Publications by authors named "Marta Blangiardo"

69 Publications

Community factors and excess mortality in first wave of the COVID-19 pandemic in England.

Nat Commun 2021 06 18;12(1):3755. Epub 2021 Jun 18.

UK Small Area Health Statistics Unit, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK.

Risk factors for increased risk of death from COVID-19 have been identified, but less is known on characteristics that make communities resilient or vulnerable to the mortality impacts of the pandemic. We applied a two-stage Bayesian spatial model to quantify inequalities in excess mortality in people aged 40 years and older at the community level during the first wave of the pandemic in England, March-May 2020 compared with 2015-2019. Here we show that communities with an increased risk of excess mortality had a high density of care homes, and/or high proportion of residents on income support, living in overcrowded homes and/or with a non-white ethnicity. We found no association between population density or air pollution and excess mortality. Effective and timely public health and healthcare measures that target the communities at greatest risk are urgently needed to avoid further widening of inequalities in mortality patterns as the pandemic progresses.
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http://dx.doi.org/10.1038/s41467-021-23935-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8213785PMC
June 2021

Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study.

Lancet Planet Health 2021 04;5(4):e209-e219

Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK; Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, UK; Centre for Statistical Modelling, London School of Hygiene & Tropical Medicine, London, UK.

Background: Temperature and rainfall patterns are known to influence seasonal patterns of dengue transmission. However, the effect of severe drought and extremely wet conditions on the timing and intensity of dengue epidemics is poorly understood. In this study, we aimed to quantify the non-linear and delayed effects of extreme hydrometeorological hazards on dengue risk by level of urbanisation in Brazil using a spatiotemporal model.

Methods: We combined distributed lag non-linear models with a spatiotemporal Bayesian hierarchical model framework to determine the exposure-lag-response association between the relative risk (RR) of dengue and a drought severity index. We fit the model to monthly dengue case data for the 558 microregions of Brazil between January, 2001, and January, 2019, accounting for unobserved confounding factors, spatial autocorrelation, seasonality, and interannual variability. We assessed the variation in RR by level of urbanisation through an interaction between the drought severity index and urbanisation. We also assessed the effect of hydrometeorological hazards on dengue risk in areas with a high frequency of water supply shortages.

Findings: The dataset included 12 895 293 dengue cases reported between 2001 and 2019 in Brazil. Overall, the risk of dengue increased between 0-3 months after extremely wet conditions (maximum RR at 1 month lag 1·56 [95% CI 1·41-1·73]) and 3-5 months after drought conditions (maximum RR at 4 months lag 1·43 [1·22-1·67]). Including a linear interaction between the drought severity index and level of urbanisation improved the model fit and showed the risk of dengue was higher in more rural areas than highly urbanised areas during extremely wet conditions (maximum RR 1·77 [1·32-2·37] at 0 months lag vs maximum RR 1·58 [1·39-1·81] at 2 months lag), but higher in highly urbanised areas than rural areas after extreme drought (maximum RR 1·60 [1·33-1·92] vs 1·15 [1·08-1·22], both at 4 months lag). We also found the dengue risk following extreme drought was higher in areas that had a higher frequency of water supply shortages.

Interpretation: Wet conditions and extreme drought can increase the risk of dengue with different delays. The risk associated with extremely wet conditions was higher in more rural areas and the risk associated with extreme drought was exacerbated in highly urbanised areas, which have water shortages and intermittent water supply during droughts. These findings have implications for targeting mosquito control activities in poorly serviced urban areas, not only during the wet and warm season, but also during drought periods.

Funding: Royal Society, Medical Research Council, Wellcome Trust, National Institutes of Health, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, and Conselho Nacional de Desenvolvimento Científico e Tecnológico.

Translation: For the Portuguese translation of the abstract see Supplementary Materials section.
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http://dx.doi.org/10.1016/S2542-5196(20)30292-8DOI Listing
April 2021

Response to "Re: Long-term exposure to air-pollution and COVID-19 mortality in England: A hierarchical spatial analysis".

Environ Int 2021 05 3;150:106427. Epub 2021 Feb 3.

MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.

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http://dx.doi.org/10.1016/j.envint.2021.106427DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857037PMC
May 2021

Long-term exposure to air-pollution and COVID-19 mortality in England: A hierarchical spatial analysis.

Environ Int 2021 01 7;146:106316. Epub 2020 Dec 7.

MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.

