Mohammadi, Alireza, Mashhoodi, Bardia ORCID: https://orcid.org/0000-0002-7037-3932, Shamsoddini, Ali, Pishgar, Elahe and Bergquist, Robert (2025) Land surface temperature predicts mortality due to chronic obstructive pulmonary disease: a study based on climate variables and impact machine learning. Geospatial Health, 20 (1):1319. ISSN 1827-1987 (Print), 1970-7096 (Online) (doi:10.4081/gh.2025.1319)

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Abstract

Chronic Obstructive Pulmonary Disease (COPD) has been the focus of scientists and policymakers in the past decade with regard to mortality rates and global warming. The long-term shift in tem-perature and weather patterns, commonly called climate change, is an important public health issue, especially concerning COPD. Using the most recent county-level age-adjusted COPD mortality rates among adults older than 25 years, this study aimed to inves-tigate the spatial trajectory of COPD in the United States between 2001 and 2020. Global Moran’s I was used to investigate spatial relationships utilising data from Terra satellite for night-time Land Surface Temperatures (LSTnt), which served as an indicator of warming within the same time period across the United States. The Forest-based Classification and Regression model (FCR) was applied to predict mortality rates. It was found that COPD mortal-ity over the study period was spatially clustered in certain coun-ties. Moran’s I statistic (0.18) showed that the COPD mortality rates increased with LSTnt, with the strongest spatial association in the eastern and south-eastern counties. The FCR model success-fully predicted mortality rates in the study area using LSTnt values, achieving an R² value of 0.68, which accounted for COPD mortal-ity rates independently. Policymakers in the United States could use the findings of this study to develop long-term spatial and health-related strategies to reduce the vulnerability to global warming of patients with acute respiratory symptoms.

Item Type:	Article
Uncontrolled Keywords:	chronic obstructive pulmonary disease, spatial correlation, predictive modelling, forest-based classification and regression model, geographic information systems, remote sensing data, United States
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	08 May 2025 12:56
URI:	http://gala.gre.ac.uk/id/eprint/50345

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