Spatial Analysis of Identifying the Association between Risk Factors and Tuberculosis Cases: A Review

Authors

  • Nur Adibah Mohidem Public Health Unit, Department of Primary Health Care, Faculty of Medicine and Health Sciences, Universiti Sains Islam Malaysia, Bandar Baru Nilai, 71800 Nilai, Negeri Sembilan, Malaysia
  • Malina Osman Department of Community Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • Zailina Hashim Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • Farrah Melissa Muharam Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • Saliza Mohd Elias Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • Aminuddin Ma'pol Dungun Health District Office, Jalan Yahya Ahmad, 23000, Dungun, Terengganu, Malaysia

DOI:

https://doi.org/10.37134/ejsmt.vol12.1.4.2025

Keywords:

risk factor, trend, spatial, tuberculosis

Abstract

Tuberculosis (TB) transmission frequently occurs in a household or group within a population, resulting in a variety of spatial patterns. However, the apparent spatial clustering of TB may represent the ongoing transmission or co-location of associated risk factors, which can vary significantly based on the type of data available, the analysis methods used, and the dynamics of the underlying population. This study aims to review the spatial analyses used for monitoring the trends involving and associations between risk factors and TB cases by applying the concept of spatial epidemiology. The role of the Geographic Information System in spatial epidemiology is discussed. Previous studies involving spatial analysis of TB cases - which include kriging, spatial autocorrelation, kernel density estimation, hotspot analysis, and regression analysis - are reviewed. The type of analysis was chosen based on the purpose of each study, which could explain the role of the transmission to reactivation of the disease as a driver of TB spatial distribution. In diverse situations, a number of different spatial analysis techniques were used, with all the studies demonstrating significant heterogeneity in terms of the spatial distribution of TB. Future research is needed to determine the best methods to use in different situations and, where possible, consider unreported cases when using notification data. A combination of genotypic, molecular, and geospatial approaches to examine epidemiologically related cases could improve TB control and provide significant contributions to the current knowledge.

Downloads

Download data is not yet available.

Author Biography

Nur Adibah Mohidem, Public Health Unit, Department of Primary Health Care, Faculty of Medicine and Health Sciences, Universiti Sains Islam Malaysia, Bandar Baru Nilai, 71800 Nilai, Negeri Sembilan, Malaysia

Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia

References

Alsayed, S. S., & Gunosewoyo, H. (2023). Tuberculosis: pathogenesis, current treatment regimens and new drug targets. International journal of molecular sciences, 24(6), 5202.

Cioboata, R., Biciusca, V., Olteanu, M., & Vasile, C. M. (2023). COVID-19 and Tuberculosis: Unveiling the Dual Threat and Shared Solutions Perspective. Journal of Clinical Medicine, 12(14), 4784.

Sadowski, C., Belknap, R., Holland, D. P., Moro, R. N., Chen, M. P., Wright, A., ... & Gandhi, N. R. (2023). Symptoms and systemic drug reactions in persons receiving weekly Rifapentine plus isoniazid (3HP) treatment for latent Tuberculosis infection. Clinical Infectious Diseases, 76(12), 2090-2097.

World Health Organization (2019). Global tuberculosis report 2019: executive summary, 1- 6.

Cusack, D. A. (2020). COVID-19 pandemic: Coroner's database of death inquiries with clinical epidemiology and total and excess mortality analyses in the District of Kildare March to June 2020. Journal of Forensic and Legal Medicine, 76, 102072.

Hansen, M. A., Samannodi, M. S., Castelblanco, R. L., & Hasbun, R. (2020). Clinical epidemiology, risk factors, and outcomes of encephalitis in older adults. Clinical Infectious Diseases, 70(11), 2377-2385.

de Jezus, S. V., do Prado, T. N., Arcêncio, R. A., Mascarello, K. C., Sales, C. M. M., Fauth, M. M., Terena, N. F. M., Amorim, R. F., Araujo, V. M. S., Aragón, M. A. L. & Maciel, E. L. N. (2021). Factors associated with latent tuberculosis among international migrants in Brazil: a cross-sectional study (2020). BMC infectious diseases, 21(1), 1-9.

Mirzazadeh, A., Kahn, J. G., Haddad, M. B., Hill, A. N., Marks, S. M., Readhead, A., Barry, P. M., Flood, J. Mermin, J. H. & Shete, P. B. (2021). State-level prevalence estimates of latent tuberculosis infection in the United States by medical risk factors, demographic characteristics and nativity. PloS one, 16(4), e0249012.