Recent studies suggested a link between long-term exposure to air-pollution and COVID-19 mortality. However, due to their ecological design based on large spatial units, they neglect the strong localised air-pollution patterns, and potentially lead to inadequate confounding adjustment. We investigated the effect of long-term exposure to NO and PM on COVID-19 mortality in England using high geographical resolution. In this nationwide cross-sectional study in England, we included 38,573 COVID-19 deaths up to June 30, 2020 at the Lower Layer Super Output Area level (n = 32,844 small areas). We retrieved averaged NO and PM concentration during 2014-2018 from the Pollution Climate Mapping. We used Bayesian hierarchical models to quantify the effect of air-pollution while adjusting for a series of confounding and spatial autocorrelation. We find a 0.5% (95% credible interval: -0.2%, 1.2%) and 1.4% (95% CrI: -2.1%, 5.1%) increase in COVID-19 mortality risk for every 1 μg/m increase in NO and PM respectively, after adjusting for confounding and spatial autocorrelation. This corresponds to a posterior probability of a positive effect equal to 0.93 and 0.78 respectively. The spatial relative risk at LSOA level revealed strong patterns, similar for the different pollutants. This potentially captures the spread of the disease during the first wave of the epidemic. Our study provides some evidence of an effect of long-term NO exposure on COVID-19 mortality, while the effect of PM remains more uncertain.
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http://dx.doi.org/10.1016/j.envint.2020.106316DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7786642PMC
January 2021

Spatio-temporal model to estimate life expectancy and to detect unusual trends at the local authority level in England.

BMJ Open 2020 11 12;10(11):e036855. Epub 2020 Nov 12.

MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK

Objectives: To estimate life expectancy at the local authority level and detect those areas that have a substantially low life expectancy after accounting for deprivation.

Design: We used registration data from the Office for National Statistics on mortality and population in England, by local authority, age group and socioeconomic deprivation decile, for both men and women over the period 2001-2018. We used a statistical model within the Bayesian framework to produce robust mortality rates, which were then transformed to life expectancy estimates. A rule based on exceedance probabilities was used to detect local authorities characterised by a low life expectancy among areas with a similar deprivation level from 2012 onwards.

Results: We confirmed previous findings showing differences in the life expectancy gap between the most and least deprived areas from 2012 to 2018. We found variations in life expectancy trends across local authorities, and we detected a number of those with a low life expectancy when compared with others of a similar deprivation level.

Conclusions: There are factors other than deprivation that are responsible for low life expectancy in certain local authorities. Further investigation on the detected areas can help understand better the stalling of life expectancy which was observed from 2012 onwards and plan efficient public health policies.
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http://dx.doi.org/10.1136/bmjopen-2020-036855DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662413PMC
November 2020

Prevalence and Population Attributable Risk for Chronic Airflow Obstruction in a Large Multinational Study.

Am J Respir Crit Care Med 2020 Nov 10. Epub 2020 Nov 10.

Department of Respiratory Medicine, Maastricht University Medical Center, Maastricht, Netherlands.

The Global Burden of Disease programme identified smoking, and ambient and household air pollution as the main drivers of death and disability from Chronic Obstructive Pulmonary Disease (COPD). To estimate the attributable risk of chronic airflow obstruction (CAO), a quantifiable characteristic of COPD, due to several risk factors. The Burden of Obstructive Lung Disease study is a cross-sectional study of adults, aged≥40, in a globally distributed sample of 41 urban and rural sites. Based on data from 28,459 participants, we estimated the prevalence of CAO, defined as a post-bronchodilator one-second forced expiratory volume to forced vital capacity ratio < lower limit of normal, and the relative risks associated with different risk factors. Local RR were estimated using a Bayesian hierarchical model borrowing information from across sites. From these RR and the prevalence of risk factors, we estimated local Population Attributable Risks (PAR). Mean prevalence of CAO was 11.2% in men and 8.6% in women. Mean PAR for smoking was 5.1% in men and 2.2% in women. The next most influential risk factors were poor education levels, working in a dusty job for ≥10 years, low body mass index (BMI), and a history of tuberculosis. The risk of CAO attributable to the different risk factors varied across sites. While smoking remains the most important risk factor for CAO, in some areas poor education, low BMI and passive smoking are of greater importance. Dusty occupations and tuberculosis are important risk factors at some sites.
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http://dx.doi.org/10.1164/rccm.202005-1990OCDOI Listing
November 2020

ZanzaMapp: A Scalable Citizen Science Tool to Monitor Perception of Mosquito Abundance and Nuisance in Italy and Beyond.

Int J Environ Res Public Health 2020 10 27;17(21). Epub 2020 Oct 27.

Department of Public Health & Infectious Diseases, Laboratory affiliated to Istituto Pasteur Italia-Fondazione Cenci Bolognetti, Sapienza University of Rome, 00185 Rome, Italy.

