98 results found
Forlani C, Bhatt S, Cameletti M, et al., A Joint Bayesian Space-Time Model to Integrate Spatially Misaligned Air Pollution Data in R-INLA, Environmetrics, ISSN: 1099-095X
Marques Moralejo Bermudi P, Nunes Carneiro Castro Costa D, Maroni Nunes C, et al., Canine serological survey and dog culling ant its relationship with Human Visceral leishmaniasis in an endemic urban area., BMC Infectious Diseases, ISSN: 1471-2334
Sera F, Hashizume M, Honda Y, et al., Air conditioning and heat-related mortality: a multi-country longitudinal study, Epidemiology, ISSN: 1044-3983
Lavigne A, Freni Sterrantino A, Fecht D, et al., A spatial joint analysis of metal constituents of ambient particulate matter and mortality in England, Environmental Epidemiology, ISSN: 2474-7882
Background Few studies have investigated associations between metal components of particulate matter on mortality due to well-known issues of multicollinearity. Here, we analyze these exposures jointly to evaluate their associations with mortality on small area data.Methods We fit a Bayesian Profile Regression (BPR) to account for the multicollinearity in the elemental components (iron, copper and zinc) of PM10 and PM2.5. The models are developed in relation to mortality from cardiovascular and respiratory disease and lung cancer incidence in 2008-11 at small area level, for a population of 13.6 million in the London-Oxford area of England.Results From the BPR, we identified higher risks in the PM10 fraction cluster likely to represent the study area, excluding London, for cardiovascular mortality RR 1.07 (95%CI 1.02, 1.12) and for respiratory mortality RR 1.06 (95%CI 0.99, 1.31), compared to the study mean. For PM2.5 fraction, higher risks were seen for cardiovascular mortality RR 1.55 (CI 95% 1.38, 1.71) and respiratory mortality RR 1.51 (CI 95% 1.33, 1.72), likely to represent the 'highways' cluster. We did not find relevant associations for lung cancer incidence.Conclusion Our analysis showed small but not fully consistent adverse associations between health outcomes and particulate metal exposures. The BPR approach identified subpopulations with unique exposure profiles and provided information about the geographical location of these to help interpret findings.
Blangiardo M, Boulieri A, Diggle P, et al., 2020, Advances in spatio-temporal models for non-communicable disease surveillance, International Journal of Epidemiology, Vol: 49, Pages: i26-i37, ISSN: 1464-3685
Surveillance systems are commonly used to provide early warning detection or to assess an impact of an intervention/policy. Traditionally, the methodological and conceptual frameworks for surveillance have been designed for infectious diseases, but the rising burden of non-communicable diseases (NCDs) worldwide suggests a pressing need for surveillance strategies to detect unusual patterns in the data and to help unveil important risk factors in this setting. Surveillance methods need to be able to detect meaningful departures from expectation and exploit dependencies within such data to produce unbiased estimates of risk as well as future forecasts. This has led to the increasing development of a range of space-time methods specifically designed for NCD surveillance.We present an overview of recent advances in spatio-temporal disease surveillance for NCDs using hierarchically specified models. This provides a coherent framework for modelling complex data structures, dealing with data sparsity, exploiting dependencies between data sources and propagating the inherent uncertainties present in both the data and the modelling process. We then focus on three commonly used models within the Bayesian Hierarchical Model (BHM) framework and through a simulation study we compare their performance.We also discuss some challenges faced by researchers when dealing with NCD surveillance, including how to account for false detection and the modifiable areal unit problem. Finally, we consider how to use and interpret the complex models, how model selection may vary depending on the intended user group and how best to communicate results to stakeholders and the general public.
