https://doi.org/10.4081/gh.2021.980
Development of a web-geographical information system application for plotting tuberculosis cases
Submitted: 22 January 2021
Accepted: 26 May 2021
Published: 19 October 2021
Accepted: 26 May 2021
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
In the last few decades, public health surveillance has increasingly applied statistical methods to analyze the spatial disease distributions. Nevertheless, contact tracing and follow up control measures for tuberculosis (TB) patients remain challenging because public health officers often lack the programming skills needed to utilize the software appropriately. This study aimed to develop a more user-friendly application by applying the CodeIgniter framework for server development, ArcGIS JavaScript for data display and a web application based on JavaScript and Hypertext Preprocessor to build the server's interface, while a webGIS technology was used for mapping. The performance of this approach was tested based on 3325 TB cases and their sociodemographic data, such as age, gender, race, nationality, country of origin, educational level, employment status, health care worker status, income status, residency status, and smoking status between 1st January 2013 and 31st December 2017 in Gombak, Selangor, Malaysia. These data were collected from the Gombak District Health Office and Rawang Health Clinic. Latitude and longitude of the location for each case was geocoded by uploading spatial data using Google Earth and the main output was an interactive map displaying location of each case. Filters are available for the selection of the various sociodemographic factors of interest. The application developed should assist public health experts to utilize spatial data for the surveillance purposes comprehensively as well as for the drafting of regulations aimed at to reducing mortality and morbidity and thus minimizing the public health impact of the disease.
Abascal E, Herranz M, Acosta F, Agapito J, Cabibbe AM, Monteserin J, Chiner-Oms Ã, 2020. Screening of inmates transferred to Spain reveals a Peruvian prison as a reservoir of persistent Mycobacterium tuberculosis MDR strains and mixed infections. Sci Rep 10:1-8.
DOI: https://doi.org/10.1038/s41598-020-59373-w
Abbasi T, Luithui C, Abbasi SA, 2020. A model to forecast methane emissions from topical and subtropical reservoirs on the basis of artificial neural networks. Water 12:145.
DOI: https://doi.org/10.3390/w12010145
Adegbite BR, Edoa JR, Achimi Agbo P, Dejon-Agobé JC, Essone PN, Lotola-Mougeni F, Zinsou JF, 2020. Epidemiological, mycobacteriological, and clinical characteristics of smoking pulmonary tuberculosis patients, in Lambaréné, Gabon: a cross-sectional study. Am J Trop Med Hyg 103:2501-5.
DOI: https://doi.org/10.4269/ajtmh.20-0424
Azimi A, Bagheri N, Mostafavi SM, et al, 2021. Spatial-time analysis of cardiovascular emergency medical requests: enlightening policy and practice. BMC Public Health 21:7.
DOI: https://doi.org/10.1186/s12889-020-10064-1
Barroso EG, 2020. Factors associated with household contacts’ tuberculosis testing and evaluation. Public Health Nurs 37:705-14.
DOI: https://doi.org/10.1111/phn.12788
Ben JM, Ben AH, Koubaa M, Hammami F, Damak J, Ben JM, 2020. Is there gender inequality in the epidemiological profile of tuberculosis?. Tunis Med 98:232.
Cai J, Xie Y, Deng M, Tang X, Li Y, Shekhar, S2020. Significant spatial co-distribution pattern discovery. Comput Environ Urban Syst 84:101543.
DOI: https://doi.org/10.1016/j.compenvurbsys.2020.101543
Census, 2018. Population distribution by local authority areas and mukims. Department of Statistics Malaysia, 2001. Available from: https://www.selangor.gov.my
Cheng J, Sun YN, Zhang CY, Yu YL, Tang LH, Peng H, Zhao JM, et al, 2020. Incidence and risk factors of tuberculosis among the elderly population in China: a prospective cohort study. Infect Dis Poverty 9:13.
