Imperial College London

ProfessorMartaBlangiardo

Faculty of MedicineSchool of Public Health

Chair in Biostatistics
 
 
 
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Contact

 

m.blangiardo Website

 
 
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528Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Publication Type
Year
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84 results found

Halonen JI, Dehbi HM, Hansell AL, Gulliver J, Fecht D, Blangiardo M, Kelly FJ, Chaturvedi N, Kivimäki M, Tonne Cet al., 2016, Associations of night-time road traffic noise with carotid intima-media thickness and blood pressure: The Whitehall II and SABRE study cohorts, Environment International, Vol: 98, Pages: 54-61, ISSN: 0160-4120

BACKGROUND: Road traffic noise has been linked to increased risk of stroke, for which hypertension and carotid intima-media thickness (cIMT) are risk factors. A link between traffic noise and hypertension has been established, but there are few studies on blood pressure and no studies on cIMT. OBJECTIVES: To examine cross-sectional associations for long-term exposure to night-time noise with cIMT, systolic blood pressure (SBP), diastolic blood pressure (DBP) and hypertension. METHODS: The study population consisted of 2592 adults from the Whitehall II and SABRE cohort studies living within Greater London who had cIMT, SBP and DBP measured. Exposure to night-time road traffic noise (A-weighted dB, referred to as dBA) was estimated at each participant's residential postcode centroid. RESULTS: Mean night-time road noise levels were 52dBA (SD=4). In the pooled analysis adjusted for cohort, sex, age, ethnicity, marital status, smoking, area-level deprivation and NOx there was a 9.1μm (95% CI: -7.1, 25.2) increase in cIMT in association with 10dBA increase in night-time noise. Analyses by noise categories of 55-60dBA (16.2μm, 95% CI: -8.7, 41.2), and >60dBA (21.2μm, 95% CI: -2.5, 44.9) vs. <55dBA were also positive but non-significant, expect among those not using antihypertensive medication and exposed to >60dBA vs. <55dBA (32.6μm, 95% CI: 6.2, 59.0). Associations for SBP, DPB and hypertension were close to null. CONCLUSIONS: After adjustments, including for air pollution, the association between night-time road traffic noise and cIMT was only observed among non-medication users but associations with blood pressure and hypertension were largely null.

Journal article

Nomura S, Blangiardo M, Tsubokura M, Ochi S, Hodgson Set al., 2016, School restrictions on outdoor activities and weight status in adolescent children after Japan's 2011 Fukushima Nuclear Power Plant disaster: a mid- to long-term retrospective analysis, BMJ Open, Vol: 6, ISSN: 2044-6055

Objective Radiation fears following Japan’s 2011 Fukushima nuclear disaster impacted levels of physical activity in local children. We assessed the post- versus pre-disaster weight status in school children, and evaluated to what extent school restrictions on outdoor activities that were intended to reduce radiation exposure risk affected child weight.ParticipantsWe considered children aged 13–15 years from four of the five secondary schools in Soma City (n=1,030, 99.1% of all children in the city), located in 35–50 km from the Fukushima nuclear plant, post- (2012 and 2015) and pre-disaster (2010).MethodsWeight status, in terms of body mass index (BMI), percentage of overweight (POW), and incidence of obesity and underweight (defined as a POW ≥ 20% and ≤ -20%, respectively), were examined and compared pre- and post-disaster using regression models. We also constructed models to assess the impact of school restrictions on outdoor activity on weight status.ResultsAfter adjustment for covariates, a slight decrease in mean BMI and POW was detected in females in 2012 (-0.37, 95% CI: -0.68 to -0.06; and -1.97, 95% CI: -3.57 to -0.36, respectively). For male children, obesity incidence increased in 2012 (odds ratio for obesity: 1.45, 95% CI: 1.02 to 2.08). Compared to pre-disaster weight status, no significant weight change was identified in 2015 in either males or females. School restrictions on outdoor activities were not significantly associated with weight status.ConclusionsFour years following the disaster, weight status has recovered to the pre-disaster levels for both males and females; however a slight decrease in weight in females, and a slight increased risk of obesity was observed in males one year following the disaster. Our findings could be used to guide actions taken during the early phase of a radiological disaster to manage the post-disaster health risks in adolescent children.

