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Spatial Association Between Environmental Factors, Physical Geographic Factors and Chronic Obstructive Pulmonary Disease (COPD) in Thailand |
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| รหัสดีโอไอ | |
| Creator | Thanabodee Chumklang |
| Title | Spatial Association Between Environmental Factors, Physical Geographic Factors and Chronic Obstructive Pulmonary Disease (COPD) in Thailand |
| Contributor | Krittiyanee Thammasarn |
| Publisher | Thai Society of Higher Education Institutes on Environment |
| Publication Year | 2568 |
| Journal Title | EnvironmentAsia |
| Journal Vol. | 18 |
| Journal No. | 1 |
| Page no. | 150-163 |
| Keyword | Chronic Obstructive Pulmonary Disease (COPD), Tobacco outlet density, Nighttime light, Elderly population density, PM2.5 concentration |
| URL Website | http://www.tshe.org/ea/index.html |
| Website title | EnvironmentAsia |
| ISSN | 1906-1714 |
| Abstract | This study aimed to identify the prevalence and factors associated with tobacco outletdensity on the prevalence of chronic obstructive pulmonary disease (COPD) in Thailand.Using data from the Health Data Center (HDC) of the Ministry of Public Health from 2016 to2020, this study included 185,891 eligible participants. Data on tobacco outlet density, elderlypopulation density, average nighttime light, and average PM2.5 concentration were analyzed forspatial associations using Moran's I, Local Indicators of Spatial Autocorrelation (LISA), andspatial regression analysis. The prevalence of chronic obstructive pulmonary disease (COPD)per 100,000 population was highest in Nan province at 900.30, and lowest in Pathum Thaniprovince at 121.27. When categorized into deciles, the provinces in the highest prevalence group(622.17 900.30) included Chiang Rai, Chiang Mai, Tak, Nan, Phayao, Phatthalung, Phichit,and Lampang, as detailed. The results showed a positive spatial autocorrelation of COPDprevalence using Univariate Moran's I (Moran's I = 0.313). LISA analysis revealed high-riskclusters (hot spots or High-High) of COPD prevalence in the northern region. Bivariate Moran'sI analysis identified: Cold-spot or low-low clusters (LL) for both tobacco outlet density andCOPD prevalence in 7 provincial clusters. LL clusters for average nighttime light and COPDprevalence in 6 provincial clusters. High-High (HH) clusters for elderly population densityand COPD prevalence in 4 provincial clusters, and LL clusters in 6 provincial clusters. HHclusters for PM2.5 concentration and COPD prevalence in 3 provincial clusters, and LL clustersin 8 provincial clusters. Comparison of spatial regression models, with and without spatialconsiderations, revealed that the Spatial Lag Model (SLM) was the most appropriate. TheSLM explained 36.10% of the variance in COPD prevalence (R2 = 0.361) and identified thefollowing statistically significant spatial factors: Tobacco outlet density (coefficient = 0.223,p < 0.05), Average nighttime light (coefficient = -20.870, p < 0.01), Elderly population density(coefficient = 16.914, p < 0.01). |