Mosquitoes represent a considerable nuisance and are actual/potential vectors of human diseases in Europe. Costly and labour-intensive entomological monitoring is needed to correct planning of interventions aimed at reducing nuisance and the risk of pathogen transmission. The widespread availability of mobile phones and of massive Internet connections opens the way to the contribution of citizen in complementing entomological monitoring. ZanzaMapp is the first mobile "mosquito" application for smartphones specifically designed to assess citizens' perception of mosquito abundance and nuisance in Italy. Differently from other applications targeting mosquitoes, ZanzaMapp prioritizes the number of records over their scientific authentication by requesting users to answer four simple questions on perceived mosquito presence/abundance/nuisance and geo-localizing the records. The paper analyses 36,867 ZanzaMapp records sent by 13,669 devices from 2016 to 2018 and discusses the results with reference to either citizens' exploitation and appreciation of the app and to the consistency of the results obtained with the known biology of main mosquito species in Italy. In addition, we provide a first small-scale validation of ZanzaMapp data as predictors of biting females and examples of spatial analyses and maps which could be exploited by public institutions and administrations involved in mosquito and mosquito-borne pathogen monitoring and control.
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http://dx.doi.org/10.3390/ijerph17217872DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7672598PMC
October 2020

Total antibiotic use in a state-wide area and resistance patterns in Brazilian hospitals: an ecologic study.

Braz J Infect Dis 2020 Nov - Dec;24(6):479-488. Epub 2020 Oct 10.

Universidade de São Paulo, Faculdade de Medicina, Departamento de Doenças Infecciosas, São Paulo, SP, Brazil.

Introduction: Use of antibiotic and bacterial resistance is the result of a complex interaction not completely understood.

Objectives: To evaluate the impact of entire antimicrobial use (community plus hospitals) on the incidence of bloodstream infections in intensive care units adjusted by socioeconomic factors, quality of healthcare, and access to the healthcare system.

Design: Ecologic study using a hierarchical spatial model.

Setting: Data obtained from 309 hospitals located in the state of São Paulo, Brazil from 2008 to 2011.

Participants: Intensive care units located at participant hospitals.

Outcome: Hospital acquired bloodstream infection caused by MDRO in ICU patients was our primary outcome and data were retrieved from São Paulo Health State Department. Socioeconomic and healthcare indexes data were obtained from IBGE (Brazilian Foundation in charge of national decennial census) and SEADE (São Paulo Planning and Development Department). Information on antimicrobial sales were obtained from IMS Brazil. We divided antibiotics into four different groups (1-4).

Results: We observed a direct association between the use of group 1 of antibiotics and the incidences of bloodstream infections caused by MRSA (1.12; 1.04-1.20), and CR-Acinetobacter sp. (1.19; 1.10-1.29). Groups 2 and 4 were directly associated to VRE (1.72; 1.13-2.39 and 2.22; 1.62-2.98, respectively). Group 2 was inversely associated to MRSA (0.87; 0.78-0.96) and CR-Acinetobacter sp. (0.79; 0.62-0.97). Group 3 was inversely associated to Pseudomonas aeruginosa (0.69; 0.45-0.98), MRSA (0.85; 0.72-0.97) and VRE (0.48; 0.21-0.84). No association was observed for third generation cephalosporin-resistant Klebsiella pneumoniae and Escherichia coli.

Conclusions: The association between entire antibiotic use and resistance in ICU was poor and not consistent for all combinations of antimicrobial groups and pathogens even after adjusted by socioeconomic indexes. Selective pressure exerted at the community level seemed not to affect the incidences of MDRO infection observed in intensive care setting.
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http://dx.doi.org/10.1016/j.bjid.2020.08.012DOI Listing
December 2020

Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic.

PLoS One 2020 9;15(10):e0240286. Epub 2020 Oct 9.

Department of Statistical Sciences, University College London, London, United Kingdom.

In this study we present the first comprehensive analysis of the spatio-temporal differences in excess mortality during the COVID-19 pandemic in Italy. We used a population-based design on all-cause mortality data, for the 7,904 Italian municipalities. We estimated sex-specific weekly mortality rates for each municipality, based on the first four months of 2016-2019, while adjusting for age, localised temporal trends and the effect of temperature. Then, we predicted all-cause weekly deaths and mortality rates at municipality level for the same period in 2020, based on the modelled spatio-temporal trends. Lombardia showed higher mortality rates than expected from the end of February, with 23,946 (23,013 to 24,786) total excess deaths. North-West and North-East regions showed one week lag, with higher mortality from the beginning of March and 6,942 (6,142 to 7,667) and 8,033 (7,061 to 9,044) total excess deaths respectively. We observed marked geographical differences also at municipality level. For males, the city of Bergamo (Lombardia) showed the largest percent excess, 88.9% (81.9% to 95.2%), at the peak of the pandemic. An excess of 84.2% (73.8% to 93.4%) was also estimated at the same time for males in the city of Pesaro (Central Italy), in stark contrast with the rest of the region, which does not show evidence of excess deaths. We provided a fully probabilistic analysis of excess mortality during the COVID-19 pandemic at sub-national level, suggesting a differential direct and indirect effect in space and time. Our model can be used to help policy-makers target measures locally to contain the burden on the health-care system as well as reducing social and economic consequences. Additionally, this framework can be used for real-time mortality surveillance, continuous monitoring of local temporal trends and to flag where and when mortality rates deviate from the expected range, which might suggest a second wave of the pandemic.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240286PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7546500PMC
October 2020

