Two-stage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions

Exposure to high levels of air pollutant concentration is known to be associated with respiratory problems which can translate into higher morbidity and mortality rates. The link between air pollution and population health has mainly been assessed considering air quality and hospitalisation or mortality data. However, this approach limits the analysis to individuals characterised by severe conditions. In this paper we evaluate the link between air pollution and respiratory diseases using general practice drug prescriptions for chronic respiratory diseases, which allow to draw conclusions based on the general population. We propose a two-stage statistical approach: in the first stage we specify a space-time model to estimate the monthly NO2 concentration integrating several data sources characterised by different spatio-temporal resolution; in the second stage we link the concentration to the β2-agonists prescribed monthly by general practices in England and we model the prescription rates through a small area approach

A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data

Air pollution in urban regions remains a crucial subject of study, given its implications on health and environment, where much effort is often put into monitoring pollutants and producing accurate trend estimates over time, employing expensive tools and sensors. In this work, we study the problem of air quality estimation in the urban area of Milan (IT), proposing different machine learning approaches that combine meteorological and transit-related features to produce affordable estimates without introducing sensor measurements into the computation. We investigated different configurations employing machine and deep learning models, namely a linear regressor, an Artificial Neural Network using Bayesian regularization, a Random Forest regressor and a Long Short Term Memory network. Our experiments show that affordable estimation results over the pollutants can be achieved even with simpler linear models, therefore suggesting that reasonably accurate Air Quality Index (AQI) measurements can be obtained without the need for expensive equipment

Assessing CoDa regression for modelling daily multivariate air pollutants evolution

The application of the theory of compositional data in multivariate spatio- temporal statistical models is still scarce, even though the results obtained are robust. Actually, this kind of models are attractive to pollution model developers, due to, its versatility in the spatio-temporal variables; but nobody has tried to use it with compositional data yet. The main differences between a conventional model and two CoDa models (with two sequential binary partition, SBP) were analyzed. The first SBP was proposed by pollutants relationship interpretation, and the second one was imposed as standard SBP (R studio). Initially the conventional temporal model is used to predicting pollution levels to fill missing data or predicting pollution levels on future days. The application of compositional data theory in conventional temporal air quality models allowed to obtain acceptable quality models, whose results were adjusted to the observed values. Nash-Sutcliffe Efficiency Index (NSE) and root- mean-square error (RMSE), were used to evaluating the model quality and fitted values respectively.Postprint (published version