Publications
145 results found
Nomura S, Blangiardo M, Tsubokura M, et 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.
Halonen JI, Blangiardo M, Toledano MB, et al., 2016, Long-term exposure to traffic pollution and hospital admissions in London, Environmental pollution, Vol: 208, Pages: 48-57, ISSN: 1873-6424
Halonen JI, Blangiardo M, Toledano MB, et al., 2016, Is long-term exposure to traffic pollution associated with mortality? A small-area study in London, Environmental Pollution, Vol: 208, Pages: 25-32, ISSN: 0269-7491
Blangiardo M, Cameletti M, 2016, Computational Issues and R Packages for Spatial Data Analysis, HANDBOOK OF SPATIAL EPIDEMIOLOGY, Editors: Lawson, Banerjee, Haining, Ugarte, Publisher: CRC PRESS-TAYLOR & FRANCIS GROUP, Pages: 417-447, ISBN: 978-1-4822-5301-6
Halonen JI, Hansell AL, Gulliver J, et 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.
Blangiardo M, Cameletti M, 2015, Spatial and Spatio-temporal Bayesian Models with R - INLA, ISBN: 9781118326558
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
Halonen J, Hansell A, Gulliver J, et al., 2015, Associations of road traffic noise with mortality and hospital admissions in London, Pages: 119-123
Background and aims Previously published studies have found associations of road noise with hypertension, which is a risk factor for cardiovascular disease, especially for stroke. We aimed to examine the chronic effects of road traffic noise on mortality and hospital admissions for cardiovascular disease and stroke in a large general population. Methods The study population consisted of 8.61 million inhabitants in London. We assessed small-area level associations of day- (7:00-22:59) and night-time (23:00-06:59) road traffic noise with all-cause and cardiovascular mortality and cardiovascular hospital admissions in all adults (25+ years) with Poisson regression models applying the Integrated Nested Laplace Approximation (INLA) approach in the Bayesian framework. We adjusted the models for age and sex, area-level deprivation, ethnicity, smoking, air pollution and a random effect. Results Mean daytime exposure to road traffic noise was 55.6 dB. Daytime noise was associated with all-cause and cardiovascular mortality in adults; relative risks (RR) for all-cause mortality were 1.04 (95% CI 1.00-1.07) in areas with daytime road noise >60 dB vs. <55 dB. Exposure to daytime road traffic noise also increased the risk of hospital admission for stroke with RR 1.05 (95% CI 1.02-1.09) in areas >60 dB vs. <55 dB. Night-time noise was not associated with road traffic noise in adults of all ages. Conclusions This is the largest study to date to investigate environmental noise and cardiovascular disease. It suggests that road traffic noise is associated with small increased risks of all-cause mortality and cardiovascular disease.
Cai Y, Blangiardo M, de Hoogh K, et al., 2015, Road traffic noise, air pollution and cardiorespiratory Health in European Cohorts: A harmonised approach in the BioShare project, Pages: 137-142
Background and aims: Few studies have investigated joint effects of road traffic noise and air pollution on cardiorespiratory 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 cohorts. Residential road traffic noise exposures for each participant are estimated using a European noise model based on Common Noise Assessment Methods in Europe (CNOSSOS-EU). Road traffic air pollution estimates at home address were derived from Land Use Regression models. Cross-sectional and incident epidemiological analyses are in progress, using individual level data, virtually pooled using DataSHIELD methodology. Results: In total, 742,950 men and women are included from all five cohorts, mostly >40 years. Prevalence of self-reported myocardial infarction from these five cohorts is 2.1% (N=15,031) while prevalence of self-reported stroke is 1.4% (N=10,077). Initial pooled analysis of EPIC-Oxford, HUNT and Lifelines showed median day-time (07:00-19:00) noise estimate of 51.8 dB(A) and night-time (23:00-07:00) noise estimate of 43.5 dB(A). 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 and exposure variations needed to investigate effects of road traffic noise and ambient air pollution on cardio-respiratory diseases.