Piel F, Fecht D, Hodgson S, et al., 2020, Small-area methods for investigation of environment and health, International Journal of Epidemiology, Vol: 49, Pages: 686-699, ISSN: 1464-3685
Small-area studies offer a powerful epidemiological approach to study disease patterns at the population level and assess health risks posed by environmental pollutants. They involve a public health investigation on a geographic scale (e.g. neighbourhood) with overlay of health, environmental, demographic and potential confounder data. Recent methodological advances, including Bayesian approaches, combined with fast growing computational capabilities permit more informative analyses than previously possible, including the incorporation of data at different scales, from satellites to individual-level survey information. Better data availability has widened the scope and utility of small-area studies, but also led to greater complexity, including choice of optimal study area size and extent, duration of study periods, range of covariates and confounders to be considered, and dealing with uncertainty. The availability of data from large, well-phenotyped cohorts such as UK Biobank enables the use of mixed-level study designs and the triangulation of evidence on environmental risks from small-area and individual-level studies, therefore improving causal inference, including use of linked biomarker and -omics data. As a result, there are now improved opportunities to investigate the impacts of environmental risk factors on human health, particularly for the surveillance and prevention of non-communicable diseases.
Roca Barcelo A, Douglas P, Fecht D, et al., 2020, Risk of respiratory hospital admission associated with modelled concentrations of Aspergillus fumigatus from composting facilities in England, Environmental Research, Vol: 183, Pages: 1-10, ISSN: 0013-9351
Bioaerosols have been associated with adverse respiratory-related health effects and are emitted in elevated concentrations from composting facilities. We usedmodelledAspergillus fumigatusconcentrations, a good indicator for bioaerosol emissions,to assess associations with respiratory-related hospital admissions. Mean dailyAspergillus fumigatusconcentrationswere estimated for each composting site for first full year of permit issuefrom2005 onwardsto 2014 for Census Output Areas (COAs) within 4km of 76 composting facilities in England, as previously described (Williams et al. 2019). We fitted ahierarchicalgeneralized mixed modelto examine therisk of hospital admission witha primary diagnosis of(i) any respiratory condition,(ii) respiratory infections,(iii) asthma,(iv) COPD,(v)diseases due to organic dust,and (vi)Cystic Fibrosis,inrelation to quartilesof Aspergillus fumigatusconcentrations. Models included a random intercept for each COAto account for over-dispersion,nested within composting facility, on whicha random intercept was fitted to account for clusteringof the data, with adjustmentsfor age, sex, ethnicity, deprivation, tobacco sales (smoking proxy) and traffic load (as a proxy for traffic-related air pollution). Weincluded 249,748 respiratory-related and 3,163 Cystic Fibrosis hospital admissions in 9,606 COAswith a population-weighted centroid within 4 km of the 76 included composting facilities. After adjustment for confounders, no statistically significant effect was observed for any respiratory-related (Relative Risk (RR)=0.99; 95% Confidence Interval (CI)0.96–1.01)or for Cystic Fibrosis (RR=1.01; 95% CI 0.56-1.83)hospital admissions for COAs in the highest quartile of exposure. Similar results were observed across all respiratory disease sub-groups.This study does not provide evidence for increased risks of respiratory-related hospitalisationsfor those livingnearcomposting facilities.However, given the limitations in the dispersion modelling, risks
Cai Y, Hansell AL, Granell R, et al., 2020, Prenatal, early-life and childhood exposure to air pollution and lung function: the ALSPAC cohort., American Journal of Respiratory and Critical Care Medicine, ISSN: 1073-449X
RATIONALE: Exposure to air pollution during intrauterine development and through childhood may have lasting effects on respiratory health. OBJECTIVES: To investigate lung function at ages 8 and 15 years in relation to air pollution exposures during pregnancy, infancy and childhood in a UK population-based birth cohort. METHODS: Individual exposures to source-specific particulate matter with diameter ≤10µm (PM10) during each trimester, 0-6 months, 7-12 months (1990-1993) and up to age 15 years (1991-2008) were examined in relation to %predicted Forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) at ages 8(N=5,276) and 15(N=3,446) years, usinglinear regression models adjusted for potential confounders. A profile regression model was used to identify sensitive time periods. MEASUREMENTS AND MAIN RESULTS: We did not find clear evidence for a sensitive exposure period for PM10 from road-traffic: at age 8 years, 1µg/m3 higher exposure during the first trimester was associated with lower %predicted of FEV1(-0.826, 95%CI:-1.357 to -0.296) and FVC(-0.817, 95%CI:-1.357 to -0.276), but similar associations were seen for exposures for other trimesters, 0-6 months, 7-12 months, and 0-7 years. Associations were stronger among boys, children whose mother had a lower education level or smoked during pregnancy. For PM10 from all sources, the third trimester was associated with lower %predicted of FVC (-1.312, 95%CI: -2.100 to -0.525). At age 15 years, no adverse associations were seen with lung function. CONCLUSIONS: Exposure to road-traffic PM10 during pregnancy may result in small but significant reductions in lung function at age 8 years.
