Bayesian profile regression to study the ecologic associations of correlated environmental exposures with excess mortality risk during the first year of the Covid-19 epidemic in lombardy, Italy.

Many countries, including Italy, have experienced significant social and spatial inequalities in mortality during the Covid-19 pandemic. This study applies a multiple exposures framework to investigate how joint place-based factors influence spatial inequalities of excess mortality during the first year of the Covid -19 pandemic in the Lombardy region of Italy. For the Lombardy region, we integrated municipality-level data on all-cause mortality between 2015 and 2020 with 13 spatial covariates, including 5-year average concentrations of six air pollutants, the average temperature in 2020, and multiple socio-demographic factors, and health facilities per capita. Using the clustering algorithm Bayesian profile regression, we fit spatial covariates jointly to identify clusters of municipalities with similar exposure profiles and estimated associations between clusters and excess mortality in 2020. Cluster analysis resulted in 13 clusters. Controlling for spatial autocorrelation of excess mortality and health-protective agency, two clusters had significantly elevated excess mortality than the rest of Lombardy. Municipalities in these highest-risk clusters are in Bergamo, Brescia, and Cremona provinces. The highest risk cluster (C11) had the highest long-term particulate matter air pollution levels (PM2.5 and PM10) and significantly elevated NO2 and CO air pollutants, temperature, proportion ≤18 years, and male-to-female ratio. This cluster is significantly lower for income and ≥65 years. The other high-risk cluster, Cluster 10 (C10), is elevated significantly for ozone but significantly lower for other air pollutants. Covariates with elevated levels for C10 include proportion 65 years or older and a male-to-female ratio. Cluster 10 is significantly lower for income, temperature, per capita health facilities, ≤18 years, and population density. Our results suggest that joint built, natural, and socio-demographic factors influenced spatial inequalities of excess mortality in Lombardy in 2020. Studies must apply a multiple exposures framework to guide policy decisions addressing the complex and multi-dimensional nature of spatial inequalities of Covid-19-related mortality.

Dirichlet process mixture models for regression discontinuity designs.

The regression discontinuity design is a quasi-experimental design that estimates the causal effect of a treatment when its assignment is defined by a threshold for a continuous variable. The regression discontinuity design assumes that subjects with measurements within a bandwidth around the threshold belong to a common population, so that the threshold can be seen as a randomising device assigning treatment to those falling just above the threshold and withholding it from those who fall below. Bandwidth selection represents a compelling decision for the regression discontinuity design analysis as results may be highly sensitive to its choice. A few methods to select the optimal bandwidth, mainly from the econometric literature, have been proposed. However, their use in practice is limited. We propose a methodology that, tackling the problem from an applied point of view, considers units' exchangeability, that is, their similarity with respect to measured covariates, as the main criteria to select subjects for the analysis, irrespectively of their distance from the threshold. We cluster the sample using a Dirichlet process mixture model to identify balanced and homogeneous clusters. Our proposal exploits the posterior similarity matrix, which contains the pairwise probabilities that two observations are allocated to the same cluster in the Markov chain Monte Carlo sample. Thus we include in the regression discontinuity design analysis only those clusters for which we have stronger evidence of exchangeability. We illustrate the validity of our methodology with both a simulated experiment and a motivating example on the effect of statins on cholesterol levels.

Mapping and determinants of consumption of egg and/or flesh foods and zero vegetables or fruits among young children in SSA.

Zero vegetable or fruit and egg and/or flesh foods are the latest indicators for assessing infant and young child feeding practices. Understanding national and subnational heterogeneity and regional clustering in children with SSA is becoming increasingly essential for geographic targeting and policy prioritization. Geographical case identification, determinants, and impacts were all investigated. SSA children's consumption of vegetable or fruit, egg and/or flesh food, and both were low. In SSA, some portions of the Southern, South direction of the Western and Central regions have a lower weight of all bad conditions than others, although children continue to suffer in considerable numbers in all disadvantage circumstances. Children under the age of 1 year, from rural areas, uneducated families, and low income were all disadvantaged by both feeding techniques. To improve child nutrition status, multisectoral collaboration is essential. This framework allows for the tracking, planning, and implementation of nutritional treatments.

