Imperial College London

ProfessorMartaBlangiardo

Faculty of MedicineSchool of Public Health

Chair in Biostatistics
 
 
 
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m.blangiardo Website

 
 
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Location

 

528Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Publication Type
Year
to

99 results found

Halonen JI, Hansell AL, Gulliver J, Morley D, Blangiardo M, Fecht D, Toledano MB, Beevers S, Anderson HR, Kelly F, Tonne Cet al., 2015, Road traffic noise is associated with increased cardiovascular morbidity and mortality and all-cause mortality in London, European Heart Journal, Vol: 36, Pages: 2653-2661, ISSN: 0195-668X

AimsRoad traffic noise has been associated with hypertension but evidence for the long-term effects on hospital admissions and mortality is limited. We examined the effects of long-term exposure to road traffic noise on hospital admissions and mortality in the general population.Methods and resultsThe study population consisted of 8.6 million inhabitants of London, one of Europe's largest cities. We assessed small-area-level associations of day- (7:00–22:59) and nighttime (23:00–06:59) road traffic noise with cardiovascular hospital admissions and all-cause and cardiovascular mortality in all adults (≥25 years) and elderly (≥75 years) through Poisson regression models. We adjusted models for age, sex, area-level socioeconomic deprivation, ethnicity, smoking, air pollution, and neighbourhood spatial structure. Median daytime exposure to road traffic noise was 55.6 dB. Daytime road traffic noise increased the risk of hospital admission for stroke with relative risk (RR) 1.05 [95% confidence interval (CI): 1.02–1.09] in adults, and 1.09 (95% CI: 1.04–1.14) in the elderly in areas >60 vs. <55 dB. Nighttime noise was associated with stroke admissions only among the elderly. Daytime noise was significantly associated with all-cause mortality in adults [RR 1.04 (95% CI: 1.00–1.07) in areas >60 vs. <55 dB]. Positive but non-significant associations were seen with mortality for cardiovascular and ischaemic heart disease, and stroke. Results were similar for the elderly.ConclusionsLong-term exposure to road traffic noise was associated with small increased risks of all-cause mortality and cardiovascular mortality and morbidity in the general population, particularly for stroke in the elderly.

Journal article

Blangiardo M, Cameletti M, 2015, Preface, ISBN: 9781118326558

Book

Blangiardo M, Cameletti M, 2015, Spatial and Spatio-temporal Bayesian Models with R - INLA, ISBN: 9781118326558

© 2015 John Wiley & Sons, Ltd. All rights reserved. Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio�-temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented to real data problems. The numerous examples from the fields of epidemiology, biostatistics and social science all are coded in the R package R-INLA, which has proven to be a valid alternative to the commonly used Markov Chain Monte Carlo simulations. o

Book

Pirani M, Best N, Blangiardo M, Liverani S, Atkinson RW, Fuller GWet al., 2015, Analysing the health effects of simultaneous exposure to physical and chemical properties of airborne particles, Environment International, Vol: 79, Pages: 56-64, ISSN: 0160-4120

Background:Airborne particles are a complex mix of organic and inorganic compounds, with a range of physical and chemical properties. Estimation of how simultaneous exposure to air particles affects the risk of adverse health response represents a challenge for scientific research and air quality management. In this paper, we present a Bayesian approach that can tackle this problem within the framework of time series analysis.Methods:We used Dirichlet process mixture models to cluster time points with similar multipollutant and response profiles, while adjusting for seasonal cycles, trends and temporal components. Inference was carried out via Markov Chain Monte Carlo methods. We illustrated our approach using daily data of a range of particle metrics and respiratory mortality for London (UK) 2002–2005. To better quantify the average health impact of these particles, we measured the same set of metrics in 2012, and we computed and compared the posterior predictive distributions of mortality under the exposure scenario in 2012 vs 2005.Results:The model resulted in a partition of the days into three clusters. We found a relative risk of 1.02 (95% credible intervals (CI): 1.00, 1.04) for respiratory mortality associated with days characterised by high posterior estimates of non-primary particles, especially nitrate and sulphate. We found a consistent reduction in the airborne particles in 2012 vs 2005 and the analysis of the posterior predictive distributions of respiratory mortality suggested an average annual decrease of − 3.5% (95% CI: − 0.12%, − 5.74%).Conclusions:We proposed an effective approach that enabled the better understanding of hidden structures in multipollutant health effects within time series analysis. It allowed the identification of exposure metrics associated with respiratory mortality and provided a tool to assess the changes in health effects from various policies to control the ambient particle matter mixtures.