Pirani M, Mason A, Hansell A, et al., A flexible hierarchical framework for improving inference in area-referenced environmental health studies, Biometrical Journal: journal of mathematical methods in biosciences, ISSN: 0323-3847
Study designs where data have been aggregated by geographical areas are popular in environmental epi-demiology. These studies are commonly based on administrative databases and, providing a completespatial coverage, are particularly appealing to make inference on the entire population. However, the re-sulting estimates are often biased and difficult to interpret due to unmeasured confounders, which typicallyare not available from routinely collected data. We propose a framework to improve inference drawn fromsuch studies exploiting information derived from individual-level survey data. The latter are summarized inan area-level scalar score by mimicking at ecological-level the well-known propensity score methodology.The literature on propensity score for confounding adjustment is mainly based on individual-level studiesand assumes a binary exposure variable. Here we generalize its use to cope with area-referenced stud-ies characterized by a continuous exposure. Our approach is based upon Bayesian hierarchical structuresspecified into a two-stage design: (i) geolocated individual-level data from survey samples are up-scaled atecological-level, then the latter are used to estimate a generalizedecological propensity score(EPS) in thein-sample areas; (ii) the generalized EPS is imputed in the out-of-sample areas under different assumptionsabout the missingness mechanisms, then it is included into the ecological regression, linking the exposureof interest to the health outcome. This delivers area-level risk estimates which allow a fuller adjustment forconfounding than traditional areal studies. The methodology is illustrated by using simulations and a casestudy investigating the risk of lung cancer mortality associated with nitrogen dioxide in England (UK).
Ponzi E, Vineis P, Chung K, et al., 2020, Accounting for measurement error to assess the effect of air pollution on omics signals, PLoS One, Vol: 15, Pages: 1-16, ISSN: 1932-6203
Studies on the effects of air pollution and more generally environmental exposures onhealth require measurements of pollutants, which are affected by measurement error.This is a cause of bias in the estimation of parameters relevant to the study and canlead to inaccurate conclusions when evaluating associations among pollutants, diseaserisk and biomarkers. Although the presence of measurement error in such studies hasbeen recognized as a potential problem, it is rarely considered in applications andpractical solutions are still lacking. In this work, we formulate Bayesian measurementerror models and apply them to study the link between air pollution and omic signals.The data we use stem from the “Oxford Street II Study”, a randomized crossover trialin which 60 volunteers walked for two hours in a traffic-free area (Hyde Park) and in abusy shopping street (Oxford Street) of London. Metabolomic measurements were madein each individual as well as air pollution measurements, in order to investigate theassociation between short-term exposure to traffic related air pollution and perturbationof metabolic pathways. We implemented error-corrected models in a classical frameworkand used the flexibility of Bayesian hierarchical models to account for dependenciesamong omic signals, as well as among different pollutants. Models were implementedusing traditional Markov Chain Monte Carlo (MCMC) simulative methods as well asintegrated Laplace approximation. The inclusion of a classical measurement error termresulted in variable estimates of the association between omic signals and traffic relatedair pollution measurements, where the direction of the bias was not predictable a priori.The models were successful in including and accounting for different correlationstructures, both among omic signals and among different pollutant exposures. Ingeneral, more associations were identified when the correlation among omics and amongpollutants were modeled, and their number
Cai Y, Blangiardo M, de Hoogh K, et al., 2020, Road traffic noise, air pollution and cardiorespiratory Health in European Cohorts: A harmonised approach in the BioShare project, Pages: 137-142
Copyright © (2015) by EAA-NAG-ABAV, All rights reserved 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.