Left Atrial Enhancement Correlates With Myocardial Conduction Velocity in Patients With Persistent Atrial Fibrillation.

BACKGROUND: Conduction velocity (CV) heterogeneity and myocardial fibrosis both promote re-entry, but the relationship between fibrosis as determined by left atrial (LA) late-gadolinium enhanced cardiac magnetic resonance imaging (LGE-CMRI) and CV remains uncertain. OBJECTIVE: Although average CV has been shown to correlate with regional LGE-CMRI in patients with persistent AF, we test the hypothesis that a localized relationship exists to underpin LGE-CMRI as a minimally invasive tool to map myocardial conduction properties for risk stratification and treatment guidance. METHOD: 3D LA electroanatomic maps during LA pacing were acquired from eight patients with persistent AF following electrical cardioversion. Local CVs were computed using triads of concurrently acquired electrograms and were co-registered to allow correlation with LA wall intensities obtained from LGE-CMRI, quantified using normalized intensity (NI) and image intensity ratio (IIR). Association was evaluated using multilevel linear regression. RESULTS: An association between CV and LGE-CMRI intensity was observed at scales comparable to the size of a mapping electrode: -0.11 m/s per unit increase in NI (P < 0.001) and -0.96 m/s per unit increase in IIR (P < 0.001). The magnitude of this change decreased with larger measurement area. Reproducibility of the association was observed with NI, but not with IIR. CONCLUSION: At clinically relevant spatial scales, comparable to area of a mapping catheter electrode, LGE-CMRI correlates with CV. Measurement scale is important in accurately quantifying the association of CV and LGE-CMRI intensity. Importantly, NI, but not IIR, accounts for changes in the dynamic range of CMRI and enables quantitative reproducibility of the association.

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

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 PM10 and PM2.5. 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 PM10 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 PM2.5 fraction, higher risks were seen for cardiovascular mortality RR 1.55 (CI 95% 1.38, 1.71) and respiratory mortality RR 1.51 (CI 95% 1.33, 1.72), likely to represent the "highways" cluster. We did not find relevant associations for lung cancer incidence. 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.

Modeling tails for collinear data with outliers in the English Longitudinal Study of Ageing: Quantile profile regression.

Research has shown that high blood glucose levels are important predictors of incident diabetes. However, they are also strongly associated with other cardiometabolic risk factors such as high blood pressure, adiposity, and cholesterol, which are also highly correlated with one another. The aim of this analysis was to ascertain how these highly correlated cardiometabolic risk factors might be associated with high levels of blood glucose in older adults aged 50 or older from wave 2 of the English Longitudinal Study of Ageing (ELSA). Due to the high collinearity of predictor variables and our interest in extreme values of blood glucose we proposed a new method, called quantile profile regression, to answer this question. Profile regression, a Bayesian nonparametric model for clustering responses and covariates simultaneously, is a powerful tool to model the relationship between a response variable and covariates, but the standard approach of using a mixture of Gaussian distributions for the response model will not identify the underlying clusters correctly, particularly with outliers in the data or heavy tail distribution of the response. Therefore, we propose quantile profile regression to model the response variable with an asymmetric Laplace distribution, allowing us to model more accurately clusters that are asymmetric and predict more accurately for extreme values of the response variable and/or outliers. Our new method performs more accurately in simulations when compared to Normal profile regression approach as well as robustly when outliers are present in the data. We conclude with an analysis of the ELSA.

Predicting success in the worldwide start-up network.