Taye, H., Alemu, K., Mihret, A., Wood, J. L., Shkedy, Z., Berg, S., & Aseffa, A. (2021). Factors associated with localization of tuberculosis disease among patients in a high burden country: A health facility-based comparative study in Ethiopia. Journal of Clinical Tuberculosis and Other Mycobacterial Diseases, 23, 100231.

Fakhruzzaman, M. N. N., Abidin, N. Z., Aziz, Z. A., Lim, W. F., Richard, J. J., Noorliza, M. N., Zaki, M. S., et al. (2019). Diversified lineages and drug-resistance profiles of clinical isolates of Mycobacterium tuberculosis complex in Malaysia. International Journal of mycobacteriology, 8(4), 320.

Jani, J., Bakar, S. F. A., Mustapha, Z. A., Ling, C. K., Teo, R., & Ahmed, K. (2020). Identification and characterization of Mycobacterium tuberculosis Beijing genotype strain SBH163, Isolated in Sabah, Malaysia. Microbiology Resource Announcements, 9(2), e01322-19.

Tan, J. L., Simbun, A., Chan, K. G., & Ngeow, Y. F. (2020). Genome sequence analysis of multidrug-resistant Mycobacterium tuberculosis from Malaysia. Scientific Data, 7(1), 1-4.

Du, C. R., Wang, S. C., Yu, M. C., Chiu, T. F., Wang, J. Y., Chuang, P. C., ... & Fang, C. T. (2020). Effect of ventilation improvement during a tuberculosis outbreak in underventilated university buildings. Indoor air, 30(3), 422-432.

Atiq, M., Dosch, K., Miller, A., & Sudhagoni, R. G. (2020). Spatial Epidemiology of Pancreatic Cancer in South Dakota. Journal of Gastrointestinal Cancer, 51(1), 144-151.

Corsi, D. J. (2020). Spatial Epidemiology of Diabetes and Tuberculosis in India. JAMA Network Open, 3(5), e203892-e203892.

Qiao, M., & Huang, B. (2023). COVID-19 spread prediction using socio-demographic and mobility-related data. Cities, 138, 104360.

Zhang, L., Wei, L., & Fang, Y. (2024). Spatial–temporal distribution patterns and influencing factors analysis of comorbidity prevalence of chronic diseases among middle-aged and elderly people in China: focusing on exposure to ambient fine particulate matter (PM2. 5). BMC Public Health, 24(1), 550.

Zhou, C., Li, T., Du, J., Yin, D., Li, X., & Li, S. (2024). Toward tuberculosis elimination by understanding epidemiologic characteristics and risk factors in Hainan Province, China. Infectious Diseases of Poverty, 13(1), 20.

Teibo, T. K. A., Andrade, R. L. D. 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), 1586.

Lan, Y., & Delmelle, E. (2023). Space-time cluster detection techniques for infectious diseases: A systematic review. Spatial and Spatio-temporal Epidemiology, 44, 100563.

Siddik, M. S. M., Ahmed, T. E., Awad Ahmed, F. R., Mokhtar, R. A., Ali, E. S., & Saeed, R. A. (2023). Development of Health Digital GIS Map for Tuberculosis Disease Distribution Analysis in Sudan. Journal of Healthcare Engineering, 2023.

Walter, S. D. (2000). Disease mapping: A historical perspective. Spatial epidemiology: Methods and applications, 223-239.

Roquette, R., Painho, M., & Nunes, B. (2017). Spatial epidemiology of cancer. Geospatial health, 12(1), 1- 12.

Meyer, S., Held, L., & Höhle, M. (2014). Spatio-temporal analysis of epidemic phenomena using the R package surveillance. arXiv preprint arXiv:1411.0416.

Koch, T. (2004). The map as intent: Variations on the theme of John Snow. Cartographica. The International Journal for Geographic Information and Geovisualization, 39(4), 1-14.

Bingham, S. A., Luben, R., Welch, A., Wareham, N., Khaw, K. T., & Day, N. (2003). Are imprecise methods obscuring a relation between fat and breast cancer?. The Lancet, 362(9379), 212-214.

Britain, G., & Farr, W. (1852). Report on the mortality of cholera in England, 1848-1849. W. Clowes.

