Effects of temperature and humidity on acute myocardial infarction hospitalization in a super-aging society

Abstract

Weather conditions affect the incidence of acute myocardial infarction (AMI). However, little is known on the association of weather temperature and humidity with AMI hospitalizations in a super-aging society. This study sought to examine this association. We included 87,911 consecutive patients with AMI admitted to Japanese acute-care hospitals between April 1, 2012 and March 31, 2015. The primary outcome was the number of AMI hospitalizations per day. Multilevel mixed-effects linear regression models were used to estimate the association of the average temperature and humidity, 1 day before hospital admission, with AMI hospitalizations, after adjusting for weather, hospital, and patient demographics.Lower temperature and humidity were associated with an increased number of AMI hospitalizations (coefficient - 0.500 [- 0.524 to - 0.474] per °C change, p < 0.001 and coefficient - 0.012 [- 0.023 to - 0.001] per % change, p = 0.039, respectively). The effects of temperature and humidity on AMI hospitalization did not differ by age and sex (all interaction p > 0.05), but differed by season. However, higher temperatures in spring (coefficient 0.089 [0.025 to 0.152] per °C change, p = 0.010) and higher humidity in autumn (coefficient 0.144 [0.121 to 0.166] per % change, p < 0.001) were risk factors for AMI hospitalization. Increased average temperatures and humidity, 1 day before hospitalization, are associated with a decreased number of AMI hospitalizations.

Figure 1

Association between average weather temperature and acute myocardial infarction hospitalizations. (A) MVRS (multivariable regression splines) indicated a linear relationship with temeprature. The predicted number of AMI per day was univariate. The x-axis represents temperature (°C) as a continuous variable. The solid and dashed lines indicate 95% confidence intervals. (B) The adjusted predicted number of AMI per day was calculated using “marginsplot” after the creation of multilevel mixed random-effects and population-averaged linear models in STATA. The covariates are the same as in Table 2. The x-axis represents temperature (°C) as a categorical variable according to the weather temperature quantiles. Bars indicate 95% confidence intervals. Q, quantile. The association of hospitalization with temperature was close to linear.

Figure 2

Association between average weather humidity and acute myocardial infarction hospitalizations. (A) Linearity was checked for continuous and categorical variables using STATA's multivariable regression splines (MVRS) command. MVRS indicated non-linear relationship with humidity. The predicted number of AMI per day was univariate. The x-axis represents humidity (%) as continuous variables. The solid and dashed lines indicate 95% confidence intervals. (B) The adjusted predicted number of AMI per day was calculated using “marginsplot” after the creation of multilevel mixed random-effects and population-averaged linear models in STATA. The covariates are the same as in Table 2. The x-axis represents humidity (%) as categorical variables according to weather humidity quantiles. The bars indicate the 95% confidence intervals. Q, quantile. The association between hospitalizations and weather humidify was close to non-linear; Q4 had the lowest risk.

Figure 3

Association between average weather temperature and acute myocardial infarction hospitalizations for several subgroups. Coefficients greater than zero represent an increase in the number of cardiovascular hospitalizations by the average weather temperature. The coefficient is indicated by a dot, and the lines represent the 95% confidence intervals. Multilevel mixed random-effects and population-averaged linear models was used and the coefficients were adjusted as indicated in Table 2. Lower temperatures increase the risk of AMI hospitalization, except in spring.

Figure 4

Association between average weather humidity and acute myocardial infarction hospitalizations for several subgroups. Coefficients greater than zero represent an increase in the number of cardiovascular hospitalizations by the average weather temperature. The coefficient is indicated by a dot, and the lines represent the 95% confidence intervals. Multilevel mixed random-effects and population-averaged linear models was used and the coefficients were adjusted as indicated in Table 2. Overall, lower humidity increases the risk of AMI hospitalization in autumn.

Figure 5

Flowchart of the present study. JROAD, Japanese registry of all cardiac and vascular diseases.