Journal article

Blangiardo M, Baio G, 2014, Evidence of bias in the Eurovision song contest: modelling the votes using Bayesian hierarchical models, Journal of Applied Statistics, Vol: 41, Pages: 2312-2322, ISSN: 1360-0532

The Eurovision Song Contest is an annual musical competition held among active members of the EuropeanBroadcasting Union since 1956. The event is televised live across Europe. Each participating countrypresents a song and receive a vote based on a combination of tele-voting and jury. Over the years, thishas led to speculations of tactical voting, discriminating against some participants and thus inducing biasin the final results. In this paper we investigate the presence of positive or negative bias (which mayroughly indicate favouritisms or discrimination) in the votes based on geographical proximity, migrationand cultural characteristics of the participating countries through a Bayesian hierarchical model. Our analysisfound no evidence of negative bias, although mild positive bias does seem to emerge systematically,linking voters to performers.

Journal article

Bennett JE, Blangiardo M, Fecht D, Elliott P, Ezzati Met al., 2014, Vulnerability to the mortality effects of warm temperature in the districts of England and Wales, Nature Climate Change, Vol: 4, Pages: 269-273, ISSN: 1758-678X

Warm temperatures adversely affect disease occurrence and death, in extreme conditions as well as when the temperature changes are more modest1,2. Therefore climate change, which is expected to affect both average temperatures and temperature variability, is likely to impact health even in temperate climates. Climate change risk assessment is enriched if there is information on vulnerability and resilience to effects of temperature. Some studies have analysed socio-demographic characteristics that make individuals vulnerable to adverse effects of temperature1,2,3,4. Less is known about community-level vulnerability. We used geo-coded mortality and environmental data and Bayesian spatial methods to conduct a national small-area analysis of the mortality effects of warm temperature for all 376 districts in England and Wales. In the most vulnerable districts, those in London and south/southeast England, odds of dying from cardiorespiratory causes increased by more than 10% for 1 °C warmer temperature, compared with virtually no effect in the most resilient districts, which were in the far north. A 2 °C warmer summer may result in 1,552 (95% credible interval 1,307–1,762) additional deaths, about one-half of which would occur in 95 districts. The findings enable risk and adaptation analyses to incorporate local vulnerability to warm temperature and to quantify inequality in its effects.

Journal article

Barrera L, Leaper C, Pape UJ, Majeed A, Blangiardo M, Millett Cet al., 2014, Impact of ethnic-specific guidelines for anti-hypertensive prescribing in primary care in England: a longitudinal study, BMC HEALTH SERVICES RESEARCH, Vol: 14, ISSN: 1472-6963

Journal article

Hansell AL, Blangiardo M, Fortunato L, Floud S, De Hoogh K, Fecht D, Ghosh RE, Laszlo HE, Pearson C, Beale L, Beevers S, Gulliver J, Best N, Richardson S, Elliott Pet al., 2014, Daytime and night-time aircraft noise and cardiovascular disease near Heathrow airport in London

