109 results found
Huang G, Blangiardo M, Brown PE, et al., 2021, Long-term exposure to air pollution and COVID-19 incidence: A multi-country study, Spatial and Spatio-temporal Epidemiology, Vol: 39, Pages: 1-11, ISSN: 1877-5845
The study of the impacts of air pollution on COVID-19 has gained increasing attention. However, most of the existing studies are based on a single country, with a high degree of variation in the results reported in different papers. We attempt to inform the debate about the long-term effects of air pollution on COVID-19 by conducting a multi-country analysis using a spatial ecological design, including Canada, Italy, England and the United States. The model allows the residual spatial autocorrelation after accounting for covariates. It is concluded that the effects of PM2.5 and NO2 are inconsistent across countries. Specifically, NO2 was not found to be an important factor affecting COVID-19 infection, while a large effect for PM2.5 in the US is not found in the other three countries. The Population Attributable Fraction for COVID-19 incidence ranges from 3.4% in Canada to 45.9% in Italy, although with considerable uncertainty in these estimates.
Elfadaly FG, Adamson A, Patel J, et al., 2021, BIMAM—a tool for imputing variables missing across datasets using a Bayesian imputation and analysis model, International Journal of Epidemiology, ISSN: 0300-5771
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>Combination of multiple datasets is routine in modern epidemiology. However, studies may have measured different sets of variables; this is often inefficiently dealt with by excluding studies or dropping variables. Multilevel multiple imputation methods to impute these ‘systematically’ missing data (as opposed to ‘sporadically’ missing data within a study) are available, but problems may arise when many random effects are needed to allow for heterogeneity across studies. We show that the Bayesian IMputation and Analysis Model (BIMAM) implemented in our tool works well in this situation.</jats:p> </jats:sec> <jats:sec> <jats:title>General features</jats:title> <jats:p>BIMAM performs imputation and analysis simultaneously. It imputes both binary and continuous systematically and sporadically missing data, and analyses binary and continuous outcomes. BIMAM is a user-friendly, freely available tool that does not require knowledge of Bayesian methods. BIMAM is an R Shiny application. It is downloadable to a local machine and it automatically installs the required freely available packages (R packages, including R2MultiBUGS and MultiBUGS).</jats:p> </jats:sec> <jats:sec> <jats:title>Availability</jats:title> <jats:p>BIMAM is available at [www.alecstudy.org/bimam].</jats:p> </jats:sec>
Davies B, Parkes B, Bennett J, et al., 2021, Community factors and excess mortality in first wave of the COVID-19 pandemic in England, Nature Communications, ISSN: 2041-1723
Risk factors for increased risk of death from Coronavirus Disease 19 (COVID-19) have been identified but less is known on characteristics that make communities resilient or vulnerable to the mortality impacts of the pandemic. We applied a two-stage Bayesian spatial model to quantify inequalities in excess mortality at the community level during the first wave of the pandemic in England. We used geocoded data on all deaths in people aged 40 years and older during March-May 2020 compared with 2015-2019 in 6,791 local communities. Here we show that communities with an increased risk of excess mortality had a high density of care homes, and/or high proportion of residents on income support, living in overcrowded homes and/or high percent of people with a non-White ethnicity (including Black, Asian and other minority ethnic groups). Conversely, after accounting for other community characteristics, we found no association between population density or air pollution and excess mortality. Overall, the social and environmental variables accounted for around 15% of the variation in mortality at community level. Effective and timely public health and healthcare measures that target the communities at greatest risk are urgently needed if England and other industrialised countries are to avoid further widening of inequalities in mortality patterns as the pandemic progresses.
Burney P, Patel J, Minelli C, et al., 2021, Prevalence and population attributable risk for chronic airflow obstruction in a large multinational study, American Journal of Respiratory and Critical Care Medicine, Vol: 203, Pages: 1353-1365, ISSN: 1073-449X
Rationale: The Global Burden of Disease programme identified smoking, and ambient and household air pollution as the main drivers of death and disability from Chronic Obstructive Pulmonary Disease (COPD). Objective: To estimate the attributable risk of chronic airflow obstruction (CAO), a quantifiable characteristic of COPD, due to several risk factors. Methods: The Burden of Obstructive Lung Disease study is a cross-sectional study of adults, aged≥40, in a globally distributed sample of 41 urban and rural sites. Based on data from 28,459 participants, we estimated the prevalence of CAO, defined as a post-bronchodilator one-second forced expiratory volume to forced vital capacity ratio < lower limit of normal, and the relative risks associated with different risk factors. Local RR were estimated using a Bayesian hierarchical model borrowing information from across sites. From these RR and the prevalence of risk factors, we estimated local Population Attributable Risks (PAR). Measurements and Main Results: Mean prevalence of CAO was 11.2% in men and 8.6% in women. Mean PAR for smoking was 5.1% in men and 2.2% in women. The next most influential risk factors were poor education levels, working in a dusty job for ≥10 years, low body mass index (BMI), and a history of tuberculosis. The risk of CAO attributable to the different risk factors varied across sites. Conclusions: While smoking remains the most important risk factor for CAO, in some areas poor education, low BMI and passive smoking are of greater importance. Dusty occupations and tuberculosis are important risk factors at some sites.
