024, from https://pubmed.ncbi.nlm.nih.gov/20044459/
World Health Organization. (2023a). Global tuberculosis report 2023. Retrieved June 30, 2024, from https://www.who.int/publications/i/item/9789240083851.
World Health Organization. (2023b). Coronavirus disease (COVID-19): Tuberculosis. Retrieved August 16, 2024, from https://www.who.int/news-room/questions-and-answers/item/coronavirus-disease-covid-19-tuberculosis
World Health Organization. (2023c). Tuberculosis. Retrieved August 16, 2024, from https://www.who.int/health-topics/tuberculosis#tab=tab_2
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