Association between respiratory hospital admissions and air quality in Portugal: A count time series approach

Abstract

Although regulatory improvements for air quality in the European Union have been made, air pollution is still a pressing problem and, its impact on health, both mortality and morbidity, is a topic of intense research nowadays. The main goal of this work is to assess the impact of the exposure to air pollutants on the number of daily hospital admissions due to respiratory causes in 58 spatial locations of Portugal mainland, during the period 2005-2017. To this end, INteger Generalised AutoRegressive Conditional Heteroskedastic (INGARCH)-based models are extensively used. This family of models has proven to be very useful in the analysis of serially dependent count data. Such models include information on the past history of the time series, as well as the effect of external covariates. In particular, daily hospitalisation counts, air quality and temperature data are endowed within INGARCH models of optimal orders, where the automatic inclusion of the most significant covariates is carried out through a new block-forward procedure. The INGARCH approach is adequate to model the outcome variable (respiratory hospital admissions) and the covariates, which advocates for the use of count time series approaches in this setting. Results show that the past history of the count process carries very relevant information and that temperature is the most determinant covariate, among the analysed, for daily hospital respiratory admissions. It is important to stress that, despite the small variability explained by air quality, all models include on average, approximately two air pollutants covariates besides temperature. Further analysis shows that the one-step-ahead forecasts distributions are well separated into two clusters: one cluster includes locations exclusively in the Lisbon area (exhibiting higher number of one-step-ahead hospital admissions forecasts), while the other contains the remaining locations. This results highlights that special attention must be given to air quality in Lisbon metropolitan area in order to decrease the number of hospital admissions.

Systematic Approach to Determining True Long Memory on PM10 Data

“…Nine out of ten people today breathe air that exceeds the acceptable threshold of pollutants approved by the World Health Organization (WHO), making air pollution an international issue responsible for roughly seven million annual global deaths through stroke, heart disease, lung cancer, diabetes, asthma, and other chronic respiratory diseases (Goshua et al 2021;Martins et al 2021). Air pollution can primarily be defined in terms of air quality index (AQI), consisting of six standard pollutants, including carbon monoxide (CO), ozone (O3), particulate matter (PM10 and PM2.5), nitric oxide (NO), and nitrogen dioxide (NO2).…”

Statistical Approach to Examining the True Status of Long Memory and Volatility Persistence in PM10 Air Pollutant at Different Regions of Malaysia: A Methodical Methodology

“…Nine out of ten people today breathe air that exceeds the acceptable threshold of pollutants approved by the World Health Organization (WHO), making air pollution an international issue responsible for roughly seven million annual global deaths through stroke, heart disease, lung cancer, diabetes, asthma, and other chronic respiratory diseases (Goshua et al 2021;Martins et al 2021). Air pollution can primarily be de ned in terms of air quality index (AQI), consisting of six standard pollutants, including carbon monoxide (CO), ozone (O 3 ), particulate matter (PM 10 and PM 2.5 ), nitric oxide (NO), and nitrogen dioxide (NO 2 ).…”