Air Conditioning and Heat-related Mortality: A Multi-country Longitudinal Study.

Epidemiology 2020 11;31(6):779-787

From the Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom.

Background: Air conditioning has been proposed as one of the key factors explaining reductions of heat-related mortality risks observed in the last decades. However, direct evidence is still limited.

Methods: We used a multi-country, multi-city, longitudinal design to quantify the independent role of air conditioning in reported attenuation in risk. We collected daily time series of mortality, mean temperature, and yearly air conditioning prevalence for 311 locations in Canada, Japan, Spain, and the USA between 1972 and 2009. For each city and sub-period, we fitted a quasi-Poisson regression combined with distributed lag non-linear models to estimate summer-only temperature-mortality associations. At the second stage, we used a novel multilevel, multivariate spatio-temporal meta-regression model to evaluate effect modification of air conditioning on heat-mortality associations. We computed relative risks and fractions of heat-attributable excess deaths under observed and fixed air conditioning prevalences.

Results: Results show an independent association between increased air conditioning prevalence and lower heat-related mortality risk. Excess deaths due to heat decreased during the study periods from 1.40% to 0.80% in Canada, 3.57% to 1.10% in Japan, 3.54% to 2.78% in Spain, and 1.70% to 0.53% in the USA. However, increased air conditioning explains only part of the observed attenuation, corresponding to 16.7% in Canada, 20.0% in Japan, 14.3% in Spain, and 16.7% in the USA.

Conclusions: Our findings are consistent with the hypothesis that air conditioning represents an effective heat adaptation strategy, but suggests that other factors have played an equal or more important role in increasing the resilience of populations.
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http://dx.doi.org/10.1097/EDE.0000000000001241DOI Listing
November 2020

A spatial joint analysis of metal constituents of ambient particulate matter and mortality in England.

Environ Epidemiol 2020 Aug 16;4(4):e098. Epub 2020 Jul 16.

Small Area Health Statistics Unit, Imperial College London, United Kingdom.

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. We fit a Bayesian profile regression (BPR) to account for the multicollinearity in the elemental components (iron, copper, and zinc) of PM and PM The models are developed in relation to mortality from cardiovascular and respiratory disease and lung cancer incidence in 2008-2011 at a small area level, for a population of 13.6 million in the London-Oxford area of England. From the BPR, we identified higher risks in the PM fraction cluster likely to represent the study area, excluding London, for cardiovascular mortality relative risk (RR) 1.07 (95% credible interval [CI] 1.02, 1.12) and for respiratory mortality RR 1.06 (95%CI 0.99, 1.31), compared with the study mean. For PM 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. 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.
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http://dx.doi.org/10.1097/EE9.0000000000000098DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7423532PMC
August 2020

Long-term exposure to air-pollution and COVID-19 mortality in England: a hierarchical spatial analysis.

medRxiv 2020 Aug 11. Epub 2020 Aug 11.

MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.

Background: Recent studies suggested a link between long-term exposure to air-pollution and COVID-19 mortality. However, due to their ecological design, based on large spatial units, they neglect the strong localised air-pollution patterns, and potentially lead to inadequate confounding adjustment. We investigated the effect of long-term exposure to NO2 and PM2.5 on COVID-19 deaths up to June 30, 2020 in England using high geographical resolution.

Methods: We included 38 573 COVID-19 deaths up to June 30, 2020 at the Lower Layer Super Output Area level in England (n=32 844 small areas). We retrieved averaged NO2 and PM2.5 concentration during 2014-2018 from the Pollution Climate Mapping. We used Bayesian hierarchical models to quantify the effect of air-pollution while adjusting for a series of confounding and spatial autocorrelation.

Findings: We find a 0.5% (95% credible interval: -0.2%-1.2%) and 1.4% (-2.1%-5.1%) increase in COVID-19 mortality rate for every 1μg/m3 increase in NO2 and PM2.5 respectively, after adjusting for confounding and spatial autocorrelation. This corresponds to a posterior probability of a positive effect of 0.93 and 0.78 respectively. The spatial relative risk at LSOA level revealed strong patterns, similar for the different pollutants. This potentially captures the spread of the disease during the first wave of the epidemic.