Konstantinoudis G, Padellini T, Bennett J, et al., 2021, Response to "Re: Long-term exposure to air-pollution and COVID-19 mortality in England: A hierarchical spatial analysis", ENVIRONMENT INTERNATIONAL, Vol: 150, ISSN: 0160-4120
Lowe R, Lee SA, O'Reilly KM, et al., 2021, Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study, LANCET PLANETARY HEALTH, Vol: 5, Pages: E209-E219, ISSN: 2542-5196
Konstantinoudis G, Padellini T, Bennett J, et al., 2020, Long-term exposure to air-pollution and COVID-19 mortality in England: a hierarchical spatial analysis, Environment International, ISSN: 0160-4120
Recent studies suggested a link between long-term exposure to air-pollution and COVID-19 mortality. However, due to their ecological design based on large spatial units, they neglect the strong localised air-pollution patterns, and potentially lead to inadequate confounding adjustment. We investigated the effect of long-term exposure to NO2 and PM2.5 on COVID-19 deaths up to June 30, 2020 in England using high geographical resolution. In this nationwide cross-sectional study in England, we included 38,573 COVID-19 deaths up to June 30, 2020 at the Lower Layer Super Output Area level (n=32,844 small areas). We retrieved averaged NO2 and PM2.5 concentration during 2014-2018 from the Pollution Climate Mapping. We used Bayesian hierarchical models to quantify the effect of air-pollution while adjusting for a series of confounding and spatial autocorrelation. We find a 0.5% (95% credible interval: -0.2%, 1.2%) and 1.4% (95% CrI: -2.1%, 5.1%) increase in COVID-19 mortality risk for every 1μg/m3 increase in NO2 and PM2.5 respectively, after adjusting for confounding and spatial autocorrelation. This corresponds to a posterior probability of a positive effect equal to 0.93 and 0.78 respectively. The spatial relative risk at LSOA level revealed strong patterns, similar for the different pollutants. This potentially captures the spread of the disease during the first wave of the epidemic. Our study provides some evidence of an effect of long-term NO2 exposure on COVID-19 mortality, while the effect of PM2.5 remains more uncertain.
Boulieri A, Blangiardo M, 2020, A spatio-temporal model to estimate life expectancy and to detect unusual trends at the local authority level in England, BMJ Open, Vol: 10, ISSN: 2044-6055
Objectives To estimate life expectancy at the local authority level and detect those areas that have a substantially low life expectancy after accounting for deprivation.Design We used registration data from the Office for National Statistics on mortality and population in England, by local authority, age group and socioeconomic deprivation decile, for both men and women over the period 2001–2018. We used a statistical model within the Bayesian framework to produce robust mortality rates, which were then transformed to life expectancy estimates. A rule based on exceedance probabilities was used to detect local authorities characterised by a low life expectancy among areas with a similar deprivation level from 2012 onwards.Results We confirmed previous findings showing differences in the life expectancy gap between the most and least deprived areas from 2012 to 2018. We found variations in life expectancy trends across local authorities, and we detected a number of those with a low life expectancy when compared with others of a similar deprivation level.Conclusions There are factors other than deprivation that are responsible for low life expectancy in certain local authorities. Further investigation on the detected areas can help understand better the stalling of life expectancy which was observed from 2012 onwards and plan efficient public health policies.