DOI: https://doi.org/10.1186/s40249-019-0614-9
Cho J, You SC, Lee S, Park, D, Park B, Hripcsak G, Park RW, 2020. Application of epidemiological geographic information system: an open-source spatial analysis tool based on the OMOP Common Data Model. Int J Environ Res Public Health 17:7824.
DOI: https://doi.org/10.3390/ijerph17217824
ESRI, 2013. ArcGIS Desktop. 102nd ed. Environmental Systems Research Institute, Redlands, CA, USA.
ECDC, 2020. Available from: https://www.ecdc.europa.eu/en/publications-data/ecdc-map-maker-tool-emma Accessed: December 13, 2020.
Ferraro OE, Guido D, Zambianchi R, Lanfranchi S, Oddone E, Villani S, 2018. Mortality for neurological diseases and pesticides: etiological hypotheses by a spatial analysis in the province of Pavia. Med Lavoro 109:420-34.
Goshayeshi L, Pourahmadi A, Ghayour-Mobarhan M, Hashtarkhani S, Karimian S, Shahhosein Dastjerdi R, Eghbali B, SeyfiE, Kiani B, 2019. Colorectal cancer risk factors in north-eastern Iran: A retrospective cross-sectional study based on geographical information systems, spatial autocorrelation and regression analysis. Geospat Health 14:793.
DOI: https://doi.org/10.4081/gh.2019.793
Hamzah IS, Sarifin MR, Aziz MSA, Abdullah MFA, 2020. Malaysia as attraction of international foreign workers. J Crit Rev 7:2020.
Hill AN, Cohen T, Salomon JA, Menzies NA, 2020. High-resolution estimates of tuberculosis incidence among non-US-born persons residing in the United States, 2000-2016. Epidemics 33:100419.
DOI: https://doi.org/10.1016/j.epidem.2020.100419
Himawan AK, 2014. Performance analysis framework codeigniter and CakePHP in website creation. Int J Comput Appl 94:6-11.
DOI: https://doi.org/10.5120/16549-5946
Hoseini B, Bagheri N, Kiani B, Azizi A, Tabesh H, Tara, M, 2018. Access to dialysis services: a systematic mapping review based on geographical information systems. Geospat Health 13:3-10.
DOI: https://doi.org/10.4081/gh.2018.577
Kang W, Du J, Yang S, Yu J, Chen H, Liu J, Zong P, 2020 The prevalence and risks of major comorbidities among inpatients with pulmonary tuberculosis in China from a gender and age perspective: A large-scale multicenter observational study. Eur J Clin Microbiol Infect Dis 40:787-800.
DOI: https://doi.org/10.1007/s10096-020-04077-2
Kiani B, Bagheri N, Tara A, Hoseini B, Tabesh H, Tara M, 2017. Revealed access to haemodialysis facilities in northeastern Iran: Factors that matter in rural and urban areas. Geospat Health 12:584.
DOI: https://doi.org/10.4081/gh.2017.584
Kwak N, Winters N, Campbell JR, Chan ED, Gegia M, Lange C, Yim JJ, et al, 2020. Changes in treatment for multidrug-resistant tuberculosis according to national income. Eur Respir J 56:2001394.
DOI: https://doi.org/10.1183/13993003.01394-2020
Malaysian Meteorological Department, 2018. General climate of Malaysia. Ministry of Science, Technology and Innovation, Kuala Lumpur, Malaysia. Available from: http://www.met.gov.my/
Ministry of Health Malaysia, 2019. Annual Report 2000-2005. Disease Control Division, Ministry of Health, Kuala Lumpur, Malaysia.
Ministry of Health of Malaysia, 2019. Annual report 2018: TB control programme in Malaysia. Ministry of Health, Kuala Lumpur, Malaysia.
Mohidem NA, Hashim Z, Osman M, Shaharudin R, Muharam FM, Makeswaran, 2018. Demographic, socio-economic and behavior as risk factors of tuberculosis in Malaysia: a systematic review of the literature. Rev Environ Health 33:407-21.