Background. Few studies have investigated associations of aircraft noise with cardiovascular health. We investigated this in areas exposed to noise from London Heathrow airport. Methods. A small area study was conducted in 12,110 census output areas covering 3.6 million residents. Risks for hospital admissions and mortality in 2001-05 were assessed in relation to aircraft noise in 2001, adjusted for relevant confounders. Night (Lnight) and daytime (LAeq,16h) aircraft noise were assessed separately. Results. Higher aircraft noise was associated with higher relative risks for hospital admissions and mortality from stroke, coronary heart disease (CHD) and cardiovascular disease. Risk estimates were higher for night-time than daytime noise. Adjusted risks werehighest for stroke, with RR 1.29 [95% CI 1.14 to 1.46] for Lnight and RR 1.08 [95% CI 1.02 to 1.14] for LAeq,16hfor >55dB vs. <50dB. All linear dose-response relationships were statistically significant for hospital admissions but not for mortality, except for CHD and LAeq,16h. Discussion. This research attracted a high level of policy interest. However, the impact of this and other recent papers on policy decisions such as increased airport capacity in England is currently unclear. Priority areas for follow-up health research into aircraft noise need to be considered.

Conference paper

Cai Y, Blangiardo M, De Hoogh K, Gulliver J, Morley D, Doiron D, Elliott P, Hansell A, Hodgson Set al., 2014, Road traffic noise, air pollution and cardio-respiratory health in European cohorts: A harmonised approach in the BioSHaRE project

Background and aims: Few studies have investigated joint effects of road traffic noise and air pollution on cardiovascular outcomes. This project aims to quantify the joint and separate effects of both exposures on prevalent and incident cardiovascular disease and asthma as part of the EU-funded BioSHaRE project involving five European cohorts (EPIC-Oxford, EPIC-Turin, HUNT, Lifelines, UK Biobank). Methods: Health outcomes have been ascertained by self-report (prevalence) and medical record (incidence) and retrospectively harmonised across the five cohorts. Residential road traffic noise exposures for each participant are being estimated using a European noise model based on Common Noise Assessment Methods in Europe (CNOSSOS-EU). Cross-sectional epidemiological analyses are in progress, virtually pooled using DataSHIELD methods. Results: In total, 716,945 men and women are included, mostly >40 years. Initial analysis of EPIC-Oxford and Lifelines showed prevalence of self-reported hypertension to be 26%, high blood lipids 15% and asthma 11% and mean annual 24-hour noise estimates of 56.4 dB(A) (EPIC-Oxford) and 65.8 dB(A) (Lifelines). Correlations between noise estimates and NO2 are generally low (r~0.1 to 0.4). Conclusions: Pooling of individual level harmonised data from established cohorts offers the large sample sizes needed to investigate effects of road traffic noise and ambient air pollution on cardio-respiratory diseases.

Conference paper

Blangiardo M, Cameletti M, Baio G, Rue Het al., 2013, Erratum to "Spatial and spatio-temporal models with R-INLA" [Spat Spatio-tempor Epidemiol 4 (2013) 33-49], Spatial and Spatio-temporal Epidemiology, Vol: 7, ISSN: 1877-5845

Journal article

Blangiardo M, Cameletti M, Baio G, Rue Het al., 2013, Spatial and spatio-temporal models with R-INLA., Spat Spatiotemporal Epidemiol, Vol: 7, Pages: 39-55

During the last three decades, Bayesian methods have developed greatly in the field of epidemiology. Their main challenge focusses around computation, but the advent of Markov Chain Monte Carlo methods (MCMC) and in particular of the WinBUGS software has opened the doors of Bayesian modelling to the wide research community. However model complexity and database dimension still remain a constraint. Recently the use of Gaussian random fields has become increasingly popular in epidemiology as very often epidemiological data are characterised by a spatial and/or temporal structure which needs to be taken into account in the inferential process. The Integrated Nested Laplace Approximation (INLA) approach has been developed as a computationally efficient alternative to MCMC and the availability of an R package (R-INLA) allows researchers to easily apply this method. In this paper we review the INLA approach and present some applications on spatial and spatio-temporal data.