Boszczowski Í, Neto FC, Blangiardo M, et al., 2020, Total antibiotic use in a state-wide area and resistance patterns in Brazilian hospitals: an ecologic study, Brazilian Journal of Infectious Diseases, Vol: 24, Pages: 479-488, ISSN: 1413-8670
INTRODUCTION: Use of antibiotic and bacterial resistance is the result of a complex interaction not completely understood. OBJECTIVES: To evaluate the impact of entire antimicrobial use (community plus hospitals) on the incidence of bloodstream infections in intensive care units adjusted by socioeconomic factors, quality of healthcare, and access to the healthcare system. DESIGN: Ecologic study using a hierarchical spatial model. SETTING: Data obtained from 309 hospitals located in the state of São Paulo, Brazil from 2008 to 2011. PARTICIPANTS: Intensive care units located at participant hospitals. OUTCOME: Hospital acquired bloodstream infection caused by MDRO in ICU patients was our primary outcome and data were retrieved from São Paulo Health State Department. Socioeconomic and healthcare indexes data were obtained from IBGE (Brazilian Foundation in charge of national decennial census) and SEADE (São Paulo Planning and Development Department). Information on antimicrobial sales were obtained from IMS Brazil. We divided antibiotics into four different groups (1-4). RESULTS: We observed a direct association between the use of group 1 of antibiotics and the incidences of bloodstream infections caused by MRSA (1.12; 1.04-1.20), and CR-Acinetobacter sp. (1.19; 1.10-1.29). Groups 2 and 4 were directly associated to VRE (1.72; 1.13-2.39 and 2.22; 1.62-2.98, respectively). Group 2 was inversely associated to MRSA (0.87; 0.78-0.96) and CR-Acinetobacter sp. (0.79; 0.62-0.97). Group 3 was inversely associated to Pseudomonas aeruginosa (0.69; 0.45-0.98), MRSA (0.85; 0.72-0.97) and VRE (0.48; 0.21-0.84). No association was observed for third generation cephalosporin-resistant Klebsiella pneumoniae and Escherichia coli. CONCLUSIONS: The association between entire antibiotic use and resistance in ICU was poor and not consistent for all combinations of antimicrobial groups and pathogens even after adjusted by socioeconomic indexes. Selective pressure exerted
Sera F, Hashizume M, Honda Y, et al., 2020, Air conditioning and heat-related mortality: a multi-country longitudinal study, Epidemiology, Vol: 31, Pages: 779-787, ISSN: 1044-3983
Background: Air conditioning has been proposed as one of the key factors explaining reductions of heat-related mortality risks observed in the last decades. However, direct evidence is still limited.Methods: We used a multi-country, multi-city, longitudinal design to quantify the independent role of air conditioning in reported attenuation in risk. We collected daily time series of mortality, mean temperature, and yearly air conditioning prevalence for 311 locations in Canada, Japan, Spain, and the USA between 1972 and 2009. For each city and sub-period, we fitted a quasi-Poisson regression combined with distributed lag non-linear models to estimate summer-only temperature–mortality associations. At the second stage, we used a novel multilevel, multivariate spatio-temporal meta-regression model to evaluate effect modification of air conditioning on heat–mortality associations. We computed relative risks and fractions of heat-attributable excess deaths under observed and fixed air conditioning prevalences.Results: Results show an independent association between increased air conditioning prevalence and lower heat-related mortality risk. Excess deaths due to heat decreased during the study periods from 1.40% to 0.80% in Canada, 3.57% to 1.10% in Japan, 3.54% to 2.78% in Spain, and 1.70% to 0.53% in the USA. However, increased air conditioning explains only part of the observed attenuation, corresponding to 16.7% in Canada, 20.0% in Japan, 14.3% in Spain, and 16.7% in the USA.Conclusions: Our findings are consistent with the hypothesis that air conditioning represents an effective heat adaptation strategy, but suggests that other factors have played an equal or more important role in increasing the resilience of populations.
Pirani M, Mason A, Hansell A, et al., 2020, A flexible hierarchical framework for improving inference in area-referenced environmental health studies, Biometrical Journal: journal of mathematical methods in biosciences, Vol: 62, Pages: 1650-1669, ISSN: 0323-3847
Study designs where data have been aggregated by geographical areas are popular in environmental epi-demiology. These studies are commonly based on administrative databases and, providing a completespatial coverage, are particularly appealing to make inference on the entire population. However, the re-sulting estimates are often biased and difficult to interpret due to unmeasured confounders, which typicallyare not available from routinely collected data. We propose a framework to improve inference drawn fromsuch studies exploiting information derived from individual-level survey data. The latter are summarized inan area-level scalar score by mimicking at ecological-level the well-known propensity score methodology.The literature on propensity score for confounding adjustment is mainly based on individual-level studiesand assumes a binary exposure variable. Here we generalize its use to cope with area-referenced stud-ies characterized by a continuous exposure. Our approach is based upon Bayesian hierarchical structuresspecified into a two-stage design: (i) geolocated individual-level data from survey samples are up-scaled atecological-level, then the latter are used to estimate a generalizedecological propensity score(EPS) in thein-sample areas; (ii) the generalized EPS is imputed in the out-of-sample areas under different assumptionsabout the missingness mechanisms, then it is included into the ecological regression, linking the exposureof interest to the health outcome. This delivers area-level risk estimates which allow a fuller adjustment forconfounding than traditional areal studies. The methodology is illustrated by using simulations and a casestudy investigating the risk of lung cancer mortality associated with nitrogen dioxide in England (UK).
Caputo B, Manica M, Filipponi F, et al., 2020, ZanzaMapp: a scalable citizen science tool to monitor perception of mosquito abundance and nuisance in Italy and beyond, International Journal of Environmental Research and Public Health, Vol: 17, ISSN: 1660-4601
Mosquitoes represent a considerable nuisance and are actual/potential vectors of human diseases in Europe. Costly and labour-intensive entomological monitoring is needed to correct planning of interventions aimed at reducing nuisance and the risk of pathogen transmission. The widespread availability of mobile phones and of massive Internet connections opens the way to the contribution of citizen in complementing entomological monitoring. ZanzaMapp is the first mobile “mosquito” application for smartphones specifically designed to assess citizens’ perception of mosquito abundance and nuisance in Italy. Differently from other applications targeting mosquitoes, ZanzaMapp prioritizes the number of records over their scientific authentication by requesting users to answer four simple questions on perceived mosquito presence/abundance/nuisance and geo-localizing the records. The paper analyses 36,867 ZanzaMapp records sent by 13,669 devices from 2016 to 2018 and discusses the results with reference to either citizens’ exploitation and appreciation of the app and to the consistency of the results obtained with the known biology of main mosquito species in Italy. In addition, we provide a first small-scale validation of ZanzaMapp data as predictors of Aedes albopictus biting females and examples of spatial analyses and maps which could be exploited by public institutions and administrations involved in mosquito and mosquito-borne pathogen monitoring and control.