By drawing on large-scale online data we are able to construct and analyze the time-varying worldwide network of professional relationships among start-ups. The nodes of this network represent companies, while the links model the flow of employees and the associated transfer of know-how across companies. We use network centrality measures to assess, at an early stage, the likelihood of the long-term positive economic performance of a start-up. We find that the start-up network has predictive power and that by using network centrality we can provide valuable recommendations, sometimes doubling the current state of the art performance of venture capital funds. Our network-based approach supports the theory that the position of a start-up within its ecosystem is relevant for its future success, while at the same time it offers an effective complement to the labour-intensive screening processes of venture capital firms. Our results can also enable policy-makers and entrepreneurs to conduct a more objective assessment of the long-term potentials of innovation ecosystems, and to target their interventions accordingly.

Incidence of falls among adults with cerebral palsy: a cohort study using primary care data.

AIM: To compare the rate of falls between adults with and without cerebral palsy (CP). METHOD: We used primary care data on 1705 adults with CP and 5115 adults without CP matched for age, sex, and general practice attended. We compared odds of experiencing a fall between adults with and without CP using conditional logistic regression. We compared the rate of falls using a negative binomial model. RESULTS: Participants were 3628 males (53%) and 3192 females (47%) (median age 29y, interquartile range 20-42y) at the start of follow-up. Follow-up was 14 617 person-years for adults with CP and 56 816 person-years for adults without CP. Of adults with CP, 15.3% experienced at least one fall compared to 5.7% of adults without CP. Adults with CP had 3.64 times (95% confidence interval [CI] 2.98-4.45) the odds of experiencing a fall compared to adults without CP. The rate of falls was 30.5 per 1000 person-years and 6.7 per 1000 person-years for adults with and without CP respectively (rate ratio 5.83, 95% CI 4.84-7.02) INTERPRETATION: Adults with CP are more likely to fall, and fall more often, than adults without CP. The causes and consequences of falls in adults with CP need examination. WHAT THIS PAPER ADDS: Twenty adults with CP and 5.3 adults without CP experienced at least one fall per 1000 person-years. Adults with CP experienced 30.5 falls per 1000 person-years compared to 6.7 falls per 1000 person-years among adults without CP. Adults with CP had 3.64 times the odds of experiencing a fall compared to adults without CP. Adults with CP experienced 5.83 times more falls than adults without CP.

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

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 PM2.5 copper with interdecile range (IDR 2.6-5.7 ng/m3) and IDR relative risk (RR) 1.005 (95%CI 1.001 to 1.009) and between respiratory mortality and PM10 zinc (IDR 1135-153 ng/m3) 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.

Noncommunicable disease among adults with cerebral palsy: A matched cohort study.

OBJECTIVE: To compare the incidence of noncommunicable diseases between adults with and without cerebral palsy (CP). METHODS: A cohort study was conducted using primary care data from the Clinical Practice Research Datalink. Cox models, stratified by matched set and adjusted for potential confounders, were fitted to compare the risk of any noncommunicable disease, cancer, cardiovascular disease, type 2 diabetes mellitus, and respiratory disease between adults with and without CP. RESULTS: The analysis included 1,705 adults with CP and 5,115 age-, sex-, and general practice-matched adults without CP. There was evidence from adjusted analyses that adults with CP had 75% increased risk of developing any noncommunicable disease compared to adults without CP (hazard ratio [HR] 1.75, 95% confidence interval [CI] 1.58-1.94). Specifically, they had increased risk of cardiovascular disease (HR 1.76, 95% CI 1.48-2.11) and respiratory disease (HR 2.61, 95% CI 2.14-3.19). There was no evidence of increased risk of cancer or type 2 diabetes mellitus. CONCLUSIONS: Adults with CP had increased risk of noncommunicable disease, specifically cardiovascular and respiratory disease. These findings highlight the need for clinical vigilance regarding identification of noncommunicable disease in people with CP and further research into the etiology and management of noncommunicable disease in this population.

Incidence of osteoarthritis, osteoporosis and inflammatory musculoskeletal diseases in adults with cerebral palsy: A population-based cohort study.