Interpretation: Our study provides some evidence of an effect of long-term NO2 exposure on COVID-19 mortality, while the effect of PM2.5 remains more uncertain.

Funding: Medical Research Council, Wellcome Trust, Environmental Protection Agency and National Institutes of Health.
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http://dx.doi.org/10.1101/2020.08.10.20171421DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7430619PMC
August 2020

A flexible hierarchical framework for improving inference in area-referenced environmental health studies.

Biom J 2020 11 22;62(7):1650-1669. Epub 2020 Jun 22.

MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK.

Study designs where data have been aggregated by geographical areas are popular in environmental epidemiology. These studies are commonly based on administrative databases and, providing a complete spatial coverage, are particularly appealing to make inference on the entire population. However, the resulting estimates are often biased and difficult to interpret due to unmeasured confounders, which typically are not available from routinely collected data. We propose a framework to improve inference drawn from such studies exploiting information derived from individual-level survey data. The latter are summarized in an 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 studies and assumes a binary exposure variable. Here, we generalize its use to cope with area-referenced studies characterized by a continuous exposure. Our approach is based upon Bayesian hierarchical structures specified into a two-stage design: (i) geolocated individual-level data from survey samples are up-scaled at ecological level, then the latter are used to estimate a generalized ecological propensity score (EPS) in the in-sample areas; (ii) the generalized EPS is imputed in the out-of-sample areas under different assumptions about the missingness mechanisms, then it is included into the ecological regression, linking the exposure of interest to the health outcome. This delivers area-level risk estimates, which allow a fuller adjustment for confounding than traditional areal studies. The methodology is illustrated by using simulations and a case study investigating the risk of lung cancer mortality associated with nitrogen dioxide in England (UK).
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http://dx.doi.org/10.1002/bimj.201900241DOI Listing
November 2020

Canine serological survey and dog culling ant its relationship with human visceral leishmaniasis in an endemic urban area.

BMC Infect Dis 2020 Jun 5;20(1):401. Epub 2020 Jun 5.

Department of Epidemiology, School of Public Health, Universidade de São Paulo (USP), Avenida Doutor Arnaldo 715, São Paulo, SP, 01246-904, Brazil.

Background: Visceral leishmaniasis is an important but neglected disease that is spreading and is highly lethal when left untreated. This study sought to measure the Leishmania infantum seroprevalence in dogs, the coverage of its control activities (identification of the canine reservoir by serological survey, dog culling and insecticide spraying) and to evaluate its relationship with the occurrence of the disease in humans in the municipalities of Araçatuba and Birigui, state of São Paulo, Brazil.

Methods: Information from 2006 to 2015 was georeferenced for each municipality and modeling was performed for the two municipalities together. To do this, latent Gaussian Bayesian models with the incorporation of a spatio-temporal structure and Poisson distribution were used. The Besag-York-Mollie models were applied for random spatial effects, as also were autoregressive models of order 1 for random temporal effects. The modeling was performed using the INLA (Integrated Nested Laplace Approximations) deterministic approach, considering both the numbers of cases as well as the coverage paired year by year and lagged at one and two years.

Results: Control activity coverage was observed to be generally low. The behavior of the temporal tendency in the human disease presented distinct patterns in the two municipalities, however, in both the tendency was to decline. The canine serological survey presented as a protective factor only in the two-year lag model.

Conclusions: The canine serological coverage, even at low intensity, carried out jointly with the culling of the positive dogs, suggested a decreasing effect on the occurrence of the disease in humans, whose effects would be seen two years after it was carried out.
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http://dx.doi.org/10.1186/s12879-020-05125-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7275440PMC
June 2020

Advances in spatiotemporal models for non-communicable disease surveillance.

Int J Epidemiol 2020 04;49 Suppl 1:i26-i37

UK Small Area Health Statistics Unit.

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 spatiotemporal 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.
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http://dx.doi.org/10.1093/ije/dyz181DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7158067PMC
April 2020

Small-area methods for investigation of environment and health.

Int J Epidemiol 2020 04;49(2):686-699

UK Small Area Health Statistics Unit, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK.

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 geographical 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 has 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.
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http://dx.doi.org/10.1093/ije/dyaa006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7266556PMC
April 2020

Prenatal, Early-Life, and Childhood Exposure to Air Pollution and Lung Function: The ALSPAC Cohort.

Am J Respir Crit Care Med 2020 07;202(1):112-123

MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, and.

