Association between rapid weather changes and incidence of chiefly cardiovascular complaints in the emergency department

Random Forest and Feature Importance Measures for Discriminating the Most Influential Environmental Factors in Predicting Cardiovascular and Respiratory Diseases

BACKGROUND

Several studies suggest that environmental and climatic factors are linked to the risk of mortality due to cardiovascular and respiratory diseases; however, it is still unclear which are the most influential ones. This study sheds light on the potentiality of a data-driven statistical approach by providing a case study analysis.

METHODS

Daily admissions to the emergency room for cardiovascular and respiratory diseases are jointly analyzed with daily environmental and climatic parameter values (temperature, atmospheric pressure, relative humidity, carbon monoxide, ozone, particulate matter, and nitrogen dioxide). The Random Forest (RF) model and feature importance measure (FMI) techniques (permutation feature importance (PFI), Shapley Additive exPlanations (SHAP) feature importance, and the derivative-based importance measure (κALE)) are applied for discriminating the role of each environmental and climatic parameter. Data are pre-processed to remove trend and seasonal behavior using the Seasonal Trend Decomposition (STL) method and preliminary analyzed to avoid redundancy of information.

RESULTS

The RF performance is encouraging, being able to predict cardiovascular and respiratory disease admissions with a mean absolute relative error of 0.04 and 0.05 cases per day, respectively. Feature importance measures discriminate parameter behaviors providing importance rankings. Indeed, only three parameters (temperature, atmospheric pressure, and carbon monoxide) were responsible for most of the total prediction accuracy.

CONCLUSIONS

Data-driven and statistical tools, like the feature importance measure, are promising for discriminating the role of environmental and climatic factors in predicting the risk related to cardiovascular and respiratory diseases. Our results reveal the potential of employing these tools in public health policy applications for the development of early warning systems that address health risks associated with climate change, and improving disease prevention strategies.

An effect of 24-hour temperature change on outpatient and emergency and inpatient visits for cardiovascular diseases in northwest China

Some studies suggested that 24-h temperature change (TC24) was one of the potential risk factors for human health. However, evidence of the short-term effect of TC24 on outpatient and emergency department (O&ED) visits and hospitalizations for cause-specific cardiovascular diseases (CVDs) is still limited. The aim of this study is to explore the short-term effects of TC24 on O&ED visits and hospitalizations for CVDs in northwest China which is an area with large temperature variation. The O&ED visits records for CVDs of 3 general hospitals and the inpatient records for CVDs of 4 general hospitals were collected from January 1, 2013, to December 31, 2016, in Jinchang City, northwest China. Meteorological and air pollution data were also obtained during the same study period from local meteorological monitoring station and environmental monitoring station, respectively. A generalized additive model (GAM) with Poisson regression was employed to analyze the effects of TC24 on O&ED visits and hospitalizations for CVDs. V-shaped relationship were found between TC24 and O&ED visits and hospitalizations for CVDs, including total CVD, hypertension, coronary heart disease (CHD) and stroke. Stratified analysis showed that men and patients over 65 years old were more susceptible to temperature changes. The estimates in non-heating months were higher than in full year. TC24 can affect the O&ED visits and hospitalizations for CVDs in this study. This study provides useful data for policy makers to better prepare local responses to the impact of changes in temperature on population health.

No impact of weather conditions on the outcome of intensive care unit patients

Global warming leads to increased exposure of humankind to meteorological variation, including short-term weather changes. Weather conditions involve changes in temperature, heat and cold, in air pressure and in air humidity. Every single condition influences the incidence and mortality of different diseases such as myocardial infarction and stroke. This study investigated the impact of weather conditions on short- and long-term mortality of 4321 critically ill patients (66 ± 14 years, 2638 men) admitted to an intensive care unit (ICU) over a period of 5 years. Meteorological information (air temperature, air pressure and humidity) for the same period was retrieved. The influence of absolute weather parameters, different seasons, sudden weather changes including "warm" and "cold" spells on ICU and long-term mortality was analyzed. After correction for Simplified Acute Physiology Score (SAPS-2), no impact of meteorological conditions on mortality was found. Different seasons, sudden weather changes, "warm spells" or "cold spells" did not affect the outcome of critically ill patients.