https://doi.org/10.4081/gh.2023.1203
Spatial pattern and heterogeneity of chronic respiratory diseases and relationship to socio-demographic factors in Thailand in the period 2016 to 2019
Submitted: 29 March 2023
Accepted: 1 May 2023
Published: 25 May 2023
Accepted: 1 May 2023
Abstract Views: 578
PDF: 321
HTML: 5
HTML: 5
Publisher's note
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.
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
Chronic respiratory diseases (CRDs) constitute 4% of the global disease burden and cause 4 million deaths annually. This cross-sectional study used QGIS and GeoDa to explore the spatial pattern and heterogeneity of CRDs morbidity and spatial autocorrelation between socio-demographic factors and CRDs in Thailand from 2016 to 2019. We found an annual, positive, spatial autocorrelation (Moran’s I >0.66, p<0.001) showing a strong clustered distribution. The local indicators of spatial association (LISA) identified hotspots mostly in the northern region, while coldspots were mostly seen in the central and north-eastern regions throughout the study period. Of the socio-demographic factors, the density of population, households, vehicles, factories and agricultural areas, correlated with the CRD morbidity rate, with statistically significant negative spatial autocorrelations and coldspots in the north-eastern and central areas (except for agricultural land) and two hotspots between farm household density and CRD in the southern region in 2019. This study identified vulnerable provinces with high risk of CRDs and can guide prioritization of resource allocation and provide target interventions for policy makers.
Alvarez-Mendoza CI, Teodoro A, Freitas A, Fonseca J, 2020. Spatial estimation of chronic respiratory diseases based on machine learning procedures—an approach using remote sensing data and environmental variables in Quito, Ecuador. Appl Geogr 123:102273.
DOI: https://doi.org/10.1016/j.apgeog.2020.102273
Anselin L, Syabri I, Kho Y, 2010. GeoDa: an introduction to spatial data analysis, in: Fischer MM, Getis A. (Eds.), Handbook of applied spatial analysis. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 73–89.
DOI: https://doi.org/10.1007/978-3-642-03647-7_5
Anselin L, Syabri I, Kho Y, 2006. GeoDa: an introduction to spatial data analysis. Geographical Analysis 38: 5–22.
DOI: https://doi.org/10.1111/j.0016-7363.2005.00671.x
Bhandari R, Sharma, 2012. Epidemiology of chronic obstructive pulmonary disease: a descriptive study in the mid-western region of Nepal. Int J Chron Obstruct Pulmon Dis 7:253-257.
DOI: https://doi.org/10.2147/COPD.S28602
Brashier BB, Kodgule R, 2012. Risk factors and pathophysiology of chronic obstructive pulmonary disease (COPD) J Assoc Physicians India 60: 17-21.
Fradelos E, Papathanasiou I, Mitsi D, Tsaras K, Kleisiaris C, Kourkouta L, 2014. Health based geographic information systems (GIS) and their applications. Acta Inform Med 22:402-405.
DOI: https://doi.org/10.5455/aim.2014.22.402-405
Hill K, Goldstein RS, Guyatt GH, Blouin M, Tan WC, Davis LL, Heels-Ansdell DM, Erak M, Bragaglia PJ, Tamari IE, Hodder R, Stanbrook MB, 2010. Prevalence and underdiagnosis of chronic obstructive pulmonary disease among patients at risk in primary care. CMAJ:Can Med Assoc J 182:673–678.
DOI: https://doi.org/10.1503/cmaj.091784
Kitjakrancharoensin P, Yasan K, Hongyantarachai K, Ratanachokthorani K, Thammasarn J, Kuwuttiwai D, Ekanaprach T, Jittakarm R, Nuntapravechpun R, Hotarapavanon S, Kulrattanarak S, Tongkaew S, Deemeechai S, Mungthin M, Rangsin R, Wongsrichanalai V, Sakboonyarat B, 2020. Prevalence and risk factors of chronic obstructive pulmonary disease among agriculturists in a rural community, Central Thailand. Int J ChronObstruct Pulmon Dis 15: 2189–98.
DOI: https://doi.org/10.2147/COPD.S262050
Labaki WW, Han MK, 2020. Chronic respiratory diseases: a global view. Lancet Respir Med 8:531–533.
DOI: https://doi.org/10.1016/S2213-2600(20)30157-0
Laohasiriwong W, Puttanapong N, Luenam A, 2018. A comparison of spatial heterogeneity with local cluster detection methods for chronic respiratory diseases in Thailand. F1000Res 6: 1819.
DOI: https://doi.org/10.12688/f1000research.12128.2
Long X, Tie X, Cao J, Huang R, Feng T, Li N, Zhao S, Tian J, Li G, Zhang Q, 2016. Impact of crop field burning and mountains on heavy haze in the North China Plain: a case study. Atmos Chem Phys 16:9675–91.
DOI: https://doi.org/10.5194/acp-16-9675-2016
Lotfata A, Hohl A, 2021. Spatial association of respiratory health with social and environmental factors: case study of Cook County, Illinois USA Cities Health, 1-13.
DOI: https://doi.org/10.1101/2021.04.29.21256319
Noble D, Smith D, Mathur R, Robson J, Greenhalgh T, 2012. Feasibility study of geospatial mapping of chronic disease risk to inform public health commissioning. BMJ Open 2:000711.
DOI: https://doi.org/10.1136/bmjopen-2011-000711
Pallasaho P, Kainu A, Sovijärvi A, Lindqvist A, Piirilä PL, 2014. Combined effect of smoking and occupational exposure to dusts, gases or fumes on the incidence of COPD. COPD: J Chron Obstruct Pulmon Dis 11:88–95.
