Land surface temperature predicts mortality due to chronic obstructive pulmonary disease: a study based on climate variables and impact machine learning
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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.
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Land surface temperature predicts mortality due to chronic obstructive pulmonary disease: a study based on climate variables and impact machine learning. (2025). Geospatial Health, 20(1). https://doi.org/10.4081/gh.2025.1319
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