: Exposure to air pollution during intrauterine development and through childhood may have lasting effects on respiratory health.: 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.: Individual exposures to source-specific particulate matter ≤10 μm in aerodynamic diameter (PM) during each trimester, 0-6 months, 7-12 months (1990-1993), and up to age 15 years (1991-2008) were examined in relation to FEV% predicted and FVC% predicted at ages 8 ( = 5,276) and 15 ( = 3,446) years using linear regression models adjusted for potential confounders. A profile regression model was used to identify sensitive time periods.: We did not find clear evidence of a sensitive exposure period for PM from road traffic. At age 8 years, 1 μg/m higher exposure during the first trimester was associated with lower FEV% predicted (-0.826; 95% confidence interval [CI], -1.357 to -0.296) and FVC% predicted (-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, as well as children whose mother had a lower education level or smoked during pregnancy. For PM from all sources, the third trimester was associated with lower FVC% predicted (-1.312; 95% CI, -2.100 to -0.525). At age 15 years, no adverse associations with lung function were seen.: Exposure to road-traffic PM during pregnancy may result in small but significant reductions in lung function at age 8 years.
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http://dx.doi.org/10.1164/rccm.201902-0286OCDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328307PMC
July 2020

Risk of respiratory hospital admission associated with modelled concentrations of Aspergillus fumigatus from composting facilities in England.

Environ Res 2020 04 3;183:108949. Epub 2020 Jan 3.

UK Small Area Health Statistics Unit, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, W2 1PG, UK; Centre for Environmental Health and Sustainability, University of Leicester, Leicester, LE1 7RH, UK; Directorate of Public Health and Primary Care, Imperial College Healthcare NHS Trust, London, W2 1NY, UK. Electronic address:

Bioaerosols have been associated with adverse respiratory-related health effects and are emitted in elevated concentrations from composting facilities. We used modelled Aspergillus fumigatus concentrations, a good indicator for bioaerosol emissions, to assess associations with respiratory-related hospital admissions. Mean daily Aspergillus fumigatus concentrations were estimated for each composting site for first full year of permit issue from 2005 onwards to 2014 for Census Output Areas (COAs) within 4 km of 76 composting facilities in England, as previously described (Williams et al., 2019). We fitted a hierarchical generalized mixed model to examine the risk of hospital admission with a primary diagnosis of (i) any respiratory condition, (ii) respiratory infections, (iii) asthma, (iv) COPD, (v) diseases due to organic dust, and (vi) Cystic Fibrosis, in relation to quartiles of Aspergillus fumigatus concentrations. Models included a random intercept for each COA to account for over-dispersion, nested within composting facility, on which a random intercept was fitted to account for clustering of the data, with adjustments for age, sex, ethnicity, deprivation, tobacco sales (smoking proxy) and traffic load (as a proxy for traffic-related air pollution). We included 249,748 respiratory-related and 3163 Cystic Fibrosis hospital admissions in 9606 COAs with 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 hospitalisations for those living near composting facilities. However, given the limitations in the dispersion modelling, risks cannot be completely ruled out. Hospital admissions represent severe respiratory episodes, so further study would be needed to investigate whether bioaerosols emitted from composting facilities have impacts on less severe episodes or respiratory symptoms.
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http://dx.doi.org/10.1016/j.envres.2019.108949DOI Listing
April 2020

Accounting for measurement error to assess the effect of air pollution on omic signals.

PLoS One 2020 2;15(1):e0226102. Epub 2020 Jan 2.

Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.

Studies on the effects of air pollution and more generally environmental exposures on health 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 can lead to inaccurate conclusions when evaluating associations among pollutants, disease risk and biomarkers. Although the presence of measurement error in such studies has been recognized as a potential problem, it is rarely considered in applications and practical solutions are still lacking. In this work, we formulate Bayesian measurement error 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 trial in which 60 volunteers walked for two hours in a traffic-free area (Hyde Park) and in a busy shopping street (Oxford Street) of London. Metabolomic measurements were made in each individual as well as air pollution measurements, in order to investigate the association between short-term exposure to traffic related air pollution and perturbation of metabolic pathways. We implemented error-corrected models in a classical framework and used the flexibility of Bayesian hierarchical models to account for dependencies among omic signals, as well as among different pollutants. Models were implemented using traditional Markov Chain Monte Carlo (MCMC) simulative methods as well as integrated Laplace approximation. The inclusion of a classical measurement error term resulted in variable estimates of the association between omic signals and traffic related air pollution measurements, where the direction of the bias was not predictable a priori. The models were successful in including and accounting for different correlation structures, both among omic signals and among different pollutant exposures. In general, more associations were identified when the correlation among omics and among pollutants were modeled, and their number increased when a measurement error term was additionally included in the multivariate models (particularly for the associations between metabolomics and NO2).
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0226102PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6940143PMC
April 2020

Associations between metal constituents of ambient particulate matter and mortality in England: an ecological study.