Journal article

Floud S, Blangiardo M, Clark C, de Hoogh K, Babisch W, Houthuijs D, Swart W, Pershagen G, Katsouyanni K, Velonakis M, Vigna-Taglianti F, Cadum E, Hansell ALet al., 2013, Exposure to aircraft and road traffic noise and associations with heart disease and stroke in six European countries: a cross-sectional study, ENVIRONMENTAL HEALTH, Vol: 12

Journal article

Tang R, Blangiardo M, Gulliver J, 2013, Using Building Heights and Street Configuration to Enhance Intraurban PM10, NOX, and NO2 Land Use Regression Models, ENVIRONMENTAL SCIENCE & TECHNOLOGY, Vol: 47, Pages: 11643-11650, ISSN: 0013-936X

Journal article

Blangiardo M, Cameletti M, Baio G, Rue Het al., 2013, Spatial and spatio-temporal models with R-INLA., Spat Spatiotemporal Epidemiol, Vol: 4, Pages: 33-49

During the last three decades, Bayesian methods have developed greatly in the field of epidemiology. Their main challenge focusses around computation, but the advent of Markov Chain Monte Carlo methods (MCMC) and in particular of the WinBUGS software has opened the doors of Bayesian modelling to the wide research community. However model complexity and database dimension still remain a constraint. Recently the use of Gaussian random fields has become increasingly popular in epidemiology as very often epidemiological data are characterised by a spatial and/or temporal structure which needs to be taken into account in the inferential process. The Integrated Nested Laplace Approximation (INLA) approach has been developed as a computationally efficient alternative to MCMC and the availability of an R package (R-INLA) allows researchers to easily apply this method. In this paper we review the INLA approach and present some applications on spatial and spatio-temporal data.

Journal article

Zucca M, Ugalde J, Arteaga FS, Biggio G, Flore V, Nonne T, Satta G, Blangiardo M, Cocco P, Ennas MGet al., 2013, Leukemia in children and youths of the Azuay province, Ecuador: 2000-2010, INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH, Vol: 23, Pages: 58-65, ISSN: 0960-3123

Journal article

Floud S, Blangiardo M, Clark C, De Hoogh K, Babisch W, Houthuijs D, Swart W, Pershagen G, Katsouyanni K, Velonakis M, Vigna- Taglianti F, Cadum E, Hansell Aet al., 2013, Heart disease and stroke in relation to aircraft noise and road traffic noise - The HYENA study, 42nd International Congress and Exposition on Noise Control Engineering 2013, INTER-NOISE 2013: Noise Control for Quality of Life, Vol: 6, Pages: 5056-5059

Journal article

Pirani M, Gulliver J, Fuller G, Blangiardo Met al., 2013, Bayesian spatiotemporal modelling for the assessment of short-term exposure to particle pollution in urban areas, Journal of exposure science & environmental epidemiology, Vol: N/A, ISSN: 1559-064X

This paper describes a Bayesian hierarchical approach to predict short-term concentrations of particle pollution in an urban environment, with application to inhalable particulate matter (PM10) in Greater London. We developed and compared several spatiotemporal models that differently accounted for factors affecting the spatiotemporal properties of particle concentrations. We considered two main source contributions to ambient measurements: (i) the long-range transport of the secondary fraction of particles, which temporal variability was described by a latent variable derived from rural concentrations; and (ii) the local primary component of particles (traffic- and non-traffic-related) captured by the output of the dispersion model ADMS-Urban, which site-specific effect was described by a Bayesian kriging. We also assessed the effect of spatiotemporal covariates, including type of site, daily temperature to describe the seasonal changes in chemical processes affecting local PM10 concentrations that are not considered in local-scale dispersion models and day of the week to account for time-varying emission rates not available in emissions inventories. The evaluation of the predictive ability of the models, obtained via a cross-validation approach, revealed that concentration estimates in urban areas benefit from combining the city-scale particle component and the long-range transport component with covariates that account for the residual spatiotemporal variation in the pollution process.

Journal article

Hansell AL, Blangiardo M, Fortunato L, Floud S, de Hoogh K, Fecht D, Ghosh RE, Laszlo HE, Pearson C, Beale L, Beevers S, Gulliver J, Best N, Richardson S, Elliott Pet al., 2013, Aircraft noise and cardiovascular disease near Heathrow airport in London: small area study., BMJ, Vol: 347, ISSN: 0959-535X

To investigate the association of aircraft noise with risk of stroke, coronary heart disease, and cardiovascular disease in the general population.

