Short-Term Lag Effects of Climate-Pollution Interactions on Cardiopulmonary Hospitalizations: A Multi-City Predictive Study Using the AE+LSTM Hybrid Model in Japan.

Yi Jia CHEN; Fan ZHAO; Qing Yang WU; Yukitaka OHASHI; Tomohiko IHARA

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Short-Term Lag Effects of Climate-Pollution Interactions on Cardiopulmonary Hospitalizations: A Multi-City Predictive Study Using the AE+LSTM Hybrid Model in Japan.

Author: Yi Jia CHEN ¹ ; Fan ZHAO ¹ ; Qing Yang WU ² ; Yukitaka OHASHI ³ ; Tomohiko IHARA ¹
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1. Department of Environment Systems, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8563, Japan.
2. Department of Environmental Health Sciences, Fielding School of Public Health, University of California, Los Angles CA90095, US.
3. Department of Biosphere-Geosphere Science, Faculty of Biosphere-Geosphere Science, Okayama University of Science, Okayama, Okayama 700-0005, Japan.
Publication Type:Journal Article
Keywords: Air pollution; Cardiovascular diseases; Climate change; Deep learning; Hospitalization; Respiratory diseases
MeSH: Humans; Hospitalization/statistics & numerical data*; Cardiovascular Diseases/epidemiology*; Japan/epidemiology*; Air Pollutants/analysis*; Air Pollution/adverse effects*; Cities/epidemiology*; Climate; Respiratory Tract Diseases/epidemiology*; Deep Learning; Male
From: Biomedical and Environmental Sciences 2025;38(11):1378-1387
CountryChina
Language:English
Abstract: OBJECTIVE:To assess the short-term lag effects of climate and air pollution on hospital admissions for cardiovascular and respiratory diseases, and to develop deep learning-based models for daily hospital admission prediction.
METHODS:A multi-city study was conducted in Tokyo's 23 wards, Osaka City, and Nagoya City. Random forest models were employed to assess the synergistic short-term lag effects (lag0, lag3, and lag7) of climate and air pollutants on hospitalization for five cardiovascular diseases (CVDs) and two respiratory diseases (RDs). Furthermore, we developed hybrid deep learning models that integrated an autoencoder (AE) with a Long Short-Term Memory network (AE+LSTM) to predict daily hospital admissions.
RESULTS:On the day of exposure (lag0), air pollutants, particularly nitrogen oxides (NO x), exhibited the strongest influence on hospital admissions for CVD and RD, with pronounced effects observed for hypertension (I10-I15), ischemic heart disease (I20), arterial and capillary diseases (I70-I79), and lower respiratory infections (J20-J22 and J40-J47). At longer lags (lag3 and lag7), temperature and precipitation were more influential predictors. The AE+LSTM model outperformed the standard LSTM, improving the prediction accuracy by 32.4% for RD in Osaka and 20.94% for CVD in Nagoya.
CONCLUSION:Our findings reveal the dynamic, time-varying health risks associated with environmental exposure and demonstrate the utility of deep learnings in predicting short-term hospital admissions. This framework can inform early warning systems, enhance healthcare resource allocation, and support climate-adaptive public health strategies.