88 results found
Smith R, Beevers S, Gulliver J, et al., 2020, Impacts of air pollution and noise on risk of preterm birth and stillbirth in London, Environment International, Vol: 134, ISSN: 0160-4120
Background: Evidence 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. Objectives: To analyse the relationship between long-term exposure to air pollution and noise at address level during pregnancy and risk of preterm birth and stillbirth.Methods: The 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.Results: An 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. Discussion: Our 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
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.
Ponzi E, Vineis P, Chung K, et al., Accounting for measurement error to assess the effect of air pollution on omics signals, PLoS One, 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
Roca Barcelo A, Douglas P, Fecht D, et al., Risk of respiratory hospital admission associated with modelled concentrations of Aspergillus fumigatus from composting facilities in England, Environmental Research, 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
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.
Piel F, Fecht D, Hodgson S, et al., Small-area methods for investigation of environment and health, International Journal of Epidemiology, 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.
Blangiardo M, Boulieri A, Diggle P, et al., Advances in spatio-temporal models for non-communicable disease surveillance, International Journal of Epidemiology, 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.
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.
Font A, Guiseppin L, Blangiardo M, et al., 2019, A tale of two cities: is air pollution improving in Paris and London?, Environmental Pollution, Vol: 249, Pages: 1-12, ISSN: 0269-7491
Paris and London are Europe's two megacities and both experience poor air quality with systemic breaches of the NO2 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 NOX, NO2 and particulate matter (PM10, PM2.5) 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 NO2 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 NO2 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 PM10. An increase in motorcycles in London since 2010 contributed to the lack of significant trend in PM2.5 roadside increment in 2010–16.
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
Blangiardo M, Pirani M, Kanapka L, et al., 2019, A hierarchical modelling approach to assess multi pollutant effects in time-series studies, PLoS 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.
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.
Ghosh R, 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
BackgroundSome 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.MethodsAssociations 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.ResultsAnalyses 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.ConclusionsWe 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.
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.
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.
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 Veterinary Research, Vol: 14, ISSN: 1746-6148
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.
Cai Y, Hansell A, Hodgson S, et al., Road traffic noise, air pollution and incident cardiovascular disease: a joint analysis of the HUNT, EPIC-Oxford and UK Biobank cohorts, Environment International, ISSN: 0160-4120
Background: This study aimed to investigate the effects of long-term exposure to road traffic noiseand air pollutionon incident cardiovascular disease (CVD)in three large cohorts: HUNT, EPIC-Oxford and UK Biobank. Methods: In pooled complete-casesample of the three cohorts from Norway and the United Kingdom(N=355,732), 21,081 incident all CVD cases including 5,259ischemic heart disease (IHD)and 2,871cerebrovascular cases were ascertained between baseline (1993-2010)and end of follow-up (2008-2013)through medical recordlinkage. Annual mean 24-hour weighted road traffic noise(Lden) and air pollution (particulate matter with aerodynamic diameter ≤10 μm [PM10],≤2.5 μm [PM2.5]andnitrogen 39dioxide[NO2])exposure at baseline address was modelled using a simplified version of the Common Noise Assessment Methods in Europe (CNOSSOS-EU)and European-wide Land Use Regression models.Individual-level covariate data were harmonised and physically pooled across the three cohorts. Analysis was via Cox proportional hazard model with mutual adjustmentsforboth noise and air pollution andpotential confounders. Results: No significant associations were found between annual mean Ldenand incidentCVD,IHD or cerebrovascular disease in the overall populationexcept that the association withincident IHD was significantamong current-smokers.In the fully adjusted models including adjustmentfor Lden, an interquartile range (IQR) higher PM10(4.1μg/m3) or PM2.5(1.4μg/m3) was associated witha5.8% (95%CI: 2.5%-9.3%) and 3.7% (95%CI: 0.2%-7.4%) higherrisk for all incident CVD respectively. No significant associations were found between NO2and any of the CVD outcomes. Conclusions: We found suggestive evidence of a possible association between road traffic noise and incident IHD, consistent with current literature. Long-term particulate air pollution exposure, even at concentrations below current European air quality standards, w