Halonen J, Hansell A, Gulliver J, et al., 2020, Associations of road traffic noise with mortality and hospital admissions in London, Pages: 119-123
Copyright © (2015) by EAA-NAG-ABAV, All rights reserved 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.
Smith RB, Beevers SD, Gulliver J, et al., 2020, Impacts of air pollution and noise on risk of preterm birth and stillbirth in London, Environment International, Vol: 134, Pages: 105290-105290, ISSN: 0160-4120
BackgroundEvidence for associations between ambient air pollution and preterm birth and stillbirth is inconsistent. Road traffic produces both air pollutants and noise, but few studies have examined these co-exposures together and none to date with all-cause or cause-specific stillbirths.ObjectivesTo analyse the relationship between long-term exposure to air pollution and noise at address level during pregnancy and risk of preterm birth and stillbirth.MethodsThe study population comprised 581,774 live and still births in the Greater London area, 2006–2010. Outcomes were preterm birth (<37 completed weeks gestation), all-cause stillbirth and cause-specific stillbirth. Exposures during pregnancy to particulate matter with diameter <2.5 μm (PM2.5) and <10 μm (PM10), ozone (O3), primary traffic air pollutants (nitrogen dioxide, nitrogen oxides, PM2.5 from traffic exhaust and traffic non-exhaust), and road traffic noise were estimated based on maternal address at birth.ResultsAn interquartile range increase in O3 exposure was associated with elevated risk of preterm birth (OR 1.15 95% CI: 1.11, 1.18, for both Trimester 1 and 2), all-cause stillbirth (Trimester 1 OR 1.17 95% CI: 1.07, 1.27; Trimester 2 OR 1.20 95% CI: 1.09, 1.32) and asphyxia-related stillbirth (Trimester 1 OR 1.22 95% CI: 1.01, 1.49). Odds ratios with the other air pollutant exposures examined were null or <1, except for primary traffic non-exhaust related PM2.5, which was associated with 3% increased odds of preterm birth (Trimester 1) and 7% increased odds stillbirth (Trimester 1 and 2) when adjusted for O3. Elevated risk of preterm birth was associated with increasing road traffic noise, but only after adjustment for certain air pollutant exposures.DiscussionOur findings suggest that exposure to higher levels of O3 and primary traffic non-exhaust related PM2.5 during pregnancy may increase risk of preterm birth and stillbirth; and a possible relationship between long-term traff
Sera F, Armstrong B, Blangiardo M, et al., 2019, An extended mixed-effects framework for meta-analysis, Statistics in Medicine, Vol: 38, Pages: 5429-5444, ISSN: 0277-6715
Standard methods for meta‐analysis are limited to pooling tasks in which a single effect size is estimated from a set of independent studies. However, this setting can be too restrictive for modern meta‐analytical applications. In this contribution, we illustrate a general framework for meta‐analysis based on linear mixed‐effects models, where potentially complex patterns of effect sizes are modeled through an extended and flexible structure of fixed and random terms. This definition includes, as special cases, a variety of meta‐analytical models that have been separately proposed in the literature, such as multivariate, network, multilevel, dose‐response, and longitudinal meta‐analysis and meta‐regression. The availability of a unified framework for meta‐analysis, complemented with the implementation in a freely available and fully documented software, will provide researchers with a flexible tool for addressing nonstandard pooling problems.
