Remote Sensing and Geographic Information System AoS, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani, 12120, Thailand.
Department of Geography, University of Peradeniya, Peradeniya, Sri Lanka.
Environ Monit Assess. 2019 Jun 28;191(Suppl 2):366. doi: 10.1007/s10661-019-7417-0.
The spatial distribution of the prevalence of asthma and chronic obstructive pulmonary disease (COPD) remains under the influence of a wide array of environmental, climatic, and socioeconomic determinants. However, a large proportion of these influences remain unexplained. In completion, this study examined the spatial associations between asthma/COPD morbidity and their determinants using ordinary least squares (OLS) and geographically weighted regressions (GWR). Inpatient records collected from the secondary and tertiary care hospitals in Kandy from 2010 to 2014 were considered as the dependent variable. Potential risk factors (explanatory variables) were identified in four distinguished classes: 1) meteorological factors, (2) direct and indirect factors of air pollution, (3) socioeconomic factors, and (4) characteristics of the physical environment. All possible combinations of candidate explanatory variables were evaluated through an exploratory regression. A comparison between the regression models was also explored. The best OLS regression models revealed about 55% of asthma variation and 62% of COPD variation while GWR models yielded 78% and 74% of the variation of asthma and COPD occurrences respectively. Relative humidity, proximity to roads (0-200 m), road density, use of firewood as a source of fuel, and elevation play a vital role in predicting morbidity from asthma and COPD. Both local and global regression models are important in assessing spatial relationships of asthma and COPD. However, the local models exhibit a better prediction capability for assessing non-stationary relationships of asthma and COPD than global models. The geostatistical aspects used in this study may also provide insights for evaluating heterogeneous environmental risk factors in other epidemiological studies across different spatial settings.
哮喘和慢性阻塞性肺疾病（COPD）的流行的空间分布仍然受到广泛的环境、气候和社会经济决定因素的影响。然而，很大一部分影响仍然无法解释。本研究使用普通最小二乘法（OLS）和地理加权回归（GWR）检查了哮喘/COPD 发病率及其决定因素之间的空间关联。考虑了 2010 年至 2014 年从康提的二级和三级保健医院收集的住院记录作为因变量。潜在的危险因素（解释变量）被确定为四个不同的类别：1）气象因素，2）空气污染的直接和间接因素，3）社会经济因素，4）物理环境特征。通过探索性回归评估了候选解释变量的所有可能组合。还探讨了回归模型之间的比较。最佳 OLS 回归模型揭示了哮喘变化的约 55%和 COPD 变化的约 62%，而 GWR 模型分别产生了哮喘和 COPD 发生的 78%和 74%的变化。相对湿度、靠近道路（0-200m）、道路密度、使用木柴作为燃料源以及海拔高度对预测哮喘和 COPD 的发病率起着至关重要的作用。局部和全局回归模型都对评估哮喘和 COPD 的空间关系很重要。然而，局部模型在评估哮喘和 COPD 的非平稳关系方面比全局模型具有更好的预测能力。本研究中使用的地统计学方面也可能为评估不同空间环境中其他流行病学研究中的异质环境危险因素提供见解。