Smith, A. M., Stull, J. W., Evason, M. D., Weese, J. S., Wittum, T. E., Szlosek, D., & Arruda, A. G. (2021). Investigation of spatio‐temporal clusters of positive leptospirosis polymerase chain reaction test results in dogs in the United States, 2009 to 2016. Journal of Veterinary Internal Medicine, 35(3), 1355-1360.

Snow, J. (1855). On the mode of communication of cholera. John Churchill.

Weng, D., Chen, R., Deng, Z., Wu, F., Chen, J., & Wu, Y. (2018). Srvis: Towards better spatial integration in ranking visualization. IEEE Transactions on Visualization and Computer Graphics, 25(1), 459-469.

Elliott, P., & Wartenberg, D. (2004). Spatial Epidemiology : Current Approaches and Future Challenges. Environmental Health Perspectives, 112(9), 998–1006.

Diez-Roux, A. V. (2007). Neighborhoods and health: Where are we and where do we go from here?. Revue d’Epidemiologie et de Sante Publique, 55(1), 13-21.

Kulldorff, M. (1999). Geographic information systems (GIS) and community health: some statistical issues. Journal of Public Health Management and Practice, 5(2), 100-106.

Elliott, P., & Wartenberg, D. (2004). Spatial epidemiology: current approaches and future challenges. Environmental health perspectives, 112(9), 998-1006.

Gatrell, A. C., & Löytönen, M. (1997). GIS and health. Taylor and Francis.

Langford, I. H., & Bentham, G. (1996). Regional variations in mortality rates in England and Wales: an analysis using multi-level modelling. Social Science and Medicine, 42(6), 897-908.

Manda, S., Haushona, N., & Bergquist, R. (2020). A scoping review of spatial analysis approaches using health survey data in Sub-Saharan Africa. International Journal of Environmental Research and Public Health, 17(9), 1-20.

Huang, K., Ding, K., Yang, X. J., Hu, C. Y., Jiang, W., Hua, X. G., Zhang, X. J., et al. (2020). Association between short-term exposure to ambient air pollutants and the risk of tuberculosis outpatient visits: A time-series study in Hefei, China. Environmental Research, 184, 109343.

Li, X. X., Ren, Z. P., Wang, L. X., Zhang, H., Jiang, S. W., Chen, J. X., ... & Zhou, X. N. (2016). Co-endemicity of pulmonary tuberculosis and intestinal helminth infection in the people’s Republic of China. PLoS neglected tropical diseases, 10(4), e0004580.

Bonell, A., Contamin, L., Thai, P. Q., Thuy, H. T. T., van Doorn, H. R., White, R., Choisy, M., et al. (2020). Does sunlight drive seasonality of TB in Vietnam? A retrospective environmental ecological study of tuberculosis seasonality in Vietnam from 2010 to 2015. BMC infectious diseases, 20(1), 1-11.

Tadesse, S., Enqueselassie, F., & Gebreyesus, S. H. (2018). Estimating the spatial risk of tuberculosis distribution in Gurage zone, southern Ethiopia: a geostatistical kriging approach. BMC Public Health, 18(1), 1-10.

Shojaei, S. R. H., Waghei, Y., & Mohammadzadeh, M. (2018). Geostatistical analysis of disease data: a case study of tuberculosis incidence in Iran. Journal of Applied Statistics, 45(8), 1476-1483.

Hassan, H., Shohaimi, S., & Hashim, N. R. (2012). Risk mapping of dengue in Selangor and Kuala Lumpur, Malaysia. Geospatial Health, 7(1), 21-25.

Lepot, M., Aubin, J. B., & Clemens, F. H. (2017). Interpolation in time series: An introductive overview of existing methods, their performance criteria and uncertainty assessment. Water, 9(10), 796.

Okunlola, O. A., Alobid, M., Olubusoye, O. E., Ayinde, K., Lukman, A. F., & Szűcs, I. (2021). Spatial regression and geostatistics discourse with empirical application to precipitation data in Nigeria. Scientific Reports, 11(1), 16848.

Koutsos, T. M., Menexes, G. C., Eleftherohorinos, I. G., & Alexandridis, T. K. (2023). Using Block Kriging as a Spatial Smooth Interpolator to Address Missing Values and Reduce Variability in Maize Field Yield Data. Agronomy, 13(7), 1685.