Blangiardo M, Cameletti M, Pirani M, et al., 2020, Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic, PLoS One, Vol: 15, ISSN: 1932-6203
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.
Lavigne A, Freni Sterrantino A, Fecht D, et al., 2020, A spatial joint analysis of metal constituents of ambient particulate matter and mortality in England, Environmental Epidemiology, Vol: 4, Pages: e098-e098, ISSN: 2474-7882
Background Few studies have investigated associations between metal components of particulate matter on mortality due to well-known issues of multicollinearity. Here, we analyze these exposures jointly to evaluate their associations with mortality on small area data.Methods We fit a Bayesian Profile Regression (BPR) to account for the multicollinearity in the elemental components (iron, copper and zinc) of PM10 and PM2.5. The models are developed in relation to mortality from cardiovascular and respiratory disease and lung cancer incidence in 2008-11 at small area level, for a population of 13.6 million in the London-Oxford area of England.Results From the BPR, we identified higher risks in the PM10 fraction cluster likely to represent the study area, excluding London, for cardiovascular mortality RR 1.07 (95%CI 1.02, 1.12) and for respiratory mortality RR 1.06 (95%CI 0.99, 1.31), compared to the study mean. For PM2.5 fraction, higher risks were seen for cardiovascular mortality RR 1.55 (CI 95% 1.38, 1.71) and respiratory mortality RR 1.51 (CI 95% 1.33, 1.72), likely to represent the 'highways' cluster. We did not find relevant associations for lung cancer incidence.Conclusion Our analysis showed small but not fully consistent adverse associations between health outcomes and particulate metal exposures. The BPR approach identified subpopulations with unique exposure profiles and provided information about the geographical location of these to help interpret findings.
Boulieri A, Bennett JE, Blangiardo M, 2020, A Bayesian mixture modelling approach for public health surveillance, Biostatistics, Vol: 21, Pages: 369-383, ISSN: 1465-4644
Spatial monitoring of trends in health data plays an important part of public health surveillance. Most commonly, it is used to understand the etiology of a public health issue, to assess the impact of an intervention, or to provide detection of unusual behavior. In this article, we present a Bayesian mixture model for public health surveillance, which is able to provide estimates of the disease risk in space and time, and also to detect areas with unusual behavior. The model is designed to deal with a range of spatial and temporal patterns in the data, and with time series of different lengths. We carry out a simulation study to assess the performance of the model under different scenarios, and we compare it against a recently proposed Bayesian model for short time series. Finally, the proposed model is used for surveillance of road traffic accidents data in England over the years 2005–2015.
Cai Y, Hansell AL, Granell R, et al., 2020, Prenatal, early-life and childhood exposure to air pollution and lung function: the ALSPAC cohort, American Journal of Respiratory and Critical Care Medicine, Vol: 202, Pages: 112-123, ISSN: 1073-449X
RATIONALE: Exposure to air pollution during intrauterine development and through childhood may have lasting effects on respiratory health. OBJECTIVES: To investigate lung function at ages 8 and 15 years in relation to air pollution exposures during pregnancy, infancy and childhood in a UK population-based birth cohort. METHODS: Individual exposures to source-specific particulate matter with diameter ≤10µm (PM10) during each trimester, 0-6 months, 7-12 months (1990-1993) and up to age 15 years (1991-2008) were examined in relation to %predicted Forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) at ages 8(N=5,276) and 15(N=3,446) years, usinglinear regression models adjusted for potential confounders. A profile regression model was used to identify sensitive time periods. MEASUREMENTS AND MAIN RESULTS: We did not find clear evidence for a sensitive exposure period for PM10 from road-traffic: at age 8 years, 1µg/m3 higher exposure during the first trimester was associated with lower %predicted of FEV1(-0.826, 95%CI:-1.357 to -0.296) and FVC(-0.817, 95%CI:-1.357 to -0.276), but similar associations were seen for exposures for other trimesters, 0-6 months, 7-12 months, and 0-7 years. Associations were stronger among boys, children whose mother had a lower education level or smoked during pregnancy. For PM10 from all sources, the third trimester was associated with lower %predicted of FVC (-1.312, 95%CI: -2.100 to -0.525). At age 15 years, no adverse associations were seen with lung function. CONCLUSIONS: Exposure to road-traffic PM10 during pregnancy may result in small but significant reductions in lung function at age 8 years.
