73 results found
Boulieri A, Bennett JE, Blangiardo M, 2018, A Bayesian mixture modeling approach for public health surveillance., Biostatistics
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, Hodgson S, Blangiardo M, et al., 2018, Road traffic noise, air pollution and incident cardiovascular disease: A joint analysis of the HUNT, EPIC-Oxford and UK Biobank cohorts, ENVIRONMENT INTERNATIONAL, Vol: 114, Pages: 191-201, ISSN: 0160-4120
Costa DNCC, Blangiardo M, Rodas LAC, et al., 2018, Canine visceral leishmaniasis in Araçatuba, state of São Paulo, Brazil, and its relationship with characteristics of dogs and their owners: a cross-sectional and spatial analysis using a geostatistical approach., BMC Vet Res, Vol: 14
BACKGROUND: The incidence of visceral leishmaniasis (VL), one of the most important neglected diseases worldwide, is increasing in Brazil. The objectives of this study were to determine the canine VL (CanL) seroprevalence in an urban area of Araçatuba municipality and to evaluate its relationship with the characteristics of dogs and their owners. RESULTS: The CanL seroprevalence in the study area was 0.081 (95% credible interval [CI]: 0.068-0.096). The following covariates/categories were positively associated with the occurrence of a seropositive dog: more than 10 dogs that had lived in the house (odds ratio [OR] = 2.36; 95% CI: 1.03-5.43) (baseline: 0-10 dogs); house with dogs that previously died of VL (OR = 4.85; 95% CI: 2.65-8.86) or died of causes other than old age (OR = 2.26; 95% CI: 1.12-4.46) (baseline: natural or no deaths); dogs that spent the day in a sheltered backyard (OR = 2.14; 95% CI: 1.05-4.40); dogs that spent the day in an unsheltered backyard or the street (OR = 2.67; 95% CI: 1.28-5.57) (baseline: inside home). Spatial dependence among observations occurred within about 45.7 m. CONCLUSIONS: The number of dogs that had lived in the house, previous deaths by VL or other cause, and the place the dog stayed during the day were associated with the occurrence of a VL seropositive dog. The short-distance spatial dependence could be related to the vector characteristics, producing a local neighbourhood VL transmission pattern. The geostatistical approach in a Bayesian context using integrated nested Laplace approximation (INLA) allowed to identify the covariates associated with VL, including its spatially dependent transmission pattern.
Python A, Illian JB, Jones-Todd CM, et al., 2018, A Bayesian approach to modelling subnational spatial dynamics of worldwide non-state terrorism, 2010-2016, Journal of the Royal Statistical Society. Series A: Statistics in Society, ISSN: 0964-1998
© 2018 The Royal Statistical Society and Blackwell Publishing Ltd. Terrorism persists as a worldwide threat, as exemplified by the on-going lethal attacks perpetrated by Islamic State in Iraq and Syria, Al Qaeda in Yemen and Boko Haram in Nigeria. In response, states deploy various counterterrorism policies, the costs of which could be reduced through efficient preventive measures. Statistical models that can account for complex spatiotemporal dependences have not yet been applied, despite their potential for providing guidance to explain and prevent terrorism. To address this shortcoming, we employ hierarchical models in a Bayesian context, where the spatial random field is represented by a stochastic partial differential equation. Our main findings suggest that lethal terrorist attacks tend to generate more deaths in ethnically polarized areas and in locations within democratic countries. Furthermore, the number of lethal attacks increases close to large cities and in locations with higher levels of population density and human activity.
Wilunda C, Yoshida S, Blangiardo M, et al., 2018, Caesarean delivery and anaemia risk in children in 45 low- and middle-income countries, MATERNAL AND CHILD NUTRITION, Vol: 14, ISSN: 1740-8695
de Rivera OR, Blangiardo M, López-Quílez A, et al., 2018, Species distribution modelling through Bayesian hierarchical approach, Theoretical Ecology, Pages: 1-11, ISSN: 1874-1738
© 2018 Springer Nature B.V. Usually in Ecology, the availability and quality of the data is not as good as we would like. For some species, the typical environmental study focuses on presence/absence data, and particularly with small animals as amphibians and reptiles, the number of presences can be rather small. The aim of this study is to develop a spatial model for studying animal data with a low level of presences; we specify a Gaussian Markov Random Field for modelling the spatial component and evaluate the inclusion of environmental covariates. To assess the model suitability, we use Watanabe-Akaike information criteria (WAIC) and the conditional predictive ordinate (CPO). We apply this framework to model each species of amphibian and reptiles present in the Las Tablas de Daimiel National Park (Spain).
de Rivera ÓR, López-Quílez A, Blangiardo M, 2018, Assessing the spatial and spatio-temporal distribution of forest species via Bayesian hierarchical modeling, Forests, Vol: 9
© 2018 by the authors. Climatic change is expected to affect forest development in the short term, as well as the spatial distribution of species in the long term. Species distribution models are potentially useful tools for guiding species choices in reforestation and forest management prescriptions to address climate change. The aim of this study is to build spatial and spatio-temporal models to predict the distribution of four different species present in the Spanish Forest Inventory. We have compared the different models and showed how accounting for dependencies in space and time affect the relationship between species and environmental variables.