Lavigne A, Freni Sterrantino A, Liverani S, et al., 2019, Associations between metal constituents of ambient particulate matter and mortality in England; an ecological study, BMJ Open, Vol: 9, ISSN: 2044-6055
Objectives To investigate long-term associations between metal components of particulate matter and mortality and lung cancer incidenceDesign Small area (ecological) study Setting Population living in all wards (~9000 individuals per ward) in the London and Oxford area of England, comprising 13.6 million individuals Exposure and Outcome measures We used land use regression (LUR) models originally used in the Transport related Air Pollution and Health impacts – Integrated Methodologies for Assessing Particulate Matter (TRANSPHORM) study to estimate exposure to copper, iron and zinc in ambient air particulate matter. We examined associations of metal exposure with Office for National Statistics mortality data from cardiovascular (CVD) and respiratory causes and with lung cancer incidence in 2008-11.Results There were 108,478 CVD deaths, 48,483 respiratory deaths and 24,849 incident cases of lung cancer in the study period and area. Using Poisson regression models adjusted for area-level deprivation, tobacco sales and ethnicity, we found associations between cardiovascular mortality and PM2.5 copper with interdecile range (IDR-2.6-5.7 ng/m3) and IDR Relative risk (RR) 1.005 (95%CI 1.001, 1.009) and between respiratory mortality and PM10 zinc (IDR 1135-153 ng/m3) and IDR RR 1.136 (95%CI 1.010, 1.277). We did not find relevant associations for lung cancer incidence. Metal elements were highly correlated.Conclusion Our analysis showed small but not fully consistent adverse associations between mortality and particulate metal exposures likely derived from non-tailpipe road traffic emissions (brake and tyre-wear), which have previously been associated with increases in inflammatory markers in the blood.
Hansell A, Cai Y, Granell R, et al., 2019, Prenatal, early-life and childhood exposure to air pollution and lung function in the UK Avon Longitudinal Study of Parents and Children (ALSPAC) cohort, European-Respiratory-Society (ERS) International Congress, Publisher: EUROPEAN RESPIRATORY SOC JOURNALS LTD, ISSN: 0903-1936
Manica M, Caputo B, Screti A, et al., 2019, Applying the N‐mixture model approach to estimate mosquito population absolute abundance from monitoring data, Journal of Applied Ecology, Vol: 56, Pages: 2225-2235, ISSN: 0021-8901
1. Estimating population abundance is a key objective of surveillance programmes, particularly for vector species of public health interest. For mosquitos, which are vectors of human pathogens, established methods to measure absolute population abundance such as mark‐release‐recapture are difficult to implement and usually spatially limited. Typically, regional monitoring schemes assess species relative abundance (counting captured individuals) to prioritize control efforts and study species distribution. However, assessing absolute abundance is crucial when the focus is on pathogen transmission by contacts between vectors and hosts. Here, we applied the N‐mixture model approach to estimate mosquito abundance from standard monitoring data.2. We extended the N‐mixture model approach in a Bayesian framework by considering a beta‐binomial distribution for the detection process. We ran a simulation study to explore model performance under a low detection probability, a time‐varying population and different sets of independent variables.3. When informative priors were used and the model was well specified, estimates by N‐mixture model well correlated (>0.9) with synthetic data and had a mean absolute deviation of about 20%. Correlation decreased and biased increased with uninformative priors or model misspecification.4. When fed with field monitoring data to estimate the absolute abundance of the mosquito arbovirus vector Aedes albopictus within the metropolitan city of Rome (Italy), the N‐mixture model showed higher population size in residential neighbourhoods than in large green areas and revealed that traps located adjacent to vegetated sites have a higher probability of capturing mosquitoes.5. Synthesis and applications. Our results show that, if supported by a good knowledge of the target species biology and by informative priors (e.g. from previous studies of capture rates), the N‐mixture model represents a valuable tool to exploit field monitoring data to esti
Can Bayesian models reveal the underlying processes that drive the lethality of non‐state terrorism at a local level? Andre Python, Janine B. Illian, Charlotte M. Jones‐Todd and Marta Blangiardo investigate Terrorism and terror‐related deaths are rarely out of the news. But not all terrorism is lethal. On a global scale, summary statistics tell us that in the last 20 years about 50% of terrorist attacks resulted in loss of life, while the remainder consisted of acts of sabotage, threats, and so on, which spread fear without necessarily killing anyone.