Smith RB, Fecht D, Gulliver J, et al., 2017, Impacts of London's road traffic air and noise pollution on birth weight: a retrospective population-based cohort study, BMJ, Vol: 359, ISSN: 0959-8138
Objective To investigate the relation between exposure to both air and noise pollution from road traffic and birth weight outcomes.Design Retrospective population based cohort study.Setting Greater London and surrounding counties up to the M25 motorway (2317 km2), UK, from 2006 to 2010.Participants 540 365 singleton term live births.Main outcome measures Term low birth weight (LBW), small for gestational age (SGA) at term, and term birth weight.Results Average air pollutant exposures across pregnancy were 41 μg/m3 nitrogen dioxide (NO2), 73 μg/m3 nitrogen oxides (NOx), 14 μg/m3 particulate matter with aerodynamic diameter <2.5 μm (PM2.5), 23 μg/m3 particulate matter with aerodynamic diameter <10 μm (PM10), and 32 μg/m3 ozone (O3). Average daytime (LAeq,16hr) and night-time (Lnight) road traffic A-weighted noise levels were 58 dB and 53 dB respectively. Interquartile range increases in NO2, NOx, PM2.5, PM10, and source specific PM2.5 from traffic exhaust (PM2.5 traffic exhaust) and traffic non-exhaust (brake or tyre wear and resuspension) (PM2.5 traffic non-exhaust) were associated with 2% to 6% increased odds of term LBW, and 1% to 3% increased odds of term SGA. Air pollutant associations were robust to adjustment for road traffic noise. Trends of decreasing birth weight across increasing road traffic noise categories were observed, but were strongly attenuated when adjusted for primary traffic related air pollutants. Only PM2.5 traffic exhaust and PM2.5 were consistently associated with increased risk of term LBW after adjustment for each of the other air pollutants. It was estimated that 3% of term LBW cases in London are directly attributable to residential exposure to PM2.5>13.8 μg/m3during pregnancy.Conclusions The findings suggest that air pollution from road traffic in London is adversely affecting fetal growth. The results suggest little evidence for an independent exposure-response effect of traffic related noise on b
Wilunda C, Yoshida S, Blangiardo MAG, et al., 2017, Caesarean delivery and anaemia risk in children in 45 low‐ and middle‐income countries, Maternal and Child Nutrition, Vol: 14, ISSN: 1740-8709
Caesarean delivery (CD) may reduce placental transfusion and cause poor iron-related haematological indices in the neonate. We aimed to explore the association between CD and anaemia in children aged <5 years utilising data from Demographic and Health Surveys conducted between 2005 and 2015 in 45 low- and middle-income countries (N = 132,877). We defined anaemia categories based on haemoglobin levels, analysed each country's data separately using propensity-score weighting, pooled the country-specific odds ratios (ORs) using random effects meta-analysis, and performed meta-regression to determine whether the association between CD and anaemia varies by national CD rate, anaemia prevalence, and gross national income. Individual-level CD was not associated with any anaemia (OR 0.95, 95% confidence interval (CI) [0.86, 1.06]; I2 = 40.2%), mild anaemia (OR 0.91, 95% CI [0.81, 1.02]; I2 = 24.8%), and moderate/severe anaemia (OR 0.97, 95% CI [0.85, 1.11]; I2 = 47.7%). CD tended to be positively associated with moderate/severe anaemia in upper middle-income countries and negatively associated with mild anaemia in lower middle-income countries; however, meta-regression did not detect any variation in the association between anaemia and CD by the level of income, CD rate, and anaemia prevalence. In conclusion, there was no evidence for an association between CD and anaemia in children younger than 5 years in low- and middle-income countries. Our conclusions were consistent when we looked at only countries with CD rate >15% with data stratified by individual-level wealth status and type of health facility of birth.
Cai Y, Hodgson S, Blangiardo M, et al., 2017, Road traffic noise and incident cardiovascular disease: a joint analysis of HUNT, EPIC-Oxford and UK Biobank, ICBEN 2017 Proceedings
Aims: This study aimed to investigate the effects of long-term exposure to road traffic noise on incident CVD in three large cohorts: HUNT, EPIC-Oxford and UK Biobank. Methods: In a complete-case sample (N=361,699), 4,014 IHD and 2,109 cerebrovascular incident cases were ascertained between baseline (1993-2010) and end of follow-up (2008-2015) through medical record linkage. Annual mean road traffic noise exposure was modelled at baseline address. Individual-level covariate data were harmonised and data were pooled. Analyses used Cox proportional hazards model with adjustments for confounders, including air pollution. Results: For an interquartile range (IQR) (3.9 dBA) higher daytime noise, a non-significant association with incident IHD was seen (Hazard ratio (HR): 1.015, 95% Confidence Interval (CI): 0.989-1.042), fully adjusted. Statistically significant associations and interaction terms were seen in obese individuals (HR: 1.099, 95%CI: 1.029-1.174), and current-smokers (HR: 1.054, 95%CI: 1.007-1.103). No associations were found for ischemic or hemorrhagic stroke. Conclusions: Our study strengthens the evidence base for an effect of road traffic noise on incident IHD, whilst the association with incident stroke remains unclear.