BACKGROUND: People with cerebral palsy (CP) may be at increased risk of musculoskeletal conditions due to various factors including malnutrition and abnormal levels of skeletal loading. This study aimed to compare the incidence of osteoporosis, osteoarthritis and inflammatory musculoskeletal diseases between adults with and without CP. METHODS: A population based cohort study was conducted using data from the Clinical Practice Research Datalink collected between 1987 and 2015. Adults with CP were matched to adults without CP for age, sex and general practice. Cox models, stratified by matched set and adjusted for potential confounders, were fitted to compare the risk of osteoporosis, osteoarthritis and inflammatory musculoskeletal diseases. RESULTS: 1705 adults with CP were matched to 5115 adults without CP. Adults with CP had an increased risk of osteoporosis in unadjusted (Hazard Ratio (HR) 3.67, 95% Confidence Interval (CI) 2.32 to 5.80, p < 0.001) and adjusted (HR 6.19, 95% CI 3.37 to 11.39, p < 0.001) analyses. No evidence of increased risk of inflammatory musculoskeletal diseases was observed in unadjusted or adjusted analyses. For osteoarthritis no evidence of increased risk was seen in the unadjusted analysis, but evidence of an increased risk was seen when the analysis was adjusted for alcohol consumption, smoking status, and mean yearly general practice (GP) visits (HR 1.54, 95% CI 1.17 to 2.02, p < 0.001). CONCLUSIONS: After accounting for potential confounding variables, we found that CP is associated with increased risk of osteoporosis and osteoarthritis. These findings provide the strongest epidemiological evidence to date for increased risk of osteoporosis and osteoarthritis in people with CP, and highlight need for clinical awareness of such conditions in this population.

Mortality due to cardiovascular disease, respiratory disease, and cancer in adults with cerebral palsy.

AIM: To compare mortality rates for cardiovascular disease, cancer, and respiratory disease between adults with cerebral palsy (CP) and the general population. METHOD: A cohort study was conducted using data from adults with CP in England, identified through a primary care data set (the Clinical Practice Research Datalink), with linked data on death registrations from the Office for National Statistics. Cause of death was categorized according to International Classification of Diseases codes. Standardized mortality ratios (SMRs) were calculated to compare mortality rates between adults with CP and the general population, adjusted for age, sex, and calendar year. RESULTS: Nine hundred and fifty-eight adults with CP were identified (52.5% males, 47.5% females; median age at start of follow-up 31y [interquartile range 22-43y]) and followed for a total of 7693 person-years. One hundred and forty-two patients (15%) died during follow-up. Adults with CP had an increased risk of death due to cardiovascular disease (SMR: 3.19, 95% confidence interval [CI] 2.20-4.62) and respiratory disease (SMR: 13.59, 95% CI 9.89-18.67), but not from malignant neoplasms (SMR: 1.42, 95% CI 0.83-2.45). INTERPRETATION: We found that adults with CP in England have increased risk of death due to diseases of the circulatory and respiratory systems, supporting findings from two studies that compared cause-specific mortality rates between adults with CP in the USA and the general population. Further research is required into primary and secondary prevention of cardiovascular and respiratory disease in people with CP worldwide. WHAT THIS PAPER ADDS: Adults with cerebral palsy (CP) in England have 14-fold increased risk of mortality due to diseases of the respiratory system. They have a 3-fold increased risk of mortality due to diseases of the circulatory system. Adults with CP had an increased risk of death due to cerebrovascular disease and ischaemic heart disease. The elevated risk of ischaemic heart disease, however, did not reach statistical significance at the 5% per cent level.