Forlani C, Bhatt S, Cameletti M, et al., 2020, A joint bayesian space-time model to integrate spatially misaligned air pollution data in R-INLA, Environmetrics, Vol: 31, Pages: 1-17, ISSN: 1099-095X
In air pollution studies, dispersion models provide estimates of concentration at grid level covering the entire spatial domain, and are then calibrated against measurements from monitoring stations. However, these different data sources are misaligned in space and time. If misalignment is not considered, it can bias the predictions. We aim at demonstrating how the combination of multiple data sources, such as dispersion model outputs, ground observations and covariates, leads to more accurate predictions of air pollution at grid level. We consider nitrogen dioxide (NO2) concentration in Greater London and surroundings for the years 2007‐2011, and combine two different dispersion models. Different sets of spatial and temporal effects are included in order to obtain the best predictive capability. Our proposed model is framed in between calibration and Bayesian melding techniques for data fusion. Unlike other examples, we jointly model the response (concentration level at monitoring stations) and the dispersion model outputs on different scales, accounting for the different sources of uncertainty. Our spatio‐temporal model allows us to reconstruct the latent fields of each model component, and to predict daily pollution concentrations. We compare the predictive capability of our proposed model with other established methods to account for misalignment (e.g. bilinear interpolation), showing that in our case study the joint model is a better alternative.
Marques Moralejo Bermudi P, Nunes Carneiro Castro Costa D, Maroni Nunes C, et al., 2020, Canine serological survey and dog culling ant its relationship with Human Visceral leishmaniasis in an endemic urban area., BMC Infectious Diseases, Vol: 20, Pages: 1-11, ISSN: 1471-2334
BackgroundVisceral leishmaniasis is an important but neglected disease that is spreading and is highly lethal when left untreated. This study sought to measure the Leishmania infantum seroprevalence in dogs, the coverage of its control activities (identification of the canine reservoir by serological survey, dog culling and insecticide spraying) and to evaluate its relationship with the occurrence of the disease in humans in the municipalities of Araçatuba and Birigui, state of São Paulo, Brazil.MethodsInformation from 2006 to 2015 was georeferenced for each municipality and modeling was performed for the two municipalities together. To do this, latent Gaussian Bayesian models with the incorporation of a spatio-temporal structure and Poisson distribution were used. The Besag-York-Mollie models were applied for random spatial effects, as also were autoregressive models of order 1 for random temporal effects. The modeling was performed using the INLA (Integrated Nested Laplace Approximations) deterministic approach, considering both the numbers of cases as well as the coverage paired year by year and lagged at one and two years.ResultsControl activity coverage was observed to be generally low. The behavior of the temporal tendency in the human disease presented distinct patterns in the two municipalities, however, in both the tendency was to decline. The canine serological survey presented as a protective factor only in the two-year lag model.ConclusionsThe canine serological coverage, even at low intensity, carried out jointly with the culling of the positive dogs, suggested a decreasing effect on the occurrence of the disease in humans, whose effects would be seen two years after it was carried out.
Blangiardo M, Boulieri A, Diggle P, et al., 2020, Advances in spatio-temporal models for non-communicable disease surveillance, International Journal of Epidemiology, Vol: 49, Pages: i26-i37, ISSN: 1464-3685
Surveillance systems are commonly used to provide early warning detection or to assess an impact of an intervention/policy. Traditionally, the methodological and conceptual frameworks for surveillance have been designed for infectious diseases, but the rising burden of non-communicable diseases (NCDs) worldwide suggests a pressing need for surveillance strategies to detect unusual patterns in the data and to help unveil important risk factors in this setting. Surveillance methods need to be able to detect meaningful departures from expectation and exploit dependencies within such data to produce unbiased estimates of risk as well as future forecasts. This has led to the increasing development of a range of space-time methods specifically designed for NCD surveillance.We present an overview of recent advances in spatio-temporal disease surveillance for NCDs using hierarchically specified models. This provides a coherent framework for modelling complex data structures, dealing with data sparsity, exploiting dependencies between data sources and propagating the inherent uncertainties present in both the data and the modelling process. We then focus on three commonly used models within the Bayesian Hierarchical Model (BHM) framework and through a simulation study we compare their performance.We also discuss some challenges faced by researchers when dealing with NCD surveillance, including how to account for false detection and the modifiable areal unit problem. Finally, we consider how to use and interpret the complex models, how model selection may vary depending on the intended user group and how best to communicate results to stakeholders and the general public.