BMJ Open 2019 12 3;9(12):e030140. Epub 2019 Dec 3.

University of Leicester, Leicester, UK

Objectives: To investigate long-term associations between metal components of particulate matter (PM) and mortality and lung cancer incidence.

Design: 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 models originally used in the Transport related Air Pollution and Health Impacts-Integrated Methodologies for Assessing Particulate Matter study to estimate exposure to copper, iron and zinc in ambient air PM. We examined associations of metal exposure with Office for National Statistics mortality data from cardiovascular disease (CVD) and respiratory causes and with lung cancer incidence during 2008-2011.

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 PM copper with interdecile range (IDR 2.6-5.7 ng/m) and IDR relative risk (RR) 1.005 (95%CI 1.001 to 1.009) and between respiratory mortality and PM zinc (IDR 1135-153 ng/m) and IDR RR 1.136 (95%CI 1.010 to 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.
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http://dx.doi.org/10.1136/bmjopen-2019-030140DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6924721PMC
December 2019

Impacts of air pollution and noise on risk of preterm birth and stillbirth in London.

Environ Int 2020 01 26;134:105290. Epub 2019 Nov 26.

MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK; NIHR HPRU in Health Impact of Environmental Hazards, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK. Electronic address:

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 (PM) and <10 μm (PM), ozone (O), primary traffic air pollutants (nitrogen dioxide, nitrogen oxides, PM 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 O 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 PM, which was associated with 3% increased odds of preterm birth (Trimester 1) and 7% increased odds stillbirth (Trimester 1 and 2) when adjusted for O. 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 O and primary traffic non-exhaust related PM during pregnancy may increase risk of preterm birth and stillbirth; and a possible relationship between long-term traffic-related noise and risk of preterm birth. These findings extend and strengthen the evidence base for important public health impacts of ambient ozone, particulate matter and noise in early life.
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http://dx.doi.org/10.1016/j.envint.2019.105290DOI Listing
January 2020

An extended mixed-effects framework for meta-analysis.

Stat Med 2019 12 24;38(29):5429-5444. Epub 2019 Oct 24.

Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, UK.

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.
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http://dx.doi.org/10.1002/sim.8362DOI Listing
December 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 Infect Dis 2019 May 20;19(1):441. Epub 2019 May 20.

Laboratório de Pesquisas em Virologia, Departamento de Doenças Dermatológicas Infecciosas e Parasitárias, Faculdade de Medicina de São José do Rio Preto (FAMERP), Avenida Brigadeiro Faria Lima, 5416, São José do Rio Preto, SP, 15090-000, Brazil.

Background: São 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.

Methods: A 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).

Results: We 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.

Conclusions: Only one in four people aged 10 years or older did not have contact with any of the serotypes of dengue virus in Vila Toninho neighbourhood in São José do Rio Preto. Age, race and type of house were associated with the occurrence of the disease. The use of INLA in a geostatistical approach in a Bayesian context allowed us to take into account the spatial dependence between the observations and identify the associated covariates to dengue seroprevalence.
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http://dx.doi.org/10.1186/s12879-019-4074-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6528304PMC
May 2019

A tale of two cities: is air pollution improving in Paris and London?

Environ Pollut 2019 Jun 14;249:1-12. Epub 2019 Jan 14.

School of Population Health & Environmental Sciences, Faculty of Life Sciences & Medicine, King's College London, UK; MRC PHE Centre for Environment and Health, UK.

Paris and London are Europe's two megacities and both experience poor air quality with systemic breaches of the NO 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, NO and particulate matter (PM, PM) 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 in 2005-2009 in both cities followed by a decrease of ∼5% year from 2010. Downward trends were associated with the introduction of Euro V heavy vehicles. Despite NO decreasing, at current rates, roads will need 20 (Paris) and 193 years (London) to achieve the European Limit Value (40 μg m annual mean). Euro 5 light diesel vehicles were associated with the decrease in roadside PM. An increase in motorcycles in London since 2010 contributed to the lack of significant trend in PM roadside increment in 2010-16.
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http://dx.doi.org/10.1016/j.envpol.2019.01.040DOI Listing
June 2019

A hierarchical modelling approach to assess multi pollutant effects in time-series studies.

PLoS One 2019 4;14(3):e0212565. Epub 2019 Mar 4.

MRC-PHE Centre for Environment and Health, Environmental Research Group, King's College, London, United Kingdom.

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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0212565PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6398830PMC
November 2019

Fetal growth, stillbirth, infant mortality and other birth outcomes near UK municipal waste incinerators; retrospective population based cohort and case-control study.