Journal article

Hansell A, Ghosh R, Blangiardo M, Perkins C, Vienneau D, Goffe K, Briggs D, Gulliver Jet al., 2016, Respiratory mortality risks in England and Wales associated with air pollution exposures up to 38 years previously, Publisher: EUROPEAN RESPIRATORY SOC JOURNALS LTD, ISSN: 0903-1936

Conference paper

Liverani S, Lavigne A, Blangiardo M, 2016, Modelling collinear and spatially correlated data, Publisher: ELSEVIER SCI LTD

Working paper

Boulieri A, Blangiardo MB, Hansell AH, 2016, Investigating trends in asthma and COPD through multiple data sources: a small area study, Spatial and Spatio-temporal Epidemiology, Vol: 19, Pages: 28-36, ISSN: 1877-5853

This paper investigates trends in asthma and COPD by using multiple datasources to help understanding the relationships between disease prevalence, morbidityand mortality. GP drug prescriptions, hospital admissions, and deaths are analysedat clinical commissioning group (CCG) level in England from August 2010 to March2011. A Bayesian hierarchical model is used for the analysis, which takes into accountthe complex space and time dependencies of asthma and COPD, while it is alsoable to detect unusual areas. Main findings show important discrepancies across thedifferent data sources, reflecting the different groups of patients that are represented.In addition, the detection mechanism that is provided by the model, together withinference on the spatial, and temporal variation, provide a better picture of therespiratory health problem.

Journal article

Liverani S, Lavigne A, Blangiardo MAG, 2016, Modelling collinear and spatially correlated data, Spatial and Spatio-temporal Epidemiology, ISSN: 1877-5853

In this work we present a statistical approach to distinguish and interpret the complexrelationship between several predictors and a response variable at the small area level, in thepresence of i) high correlation between the predictors and ii) spatial correlation for the response.Covariates which are highly correlated create collinearity problems when used in a standardmultiple regression model. Many methods have been proposed in the literature to address thisissue. A very common approach is to create an index which aggregates all the highly correlatedvariables of interest. For example, it is well known that there is a relationship between socialdeprivation measured through the Multiple Deprivation Index (IMD) and air pollution; thisindex is then used as a confounder in assessing the effect of air pollution on health outcomes(e.g. respiratory hospital admissions or mortality). However it would be more informative tolook specifically at each domain of the IMD and at its relationship with air pollution to betterunderstand its role as a confounder in the epidemiological analyses.In this paper we illustrate how the complex relationships between the domains of IMD and airpollution can be deconstructed and analysed using profile regression, a Bayesian non-parametricmodel for clustering responses and covariates simultaneously. Moreover, we include an intrinsicspatial conditional autoregressive (ICAR) term to account for the spatial correlation of theresponse variable.

Journal article

Scheelbeek PFD, Chowdhury MAH, Haines A, Alam A, Hoque MA, Butler AP, Khan AE, Mojumder SK, Blangiardo MAG, Elliott P, Vineis Pet al., 2016, High concentrations of sodium in drinking water and raised blood pressure in coastal deltas affected by episodic seawater inundations, Lancet Global Health, Vol: 4, ISSN: 2214-109X

Background In times of seawater inundation in coastal deltas, unprotected drinking water sources, such as ponds andshallow tube wells, take on salt water with each inundation. Daily consumption of these saline sources contributes tooverall sodium intake. Although there is evidence that a high dietary salt intake is an important risk factor forhypertension, little is known about the eff ect of high concentrations of sodium in drinking water on populationhealth. In this longitudinal study, we aimed to measure the eff ect of high concentrations of sodium in drinking wateron blood pressure and to assess the reversibility of raised blood pressure when conventional drinking water sourceswere replaced by low-saline water.Methods We used a multistage sampling process to recruit participants aged 18 years or older from the salinityaffectedsub-districts of Dacope, Batiagatha, and Paikgatchha in coastal Bangladesh. Most participants consumeddrinking water from highly saline sources, such as ponds and tube-wells, while a small percentage had access torainwater. In March, 2013, we recorded: baseline concentrations of sodium in drinking water; participants’ bloodpressure; and personal, lifestyle, and environmental characteristics. During the study period, some study participantsgained access to low-saline drinking water alternatives that were installed for use in the dry season, when water fromponds becomes more saline. In March, 2014, and May, 2014, we made follow-up assessments of drinking watersodium, blood pressure, and repeated the questionnaire about personal, lifestyle, and environmental characteristics.We used generalised linear mixed methods to model the eff ect of drinking water sodium on blood pressure andassess reversibility of raised blood pressure when participants switched from conventional drinking water sources tolow-saline alternatives.Findings We included data from 581 participants in analysis, of which 277 (48%) were male. Median age was 38 years(IQR 30&

