Association between climate variables, pollutants, aerosols and hospitalizations due to asthma

Abstract

Many studies have shown that climatic and pollutant variables are directly related to the increase of hospitalizations due to respiratory diseases, mainly asthma. The aim of this study was to estimate the association between exposure to climate variables, pollutants, aerosols and hospitalizations due to asthma. This ecological study used time series with daily asthma hospitalization indicators, and concentrations of pollutants, climate data and aerosols, between January 2013 and December 2013. A generalized additive model using Poisson regression was used to estimate the relative risk with a two-day lag after exposure; the unipollutant model was adjusted by the apparent temperature, a measure defined from the temperature and relative humidity of the air, and a variable was added to control the seasonality and the day of the week. As a result, the values of relative risks (RR) for hospitalizations due to asthma were: for minimum temperature RR= 0.8985; maximum humidity RR= 0.9819; wind speed RR= 0.9419; rain RR= 0.9834; ozone RR= 0.9735; aerosols (AOT) RR= 1.0078; clearness index (Kt) RR= 0.0492 and carbon monoxide RR= 1.0865 for a two-day lag. After considering the aforementioned factors, we found the values of B coefficients. Exposure to climatic variables, pollutants, aerosols and clearness index was associated with hospitalizations due to asthma, and provided subsidies for the implementation of preventive measures to decrease these outcomes.Keywords: Hospitalization. Asthma. Environmental pollutants. Atmosphere. ResumoMuitos estudos vêm demonstrando que variáveis climáticas e poluentes tem uma relação direta no aumento de internações por doenças respiratórias, principalmente por asma. O objetivo deste estudo foi estimar a associação entre exposição às variáveis climáticas, poluentes, aerossóis nas internações por asma. O presente estudo ecológico utilizou séries temporais com indicadores diários de internação por asma, e concentrações de poluentes, dados de clima e aerossóis, entre janeiro de 2013 e dezembro de 2013. Um modelo aditivo generalizado utilizando regressão de Poisson foi usado para estimar o risco relativo, com defasagem de dois dias após a exposição; o modelo unipoluente foi ajustado pela temperatura aparente, medida definida a partir da temperatura e umidade relativa do ar, foi adicionada uma variável para controlar a sazonalidade e o dia da semana. Como resultados, os valores dos riscos relativos para internações por asma foram para Temperatura mínima: RR=0,8985; umidade máxima RR=0,9819; velocidade dos ventos RR=0,9419; precipitação RR= 0,9834; ozônio RR=0,9735; aerossóis (AOT; RR=1,0078; índice de claridade (Kt) RR=0,0492 e monóxido de carbono RR=1,0865 para defasagem temporal de dois dias. Após considerar os fatores mencionados, encontramos os valores dos coeficientes B's. A exposição às variáveis climáticas, poluentes, aerossóis e índice de claridade esteve associada às internações por asma, e forneceu subsídios para implantação de medidas preventivas para diminuírem esses desfechos.

Environmental risk factor assessment for major respiratory disorders in metropolitan cities of India using VIIRS Suomi Aerosol data and Google Trends

“…It may be mentioned here that many researchers have studied the relation between respiratory diseases and aerosols (De Miguel-Diez et al 2019 ). As for example, De Souza et al ( 2017 ) have shown the linkage between aerosols and prevalence of Asthma in Brazil, and Cao et al ( 2021 ) have discussed about how COVID-19 (COronaVIrus Disease 2019) can be transmitted through aerosols.…”

Investigating the Relationship between Climate and Hospital Admissions for Respiratory Diseases before and during the COVID-19 Pandemic in Brazil

“…Costa et al [13] tried to identify patterns of relationship between extreme weather conditions and hospitalization entries for cardiovascular diseases, obtained from the statistics sector at the University Hospital of Santa Maria in southern Brazil, using a time-series between 2012 and 2017. Souza and colleagues evaluated asthma-related hospitalization against climatic variables, pollutants and aerosols over time in Campo Grande, Mato Grosso do Sul [14]. Ikefuti et al [15] used a similar method of regression analysis (Poisson regression model) to identify relationships between stroke mortality and air temperature in São Paulo using statistical data from the municipality.…”

Modeling of hospital admissions for respiratory diseases as a function of probability distribution functions

“…Sensitive and vulnerable groups, such as pregnant women, children, the elderly, and those who already suffer from respiratory illnesses and other serious diseases, or from low-income groups, are especially affected by climatic variation. Studies have shown that the number of respiratory diseases in children and the elderly increases due to the higher concentrations of air pollution (Souza, et al, 2018c;Souza, et al, 2018d;Souza, et al, 2017;Santos, et al, 2017;Braga, et al, 1999;Braga, et al, 2001;Viegi, et al, 2009). According to these studies, children are more susceptible because they need twice the amount of air inhaled by adults, and the elderly are more affected because of their weakened immune and respiratory systems and have been exposed to a large amount of air pollution in all your life.…”