Hassim, M., Yuzir, A., Razali, M. N., Ros, F. C., Chow, M. F., & Othman, F. (2020). Comparison of Rainfall Interpolation Methods in Langat River Basin. In IOP Conference Series: Earth and Environmental Science, 479(1), 012018.

Etherington, T. R. (2020). Discrete natural neighbour interpolation with uncertainty using cross-validation error-distance fields. PeerJ Computer Science, 6, e282.

AlQadi, H., Bani-Yaghoub, M., Balakumar, S., Wu, S., & Francisco, A. (2021). Assessment of retrospective COVID-19 spatial clusters with respect to demographic factors: Case Study of Kansas City, Missouri, United States. International Journal of Environmental Research and Public Health, 18(21), 11496.

Damasceno, D. M., da Paz, W. S., de Souza, C. D. F., Dos Santos, A. D., & Bezerra‐Santos, M. (2021). High‐risk transmission clusters of leprosy in an endemic area in the Northeastern Brazil: A retrospective spatiotemporal modelling (2001–2019). Tropical Medicine & International Health.

Smith, A. M., Stull, J. W., Evason, M. D., Weese, J. S., Wittum, T. E., Szlosek, D., & Arruda, A. G. (2021). Investigation of spatio‐temporal clusters of positive leptospirosis polymerase chain reaction test results in dogs in the United States, 2009 to 2016. Journal of Veterinary Internal Medicine, 35(3), 1355-1360.

Asemahagn, M. A., Alene, G. D., & Yimer, S. A. (2021). Spatial-temporal clustering of notified pulmonary tuberculosis and its predictors in East Gojjam Zone, Northwest Ethiopia. PLoS One, 16(1), e0245378.

Carrasco-Escobar, G., Schwalb, A., Tello-Lizarraga, K., Vega-Guerovich, P., & Ugarte-Gil, C. (2020). Spatio-temporal co-occurrence of hotspots of tuberculosis, poverty and air pollution in Lima, Peru. Infectious Diseases of Poverty, 9, 1-6.

Liao, W. B., Ju, K., Gao, Y. M., & Pan, J. (2020). The association between internal migration and pulmonary tuberculosis in China, 2005–2015: A spatial analysis. Infectious Diseases of Poverty, 9(1), 1-12.

Rajab, N. A., Hashim, N., & Rasam, A. R. A. (2020). Spatial Mapping and Analysis of Tuberculosis Cases in Kuala Lumpur, Malaysia. In 2020 IEEE 10th International Conference on System Engineering and Technology, 38-43.

Bui, D. P., Oren, E., Roe, D. J., Brown, H. E., Harris, R. B., Knight, G. M., ... & Grandjean, L. (2019). A case-control study to identify community venues associated with genetically-clustered, multidrug-resistant tuberculosis disease in Lima, Peru. Clinical Infectious Diseases, 68(9), 1547-1555.

Ogbudebe, C., Jeong, D., Odume, B., Chukwuogo, O., Dim, C., Useni, S., ... & Gidado, M. (2023). Identifying hot spots of tuberculosis in Nigeria using an early warning outbreak recognition system: retrospective analysis of implications for active case finding interventions. JMIR Public Health and Surveillance, 9(1), e40311.

Mohidem, N. A., Osman, M., Hashim, Z., Muharam, F. M., Mohd Elias, S., & Shaharudin, R. (2021). Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia. PLoS One, 16(6), e0252146.

Chun, Z. M., Jun, J. Q., & Yan, H. Y. (2020). The Spatiotemporal dynamic distributions of new tuberculosis in Hangzhou, China. Biomedical and Environmental Sciences, 33(4), 277-281.

Jiang, Q., Liu, Q., Ji, L., Li, J., Zeng, Y., Meng, L., Gao, Q., et al. (2019). Citywide transmission of MDR-TB under China's rapid urbanization: a retrospective population-based genomic spatial epidemiological study. Clinical Infectious Diseases: An official publication of the Infectious Diseases Society of America.

Alves, L. S., Dos Santos, D. T., Arcoverde, M. A. M., Berra, T. Z., Arroyo, L. H., Ramos, A. C. V., Arcêncio, R. A., et al. (2019). Detection of risk clusters for deaths due to tuberculosis specifically in areas of southern Brazil where the disease was supposedly a non-problem. BMC Infectious Diseases, 19(1), 1-13.