Boulieri A, Liverani S, de Hoogh K, et al., 2017, 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
Cai Y, Hansell AL, Blangiardo M, et al., 2017, Long-termexposure to road traffic noise, ambient air pollution, and cardiovascular risk factors in the HUNT and lifelines cohorts, EUROPEAN HEART JOURNAL, Vol: 38, Pages: 2290-+, ISSN: 0195-668X
Cai Y, Hodgson S, Blangiardo M, et al., 2017, Ambient Air Pollution, Traffic Noise And Adult-Onset Asthma: The Hunt Study, Norway, International Conference of the American-Thoracic-Society (ATS), Publisher: AMER THORACIC SOC, ISSN: 1073-449X
Cai Y, Zijlema WL, Doiron D, et al., 2017, Ambient air pollution, traffic noise and adult asthma prevalence: a BioSHaRE approach, EUROPEAN RESPIRATORY JOURNAL, Vol: 49, ISSN: 0903-1936
Dehbi H-M, Blangiardo M, Gulliver J, et al., 2017, Air pollution and cardiovascular mortality with over 25 years follow-up: A combined analysis of two British cohorts, ENVIRONMENT INTERNATIONAL, Vol: 99, Pages: 275-281, ISSN: 0160-4120
Douglas P, Freni-Sterrantino A, Sanchez ML, et al., 2017, Estimating Particulate Exposure from Modern Municipal Waste Incinerators in Great Britain, ENVIRONMENTAL SCIENCE & TECHNOLOGY, Vol: 51, Pages: 7511-7519, ISSN: 0013-936X
Halonen JI, Dehbi H-M, Hansell AL, et al., 2017, 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
Nomura S, Tsubokura M, Ozaki A, et al., 2017, Towards a Long-Term Strategy for Voluntary-Based Internal Radiation Contamination Monitoring: A Population-Level Analysis of Monitoring Prevalence and Factors Associated with Monitoring Participation Behavior in Fukushima, Japan, INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, Vol: 14, ISSN: 1660-4601
Python A, Illian J, Jones-Todd C, et al., 2017, Explaining the Lethality of Boko Haram's Terrorist Attacks in Nigeria, 2009-2014: A Hierarchical Bayesian Approach, 3rd Bayesian Young Statisticians Meeting (BAYSM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 231-239, ISSN: 2194-1009
Scheelbeek PFD, Chowdhury MAH, Haines A, et al., 2017, Drinking Water Salinity and Raised Blood Pressure: Evidence from a Cohort Study in Coastal Bangladesh, ENVIRONMENTAL HEALTH PERSPECTIVES, Vol: 125, ISSN: 0091-6765
Smith RB, Fecht D, Gulliver J, et al., 2017, Impact of London's road traffic air and noise pollution on birth weight: retrospective population based cohort study, BMJ-BRITISH MEDICAL JOURNAL, Vol: 359, ISSN: 1756-1833
Wang Y, Pirani M, Hansell AL, et al., 2017, Using ecological propensity score to adjust for missing confounders in small area studies., Biostatistics
Small area ecological studies are commonly used in epidemiology to assess the impact of area level risk factors on health outcomes when data are only available in an aggregated form. However, the resulting estimates are often biased due to unmeasured confounders, which typically are not available from the standard administrative registries used for these studies. Extra information on confounders can be provided through external data sets such as surveys or cohorts, where the data are available at the individual level rather than at the area level; however, such data typically lack the geographical coverage of administrative registries. We develop a framework of analysis which combines ecological and individual level data from different sources to provide an adjusted estimate of area level risk factors which is less biased. Our method (i) summarizes all available individual level confounders into an area level scalar variable, which we call ecological propensity score (EPS), (ii) implements a hierarchical structured approach to impute the values of EPS whenever they are missing, and (iii) includes the estimated and imputed EPS into the ecological regression linking the risk factors to the health outcome. Through a simulation study, we show that integrating individual level data into small area analyses via EPS is a promising method to reduce the bias intrinsic in ecological studies due to unmeasured confounders; we also apply the method to a real case study to evaluate the effect of air pollution on coronary heart disease hospital admissions in Greater London.
Blangiardo M, Cameletti M, 2016, Computational issues and R packages for spatial data analysis, Handbook of Spatial Epidemiology, Pages: 417-447, ISBN: 9781482253023
Blangiardo M, 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: 1877-5845
Boulieri A, Hansell A, Blangiardo M, 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: 1877-5845
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: 0269-7491
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
Hansell A, Ghosh R, Blangiardo M, et 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
Hansell A, Ghosh RE, Blangiardo M, et 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: 0040-6376
Liverani S, Lavigne A, Blangiardo M, 2016, Modelling collinear and spatially correlated data, Publisher: ELSEVIER SCI LTD
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.
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: 0091-7435
Nomura S, Blangiardo M, Tsubokura M, et al., 2016, School restrictions on outdoor activities and weight status in adolescent children after Japan's 2011 Fukushima Nuclear Power Plant disaster: a mid-term to long-term retrospective analysis, BMJ OPEN, Vol: 6, ISSN: 2044-6055
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