Spatial pattern and heterogeneity of chronic respiratory diseases and relationship to socio-demographic factors in Thailand in the period 2016 to 2019
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 CRD...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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PAGEPress Publications
2023-05-01
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| Series: | Geospatial Health |
| Subjects: | |
| Online Access: | https://geospatialhealth.net/index.php/gh/article/view/1203 |
| _version_ | 1797820118523183104 |
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| author | Zar Chi Htwe Wongsa Laohasiriwong Kittipong Sornlorm Roshan Mahato |
| author_facet | Zar Chi Htwe Wongsa Laohasiriwong Kittipong Sornlorm Roshan Mahato |
| author_sort | Zar Chi Htwe |
| collection | DOAJ |
| description |
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.
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| first_indexed | 2024-03-13T09:33:40Z |
| format | Article |
| id | doaj.art-b0bbabbff50d4ad1b0f972890eca6cac |
| institution | Directory Open Access Journal |
| issn | 1827-1987 1970-7096 |
| language | English |
| last_indexed | 2024-03-13T09:33:40Z |
| publishDate | 2023-05-01 |
| publisher | PAGEPress Publications |
| record_format | Article |
| series | Geospatial Health |
| spelling | doaj.art-b0bbabbff50d4ad1b0f972890eca6cac2023-05-25T18:21:12ZengPAGEPress PublicationsGeospatial Health1827-19871970-70962023-05-0118110.4081/gh.2023.1203Spatial pattern and heterogeneity of chronic respiratory diseases and relationship to socio-demographic factors in Thailand in the period 2016 to 2019Zar Chi Htwe0Wongsa Laohasiriwong1Kittipong Sornlorm2Roshan Mahato3Faculty of Public Health, KhonKaen University, KhonKaenFaculty of Public Health, KhonKaen University, KhonKaenFaculty of Public Health, KhonKaen University, KhonKaenFaculty of Public Health, KhonKaen University, KhonKaen 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. https://geospatialhealth.net/index.php/gh/article/view/1203chronic respiratory diseasesnorthern regionsocio-demographic factorsspatial analysisThailand |
| spellingShingle | Zar Chi Htwe Wongsa Laohasiriwong Kittipong Sornlorm Roshan Mahato Spatial pattern and heterogeneity of chronic respiratory diseases and relationship to socio-demographic factors in Thailand in the period 2016 to 2019 Geospatial Health chronic respiratory diseases northern region socio-demographic factors spatial analysis Thailand |
| title | Spatial pattern and heterogeneity of chronic respiratory diseases and relationship to socio-demographic factors in Thailand in the period 2016 to 2019 |
| title_full | Spatial pattern and heterogeneity of chronic respiratory diseases and relationship to socio-demographic factors in Thailand in the period 2016 to 2019 |
| title_fullStr | Spatial pattern and heterogeneity of chronic respiratory diseases and relationship to socio-demographic factors in Thailand in the period 2016 to 2019 |
| title_full_unstemmed | Spatial pattern and heterogeneity of chronic respiratory diseases and relationship to socio-demographic factors in Thailand in the period 2016 to 2019 |
| title_short | Spatial pattern and heterogeneity of chronic respiratory diseases and relationship to socio-demographic factors in Thailand in the period 2016 to 2019 |
| title_sort | spatial pattern and heterogeneity of chronic respiratory diseases and relationship to socio demographic factors in thailand in the period 2016 to 2019 |
| topic | chronic respiratory diseases northern region socio-demographic factors spatial analysis Thailand |
| url | https://geospatialhealth.net/index.php/gh/article/view/1203 |
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