Journal article

Barrera L, Millett C, Blangiardo M, Pape UJ, Majeed Aet al., 2012, Differences in the classification of hypertensive controlled patient in primary care: Cross sectional study., JRSM Short Rep, Vol: 3

OBJECTIVES: To examine differences in blood pressure control using the 2006 National Institute for Health and Clinical Excellence (NICE) guidelines and the 2007 Quality and Outcome Framework (QOF) standards. DESIGN: Cross-sectional study. SETTING: 28 general practices located in Wandsworth, London. PARTICIPANTS: Hypertensive patients aged 17 years and over. MAIN OUTCOMES MEASURES: Percentage of hypertensive patients classified as a hypertensive controlled patient (HCP) by each standard. RESULTS: 79.5% of patients were classified as a HCP by the QOF target and 60.7% by the NICE target. 93% and 14% of practices had more than 70% of patients classified as a HPC by using the QOF and NICE targets respectively. By applying the QOF target, men aged 45-64 years and 65 years and over had significantly higher probability of being classified as a HCP compared to those aged 17-44 years, OR 1.34 (1.08-.165) and OR 2.15 (1.61-2.87) respectively. Regardless of the target, for men the probability of being classified as a HCP increased with age. CONCLUSION: Better achievement of blood pressure control targets is present when the less stringent QOF target is used. Men aged 65 years and over were more likely to be classified as a HCP. Greater consistency is needed between the clinical targets in QOF and NICE guidance.

Journal article

Petit C, Blangiardo M, Richardson S, Coquet F, Chevrier C, Cordier Set al., 2012, Association of Environmental Insecticide Exposure and Fetal Growth With a Bayesian Model Including Multiple Exposure Sources, AMERICAN JOURNAL OF EPIDEMIOLOGY, Vol: 175, Pages: 1182-1190, ISSN: 0002-9262

Journal article

Carreras G, Chellini E, Blangiardo M, 2012, A Bayesian model for studying urban air pollution and respiratory symptoms in children, International Journal of Environment and Health, Vol: 6, Pages: 125-140, ISSN: 1743-4955

The association between traffic-related air pollution and long-term respiratory health problems has been extensively studied. In this work we evaluated the effect of traffic-related air pollution on respiratory symptoms in children living in Florence, Italy. Children were selected from different schools part of the Italian Studies on Respiratory Disorders in Children and the Environment 2. Exposure to traffic air pollution was assessed through a dispersion model and weighted by distance using four different criteria. A Bayesian hierarchical logistic regression model was specified to assess the impact of traffic air pollution on cough or phlegm and asthma. Potential confounders were included in the analysis. Familiarity of asthma and exposure to second-hand smoking showed the strongest positive association with respiratory symptoms. No evidence of increasing risk of asthma with urban air pollution was found, while some evidence of an association was observed for carbon dioxide and cough or phlegm. Copyright © 2012 Inderscience Enterprises Ltd.

Journal article

Mustapha BA, Blangiardo M, Briggs DJ, Hansell ALet al., 2011, Traffic Air Pollution and Other Risk Factors for Respiratory Illness in Schoolchildren in the Niger-Delta Region of Nigeria, Environmental Health Perspectives, Vol: 119, Pages: 1478-1482, ISSN: 0091-6765