MORTALIDAD POR ENFERMEDAD CARDIOVASCULAR, ENFERMEDAD RESPIRATORIA Y CÁNCER EN ADULTOS CON PARÁLISIS CEREBRAL: OBJETIVO: Comparar tasas de mortalidad por enfermedad cardiovascular, cáncer y enfermedad respiratoria entre adultos con parálisis cerebral (PC) y la población general. MÉTODO: Se llevó a cabo un estudio de cohorte utilizando datos de adultos con PC en Inglaterra, identificados a través de un set de datos de atención primaria (the Clinical Practice Research Datalink) con datos vinculados sobre los registros de defunciones de la Oficina Nacional de Estadísticas. Las causas de muertes fueron categorizadas de acuerdo con los códigos de la Clasificación Internacional de Enfermedades. Las tasas de mortalidad estandarizadas (TME) fueron calculadas a fin de comparar las tasas de mortalidad entre adultos con PC y la población general, ajustadas por edad, sexo y año calendario. RESULTADOS: Se identificaron 958 adultos con PC (52,5% varones, 47,5% mujeres; edad mediana al comienzo del seguimiento 31 años [rango intercuartilo 22-43 años] y fueron seguidos por un total de 7.693 años-persona. Ciento cuarenta y dos pacientes (15%) fallecieron durante el seguimiento. Los adultos con PC tuvieron un mayor riesgo de muerte por enfermedad cardiovascular (TME:3,19, 95% intervalo de confidencia [IC] 2,20-4,62) y enfermedad respiratoria (TME:13,59,95% IC 9,89-18,67), pero no tuvieron mayor riesgo de neoplasias malignas (TME:1,42, 95% IC 0,83-2,45). INTERPRETACIÓN: Encontramos que los adultos con PC en Inglaterra tienen un riesgo incrementado de muerte por enfermedades de los sistemas circulatorio y respiratorio, sosteniendo los hallazgos con dos estudios que compararon tasas de mortalidad causa-específicas entre adultos con PC en los EEUU y la población general. Se necesita avanzar con la investigación hacia prevención primaria y secundaria de enfermedades cardiovascular y respiratoria en personas con PC en todo el mundo.

MORTALIDADE DEVIDO A DOENÇA CARDIOVASCULAR, RESPIRATÓRIA, E CÂNCER EM ADULTOS COM PARALISIA CEREBRAL: OBJETIVO: Comparar as taxas de mortalidade por doença cardiovascular, câncer, e doença respiratória em adultos com paralisia cerebral (PC) e a população em geral. MÉTODO: Um estudo de coorte foi realizado usando dados de adultos com PC na Inglaterra, identificados por meio de um conjunto de dados de atenção primária (o Datalink Pesquisa em Prática Clínica,), com dados sobre registros de óbitos do Escritório Nacional de Estatística. A causa da morte foi categorizada de acordo com os códigos da Classificação Internacional de Doenças. Taxas de mortalidade padronizadas (TMPs) foram calculadas para comparar as taxas de mortalidade entre adultos com PC e a população em geral, ajustadas por idade, sexo e ano calendário. RESULTADOS: Novecentos e cinquenta e oito adultos com PC foram identificados (52,5% do sexo masculino, 47,5% do sexo feminino; idade mediana no início do acompanhamento 31a [intervalo interquartil 22-43a]) e acompanhados por um total de 7,693 anos-pessoa. Cento e quarenta e dois pacientes (15%) morreram durante o acompanhamento. Adultos com PC tiveram risco aumentado de morte por doença (TMP: 3,19, intervalo de confiança [IC] a 95% 2,20-4,62) e doença respiratória (TMP: 13,59, IC 95% 9,89-18,67), mas não por neoplasias malignas (TMP: 1,42, IC 95% 0,83-2,45). INTERPRETAÇÃO: Observamos que adultos com PC na Inglaterra têm risco aumentado de more por doenças dos sistemas circulatório e respiratório, o que apóia achados de dois estudos qu compararam taxas de mortalidade causa-específica em adultos com PC nos EUA e na população em geral. Mais pesquisas são necessárias sobre a prevenção primária e secundária de doenças cardiovasculares e respiratórias em pessoas com PC em todo o mundo.

Risk of Depression and Anxiety in Adults With Cerebral Palsy.