Rahim, S. S. S. A., Shah, S. A., Idrus, S., & Azhar, Z. I. (2020). Spatial Analysis of Food and Waterborne Diseases in Sabah, Malaysia. Sains Malaysiana, 49(7), 1627-1638.

Alves, L. S., Dos Santos, D. T., Arcoverde, M. A. M., Berra, T. Z., Arroyo, L. H., Ramos, A. C. V., Arcêncio, R. A., et al. (2019). Detection of risk clusters for deaths due to tuberculosis specifically in areas of southern Brazil where the disease was supposedly a non-problem. BMC Infectious Diseases, 19(1), 1-13.

Chirenda, J., Gwitira, I., Warren, R. M., Sampson, S. L., Murwira, A., Masimirembwa, C., Streicher, E. M., et al. (2020). Spatial distribution of Mycobacterium tuberculosis in metropolitan Harare, Zimbabwe. PLoS One, 15(4), e0231637.

Gwitira, I., Karumazondo, N., Shekede, M. D., Sandy, C., Siziba, N., & Chirenda, J. (2021). Spatial patterns of pulmonary tuberculosis (TB) cases in Zimbabwe from 2015 to 2018. Plos one, 16(4), e0249523.

Chirenda, J., Gwitira, I., Warren, R. M., Sampson, S. L., Murwira, A., Masimirembwa, C., Streicher, E. M., et al. (2020). Spatial distribution of Mycobacterium tuberculosis in metropolitan Harare, Zimbabwe. PLoS One, 15(4), e0231637.

Yu, Y., Wu, B., Wu, C., Wang, Q., Hu, D., & Chen, W. (2020). Spatial-temporal analysis of tuberculosis in Chongqing, China 2011-2018. BMC Infectious Diseases, 20(1), 1-12.

Seman, B. B., & Masron, T. (2019). Hotspot Analysis of Hand Foot and Mouth Disease (HFMD) Using GIS in Kuching, Sarawak, Malaysia. Humanities & Social Sciences Reviews, 7(2), 36-44.

Bui, L. V., Mor, Z., Chemtob, D., Ha, S. T., & Levine, H. (2018). Use of geographically weighted poisson regression to examine the effect of distance on tuberculosis incidence: A case study in Nam Dinh, Vietnam. PloS one, 13(11), e0207068.

Zhang, X., Ma, S. L., & He, J. (2019). Correlation analysis of rubella incidence and meteorological variables based on Chinese medicine theory of Yunqi. Chinese Journal of integrative Medicine, 25(12), 911-916.

Makalew, L. A., Otok, B. W., & Barung, E. N. (2019). Spatio of lungs tuberculosis (TB Lungs) in East Java using geographically weighted poisson regression (GWPR). Indian Journal of Public Health Research & Development, 10(8), 1757-1760

Dangisso, M. H., Datiko, D. G., & Lindtjørn, B. (2020). Identifying geographical heterogeneity of pulmonary tuberculosis in southern Ethiopia: A method to identify clustering for targeted interventions. Global Health Action, 13(1), 1785737.

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. Tropical Medicine and International Health, 24(9), 1104-1113.

Zhang, Y., Liu, M., Wu, S. S., Jiang, H., Zhang, J., Wang, S., Ma, W., Li, Q, Ma, Y., Liu, Y., Feng, W., Amsalu, E., Li, X., Wang, W., Li, W. & Guo, X. (2019). Spatial distribution of tuberculosis and its association with meteorological factors in mainland China. BMC Infectious Diseases, 19(1), 1-7.

Arroyo, L. A. H., Arcoverde, M. A. M., Alves, J. D., Fuentealba-Torres, M., Cartagena-Ramos, D., Scholze, A. R., Arcêncio, R. A. , et al. (2019). Spatial analysis of cases of Tuberculosis with Mental Disorders in São Paulo. Revista brasileira de enfermagem, 72(3), 654-662.

Downloads

Published

2024-08-19

How to Cite

Mohidem, N. A., Osman, M., Hashim, Z., Muharam, F. M., Mohd Elias, S., & Ma’pol, A. (2024). Spatial Analysis of Identifying the Association between Risk Factors and Tuberculosis Cases: A Review. EDUCATUM Journal of Science, Mathematics and Technology, 12(1), 24–34. https://doi.org/10.37134/ejsmt.vol12.1.4.2025