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

Introduction: Chronic Obstructive Pulmonary Disease (COPD) mortality rates and global warming have been in the focus of scientists and policymakers in the past decade. The long-term shifts in temperature and weather patterns, commonly referred to as climate change, is an important public health issue, especially with regard to COPD.
Method: Using the most recent county-level age-adjusted COPD mortality rates among adults older than 25 years, this study aimed to investigate 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.
Results: It was found that COPD mortality over the 20-year period was spatially clustered in certain counties. Moran's I statistic (I=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 was able to predict mortality rates based on LSTnt values in the study area with a R2 value of 0.68.
Conclusion: 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
Additional Information:	Funding: this study received financial support from the University of Mohaghegh Ardabili (#19308). - MP
Uncontrolled Keywords:	land surface temperature, mortality, health geography, climate change, machine learning
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:	24 Sep 2025 13:17
URI:	https://gala.gre.ac.uk/id/eprint/51069

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