Analysis of the Association Between Meteorological Variables and Mortality in the Elderly Applied to Different Climatic Characteristics of the State of São Paulo, Brazil

Abstract

With the rising trends in elderly populations around the world, there is a growing interest in understanding how climate sensitivity is related to their thermal perception. Therefore, we analyzed the associations between mortality in the elderly due to cardiovascular (CVD) and respiratory diseases (RD) and meteorological variables, for three cities in the State of São Paulo, Brazil: Campos do Jordão, Ribeirão Preto and Santos, from 1996 to 2017. We applied the Autoregressive Model Integrated with Moving Average (ARIMA) and the Principal Component Analysis (PCA) in order to evaluate statistical associations. Results showed CVD as a major cause of mortality, particularly in the cold period, when a high mortality rate is also observed due to RD. The mortality rate was higher in Campos do Jordão and lower in Santos (and intermediate values in Ribeirão Preto). Campos do Jordão results indicate an increased probability of mortality from CVD and RD due to lower temperatures. In Ribeirão Preto, the lower relative humidity may be related to the increase in CVD and RD deaths. This study emphasizes that, even among subtropical climates, there are significant differences. Therefore, this can assist decision makers in the implementation of mitigating and adaptive measures.

Impact of Meteorological Conditions on the Dynamics of the COVID-19 Pandemic in Poland

“…The solution is to use one of the common techniques of multivariate analysis-principal component analysis (PCA). This approach is also used in different areas of human health—e.g., clinical studies [ 33 ], schizophrenia [ 34 ], health system readiness [ 35 ], mortality [ 36 ], oral and oropharyngeal cancer [ 37 ]. This method was mainly developed for data reduction but also can be efficiently used for finding patterns in the data and to highlight their similarities and differences.…”