Blangiardo M, Cameletti M, 2015, Advanced modeling, SPATIAL AND SPATIO-TEMPORAL BAYESIAN MODELS WITH R-INLA, Publisher: JOHN WILEY & SONS LTD, Pages: 259-304, ISBN: 978-1-118-32655-8
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Blangiardo M, Cameletti M, 2015, Bayesian regression and hierarchical models, SPATIAL AND SPATIO-TEMPORAL BAYESIAN MODELS WITH R-INLA, Publisher: JOHN WILEY & SONS LTD, Pages: 127-172, ISBN: 978-1-118-32655-8
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- Citations: 3
Blangiardo M, Cameletti M, 2015, Bayesian computing, SPATIAL AND SPATIO-TEMPORAL BAYESIAN MODELS WITH R-INLA, Publisher: JOHN WILEY & SONS LTD, Pages: 75-126, ISBN: 978-1-118-32655-8
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- Citations: 2
Blangiardo M, Cameletti M, 2015, Introduction to Bayesian methods, SPATIAL AND SPATIO-TEMPORAL BAYESIAN MODELS WITH R-INLA, Publisher: JOHN WILEY & SONS LTD, Pages: 47-74, ISBN: 978-1-118-32655-8
Blangiardo M, Cameletti M, 2015, Introduction to R, SPATIAL AND SPATIO-TEMPORAL BAYESIAN MODELS WITH R-INLA, Publisher: JOHN WILEY & SONS LTD, Pages: 19-46, ISBN: 978-1-118-32655-8
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Blangiardo M, Cameletti M, 2015, Spatial and Spatio-temporal Bayesian Models with R-INLA Preface, SPATIAL AND SPATIO-TEMPORAL BAYESIAN MODELS WITH R-INLA, Publisher: JOHN WILEY & SONS LTD, Pages: XI-XII, ISBN: 978-1-118-32655-8
Blangiardo M, Cameletti M, 2015, Spatio-temporal models, SPATIAL AND SPATIO-TEMPORAL BAYESIAN MODELS WITH R-INLA, Publisher: JOHN WILEY & SONS LTD, Pages: 235-258, ISBN: 978-1-118-32655-8
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Blangiardo M, Cameletti M, 2015, Spatial modeling, SPATIAL AND SPATIO-TEMPORAL BAYESIAN MODELS WITH R-INLA, Publisher: JOHN WILEY & SONS LTD, Pages: 173-234, ISBN: 978-1-118-32655-8
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Blangiardo M, Cameletti M, 2015, Spatial and Spatio-temporal Bayesian Models with R-INLA Introduction, SPATIAL AND SPATIO-TEMPORAL BAYESIAN MODELS WITH R-INLA, Publisher: JOHN WILEY & SONS LTD, Pages: 1-18, ISBN: 978-1-118-32655-8
Pirani M, Best N, Blangiardo M, et 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.
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.
Bennett JE, Blangiardo M, Fecht D, et 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.
Barrera L, Leaper C, Pape UJ, et 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
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Cai Y, Blangiardo M, De Hoogh K, et 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.
Hansell AL, Blangiardo M, Fortunato L, et 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.
Blangiardo M, Cameletti M, Baio G, et 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
Blangiardo M, Cameletti M, Baio G, et 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.
Floud S, Blangiardo M, Clark C, et 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
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Tang R, Blangiardo M, Gulliver J, 2013, Using Building Heights and Street Configuration to Enhance Intraurban PM<sub>10</sub>, NO<sub>X</sub>, and NO<sub>2</sub> Land Use Regression Models, ENVIRONMENTAL SCIENCE & TECHNOLOGY, Vol: 47, Pages: 11643-11650, ISSN: 0013-936X
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Blangiardo M, Cameletti M, Baio G, et 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.
Floud S, Blangiardo M, Clark C, et 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
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- Citations: 2
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