Journal article

Blangiardo M, Cameletti M, 2016, Computational issues and R packages for spatial data analysis, Handbook of Spatial Epidemiology, Pages: 417-447, ISBN: 9781482253016

Book chapter

Blangiardo MAG, Finazzi F, Cameletti M, 2016, Two-stage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions, Spatial and Spatio-temporal Epidemiology, Vol: 18, Pages: 1-12, ISSN: 1877-5853

Exposure to high levels of air pollutant concentration is known to be associatedwith respiratory problems which can translate into higher morbidity andmortality rates. The link between air pollution and population health hasmainly been assessed considering air quality and hospitalization or mortalitydata. However this approach limits the analysis to individuals characterizedby severe conditions. In this paper we evaluate the link between air pollutionand respiratory diseases using general practice drug prescriptions for chronicrespiratory diseases, which allow to draw conclusions based on the generalpopulation.We propose a two-stage statistical approach: in the first stage we specifya space-time model to estimate the monthly NO2 concentration integratingseveral data sources characterized by different spatio-temporal resolution;in the second stage we link the concentration to the β2-agonists prescribedmonthly by general practices in England and we model the prescription ratesthrough a small area approach.

Journal article

Hansell A, Ghosh RE, Blangiardo M, Perkins C, Vienneau D, Goffe K, Briggs D, Gulliver Jet al., 2016, Historic air pollution exposure and long-term mortality risks in England and Wales: prospective longitudinal cohort study, Thorax, Vol: 71, Pages: 330-338, ISSN: 1468-3296

Introduction Long-term air pollution exposure contributes to mortality but there are few studies examining effects of very long-term (>25 years) exposures.Methods This study investigated modelled air pollution concentrations at residence for 1971, 1981, 1991 (black smoke (BS) and SO2) and 2001 (PM10) in relation to mortality up to 2009 in 367 658 members of the longitudinal survey, a 1% sample of the English Census. Outcomes were all-cause (excluding accidents), cardiovascular (CV) and respiratory mortality.Results BS and SO2 exposures remained associated with mortality decades after exposure—BS exposure in 1971 was significantly associated with all-cause (OR 1.02 (95% CI 1.01 to 1.04)) and respiratory (OR 1.05 (95% CI 1.01 to 1.09)) mortality in 2002–2009 (ORs expressed per 10 μg/m3). Largest effect sizes were seen for more recent exposures and for respiratory disease. PM10 exposure in 2001 was associated with all outcomes in 2002–2009 with stronger associations for respiratory (OR 1.22 (95% CI 1.04 to 1.44)) than CV mortality (OR 1.12 (95% CI 1.01 to 1.25)). Adjusting PM10 for past BS and SO2 exposures in 1971, 1981 and 1991 reduced the all-cause OR to 1.16 (95% CI 1.07 to 1.26) while CV and respiratory associations lost significance, suggesting confounding by past air pollution exposure, but there was no evidence for effect modification. Limitations include limited information on confounding by smoking and exposure misclassification of historic exposures.Conclusions This large national study suggests that air pollution exposure has long-term effects on mortality that persist decades after exposure, and that historic air pollution exposures influence current estimates of associations between air pollution and mortality.

Journal article

Nomura S, Blangiardo M, Tsubokura M, Ozaki A, Morita T, Hodgson Set al., 2016, Postnuclear disaster evacuation and chronic health in adults in Fukushima, Japan: a long-term retrospective analysis, BMJ Open, Vol: 6, ISSN: 2044-6055