Importance: Cerebral palsy (CP) is considered a pediatric condition despite most individuals with CP living into adulthood. Thus, there is a lack of evidence in adults with CP, which includes a paucity of research examining mental health in this population. Objectives: To determine the risk of depression and anxiety in adults with CP compared with an age-, sex-, and practice-matched reference group of adults without CP, using primary care data. Design, Setting, and Participants: Retrospective longitudinal cohort study set in UK primary care. Data were analyzed using Cox proportional hazards regression analyses adjusted for chronic conditions and visits to their physician. The study period ran from January 1987 to November 2015. Data of entry into the study ranged from January 1987 to September 2015. Data for 1705 adults 18 years or older with CP and 5115 matched adults without CP were extracted. Cerebral palsy was identified using diagnostic codes, and each person with CP was compared with 3 age-, sex-, and practice-matched controls. Exposures: Diagnosis of CP, with a second analysis accounting for comorbidity of intellectual disability (ID). Main Outcomes and Measures: Time to diagnosis for depression or anxiety following the date of entry into the study in adults with CP (with and without ID) compared with matched controls. Results: The mean (SD) age of the 1705 patients with CP and the 5115 adults without CP was 33.3 (15.5) years, and 798 participants (46.8%) were women. Individuals with CP had an increased adjusted hazard of depression (hazard ratio [HR], 1.28; 95% CI, 1.09-1.51) and anxiety (HR, 1.40; 95% CI, 1.21-1.63) compared with the matched reference group. When we accounted for ID comorbidity, there were 363 adults with CP who also had ID (mean [SD] age, 32.1 [13.2] years; 159 women [47.6%]) and 1342 adults with CP who did not have ID (mean [SD] age, 33.6 [16.1] years; 639 women [43.8%]). Only those individuals with CP and no comorbid ID had a higher risk of incident depression (HR, 1.44; 95% CI, 1.20-1.72) and anxiety (HR, 1.55; 95% CI, 1.28-1.87) than their matched controls. Conclusions and Relevance: Adults with CP have an increased risk of depression or anxiety. In particular, these results indicate that this association is driven largely by those individuals with CP with no co-occurring ID. Future work is needed in community-based samples to fully elucidate the causal mechanisms driving these associations.

Circadian clock components control daily growth activities by modulating cytokinin levels and cell division-associated gene expression in Populus trees.

Trees are carbon dioxide sinks and major producers of terrestrial biomass with distinct seasonal growth patterns. Circadian clocks enable the coordination of physiological and biochemical temporal activities, optimally regulating multiple traits including growth. To dissect the clock's role in growth, we analysed Populus tremula × P. tremuloides trees with impaired clock function due to down-regulation of central clock components. late elongated hypocotyl (lhy-10) trees, in which expression of LHY1 and LHY2 is reduced by RNAi, have a short free-running period and show disrupted temporal regulation of gene expression and reduced growth, producing 30-40% less biomass than wild-type trees. Genes important in growth regulation were expressed with an earlier phase in lhy-10, and CYCLIN D3 expression was misaligned and arrhythmic. Levels of cytokinins were lower in lhy-10 trees, which also showed a change in the time of peak expression of genes associated with cell division and growth. However, auxin levels were not altered in lhy-10 trees, and the size of the lignification zone in the stem showed a relative increase. The reduced growth rate and anatomical features of lhy-10 trees were mainly caused by misregulation of cell division, which may have resulted from impaired clock function.

Multi-pollutant Modeling Through Examination of Susceptible Subpopulations Using Profile Regression.