DOI: https://doi.org/10.3109/15412555.2013.830095
Postma DS, Bush A, van den Berge M, 2015. Risk factors and early origins of chronic obstructive pulmonary disease. Lancet 385:899–909.
DOI: https://doi.org/10.1016/S0140-6736(14)60446-3
Pothirat C, Chaiwong W, Phetsuk N, Pisalthanapuna S, Chetsadaphan N, Inchai J, 2015. A comparative study of COPD burden between urban vs rural communities in northern Thailand. Int J Chron Obstruct Pulmon Dis 10:1035-42.
DOI: https://doi.org/10.2147/COPD.S82303
Ramírez-Aldana R, Gomez-Verjan JC, Bello-Chavolla OY, 2020. Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level. PLoS Negl Trop Dis 14:0008875.
DOI: https://doi.org/10.1371/journal.pntd.0008875
Rankantha A, Chitapanarux I, Pongnikorn D, Prasitwattanaseree S, Bunyatisai W, Sripan P, Traisathit P, 2018. Risk patterns of lung cancer mortality in northern Thailand. BMC Public Health 18:1138.
DOI: https://doi.org/10.1186/s12889-018-6025-1
Reddington CL, Butt EW, Ridley DA, Artaxo P, Morgan WT, Coe H, Spracklen DV, 2015. Air quality and human health improvements from reductions in deforestation-related fire in Brazil. Nat Geosci 8:768–71.
DOI: https://doi.org/10.1038/ngeo2535
Rujivanarom P, 2019. Medical study links smog in north with rising cases of respiratory diseases. Nation Thailand. May 7, 2019
Salvi SS, Barnes PJ, 2009. Chronic obstructive pulmonary disease in non-smokers. Lancet 374:733-743.
DOI: https://doi.org/10.1016/S0140-6736(09)61303-9
Steiniger S, Hunter AJS, 2013. The 2012 free and open source GIS software map – A guide to facilitate research, development, and adoption. Comput Environ Urban Syst 39:136–50.
DOI: https://doi.org/10.1016/j.compenvurbsys.2012.10.003
Surendran S, Mohan A, Valamparampil M, Nair S, Balakrishnan S, Laila A, Reghunath R, Jose C, Rajeevan A, Vasudevakaimal P, Surendrannair A, Nujum Z, Varghese S, Mohan A, 2022. Spatial analysis of chronic obstructive pulmonary disease and its risk factors in an urban area of Trivandrum, Kerala, India. Lung India 39:110-15.
DOI: https://doi.org/10.4103/lungindia.lungindia_454_21
Tee K, 2013. Re-emergence of chronic obstructive pulmonary disease: it is time to think COPDifferently. Singapore Med J 54:673–7.
DOI: https://doi.org/10.11622/smedj.2013240
Thanaviratananich S, Cho SH, Ghoshal AG, Muttalif ARBA, Lin HC, Pothirat C, Chuaychoo B, Aeumjaturapat S, Bagga S, Faruqi R, Sajjan S, Baidya S, Wang DY, 2016. Burden of respiratory disease in Thailand: Results from the APBORD observational study. Medicine 95:4090.
DOI: https://doi.org/10.1097/MD.0000000000004090
Trisurat Y, Alkemade R, Verburg PH, 2010. Projecting land-use change and its consequences for biodiversity in Northern Thailand. J Environ Manage 45:626–39.
DOI: https://doi.org/10.1007/s00267-010-9438-x
Wang S, Zhang CY, 2008. Spatial and temporal distribution of air pollutant emissions from open burning of crop residues in China. Sciencepaper Online 3:329–33.
Wang W, Ying Y, Wu Q, Zhang H, Ma D, Xiao W, 2015. A GIS-based spatial correlation analysis for ambient air pollution and AECOPD hospitalizations in Jinan, China Respir Med 109:372–8.
DOI: https://doi.org/10.1016/j.rmed.2015.01.006
World Health Organization, 2022. Chronic obstructive pulmonary disease (COPD) World Health Organization. Available from: https://www.who.int/news-room/fact-sheets/detail/chronic-obstructive-pulmonary-disease-(copd)
Yin S, 2020. Biomass burning spatiotemporal variations over South and Southeast Asia. Environ Int 145:106153.
DOI: https://doi.org/10.1016/j.envint.2020.106153
Zhang L, Liu Y, Hao L, 2016. Contributions of open crop straw burning emissions to PM 2.5 concentrations in China. Environ Res Lett 11:014014.
DOI: https://doi.org/10.1088/1748-9326/11/1/014014
Zhou Y, Wang D, Liu S, Lu J, Zheng J, Zhong N, Ran P, 2013. The Association between BMI and COPD: The Results of Two Population-based Studies in Guangzhou, China. COPD: J Chron Obstruct Pulmon Dis 10:567–572.
DOI: https://doi.org/10.3109/15412555.2013.781579
How to Cite
Htwe, Z. C., Laohasiriwong, W. ., Sornlorm, K., & Mahato, R. (2023). Spatial pattern and heterogeneity of chronic respiratory diseases and relationship to socio-demographic factors in Thailand in the period 2016 to 2019. Geospatial Health, 18(1). https://doi.org/10.4081/gh.2023.1203
Copyright (c) 2023 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Ricardo Ramìrez-Aldana , Juan Carlos Gomez-Verjan, Omar Yaxmehen Bello-Chavolla , Lizbeth Naranjo, A spatio-temporal study of state-wide case-fatality risks during the first wave of the COVID-19 pandemic in Mexico , Geospatial Health: Vol. 17 No. s1 (2022): Special issue on COVID-19
You may also start an advanced similarity search for this article.