Freni Sterrantino A, Elliott P, Blangiardo M, et al., 2019, Bayesian spatial modelling for quasi-experimental designs: an interrupted time series study of the opening of Municipal Waste Incinerators in relation to infant mortality and sex ratio, Environment International, Vol: 128, Pages: 109-115, ISSN: 0160-4120
BackgroundThere is limited evidence on potential health risks from Municipal Waste Incinerators (MWIs), and previous studies on birth outcomes show inconsistent results. Here, we evaluate whether the opening of MWIs is associated with infant mortality and sex ratio in the surrounding areas, extending the Interrupted Time Series (ITS) methodological approach to account for spatial dependencies at the small area level.MethodsWe specified a Bayesian hierarchical model to investigate the annual risks of infant mortality and sex-ratio (female relative to male) within 10 km of eight MWIs in England and Wales, during the period 1996–2012. We included comparative areas matched one-to-one of similar size and area characteristics.ResultsDuring the study period, infant mortality rates decreased overall by 2.5% per year in England. The opening of an incinerator in the MWI area was associated with −8 deaths per 100,000 infants (95% CI −62, 40) and with a difference in sex ratio of −0.004 (95% CI −0.02, 0.01), comparing the period after opening with that before, corrected for before-after trends in the comparator areas.ConclusionOur method is suitable for the analysis of quasi-experimental time series studies in the presence of spatial structure and when there are global time trends in the outcome variable. Based on our approach, we do not find evidence of an association of MWI opening with changes in risks of infant mortality or sex ratio in comparison with control areas.
Cameletti M, Gomez-Rubio V, Blangiardo M, 2019, Bayesian modeling for spatially misaligned health and air pollution data through the INLA-SPDE approach, Spatial Statistics, Vol: 31, ISSN: 2211-6753
In air pollution studies a key issue concerns the change of support: pollutant concentrations are continuous phenomena in space but their measurements are typically available at a finite number of point-referenced monitoring stations or result from numerical models. When linking exposure to health outcomes, the latter are usually available at administrative level, hence on an irregular lattice, providing challenges in terms of data misalignment.In this paper we tackle the change of support problem for air pollution and health studies through a two-stage Bayesian approach; in the first stage our model estimates the air pollution concentration at the area level and then in the second stage it links the exposure to the health outcome, accounting for the uncertainty on the exposure estimates. We show through an extensive and realistic simulation that our model is able to predict the concentration accurately at the administrative level as well as estimate the association between exposure and health outcome. We use the Integrated Nested Laplace Approximation, coupled with the Stochastic Partial Differential Equation method for model implementation. Finally we apply the proposed model to evaluate the effect of NOconcentration on hospital admissions for respiratory diseases in the Piemonte region (Italy). We found that the upscaling method and the approach used to propagate uncertainty from the first to the second stage have an impact on the posterior distribution of the relative risk. Moreover, we found a significant increased risk of 1.6% and 1.8% associated with an increase of 10 in NO concentration.
Chiaravalloti-Neto F, Alves da Silva R, Zini N, et al., 2019, Seroprevalence for dengue virus in a hyperendemic area and associated socioeconomic and demographic factors using a cross-sectional design and a geostatistical approach, state of São Paulo, Brazil, BMC Infectious Diseases, Vol: 19, ISSN: 1471-2334
BackgroundSão José do Rio Preto is one of the cities of the state of São Paulo, Brazil, that is hyperendemic for dengue, with the presence of the four dengue serotypes.Objectives: to calculate dengue seroprevalence in a neighbourhood of São José do Rio Preto and identify if socioeconomic and demographic covariates are associated with dengue seropositivity.MethodsA cohort study to evaluate dengue seroprevalence and incidence and associated factors on people aged 10 years or older, was assembled in Vila Toninho neighbourhood, São José do Rio Preto. The participant enrolment occurred from October 2015 to March 2016 (the first wave of the cohort study), when blood samples were collected for serological test (ELISA IgG anti-DENV) and questionnaires were administrated on socio-demographic variables. We evaluated the data collected in this first wave using a cross-sectional design. We considered seropositive the participants that were positive in the serological test (seronegative otherwise). We modelled the seroprevalence with a logistic regression in a geostatistical approach. The Bayesian inference was made using integrated nested Laplace approximations (INLA) coupled with the Stochastic Partial Differential Equation method (SPDE).ResultsWe found 986 seropositive individuals for DENV in 1322 individuals surveyed in the study area in the first wave of the cohort study, corresponding to a seroprevalence of 74.6% (95%CI: 72.2–76.9). Between the population that said never had dengue fever, 68.4% (566/828) were dengue seropositive. Older people, non-white and living in a house (instead of in an apartment), were positively associated with dengue seropositivity. We adjusted for the other socioeconomic and demographic covariates, and accounted for residual spatial dependence between observations, which was found to present up to 800 m.ConclusionsOnly one in four people aged 10 years or older did not hav
Rodriguez de River Ortega O, Blangiardo MAG, López-Quílez A, et al., 2019, Species distribution modelling through Bayesian hierarchical approach, Theoretical Ecology, Vol: 12, Pages: 49-59, ISSN: 1874-1738
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).