Piel F, Fecht D, Hodgson S, et al., 2020, Small-area methods for investigation of environment and health, International Journal of Epidemiology, Vol: 49, Pages: 686-699, ISSN: 1464-3685
Small-area studies offer a powerful epidemiological approach to study disease patterns at the population level and assess health risks posed by environmental pollutants. They involve a public health investigation on a geographic scale (e.g. neighbourhood) with overlay of health, environmental, demographic and potential confounder data. Recent methodological advances, including Bayesian approaches, combined with fast growing computational capabilities permit more informative analyses than previously possible, including the incorporation of data at different scales, from satellites to individual-level survey information. Better data availability has widened the scope and utility of small-area studies, but also led to greater complexity, including choice of optimal study area size and extent, duration of study periods, range of covariates and confounders to be considered, and dealing with uncertainty. The availability of data from large, well-phenotyped cohorts such as UK Biobank enables the use of mixed-level study designs and the triangulation of evidence on environmental risks from small-area and individual-level studies, therefore improving causal inference, including use of linked biomarker and -omics data. As a result, there are now improved opportunities to investigate the impacts of environmental risk factors on human health, particularly for the surveillance and prevention of non-communicable diseases.
Roca Barcelo A, Douglas P, Fecht D, et al., 2020, Risk of respiratory hospital admission associated with modelled concentrations of Aspergillus fumigatus from composting facilities in England, Environmental Research, Vol: 183, Pages: 1-10, ISSN: 0013-9351
Bioaerosols have been associated with adverse respiratory-related health effects and are emitted in elevated concentrations from composting facilities. We usedmodelledAspergillus fumigatusconcentrations, a good indicator for bioaerosol emissions,to assess associations with respiratory-related hospital admissions. Mean dailyAspergillus fumigatusconcentrationswere estimated for each composting site for first full year of permit issuefrom2005 onwardsto 2014 for Census Output Areas (COAs) within 4km of 76 composting facilities in England, as previously described (Williams et al. 2019). We fitted ahierarchicalgeneralized mixed modelto examine therisk of hospital admission witha primary diagnosis of(i) any respiratory condition,(ii) respiratory infections,(iii) asthma,(iv) COPD,(v)diseases due to organic dust,and (vi)Cystic Fibrosis,inrelation to quartilesof Aspergillus fumigatusconcentrations. Models included a random intercept for each COAto account for over-dispersion,nested within composting facility, on whicha random intercept was fitted to account for clusteringof the data, with adjustmentsfor age, sex, ethnicity, deprivation, tobacco sales (smoking proxy) and traffic load (as a proxy for traffic-related air pollution). Weincluded 249,748 respiratory-related and 3,163 Cystic Fibrosis hospital admissions in 9,606 COAswith a population-weighted centroid within 4 km of the 76 included composting facilities. After adjustment for confounders, no statistically significant effect was observed for any respiratory-related (Relative Risk (RR)=0.99; 95% Confidence Interval (CI)0.96–1.01)or for Cystic Fibrosis (RR=1.01; 95% CI 0.56-1.83)hospital admissions for COAs in the highest quartile of exposure. Similar results were observed across all respiratory disease sub-groups.This study does not provide evidence for increased risks of respiratory-related hospitalisationsfor those livingnearcomposting facilities.However, given the limitations in the dispersion modelling, risks
Ponzi E, Vineis P, Chung K, et al., 2020, Accounting for measurement error to assess the effect of air pollution on omics signals, PLoS One, Vol: 15, Pages: 1-16, ISSN: 1932-6203
Studies on the effects of air pollution and more generally environmental exposures onhealth require measurements of pollutants, which are affected by measurement error.This is a cause of bias in the estimation of parameters relevant to the study and canlead to inaccurate conclusions when evaluating associations among pollutants, diseaserisk and biomarkers. Although the presence of measurement error in such studies hasbeen recognized as a potential problem, it is rarely considered in applications andpractical solutions are still lacking. In this work, we formulate Bayesian measurementerror models and apply them to study the link between air pollution and omic signals.The data we use stem from the “Oxford Street II Study”, a randomized crossover trialin which 60 volunteers walked for two hours in a traffic-free area (Hyde Park) and in abusy shopping street (Oxford Street) of London. Metabolomic measurements were madein each individual as well as air pollution measurements, in order to investigate theassociation between short-term exposure to traffic related air pollution and perturbationof metabolic pathways. We implemented error-corrected models in a classical frameworkand used the flexibility of Bayesian hierarchical models to account for dependenciesamong omic signals, as well as among different pollutants. Models were implementedusing traditional Markov Chain Monte Carlo (MCMC) simulative methods as well asintegrated Laplace approximation. The inclusion of a classical measurement error termresulted in variable estimates of the association between omic signals and traffic relatedair pollution measurements, where the direction of the bias was not predictable a priori.The models were successful in including and accounting for different correlationstructures, both among omic signals and among different pollutant exposures. Ingeneral, more associations were identified when the correlation among omics and amongpollutants were modeled, and their number