PURPOSE OF REVIEW: The inter-correlated nature of exposure-based risk factors in environmental health studies makes it a challenge to determine their combined effect on health outcomes. As such, there has been much research of late regarding the development and utilization of methods in the field of multi-pollutant modeling. However, much of this work has focused on issues related to variable selection in a regression context, with the goal of identifying which exposures are the "bad actors" most responsible for affecting the health outcome of interest. However, the question addressed by these approaches does not necessarily represent the only or most important questions of interest in a multi-pollutant modeling context, where researchers may be interested in health effects from co-exposure patterns and in identifying subpopulations associated with patterns defined by different levels of constituent exposures. RECENT FINDINGS: One approach to analyzing multi-pollutant data is to use a method known as Bayesian profile regression, which aids in identifying susceptible subpopulations associated with exposure mixtures defined by different levels of each exposure. Identification of exposure-level patterns that correspond to a location may provide a starting point for policy-based exposure reduction. Also, in a spatial context, identification of locations with the most health-relevant exposure-mixture profiles might provide further policy relevant information. In this brief report, we review and describe an approach that can be used to identify exposures in subpopulations or locations known as Bayesian profile regression. An example is provided in which we examine associations between air pollutants, an indicator of healthy food retailer availability, and indicators of poverty in Los Angeles County. A general tread suggesting that vulnerable individuals are more highly exposed and have limited access to healthy food retailers is observed, though the associations are complex and non-linear.

Metabolic profiling identifies trehalose as an abundant and diurnally fluctuating metabolite in the microalga Ostreococcus tauri.

INTRODUCTION: The picoeukaryotic alga Ostreococcus tauri (Chlorophyta) belongs to the widespread group of marine prasinophytes. Despite its ecological importance, little is known about the metabolism of this alga. OBJECTIVES: In this work, changes in the metabolome were quantified when O. tauri was grown under alternating cycles of 12 h light and 12 h darkness. METHODS: Algal metabolism was analyzed by gas chromatography-mass spectrometry. Using fluorescence-activated cell sorting, the bacteria associated with O. tauri were depleted to below 0.1% of total cells at the time of metabolic profiling. RESULTS: Of 111 metabolites quantified over light-dark cycles, 20 (18%) showed clear diurnal variations. The strongest fluctuations were found for trehalose. With an intracellular concentration of 1.6 mM in the dark, this disaccharide was six times more abundant at night than during the day. This fluctuation pattern of trehalose may be a consequence of starch degradation or of the synchronized cell cycle. On the other hand, maltose (and also sucrose) was below the detection limit (~10 µM). Accumulation of glycine in the light is in agreement with the presence of a classical glycolate pathway of photorespiration. We also provide evidence for the presence of fatty acid methyl and ethyl esters in O. tauri. CONCLUSIONS: This study shows how the metabolism of O. tauri adapts to day and night and gives new insights into the configuration of the carbon metabolism. In addition, several less common metabolites were identified.

Modelling collinear and spatially correlated data.

In this work we present a statistical approach to distinguish and interpret the complex relationship between several predictors and a response variable at the small area level, in the presence of (i) high correlation between the predictors and (ii) spatial correlation for the response. Covariates which are highly correlated create collinearity problems when used in a standard multiple regression model. Many methods have been proposed in the literature to address this issue. A very common approach is to create an index which aggregates all the highly correlated variables of interest. For example, it is well known that there is a relationship between social deprivation measured through the Multiple Deprivation Index (IMD) and air pollution; this index is then used as a confounder in assessing the effect of air pollution on health outcomes (e.g. respiratory hospital admissions or mortality). However it would be more informative to look specifically at each domain of the IMD and at its relationship with air pollution to better understand its role as a confounder in the epidemiological analyses. In this paper we illustrate how the complex relationships between the domains of IMD and air pollution can be deconstructed and analysed using profile regression, a Bayesian non-parametric model for clustering responses and covariates simultaneously. Moreover, we include an intrinsic spatial conditional autoregressive (ICAR) term to account for the spatial correlation of the response variable.

Multidimensional analysis of the effect of occupational exposure to organic solvents on lung cancer risk: the ICARE study.

