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

Comparing air quality among Italy, Germany and Poland using BC indexes

In this paper we discuss air quality assessment in three Italian, German and Polish regions using the index methodology proposed in Bruno and Cocchi (2002, 2007). This analysis focuses first of all on the quality of the air in each of the countries being taken into consideration, and then adopts a more general approach in order to compare pollution severity and toxicity. This is interesting in a global European perspective where all countries are commonly involved in assessing air quality and taking proper measures for improving it. In this context, air quality indexes are a powerful data-driven tool which are easily calculated and summarize a complex phenomenon, such as air pollution, in indicators which are immediately understandable. In particular, the main objective of this work is to evaluate the index performances in distinguishing different air pollution patterns. This kind of analysis can be particularly useful, for example, in the perspective of constructing an indicator of air pollution. --

Comparing Methods to Retrieve Tweets: a Sentiment Approach

[EN] In current times Internet and social media have become almost unavoidabletools to support research and decision making processes in various fields.Nevertheless, the collection and use of data retrieved from these types ofsources pose different challenges. In a previous paper we compared theefficiency of three alternative methods used to retrieve geolocated tweets overan entire country (United Kingdom). One method resulted as the bestcompromise in terms of both the effort needed to set it and quantity/quality ofdata collected. In this work we further check, in term of content, whether thethree compared methods are able to produce “similar information”. Inparticular, we aim at checking whether there are differences in the level ofsentiment estimated using tweets coming from the three methods. In doing so,we take into account both a cross-section and a longitudinal perspective. Ourresults confirm that our current best option does not show any significantdifference in the sentiment, producing globally scores in between the scoresobtained using the two alternative methods. Thus, such a flexible and reliablemethod can be implemented in the data collection of geolocated tweets in othercountries and for other studies based on the sentiment analysis.Schlosser, S.; Toninelli, D.; Cameletti, M. (2020). Comparing Methods to Retrieve Tweets: a Sentiment Approach. Editorial Universitat Politècnica de València. 299-306. https://doi.org/10.4995/CARMA2020.2020.11653OCS29930

Software for Bayesian Statistics

In this summary we introduce the papers published in the special issue on Bayesian statistics. This special issue comprises 20 papers on Bayesian statistics and Bayesian inference on different topics such as general packages for hierarchical linear model fitting, survival models, clinical trials, missing values, time series, hypothesis testing, priors, approximate Bayesian computation, and others

Missing data analysis and imputation via latent Gaussian Markov random fields

Acknowledgements. V. Gomez-Rubio has been supported by grants MTM2016-77501-P and PID2019-106341GB-I00 from the Spanish Ministry of Economy and Competitiveness co-fnanced with FEDER funds, grant SBPLY/17/180501/000491 and SBPLY/21/180501/000241 funded by Consejería de Educacion, Cultura y Deportes (JCCM, Spain) and FEDER. Marta Blangiardo acknowledges partial support through the grant R01HD092580 funded by the National Institute of Health and from the MRC Centre for Environment and Health, which is currently funded by the Medical Research Council (MR/S019669/1).This paper recasts the problem of missing values in the covariates of a regression model as a latent Gaussian Markov random field (GMRF) model in a fully Bayesian framework. The proposed approach is based on the definition of the covariate imputation sub-model as a latent effect with a GMRF structure. This formulation works for continuous covariates but for categorical covariates a typical multiple imputation approach is employed. Both techniques can be easily combined for the case in which continuous and categorical variables have missing values. The resulting Bayesian hierarchical model naturally fts within the integrated nested Laplace approximation (INLA) framework, which is used for model fitting. Hence, this work fills an important gap in the INLA methodology as it allows to treat models with missing values in the covariates. As in any other fully Bayesian framework, by relying on INLA for model fitting it is possible to formulate a joint model for the data, the imputed covariates and their missingness mechanism. In this way, it is possible to tackle the more general problem of assessing the missingness mechanism by conducting a sensitivity analysis on the different alternatives to model the non-observed covariates. Finally, the proposed approach is illustrated in two examples on modeling health risk factors and disease mapping

A statistical emulator for multivariate model outputs with missing values

Statistical emulators are used to approximate the output of complex physical models when their computational burden limits any sensitivity and uncertainty analysis of model output to variation in the model inputs. In this paper, we introduce a flexible emulator which is able to handle multivariate model outputs and missing values. The emulator is based on a spatial model and the D-STEM software, which is extended to include emulator fitting using the EM algorithm. The missing values handling capabilities of the emulator are exploited to keep the number of model output realisations as low as possible when the computing burden of each realisation is high. As a case study, we emulate the output of the Atmospheric Dispersion Modelling System (ADMS) used by the Scottish Environment Protection Agency (SEPA) to model the air quality of the city of Aberdeen (UK). With the emulator, we study the city air quality under a discrete set of realisations and identify conditions under which, with a given probability, the 40μg−m3 yearly average concentration limit for NO2 of EU legislation is not exceeded at the locations of the city monitoring stations. The effect of missing values on the emulator estimation and probability of exceedances are studied by means of simulations

Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic

In this study we present the first comprehensive analysis of the spatio-temporal differences in excess mortality during the COVID-19 pandemic in Italy. We used a population-based design on all-cause mortality data, for the 7,904 Italian municipalities. We estimated sex-specific weekly mortality rates for each municipality, based on the first four months of 2016-2019, while adjusting for age, localised temporal trends and the effect of temperature. Then, we predicted all-cause weekly deaths and mortality rates at municipality level for the same period in 2020, based on the modelled spatio-temporal trends. Lombardia showed higher mortality rates than expected from the end of February, with 23,946 (23,013 to 24,786) total excess deaths. North-West and North-East regions showed one week lag, with higher mortality from the beginning of March and 6,942 (6,142 to 7,667) and 8,033 (7,061 to 9,044) total excess deaths respectively. We observed marked geographical differences also at municipality level. For males, the city of Bergamo (Lombardia) showed the largest percent excess, 88.9% (81.9% to 95.2%), at the peak of the pandemic. An excess of 84.2% (73.8% to 93.4%) was also estimated at the same time for males in the city of Pesaro (Central Italy), in stark contrast with the rest of the region, which does not show evidence of excess deaths. We provided a fully probabilistic analysis of excess mortality during the COVID-19 pandemic at sub-national level, suggesting a differential direct and indirect effect in space and time. Our model can be used to help policy-makers target measures locally to contain the burden on the health-care system as well as reducing social and economic consequences. Additionally, this framework can be used for real-time mortality surveillance, continuous monitoring of local temporal trends and to flag where and when mortality rates deviate from the expected range, which might suggest a second wave of the pandemic