Python A, Illian J, Joness-Todd C, et al., 2019, A bayesian approach to modelling subnational spatial dynamics of worldwide non-state terrorism, 2010 - 2015, Journal of the Royal Statistical Society: Series A, Vol: 182, Pages: 323-344, ISSN: 0964-1998
Terrorism persists as a worldwide threat, as exemplified by the ongoinglethal attacks perpetrated by ISIS in Iraq, Syria, Al Qaeda in Yemen, and Boko Haramin Nigeria. In response, states deploy various counterterrorism policies, the costsof which could be reduced through efficient preventive measures. Statistical modelsable to account for complex spatio-temporal dependencies have not yet been applied,despite their potential for providing guidance to explain and prevent terrorism. In aneffort to address this shortcoming, we employ hierarchical models in a Bayesian context,where the spatial random field is represented by a stochastic partial differentialequation. Our main findings suggest that lethal terrorist attacks tend to generate moredeaths in ethnically polarised areas and in locations within democratic countries. Furthermore,the number of lethal attacks increases close to large cities and in locationswith higher levels of population density and human activity.
Williams D, Haworth J, Blangiardo MAG, et al., 2019, A spatiotemporal bayesian hierarchical approach to investigating patterns of confidence in the police at the neighbourhood level, Geographical Analysis, Vol: 51, Pages: 90-110, ISSN: 0016-7363
Public confidence in the police is crucial to effective policing. Improving understanding of public confidence at the local level will better enable the police to conduct proactive confidence interventions to meet the concerns of local communities. Conventional approachesdonot consider that public confidence varies across geographic space as well as in time.Neighbourhood level approaches to modelling public confidence in the police are hampered by the small number problem and the resulting instability in the estimates and uncertainty in the results. This research illustrates a spatiotemporal Bayesian approach for estimating and forecastingpublic confidence at theneighbourhood leveland we use it to examine trends in public confidence in the police in London, UK, for Q2 2006 to Q3 2013. Our approach overcomes the limitations of the small number problemand specifically, we investigate the effect of the spatiotemporal representation structurechosenon theestimatesof public confidence produced. We then investigate the use of the model for forecasting by producing one-step ahead forecasts ofthe final third of the time-series.The results are compared with the forecasts from traditional time-series forecasting methods like naïve, exponential smoothing, ARIMA, STARIMA and others. A model with spatially structured and unstructured random effects as well as a normally distributed spatiotemporal interaction term was the most parsimonious and produced the most realistic estimates.It alsoprovided the best forecasts at the London-wide, Borough and neighbourhood level.
Wang Y, Pirani M, Hansell A, et al., 2019, Using ecological propensity score to adjust for missing confounders in small area studies, Biostatistics, Vol: 20, Pages: 1-16, ISSN: 1465-4644
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.
Paris and London are Europe's two megacities and both experience poor air quality with systemic breaches of the NO 2 limit value. Policy initiatives have been taken to address this: some European-wide (e.g. Euro emission standards); others local (e.g. Low Emission Zone, LEZ). Trends in NO X, NO 2 and particulate matter (PM 10, PM 2.5) for 2005-2016 in background and roadside locations; and trends in traffic increments were calculated in both cities to address their impact. Trends in traffic counts and the distribution in Euro standards for diesel vehicles were also evaluated. Linear-mixed effect models were built to determine the main determinants of traffic concentrations. There was an overall increase in roadside NO 2 in 2005-2009 in both cities followed by a decrease of ∼5% year -1 from 2010. Downward trends were associated with the introduction of Euro V heavy vehicles. Despite NO 2 decreasing, at current rates, roads will need 20 (Paris) and 193 years (London) to achieve the European Limit Value (40 μg m -3 annual mean). Euro 5 light diesel vehicles were associated with the decrease in roadside PM 10. An increase in motorcycles in London since 2010 contributed to the lack of significant trend in PM 2.5 roadside increment in 2010-16.