Douglas P, Freni Sterrantino A, Leal Sanchez M, et al., 2017, Estimating particulate exposure from modern Municipal Waste Incinerators (MWIs) in Great Britain., Environmental Science & Technology, Vol: 51, Pages: 7511-7519, ISSN: 0013-936X
Municipal Waste Incineration (MWI) is regulated through the European Union Directive on Industrial Emissions (IED), but there is ongoing public concern regarding potential hazards to health. Using dispersion modeling, we estimated spatial variability in PM10 concentrations arising from MWIs at postcodes (average 12 households) within 10 km of MWIs in Great Britain (GB) in 2003–2010. We also investigated change points in PM10 emissions in relation to introduction of EU Waste Incineration Directive (EU-WID) (subsequently transposed into IED) and correlations of PM10 with SO2, NOx, heavy metals, polychlorinated dibenzo-p-dioxins/furan (PCDD/F), polycyclic aromatic hydrocarbon (PAH) and polychlorinated biphenyl (PCB) emissions. Yearly average modeled PM10 concentrations were 1.00 × 10–5 to 5.53 × 10–2 μg m–3, a small contribution to ambient background levels which were typically 6.59–2.68 × 101 μg m–3, 3–5 orders of magnitude higher. While low, concentration surfaces are likely to represent a spatial proxy of other relevant pollutants. There were statistically significant correlations between PM10 and heavy metal compounds (other heavy metals (r = 0.43, p = <0.001)), PAHs (r = 0.20, p = 0.050), and PCBs (r = 0.19, p = 0.022). No clear change points were detected following EU-WID implementation, possibly as incinerators were operating to EU-WID standards before the implementation date. Results will be used in an epidemiological analysis examining potential associations between MWIs and health outcomes.
Cai Y, Hansell A, Blangiardo M, et al., 2017, Long-term exposure to road traffic noise, ambient air pollution and cardiovascular risk factors in the HUNT and Lifelines cohorts, European Heart Journal, Vol: 38, Pages: 2290-2296, ISSN: 1522-9645
AimsBlood biochemistry may provide information on associations between road traffic noise, air pollution, and cardiovascular disease risk. We evaluated this in two large European cohorts (HUNT3, Lifelines).Methods and resultsRoad traffic noise exposure was modelled for 2009 using a simplified version of the Common Noise Assessment Methods in Europe (CNOSSOS-EU). Annual ambient air pollution (PM10, NO2) at residence was estimated for 2007 using a Land Use Regression model. The statistical platform DataSHIELD was used to pool data from 144 082 participants aged ≥20 years to enable individual-level analysis. Generalized linear models were fitted to assess cross-sectional associations between pollutants and high-sensitivity C-reactive protein (hsCRP), blood lipids and for (Lifelines only) fasting blood glucose, for samples taken during recruitment in 2006–2013. Pooling both cohorts, an inter-quartile range (IQR) higher day-time noise (5.1 dB(A)) was associated with 1.1% [95% confidence interval (95% CI: 0.02–2.2%)] higher hsCRP, 0.7% (95% CI: 0.3–1.1%) higher triglycerides, and 0.5% (95% CI: 0.3–0.7%) higher high-density lipoprotein (HDL); only the association with HDL was robust to adjustment for air pollution. An IQR higher PM10 (2.0 µg/m3) or NO2 (7.4 µg/m3) was associated with higher triglycerides (1.9%, 95% CI: 1.5–2.4% and 2.2%, 95% CI: 1.6–2.7%), independent of adjustment for noise. Additionally for NO2, a significant association with hsCRP (1.9%, 95% CI: 0.5–3.3%) was seen. In Lifelines, an IQR higher noise (4.2 dB(A)) and PM10 (2.4 µg/m3) was associated with 0.2% (95% CI: 0.1–0.3%) and 0.6% (95% CI: 0.4–0.7%) higher fasting glucose respectively, with both remaining robust to adjustment for air/noise pollution.ConclusionLong-term exposures to road traffic noise and ambient air pollution were associated with blood biochemistry, providing a possible link b
Scheelbeek P, 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
BACKGROUND: Millions of coastal inhabitants in Southeast Asia have been experiencing increasing sodium concentrations in their drinking-water sources, likely partially due to climate change. High (dietary) sodium intake has convincingly been proven to increase risk of hypertension; it remains unknown, however, whether consumption of sodium in drinking water could have similar effects on health. OBJECTIVES: We present the results of a cohort study in which we assessed the effects of drinking-water sodium (DWS) on blood pressure (BP) in coastal populations in Bangladesh. METHODS: DWS, BP, and information on personal, lifestyle, and environmental factors were collected from 581 participants. We used generalized linear latent and mixed methods to model the effects of DWS on BP and assessed the associations between changes in DWS and BP when participants experienced changing sodium levels in water, switched from "conventional" ponds or tube wells to alternatives [managed aquifer recharge (MAR) and rainwater harvesting] that aimed to reduce sodium levels, or experienced a combination of these changes. RESULTS: DWS concentrations were highly associated with BP after adjustments for confounding factors. Furthermore, for each 100 mg/L reduction in sodium in drinking water, systolic/diastolic BP was lower on average by 0.95/0.57 mmHg, and odds of hypertension were lower by 14%. However, MAR did not consistently lower sodium levels. CONCLUSIONS: DWS is an important source of daily sodium intake in salinity-affected areas and is a risk factor for hypertension. Considering the likely increasing trend in coastal salinity, prompt action is required. Because MAR showed variable effects, alternative technologies for providing reliable, safe, low-sodium fresh water should be developed alongside improvements in MAR and evaluated in "real-life" salinity-affected settings. https://doi.org/10.1289/EHP659.
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: American Thoracic Society, ISSN: 1073-449X
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