The association of myocardial infarction hospitalizations and weather conditions in a private hospital in Brazil

Abstract

Background The relationship between seasonal temperature peaks and the incidence of hospitalization for myocardial infarction has been reported in different countries. Despite increased interest in assessing the impact of temperature on hospitalization, few studies have used case-crossover designs to study non-linear and distributed delay effects temperature and thermal amplitude. In addition, is not clear how myocardial hospitalization is affected by temperature in subtropical climate cities. Objective We investigated the effects of climatic variables on the number of hospitalizations for myocardial infarctions at a private hospital at a city with a subtropical climate from 2017 to 2019. Methods Myocardial hospitalization records were extracted from database obtained through the Brazilian Information System on Hospitalization according to the municipality of residence and the CID-10 classification in elderly people. The temporal analysis of this study aimed at verifying the relationship between hospitalizations, climatic attributes, and the standard deviation of other descriptive statistics. To estimate the non-linear and time-interval effects, we used Distributed Lag Non-Linear Models (DLNM), which allowed us to observe their distributed delay effects. This model is suitable for studying the impact of environmental conditions on health, as it usually occurs a few days after exposure. The data were analyzed using the R software. Results In Figure 1, five descriptive graphs illustrate the study's primary data.Distribution of hospitalizations by gender (A), showing the predominance of males (65.81%), age group of 60 to 69 years (44.63%), followed by the group of 70 to 79 years (33.61%). Chart D shows that the medians representing hospitalization rates are higher in different bimesters throughout the year. Moreover, in chart E, the monthly averages of hospitalization rates per 100,000 elderly inhabitants, segmented by gender, show a prevalence of the male gender in all months.Based on the Poisson model for the variables combined, some lags were shown to be significant (17 and 20) at an α= 5% significance level, both for minimum temperature (p=0.00768) and relative humidity (p=0.03263). In addition to the other factors such as gender (p=4.14e-09) and all age levels (p=0.000), they can influence the number of hospitalizations. From the results shown in Table 1, we can interpret that when the Odds Ratio has a value between 0 and 1, the chance of hospitalization is reduced as the variable increases, or when the estimated value of the Odds Ratio is greater than 1, there is an increase in the chance of hospitalization as the respective variable increases. No significant results were found for maximum temperature and mean temperature. Conclusion Understanding the effects of temperature on cardiovascular risk factors over time in the elderly may help optimize prevention strategies and better organize care for elderly patients throughout the year. Funding Acknowledgement Type of funding sources: Private hospital(s). Main funding source(s): Constantini Cardiology Hospital