Smith RB, Beevers SD, Gulliver J, et al., 2020, Impacts of air pollution and noise on risk of preterm birth and stillbirth in London, Environment International, Vol: 134, ISSN: 0160-4120
BackgroundEvidence for associations between ambient air pollution and preterm birth and stillbirth is inconsistent. Road traffic produces both air pollutants and noise, but few studies have examined these co-exposures together and none to date with all-cause or cause-specific stillbirths.ObjectivesTo analyse the relationship between long-term exposure to air pollution and noise at address level during pregnancy and risk of preterm birth and stillbirth.MethodsThe study population comprised 581,774 live and still births in the Greater London area, 2006–2010. Outcomes were preterm birth (<37 completed weeks gestation), all-cause stillbirth and cause-specific stillbirth. Exposures during pregnancy to particulate matter with diameter <2.5 μm (PM2.5) and <10 μm (PM10), ozone (O3), primary traffic air pollutants (nitrogen dioxide, nitrogen oxides, PM2.5 from traffic exhaust and traffic non-exhaust), and road traffic noise were estimated based on maternal address at birth.ResultsAn interquartile range increase in O3 exposure was associated with elevated risk of preterm birth (OR 1.15 95% CI: 1.11, 1.18, for both Trimester 1 and 2), all-cause stillbirth (Trimester 1 OR 1.17 95% CI: 1.07, 1.27; Trimester 2 OR 1.20 95% CI: 1.09, 1.32) and asphyxia-related stillbirth (Trimester 1 OR 1.22 95% CI: 1.01, 1.49). Odds ratios with the other air pollutant exposures examined were null or <1, except for primary traffic non-exhaust related PM2.5, which was associated with 3% increased odds of preterm birth (Trimester 1) and 7% increased odds stillbirth (Trimester 1 and 2) when adjusted for O3. Elevated risk of preterm birth was associated with increasing road traffic noise, but only after adjustment for certain air pollutant exposures.DiscussionOur findings suggest that exposure to higher levels of O3 and primary traffic non-exhaust related PM2.5 during pregnancy may increase risk of preterm birth and stillbirth; and a possible relationship between long-term traff
Sera F, Armstrong B, Blangiardo M, et al., 2019, An extended mixed-effects framework for meta-analysis, Statistics in Medicine, Vol: 38, Pages: 5429-5444, ISSN: 0277-6715
Standard methods for meta‐analysis are limited to pooling tasks in which a single effect size is estimated from a set of independent studies. However, this setting can be too restrictive for modern meta‐analytical applications. In this contribution, we illustrate a general framework for meta‐analysis based on linear mixed‐effects models, where potentially complex patterns of effect sizes are modeled through an extended and flexible structure of fixed and random terms. This definition includes, as special cases, a variety of meta‐analytical models that have been separately proposed in the literature, such as multivariate, network, multilevel, dose‐response, and longitudinal meta‐analysis and meta‐regression. The availability of a unified framework for meta‐analysis, complemented with the implementation in a freely available and fully documented software, will provide researchers with a flexible tool for addressing nonstandard pooling problems.
Lavigne A, Freni Sterrantino A, Liverani S, et al., 2019, Associations between metal constituents of ambient particulate matter and mortality in England; an ecological study, BMJ Open, Vol: 9, ISSN: 2044-6055
Objectives To investigate long-term associations between metal components of particulate matter and mortality and lung cancer incidenceDesign Small area (ecological) study Setting Population living in all wards (~9000 individuals per ward) in the London and Oxford area of England, comprising 13.6 million individuals Exposure and Outcome measures We used land use regression (LUR) models originally used in the Transport related Air Pollution and Health impacts – Integrated Methodologies for Assessing Particulate Matter (TRANSPHORM) study to estimate exposure to copper, iron and zinc in ambient air particulate matter. We examined associations of metal exposure with Office for National Statistics mortality data from cardiovascular (CVD) and respiratory causes and with lung cancer incidence in 2008-11.Results There were 108,478 CVD deaths, 48,483 respiratory deaths and 24,849 incident cases of lung cancer in the study period and area. Using Poisson regression models adjusted for area-level deprivation, tobacco sales and ethnicity, we found associations between cardiovascular mortality and PM2.5 copper with interdecile range (IDR-2.6-5.7 ng/m3) and IDR Relative risk (RR) 1.005 (95%CI 1.001, 1.009) and between respiratory mortality and PM10 zinc (IDR 1135-153 ng/m3) and IDR RR 1.136 (95%CI 1.010, 1.277). We did not find relevant associations for lung cancer incidence. Metal elements were highly correlated.Conclusion Our analysis showed small but not fully consistent adverse associations between mortality and particulate metal exposures likely derived from non-tailpipe road traffic emissions (brake and tyre-wear), which have previously been associated with increases in inflammatory markers in the blood.