Environ Int 2019 01 22;122:151-158. Epub 2018 Nov 22.

UK Small Area Health Statistics Unit, MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, W2 1PG, UK; National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Health Impact of Environmental Hazards, Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK; MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London W2 1PG, UK; Centre for Environmental Health and Sustainability, University of Leicester, Leicester LE1 7RH, UK; Directorate of Public Health and Primary Care, Imperial College Healthcare NHS Trust, London W2 1NY, UK. Electronic address:

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 (PM) 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 PM or proximity to an MWI.

Conclusions: We found no evidence that exposure to PM 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.
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http://dx.doi.org/10.1016/j.envint.2018.10.060DOI Listing
January 2019

A Bayesian mixture modeling approach for public health surveillance.

Biostatistics 2020 07;21(3):369-383

Department of Epidemiology and Biostatistics, MRC- PHE Environment and Health, Imperial College London, Norfolk Place, London W2 1PG, UK.

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.
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http://dx.doi.org/10.1093/biostatistics/kxy038DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7307974PMC
July 2020

Canine visceral leishmaniasis in Araçatuba, state of São Paulo, Brazil, and its relationship with characteristics of dogs and their owners: a cross-sectional and spatial analysis using a geostatistical approach.

BMC Vet Res 2018 Jul 31;14(1):229. Epub 2018 Jul 31.

Departamento de Epidemiologia, Faculdade de Saúde Pública, Universidade de São Paulo (USP), Avenida Doutor Arnaldo 715, São Paulo, SP, 01246-904, Brazil.

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.
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http://dx.doi.org/10.1186/s12917-018-1550-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6102874PMC
July 2018

Road traffic noise, air pollution and incident cardiovascular disease: A joint analysis of the HUNT, EPIC-Oxford and UK Biobank cohorts.

Environ Int 2018 05 5;114:191-201. Epub 2018 Mar 5.

MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom; Directorate of Public Health and Primary Care, Imperial College Healthcare NHS Trust, London, United Kingdom.

Background: This study aimed to investigate the effects of long-term exposure to road traffic noise and air pollution on incident cardiovascular disease (CVD) in three large cohorts: HUNT, EPIC-Oxford and UK Biobank.

Methods: In pooled complete-case sample of the three cohorts from Norway and the United Kingdom (N = 355,732), 21,081 incident all CVD cases including 5259 ischemic heart disease (IHD) and 2871 cerebrovascular cases were ascertained between baseline (1993-2010) and end of follow-up (2008-2013) through medical record linkage. 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] and nitrogen dioxide [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 adjustments for both noise and air pollution and potential confounders.

Results: No significant associations were found between annual mean Lden and incident CVD, IHD or cerebrovascular disease in the overall population except that the association with incident IHD was significant among current-smokers. In the fully adjusted models including adjustment for Lden, an interquartile range (IQR) higher PM10 (4.1 μg/m3) or PM2.5 (1.4 μg/m3) was associated with a 5.8% (95%CI: 2.5%-9.3%) and 3.7% (95%CI: 0.2%-7.4%) higher risk for all incident CVD respectively. No significant associations were found between NO2 and 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, was significantly associated with incident CVD.
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http://dx.doi.org/10.1016/j.envint.2018.02.048DOI Listing
May 2018

Impact of London's road traffic air and noise pollution on birth weight: retrospective population based cohort study.

BMJ 2017 12 5;359:j5299. Epub 2017 Dec 5.

MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK.

 To investigate the relation between exposure to both air and noise pollution from road traffic and birth weight outcomes. Retrospective population based cohort study. Greater London and surrounding counties up to the M25 motorway (2317 km), UK, from 2006 to 2010. 540 365 singleton term live births. Term low birth weight (LBW), small for gestational age (SGA) at term, and term birth weight. Average air pollutant exposures across pregnancy were 41 μg/m nitrogen dioxide (NO), 73 μg/m nitrogen oxides (NO), 14 μg/m particulate matter with aerodynamic diameter <2.5 μm (PM), 23 μg/m particulate matter with aerodynamic diameter <10 μm (PM), and 32 μg/m ozone (O). Average daytime (L) and night-time (L) road traffic A-weighted noise levels were 58 dB and 53 dB respectively. Interquartile range increases in NO, NO, PM, PM, and source specific PM from traffic exhaust (PM) and traffic non-exhaust (brake or tyre wear and resuspension) (PM) 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 PM and PM 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 PM>13.8 μg/mduring pregnancy. 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 birth weight outcomes.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712860PMC
http://dx.doi.org/10.1136/bmj.j5299DOI Listing
December 2017