DOI: https://doi.org/10.1515/reveh-2018-0026
Montazeri M, Hoseini B, Firouraghi N, 2020. Spatio-temporal mapping of breast and prostate cancers in South Iran from 2014 to 2017. BMC Cancer 20:1170.
DOI: https://doi.org/10.1186/s12885-020-07674-8
Moraga P, 2017. SpatialEpiApp: A Shiny web application for the analysis of spatial and spatio-temporal disease data. Spat Spatiotemporal Epidemiol 23:47-57.
DOI: https://doi.org/10.1016/j.sste.2017.08.001
Nur HA, Choy L, 2016. Analysis of land use and land cover changes in Gombak, Selangor using remote sensing data. Sains Malays 45:1869-77.
Official Portal of Gombak Land and District Office, 2020. Available from: https://www2.selangor.gov.my/
Phyu MH, Sriplung H, Kyi MS, San CC, Chongsuvivatwong V, 2020. Comparison of latent tuberculosis infections among general versus tuberculosis health care workers in Myanmar. Tropical Med Infect Dis 5:116.
DOI: https://doi.org/10.3390/tropicalmed5030116
Pishgar E, Fanni Z, Tavakkolinia J, 2020. Mortality rates due to respiratory tract diseases in Tehran, Iran during 2008-2018: a spatiotemporal, cross-sectional study. BMC Public Health 20:1414.
DOI: https://doi.org/10.1186/s12889-020-09495-7
QGIS Geographic Information System, 2015. Open Source Geospatial Foundation Project, 2.10.1 edn.
Schultz SD, MacArthur R, 2019. Geographic information systems and rural data. Rural data, people, and policy: information systems for the 21st century. Routledge, London, UK, pp. 189-204.
DOI: https://doi.org/10.4324/9780429305016-14
Singh H, Ramamohan V, 2020. A model-based investigation into urban-rural disparities in tuberculosis treatment outcomes under the Revised National Tuberculosis Control Programme in India. PLoS One 15:e0228712.
DOI: https://doi.org/10.1371/journal.pone.0228712
Smith CM, Hayward AC, 2016. DotMapper: an open source tool for creating interactive disease point maps. BMC Infect Dis 16:1-6.
DOI: https://doi.org/10.1186/s12879-016-1475-5
Solanki N, Shah D, Shah A, 2017. A Survey on different framework of PHP. Int J Latest Technol Eng Manag Appl Sci 6:155-8.
Sweeney S, Vassall A, Guinness L, Siapka M, Chimbindi N, Mudzengi D, Gomez GB, 2020. Examining approaches to estimate the prevalence of catastrophic costs due to tuberculosis from small-scale studies in South Africa. Pharmacoeconomics 38:619-31.
DOI: https://doi.org/10.1007/s40273-020-00898-3
Taherian A, Akhlaghi M, Sadat Hosseiniun Z, Shahrestanaki E, Tiyuri A, Sahebkar M, 2020. Investigating the effect of education on knowledge and practice in preventing tuberculosis in eastern Iran. Int J Health Promot Educ 58:83-91.
DOI: https://doi.org/10.1080/14635240.2019.1678396
Takahashi K, Shimadzu H, 2020. Detecting multiple spatial disease clusters: information criterion and scan statistic approach. Int J Health Geogr 19:1-11.
DOI: https://doi.org/10.1186/s12942-020-00228-y
World Health Organization, 2020. Global tuberculosis report 2020. Available from: https://www.who.int/teams/global-tuberculosis-programme/data Accessed: December 24, 2020.
How to Cite
Mohidem, N. A., Osman, M. ., Muharam, F. M. ., Mohd Elias, S., Shaharudin, R., & Hashim, Z. . (2021). Development of a web-geographical information system application for plotting tuberculosis cases. Geospatial Health, 16(2). https://doi.org/10.4081/gh.2021.980
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