Background: Association of childhood respiratory illness with traffic air pollution has beeninvestigated largely in developed but not in developing countries, where pollution levels are oftenvery high.Objectives: In this study we investigated associations between respiratory health and outdoor andindoor air pollution in schoolchildren 7–14 years of age in low socioeconomic status areas in theNiger Delta.Methods: A cross-sectional survey was carried out among 1,397 schoolchildren. Exposure to homeoutdoor and indoor air pollution was assessed by self-report questionnaire. School air pollutionexposures were assessed using traffic counts, distance of schools to major streets, and particulatematter and carbon monoxide measurements, combined using principal components analysis.Hierarchical logistic regression was used to examine associations with reported respiratory health,adjusting for potential confounders.Results: Traffic disturbance at home (i.e., traffic noise and/or fumes evident inside the homevs. none) was associated with wheeze [odds ratio (OR) = 2.16; 95% confidence interval (CI),1.28–3.64], night cough (OR = 1.37; 95% CI, 1.03–1.82), phlegm (OR = 1.49; 95% CI,1.09–2.04), and nose symptoms (OR = 1.40; 95% CI, 1.03–1.90), whereas school exposure to acomponent variable indicating exposure to fine particles was associated with increased phlegm (OR= 1.38; 95% CI, 1.09–1.75). Nonsignificant positive associations were found between cooking withwood/coal (OR = 2.99; 95% CI, 0.88–10.18) or kerosene (OR = 2.83; 95% CI, 0.85–9.44) andphlegm compared with cooking with gas.Conclusion: Traffic pollution is associated with respiratory symptoms in schoolchildren in adeprived area of western Africa. Associations may have been underestimated because of nondifferentialmisclassification resulting from limitations in exposure measurement.

Journal article

Baio G, Blangiardo GC, Blangiardo M, 2011, Centre Sampling Technique in Foreign Migration Surveys: A Methodological Note, JOURNAL OF OFFICIAL STATISTICS, Vol: 27, Pages: 451-465, ISSN: 0282-423X

Journal article

Floud S, Vigna-Taglianti F, Hansell A, Blangiardo M, Houthuijs D, Breugelmans O, Cadum E, Babisch W, Selander J, Pershagen G, Antoniotti MC, Pisani S, Dimakopoulou K, Haralabidis AS, Velonakis V, Jarup Let al., 2011, Medication use in relation to noise from aircraft and road traffic in six European countries: results of the HYENA study, OCCUPATIONAL AND ENVIRONMENTAL MEDICINE, Vol: 68, Pages: 518-524, ISSN: 1351-0711

Journal article

Blangiardo M, Richardson S, Gulliver J, Hansell Aet al., 2011, A Bayesian analysis of the impact of air pollution episodes on cardio-respiratory hospital admissions in the Greater London area, STATISTICAL METHODS IN MEDICAL RESEARCH, Vol: 20, Pages: 69-80, ISSN: 0962-2802

Journal article

Blangiardo M, Hansell A, Richardson S, 2011, A Bayesian model of time activity data to investigate health effect of air pollution in time series studies, ATMOSPHERIC ENVIRONMENT, Vol: 45, Pages: 379-386, ISSN: 1352-2310

Journal article

Sabel C, Shaddick G, Blangiardo M, Salway R, Zenie A, Denby B, Gerharz Let al., 2011, Uncertainty Analysis Within the EU HEIMTSA (Health and Environment Integrated Methodology and Toolbox for Scenario Assessment) Project, Joint Conference of International-Society-of-Exposure-Science/International-Society-for-Environmental-Epidemiology, Publisher: LIPPINCOTT WILLIAMS & WILKINS, Pages: S176-S177, ISSN: 1044-3983

Conference paper

Hansell A, Blangiardo M, Morris C, Vienneau D, Gulliver J, Lee K, Briggs Det al., 2011, Association Between Black Smoke and SO2 Air Pollution Exposures in 1971 and Mortality 1972-2007 in Great Britain, Joint Conference of International-Society-of-Exposure-Science/International-Society-for-Environmental-Epidemiology, Publisher: LIPPINCOTT WILLIAMS & WILKINS, Pages: S29-S29, ISSN: 1044-3983

Conference paper

Haining R, Li GQ, Maheswaran R, Blangiardo M, Law J, Richardson S, Best Net al., 2010, Inference from ecological models: estimating the relative risk of stroke from air pollution exposure using small area data, Spatial and Spatio-temporal Epidemiology, Vol: 1, Pages: 123-131

Journal article

Blangiardo M, Cassese A, Richardson S, 2010, sdef: an R package to synthesize lists of significant features in related experiments, BMC BIOINFORMATICS, Vol: 11, ISSN: 1471-2105

Journal article

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