Objective Japan's 2011 Fukushima Daiichi Nuclear Power Plant incident required the evacuation of over a million people, creating a large displaced population with potentially increased vulnerability in terms of chronic health conditions. We assessed the long-term impact of evacuation on diabetes, hyperlipidaemia and hypertension.Participants We considered participants in annual public health check-ups from 2008 to 2014, administrated by Minamisoma City and Soma City, located about 10–50 km from the Fukushima nuclear plant.Methods Disease risks, measured in terms of pre-incident and post-incident relative risks, were examined and compared between evacuees and non-evacuees/temporary-evacuees. We also constructed logistic regression models to assess the impact of evacuation on the disease risks adjusted for covariates.Results Data from a total of 6406 individuals aged 40–74 years who participated in the check-ups both at baseline (2008–2010) and in one or more post-incident years were analysed. Regardless of evacuation, significant post-incident increases in risk were observed for diabetes and hyperlipidaemia (relative risk: 1.27–1.60 and 1.12–1.30, respectively, depending on evacuation status and post-incident year). After adjustment for covariates, the increase in hyperlipidaemia was significantly greater among evacuees than among non-evacuees/temporary-evacuees (OR 1.18, 95% CI 1.06 to 1.32, p<0.01).Conclusions The singularity of this study is that evacuation following the Fukushima disaster was found to be associated with a small increase in long-term hyperlipidaemia risk in adults. Our findings help identify discussion points on disaster planning, including preparedness, response and recovery measures, applicable to future disasters requiring mass evacuation.

Journal article

Boulieri A, Liverani S, de Hoogh K, Blangiardo Met al., 2016, A space-time multivariate Bayesian model to analyse road traffic accidents by severity, Journal of the Royal Statistical Society. Series A. Statistics in Society, Vol: 180, Pages: 119-139, ISSN: 0964-1998

his paper investigates the dependencies between severity levels ofroad traffic accidents, accounting at the same time for spatial and temporal cor-relations. The study analyses road traffic accidents data at ward level in Englandover the period 2005-2013. We include in our model multivariate spatially struc-tured and unstructured effects to capture the respective dependencies betweenseverities, within a Bayesian hierarchical formulation. We also include a tempo-ral component to capture the time effects and we carry out an extensive modelcomparison. The results show important associations in both spatially structuredand unstructured effects between severities, while a downward temporal trend isobserved for low and high severity levels. Maps of posterior accident rates indi-cate elevated risk within big cities for accidents of low severity and in suburbanareas in the north and on the southern coast of England for accidents of high2Boulieriet al.severity. Posterior probability of extreme rates is used to suggest the presenceof hot spots in a public health perspective.

Journal article

Nomura S, Blangiardo M, Tsubokura M, Ozaki A, Morita T, Hodgson Set al., 2016, Post-nuclear disaster evacuation and chronic health in Adults in Fukushima, Japan: A long-term retrospective analysis, Pages: 468-471

© 2016 CURRAN-CONFERENCE. All rights reserved. Objective: Japan's 2011 Fukushima Daiichi Nuclear Power Plant incident required the evacuation of over a million people, creating a large displaced population with potentially increased vulnerability in terms of chronic health conditions. We assessed the long-term impact of evacuation on diabetes, hyperlipidemia, and hypertension. Participants: We considered participants in annual public health check-ups from 2008 to 2014, administrated by Minamisoma City and Soma City, located about 10-50 km from the Fukushima nuclear plant. Methods: Disease risks, measured in terms of pre- and post-incident relative risks, were examined and compared between evacuees and non-/temporary-evacuees. We also constructed logistic regression models to assess the impact of evacuation on the disease risks adjusted for covariates. Results: Data from a total of 6, 406 individuals aged 40-74 who participated in the check-ups both at baseline (2008-2010) and in one or more post-incident years were analyzed. Regardless of evacuation, significant post-incident increases in risk were observed for diabetes and hyperlipidemia (Relative Risk: 1.27 to 1.60 and 1.12 to 1.30, respectively, depending on evacuation status and post-incident year). After adjusted covariates, the increase in hyperlipidemia was significantly greater among evacuees than among non-/temporary-evacuees (Odds Ratio: 1.18, 95% CI: 1.06-1.32, p<0.01). Conclusions: The novelty of this study is that evacuation following the Fukushima disaster was found to be associated with a small increase in long term hyperlipidemia risk in adults. Our findings help identify discussion points on disaster planning, including preparedness, response and recovery measures applicable to future disasters requiring mass evacuation.