Blangiardo M, Pirani M, Kanapka L, et al., 2019, A hierarchical modelling approach to assess multi pollutant effects in time-series studies, PL o S One, Vol: 14, ISSN: 1932-6203
When assessing the short-term effect of air pollution on health outcomes, it is common practice to consider one pollutant at a time, due to their high correlation. Multi pollutant methods have been recently proposed, mainly consisting of collapsing the different pollutants into air quality indexes or clustering the pollutants and then evaluating the effect of each cluster on the health outcome. A major drawback of such approaches is that it is not possible to evaluate the health impact of each pollutant. In this paper we propose the use of the Bayesian hierarchical framework to deal with multi pollutant concentrations in a two-component model: a pollutant model is specified to estimate the 'true' concentration values for each pollutant and then such concentration is linked to the health outcomes in a time-series perspective. Through a simulation study we evaluate the model performance and we apply the modelling framework to investigate the effect of six pollutants on cardiovascular mortality in Greater London in 2011-2012.
Ghosh RE, Freni-Sterrantino A, Douglas P, et al., 2019, Fetal growth, stillbirth, infant mortality and other birth outcomes near UK municipal waste incinerators; retrospective population based cohort and case-control study, Environment International, Vol: 122, Pages: 151-158, ISSN: 0160-4120
Background: Some studies have reported associations between municipal waste incinerator (MWI) exposures and adverse birth outcomes but there are few studies of modern MWIs operating to current European Union (EU) Industrial Emissions Directive standards. Methods: Associations between modelled ground-level particulate matter ≤10 μm in diameter (PM10) from MWI emissions (as a proxy for MWI emissions) within 10 km of each MWI, and selected birth and infant mortality outcomes were examined for all 22 MWIs operating in Great Britain 2003–10. We also investigated associations with proximity of residence to a MWI. Outcomes used were term birth weight, small for gestational age (SGA) at term, stillbirth, neonatal, post-neonatal and infant mortality, multiple births, sex ratio and preterm delivery sourced from national registration data from the Office for National Statistics. Analyses were adjusted for relevant confounders including year of birth, sex, season of birth, maternal age, deprivation, ethnicity and area characteristics and random effect terms were included in the models to allow for differences in baseline rates between areas and in incinerator feedstock. Results: Analyses included 1,025,064 births and 18,694 infant deaths. There was no excess risk in relation to any of the outcomes investigated during pregnancy or early life of either mean modelled MWI PM10 or proximity to an MWI. Conclusions: We found no evidence that exposure to PM10 from, or living near to, an MWI operating to current EU standards was associated with harm for any of the outcomes investigated. Results should be generalisable to other MWIs operating to similar standards.
Boulieri A, Bennett JE, Blangiardo M, 2018, A Bayesian mixture modelling approach for public health surveillance, Biostatistics, ISSN: 1465-4644
Spatial monitoring of trends in health data plays an important part of public health surveillance. Most commonly, it is used to understand the etiology of a public health issue, to assess the impact of an intervention, or to provide detection of unusual behavior. In this article, we present a Bayesian mixture model for public health surveillance, which is able to provide estimates of the disease risk in space and time, and also to detect areas with unusual behavior. The model is designed to deal with a range of spatial and temporal patterns in the data, and with time series of different lengths. We carry out a simulation study to assess the performance of the model under different scenarios, and we compare it against a recently proposed Bayesian model for short time series. Finally, the proposed model is used for surveillance of road traffic accidents data in England over the years 2005–2015.
Rodríguez de Rivera O, López-Quílez A, Blangiardo MAG, 2018, Assessing the spatial and spatio-temporal distribution of forest species via bayesian hierarchical modeling, Forests, Vol: 9, ISSN: 1999-4907
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.
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