Improving spatial nitrogen dioxide prediction using diffusion tubes: a case study in West Central Scotland

It has been well documented that air pollution adversely affects health, and epidemiological pollutionhealth studies utilise pollution data from automatic monitors. However, these automatic monitors are small in number and hence spatially sparse, which does not allow an accurate representation of the spatial variation in pollution concentrations required for these epidemiological health studies. Nitrogen dioxide (NO2) diffusion tubes are also used to measure concentrations, and due to their lower cost compared to automatic monitors are much more prevalent. However, even combining both data sets still does not provide sufficient spatial coverage of NO2 for epidemiological studies, and modelled concentrations on a regular grid from atmospheric dispersion models are also available. This paper proposes the first modelling approach to using all three sources of NO2 data to make fine scale spatial predictions for use in epidemiological health studies. We propose a geostatistical fusion model that regresses combined NO2 concentrations from both automatic monitors and diffusion tubes against modelled NO2 concentrations from an atmospheric dispersion model in order to predict fine scale NO2 concentrations across our West Central Scotland study region. Our model exhibits a 47% improvement in fine scale spatial prediction of NO2 compared to using the automatic monitors alone, and we use it to predict NO2 concentrations across West Central Scotland in 2006

How robust are the estimated effects of air pollution on health? Accounting for model uncertainty using Bayesian model averaging

The long-term impact of air pollution on human health can be estimated from small-area ecological studies in which the health outcome is regressed against air pollution concentrations and other covariates, such as socio-economic deprivation. Socio-economic deprivation is multi-factorial and difficult to measure, and includes aspects of income, education, and housing as well as others. However, these variables are potentially highly correlated, meaning one can either create an overall deprivation index, or use the individual characteristics, which can result in a variety of pollution-health effects. Other aspects of model choice may affect the pollution-health estimate, such as the estimation of pollution, and spatial autocorrelation model. Therefore, we propose a Bayesian model averaging approach to combine the results from multiple statistical models to produce a more robust representation of the overall pollution-health effect. We investigate the relationship between nitrogen dioxide concentrations and cardio-respiratory mortality in West Central Scotland between 2006 and 2012

Spatial modelling of air pollution, deprivation and mortality in Scotland

Air pollution is not only a major risk to the environment, but also a major environmental risk to the health of the population in developed and developing countries. The health impact of both short-term and long-term exposure to air pollution has been the focus of much research in the past few decades, which has investigated the relationship between specific air pollutants, such as carbon monoxide (CO), nitrogen dioxide (NO_2), particulate matter (PM_2.5 and PM_10), and sulphur dioxide (SO_2), to cardiovascular and respiratory diseases. The health impact of short-term exposure is conducted through time series studies, whereas long-term exposure is investigated through cohort studies. Cohort studies are considered the gold-standard research design since inference is made at the individual level and can directly assess cause and effect. However, cohort studies are costly and require a long follow-up period meaning they take a long time to conduct. To counteract these limitations, spatial ecological studies are used instead, which make use of routinely available disease data and air pollutant concentrations at a small areal level, such as census tracts or postcodes. This is to ensure the population under study is relatively homogeneous within the areal unit in terms of socio-demographic characteristics, and thus complements inference from a cohort study. These studies quantify the health impact of exposure to air pollution by relating geographical contrasts between air pollutant concentrations and disease risk across the chosen spatial resolution. The disease data are counts of the numbers of disease cases occurring in each areal unit, and Poisson log-linear models are used to assess the pollutant-health relationship. Other covariate information, such as socio-economic deprivation, is also included to help explain the spatial pattern in disease risk. However, the residual disease risk after the covariate effects have been accounted for tends to contain spatial autocorrelation, which has to be modelled in order to make sound inferences. Residual spatial autocorrelation is typically modelled by a set of random effects that utilise a neighbourhood matrix in order to induce spatial autocorrelation into the model. There are a number of specifications to model this, but this thesis makes use of the Leroux specification due to its flexibility in being able to model both strong and weak spatial autocorrelation. An important issue with using a spatial ecological study design is the estimation of spatially representative pollutant concentrations that are available in each areal unit. Studies can typically use measured data from fixed-location monitors that are spatially sparse and do not provide a pollutant concentration for each areal unit; or they make use of modelled concentrations available at a fine grid square resolution, which are known to contain biases and no measure of uncertainty. There have been numerous statistical approaches to combine both sets of information in order to estimate accurate and spatially representative concentrations. This thesis will develop previous methodology that utilises extra data sources in order to improve the prediction performance of the model for use in a Scottish context. The overarching aim of this thesis is to investigate the cardio-respiratory health effects of long-term exposure to air pollution in West Central Scotland, UK. As the majority of air pollution in this region results from vehicle emissions, nitrogen dioxide (NO_2), a traffic-related gaseous pollutant, will be used to measure air pollution. Models investigating its health effect will incorporate predicted measures of NO_2 developed in this thesis. The sensitivity of the pollutant-health effect to the choice of NO_2 concentrations, indicator of deprivation, and choice of spatial model will be investigated. Changing these factors has been shown to modify estimated pollutant-health effects.\\ Findings in this thesis demonstrated that improvements in the accuracy of fine scale spatial prediction of NO_2 concentrations can be made by utilising extra sources of data in addition to the commonly-used monitoring stations. In addition, the estimated pollutant-health effect is not robust to the choice of the aforementioned factors and the choice of these factors can have a major impact on the resulting pollutant-health effects. This justified the combination of all statistical models into a single effect size, which estimated a small, but positive effect of NO_2 concentrations on cardio-respiratory ill health. However, the estimated NO_2-health relationship was not substantial, possibly due to the NO_2 concentrations in West Central Scotland being too low. Greater variation in the exposure would be needed to observe substantial health impacts