BACKGROUND: The association between lung cancer and occupational exposure to organic solvents is discussed. Since different solvents are often used simultaneously, it is difficult to assess the role of individual substances. OBJECTIVES: The present study is focused on an in-depth investigation of the potential association between lung cancer risk and occupational exposure to a large group of organic solvents, taking into account the well-known risk factors for lung cancer, tobacco smoking and occupational exposure to asbestos. METHODS: We analysed data from the Investigation of occupational and environmental causes of respiratory cancers (ICARE) study, a large French population-based case-control study, set up between 2001 and 2007. A total of 2276 male cases and 2780 male controls were interviewed, and long-life occupational history was collected. In order to overcome the analytical difficulties created by multiple correlated exposures, we carried out a novel type of analysis based on Bayesian profile regression. RESULTS: After analysis with conventional logistic regression methods, none of the 11 solvents examined were associated with lung cancer risk. Through a profile regression approach, we did not observe any significant association between solvent exposure and lung cancer. However, we identified clusters at high risk that are related to occupations known to be at risk of developing lung cancer, such as painters. CONCLUSIONS: Organic solvents do not appear to be substantial contributors to the occupational risk of lung cancer for the occupations known to be at risk.

Multi-pollutant exposure profiles associated with term low birth weight in Los Angeles County.

Research indicates that multiple outdoor air pollutants and adverse neighborhood conditions are spatially correlated. Yet health risks associated with concurrent exposure to air pollution mixtures and clustered neighborhood factors remain underexplored. Statistical models to assess the health effects from pollutant mixtures remain limited, due to problems of collinearity between pollutants and area-level covariates, and increases in covariate dimensionality. Here we identify pollutant exposure profiles and neighborhood contextual profiles within Los Angeles (LA) County. We then relate these profiles with term low birth weight (TLBW). We used land use regression to estimate NO2, NO, and PM2.5 concentrations averaged over census block groups to generate pollutant exposure profile clusters and census block group-level contextual profile clusters, using a Bayesian profile regression method. Pollutant profile cluster risk estimation was implemented using a multilevel hierarchical model, adjusting for individual-level covariates, contextual profile cluster random effects, and modeling of spatially structured and unstructured residual error. Our analysis found 13 clusters of pollutant exposure profiles. Correlations between study pollutants varied widely across the 13 pollutant clusters. Pollutant clusters with elevated NO2, NO, and PM2.5 concentrations exhibited increased log odds of TLBW, and those with low PM2.5, NO2, and NO concentrations showed lower log odds of TLBW. The spatial patterning of pollutant cluster effects on TLBW, combined with between-pollutant correlations within pollutant clusters, imply that traffic-related primary pollutants influence pollutant cluster TLBW risks. Furthermore, contextual clusters with the greatest log odds of TLBW had more adverse neighborhood socioeconomic, demographic, and housing conditions. Our data indicate that, while the spatial patterning of high-risk multiple pollutant clusters largely overlaps with adverse contextual neighborhood cluster, both contribute to TLBW while controlling for the other.

Sampling from Dirichlet process mixture models with unknown concentration parameter: mixing issues in large data implementations.

We consider the question of Markov chain Monte Carlo sampling from a general stick-breaking Dirichlet process mixture model, with concentration parameter [Formula: see text]. This paper introduces a Gibbs sampling algorithm that combines the slice sampling approach of Walker (Communications in Statistics - Simulation and Computation 36:45-54, 2007) and the retrospective sampling approach of Papaspiliopoulos and Roberts (Biometrika 95(1):169-186, 2008). Our general algorithm is implemented as efficient open source C++ software, available as an R package, and is based on a blocking strategy similar to that suggested by Papaspiliopoulos (A note on posterior sampling from Dirichlet mixture models, 2008) and implemented by Yau et al. (Journal of the Royal Statistical Society, Series B (Statistical Methodology) 73:37-57, 2011). We discuss the difficulties of achieving good mixing in MCMC samplers of this nature in large data sets and investigate sensitivity to initialisation. We additionally consider the challenges when an additional layer of hierarchy is added such that joint inference is to be made on [Formula: see text]. We introduce a new label-switching move and compute the marginal partition posterior to help to surmount these difficulties. Our work is illustrated using a profile regression (Molitor et al. Biostatistics 11(3):484-498, 2010) application, where we demonstrate good mixing behaviour for both synthetic and real examples.