Hansell A, Cai Y, Granell R, et al., 2019, Prenatal, early-life and childhood exposure to air pollution and lung function in the UK Avon Longitudinal Study of Parents and Children (ALSPAC) cohort, European-Respiratory-Society (ERS) International Congress, Publisher: EUROPEAN RESPIRATORY SOC JOURNALS LTD, ISSN: 0903-1936
Manica M, Caputo B, Screti A, et al., 2019, Applying the N‐mixture model approach to estimate mosquito population absolute abundance from monitoring data, Journal of Applied Ecology, Vol: 56, Pages: 2225-2235, ISSN: 0021-8901
1. Estimating population abundance is a key objective of surveillance programmes, particularly for vector species of public health interest. For mosquitos, which are vectors of human pathogens, established methods to measure absolute population abundance such as mark‐release‐recapture are difficult to implement and usually spatially limited. Typically, regional monitoring schemes assess species relative abundance (counting captured individuals) to prioritize control efforts and study species distribution. However, assessing absolute abundance is crucial when the focus is on pathogen transmission by contacts between vectors and hosts. Here, we applied the N‐mixture model approach to estimate mosquito abundance from standard monitoring data.2. We extended the N‐mixture model approach in a Bayesian framework by considering a beta‐binomial distribution for the detection process. We ran a simulation study to explore model performance under a low detection probability, a time‐varying population and different sets of independent variables.3. When informative priors were used and the model was well specified, estimates by N‐mixture model well correlated (>0.9) with synthetic data and had a mean absolute deviation of about 20%. Correlation decreased and biased increased with uninformative priors or model misspecification.4. When fed with field monitoring data to estimate the absolute abundance of the mosquito arbovirus vector Aedes albopictus within the metropolitan city of Rome (Italy), the N‐mixture model showed higher population size in residential neighbourhoods than in large green areas and revealed that traps located adjacent to vegetated sites have a higher probability of capturing mosquitoes.5. Synthesis and applications. Our results show that, if supported by a good knowledge of the target species biology and by informative priors (e.g. from previous studies of capture rates), the N‐mixture model represents a valuable tool to exploit field monitoring data to esti
Can Bayesian models reveal the underlying processes that drive the lethality of non‐state terrorism at a local level? Andre Python, Janine B. Illian, Charlotte M. Jones‐Todd and Marta Blangiardo investigate Terrorism and terror‐related deaths are rarely out of the news. But not all terrorism is lethal. On a global scale, summary statistics tell us that in the last 20 years about 50% of terrorist attacks resulted in loss of life, while the remainder consisted of acts of sabotage, threats, and so on, which spread fear without necessarily killing anyone.
Freni Sterrantino A, Elliott P, Blangiardo M, et al., 2019, Bayesian spatial modelling for quasi-experimental designs: an interrupted time series study of the opening of Municipal Waste Incinerators in relation to infant mortality and sex ratio, Environment International, Vol: 128, Pages: 109-115, ISSN: 0160-4120
BackgroundThere is limited evidence on potential health risks from Municipal Waste Incinerators (MWIs), and previous studies on birth outcomes show inconsistent results. Here, we evaluate whether the opening of MWIs is associated with infant mortality and sex ratio in the surrounding areas, extending the Interrupted Time Series (ITS) methodological approach to account for spatial dependencies at the small area level.MethodsWe specified a Bayesian hierarchical model to investigate the annual risks of infant mortality and sex-ratio (female relative to male) within 10 km of eight MWIs in England and Wales, during the period 1996–2012. We included comparative areas matched one-to-one of similar size and area characteristics.ResultsDuring the study period, infant mortality rates decreased overall by 2.5% per year in England. The opening of an incinerator in the MWI area was associated with −8 deaths per 100,000 infants (95% CI −62, 40) and with a difference in sex ratio of −0.004 (95% CI −0.02, 0.01), comparing the period after opening with that before, corrected for before-after trends in the comparator areas.ConclusionOur method is suitable for the analysis of quasi-experimental time series studies in the presence of spatial structure and when there are global time trends in the outcome variable. Based on our approach, we do not find evidence of an association of MWI opening with changes in risks of infant mortality or sex ratio in comparison with control areas.
Cameletti M, Gomez-Rubio V, Blangiardo M, 2019, Bayesian modeling for spatially misaligned health and air pollution data through the INLA-SPDE approach, Spatial Statistics, Vol: 31, ISSN: 2211-6753
In air pollution studies a key issue concerns the change of support: pollutant concentrations are continuous phenomena in space but their measurements are typically available at a finite number of point-referenced monitoring stations or result from numerical models. When linking exposure to health outcomes, the latter are usually available at administrative level, hence on an irregular lattice, providing challenges in terms of data misalignment.In this paper we tackle the change of support problem for air pollution and health studies through a two-stage Bayesian approach; in the first stage our model estimates the air pollution concentration at the area level and then in the second stage it links the exposure to the health outcome, accounting for the uncertainty on the exposure estimates. We show through an extensive and realistic simulation that our model is able to predict the concentration accurately at the administrative level as well as estimate the association between exposure and health outcome. We use the Integrated Nested Laplace Approximation, coupled with the Stochastic Partial Differential Equation method for model implementation. Finally we apply the proposed model to evaluate the effect of NOconcentration on hospital admissions for respiratory diseases in the Piemonte region (Italy). We found that the upscaling method and the approach used to propagate uncertainty from the first to the second stage have an impact on the posterior distribution of the relative risk. Moreover, we found a significant increased risk of 1.6% and 1.8% associated with an increase of 10 in NO concentration.
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