Exploiting new forms of data to study the private rented sector: strengths and limitations of a database of rental listings

Reviews of official statistics for UK housing have noted that developments have not kept pace with real‐world change, particularly the rapid growth of private renting. This paper examines the potential value of big data in this context. We report on the construction of a dataset from the on‐line adverts of one national lettings agency, describing the content of the dataset and efforts to validate it against external sources. The paper specifically examines what these data might add to our understanding of changing volumes and rents in the private rented sector. Fluctuations in market share across advertising platforms make assessment of volume problematic, while rental prices appear more robust through comparison with other reference information. Focussing on one urban area, we illustrate how the dataset can shed new light on local changes. Lastly, we discuss the issues involved in making more routine use of this kind of data

Quantifying the impact of current and future concentrations of air pollutants on respiratory disease risk in England

Abstract Background Estimating the long-term health impact of air pollution in a spatio-temporal ecological study requires representative concentrations of air pollutants to be constructed for each geographical unit and time period. Averaging concentrations in space and time is commonly carried out, but little is known about how robust the estimated health effects are to different aggregation functions. A second under researched question is what impact air pollution is likely to have in the future. Methods We conducted a study for England between 2007 and 2011, investigating the relationship between respiratory hospital admissions and different pollutants: nitrogen dioxide (NO2); ozone (O3); particulate matter, the latter including particles with an aerodynamic diameter less than 2.5 micrometers (PM2.5), and less than 10 micrometers (PM10); and sulphur dioxide (SO2). Bayesian Poisson regression models accounting for localised spatio-temporal autocorrelation were used to estimate the relative risks (RRs) of pollution on disease risk, and for each pollutant four representative concentrations were constructed using combinations of spatial and temporal averages and maximums. The estimated RRs were then used to make projections of the numbers of likely respiratory hospital admissions in the 2050s attributable to air pollution, based on emission projections from a number of Representative Concentration Pathways (RCP). Results NO2 exhibited the largest association with respiratory hospital admissions out of the pollutants considered, with estimated increased risks of between 0.9 and 1.6% for a one standard deviation increase in concentrations. In the future the projected numbers of respiratory hospital admissions attributable to NO2 in the 2050s are lower than present day rates under 3 Representative Concentration Pathways (RCPs): 2.6, 6.0, and 8.5, which is due to projected reductions in future NO2 emissions and concentrations. Conclusions NO2 concentrations exhibit consistent substantial present-day health effects regardless of how a representative concentration is constructed in space and time. Thus as concentrations are predicted to remain above limits set by European Union Legislation until the 2030s in parts of urban England, it will remain a substantial health risk for some time

Additional file 2 of Quantifying the impact of current and future concentrations of air pollutants on respiratory disease risk in England

Study data. This is a .zip file containing the data used in the study. The file also contains a read-me document summarising the content of the file. (ZIP 5070 kb

Additional file 1 of Quantifying the impact of current and future concentrations of air pollutants on respiratory disease risk in England

Supplementary data analysis. This file contains additional data description, numerical and graphical summaries, and analysis not presented in the main paper. (PDF 1010 kb

Effects of pre‐operative isolation on postoperative pulmonary complications after elective surgery: an international prospective cohort study

We aimed to determine the impact of pre-operative isolation on postoperative pulmonary complications after elective surgery during the global SARS-CoV-2 pandemic. We performed an international prospective cohort study including patients undergoing elective surgery in October 2020. Isolation was defined as the period before surgery during which patients did not leave their house or receive visitors from outside their household. The primary outcome was postoperative pulmonary complications, adjusted in multivariable models for measured confounders. Pre-defined sub-group analyses were performed for the primary outcome. A total of 96,454 patients from 114 countries were included and overall, 26,948 (27.9%) patients isolated before surgery. Postoperative pulmonary complications were recorded in 1947 (2.0%) patients of which 227 (11.7%) were associated with SARS-CoV-2 infection. Patients who isolated pre-operatively were older, had more respiratory comorbidities and were more commonly from areas of high SARS-CoV-2 incidence and high-income countries. Although the overall rates of postoperative pulmonary complications were similar in those that isolated and those that did not (2.1% vs 2.0%, respectively), isolation was associated with higher rates of postoperative pulmonary complications after adjustment (adjusted OR 1.20, 95%CI 1.05-1.36, p = 0.005). Sensitivity analyses revealed no further differences when patients were categorised by: pre-operative testing; use of COVID-19-free pathways; or community SARS-CoV-2 prevalence. The rate of postoperative pulmonary complications increased with periods of isolation longer than 3 days, with an OR (95%CI) at 4-7 days or >= 8 days of 1.25 (1.04-1.48), p = 0.015 and 1.31 (1.11-1.55), p = 0.001, respectively. Isolation before elective surgery might be associated with a small but clinically important increased risk of postoperative pulmonary complications. Longer periods of isolation showed no reduction in the risk of postoperative pulmonary complications. These findings have significant implications for global provision of elective surgical care