Conference paper

Nomura S, Blangiardo M, Tsubokura M, Nishikawa Y, Gilmour S, Kami M, Hodgson Set al., 2016, Post-nuclear disaster evacuation and survival amongst elderly people in Fukushima: a comparative analysis between evacuees and non-evacuees, Preventive Medicine, Vol: 82, Pages: 77-82, ISSN: 1096-0260

BACKGROUND: Considering the health impacts of evacuation is fundamental to disaster planning especially for vulnerable elderly populations; however, evacuation-related mortality risks have not been well-investigated. We conducted an analysis to compare survival of evacuated and non-evacuated residents of elderly care facilities, following the Great East Japan Earthquake and subsequent Fukushima Dai-ichi nuclear power plant incident on 11th March 2011. OBJECTIVE: To assess associations between evacuation and mortality after the Fukushima nuclear incident; and to present discussion points on disaster planning, with reference to vulnerable elderly populations. METHODS: The study population comprised 1,215 residents admitted to seven elderly care facilities located 20-40km from the nuclear plant in the five years before the incident. Demographic and clinical characteristics were obtained from medical records. Evacuation histories were tracked until mid 2013. Main outcome measures are hazard ratios in evacuees versus non-evacuees using random-effects Cox proportional hazards models, and pre- and post-disaster survival probabilities and relative mortality incidence. RESULTS: Experiencing the disasters did not have a significant influence on mortality (hazard ratio 1.10, 95% confidence interval: 0.84-1.43). Evacuation was associated with 1.82 times higher mortality (95% confidence interval: 1.22-2.70) after adjusting for confounders, with the initial evacuation from the original facility associated with 3.37 times higher mortality risk (95% confidence interval: 1.66-6.81) than non evacuation. CONCLUSIONS: The government should consider updating its requirements for emergency planning for elderly facilities and ensure that, in a disaster setting, these facilities have the capacity and support to shelter in place for at least sufficient time to adequately prepare initial evacuation.

Journal article

Halonen JI, Blangiardo M, Toledano MB, Fecht D, Gulliver J, Ghosh R, Anderson HR, Beevers SD, Dajnak D, Kelly FJ, Wilkinson P, Tonne Cet al., 2015, Is long-term exposure to traffic pollution associated with mortality? A small-area study in London., Environmental Pollution, ISSN: 1873-6424

Long-term exposure to primary traffic pollutants may be harmful for health but few studies have investigated effects on mortality. We examined associations for six primary traffic pollutants with all-cause and cause-specific mortality in 2003-2010 at small-area level using linear and piecewise linear Poisson regression models. In linear models most pollutants showed negative or null association with all-cause, cardiovascular or respiratory mortality. In the piecewise models we observed positive associations in the lowest exposure range (e.g. relative risk (RR) for all-cause mortality 1.07 (95% credible interval (CI) = 1.00-1.15) per 0.15 μg/m(3) increase in exhaust related primary particulate matter ≤2.5 μm (PM2.5)) whereas associations in the highest exposure range were negative (corresponding RR 0.93, 95% CI: 0.91-0.96). Overall, there was only weak evidence of positive associations with mortality. That we found the strongest positive associations in the lowest exposure group may reflect residual confounding by unmeasured confounders that varies by exposure group.

Journal article

Halonen JI, Hansell AL, Gulliver J, Morley D, Blangiardo M, Fecht D, Toledano MB, Beevers SD, Anderson HR, Kelly FJ, 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: 1522-9645

Aims Road 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 results The 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.Conclusions Long-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: 1873-6750

BackgroundAirborne 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.MethodsWe 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.ResultsThe 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%).ConclusionsWe 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

Pirani M, Gulliver J, Fuller GW, Blangiardo Met al., 2013, Bayesian spatiotemporal modelling for the assessment of short-term exposure to particle pollution in urban areas, Journal of Exposure Science and Environmental Epidemiology, Vol: 24, Pages: 319-327, ISSN: 1559-064X

This paper describes a Bayesian hierarchical approach to predict short-term concentrations of particle pollution in an urbanenvironment, with application to inhalable particulate matter (PM10) in Greater London. We developed and compared severalspatiotemporal models that differently accounted for factors affecting the spatiotemporal properties of particle concentrations. Weconsidered two main source contributions to ambient measurements: (i) the long-range transport of the secondary fraction ofparticles, which temporal variability was described by a latent variable derived from rural concentrations; and (ii) the local primarycomponent of particles (traffic- and non-traffic-related) captured by the output of the dispersion model ADMS-Urban, which sitespecificeffect 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 notconsidered in local-scale dispersion models and day of the week to account for time-varying emission rates not available inemissions inventories. The evaluation of the predictive ability of the models, obtained via a cross-validation approach, revealed thatconcentration estimates in urban areas benefit from combining the city-scale particle component and the long-range transportcomponent with covariates that account for the residual spatiotemporal variation in the pollution process

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, ISSN: 1476-069X

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

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