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.

Who lives in overcrowded households in north-east London? Cross-sectional study of linked electronic health records and Energy Performance Certificate register data

ObjectivesHousehold overcrowding is associated with adverse health outcomes, including increased risk of infectious diseases, mental health problems, and poor educational attainment. We investigated inequalities in overcrowding in an urban, ethnically diverse, and disadvantaged London population by pseudonymously linking electronic health records (EHR) to Energy Performance Certificates (EPC) data. ApproachWe used pseudonymised Unique Property Reference Numbers to link EHRs for 1,066,156 currently registered patients from 321,318 households in north-east London to EPC data. We measured household occupancy and derived the bedroom standard overcrowding definition (number of rooms relative to occupants’ sex and ages) to estimate overcrowding prevalence. We examined associations with: household composition (adults only, single adult+children, ≥2 working-age adults+children, ≥1 retirement-age adults+children, three-generational household); ethnic background (White, South Asian, Black, Mixed, Other, missing); and Index of Multiple Deprivation (IMD) quintile. We used multivariable logistic regression to estimate the adjusted odds (aOR) and 95% Confidence Intervals (CI) of overcrowding. ResultsOverall, 243,793 (22.9%) people were overcrowded. People living in households with children, or three-generational households were more likely (aOR [95% CI] 3.79 [3.74 - 3.84]; 6.53 [6.41 - 6.66] respectively), and single adults or retirement age adults with children less likely (0.36 [0.35 - 0.38]; 0.36 [0.23 - 0.57] respectively), to be overcrowded. Overcrowding was more likely among people from Asian or Black ethnic backgrounds (1.24 [1.22 - 1.25] and 1.17 [1.15 - 1.19] respectively). There was a dose-response relationship between IMD quintile and overcrowding: OR 0.20 [0.20 - 0.21] in the least deprived compared to most deprived quintile. ConclusionOne in five people in north-east London live in overcrowded households with marked inequalities by ethnicity, household generational composition, and deprivation. Up-to-date estimates of household overcrowding can be derived from linked housing and health records and used to evaluate the impact of economic policies on health and housing inequalities.

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.

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.

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.

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.

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 PM_2.5 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.

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.

Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results

Twenty-nine teams involving 61 analysts used the same data set to address the same research question: whether soccer referees are more likely to give red cards to dark-skin-toned players than to light-skin-toned players. Analytic approaches varied widely across the teams, and the estimated effect sizes ranged from 0.89 to 2.93 ( Mdn = 1.31) in odds-ratio units. Twenty teams (69%) found a statistically significant positive effect, and 9 teams (31%) did not observe a significant relationship. Overall, the 29 different analyses used 21 unique combinations of covariates. Neither analysts’ prior beliefs about the effect of interest nor their level of expertise readily explained the variation in the outcomes of the analyses. Peer ratings of the quality of the analyses also did not account for the variability. These findings suggest that significant variation in the results of analyses of complex data may be difficult to avoid, even by experts with honest intentions. Crowdsourcing data analysis, a strategy in which numerous research teams are recruited to simultaneously investigate the same research question, makes transparent how defensible, yet subjective, analytic choices influence research results.

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.

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.

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.

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.

Modeling spatial effects of PM(2.5) on term low birth weight in Los Angeles County

Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM2.5) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure-response of PM2.5 on TLBW to be the same throughout a large geographical area. Health effects related to PM2.5 exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure-response relationship between individual-level exposure to PM2.5 and TLBW. Here, we examine the overall and spatially varying exposure-response relationship between PM2.5 and TLBW throughout urban Los Angeles (LA) County, California. We estimated PM2.5 from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM2.5 level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure-response for PM2.5 and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure-response estimates for PM2.5 on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective.

Analysing the health effects of simultaneous exposure to physical and chemical properties of airborne particles

BACKGROUND

Airborne particles are a complex mix of organic and inorganic compounds, with a range of physical and chemical properties. Estimation of how simultaneous exposure to air particles affects the risk of adverse health response represents a challenge for scientific research and air quality management. In this paper, we present a Bayesian approach that can tackle this problem within the framework of time series analysis.

METHODS

We used Dirichlet process mixture models to cluster time points with similar multipollutant and response profiles, while adjusting for seasonal cycles, trends and temporal components. Inference was carried out via Markov Chain Monte Carlo methods. We illustrated our approach using daily data of a range of particle metrics and respiratory mortality for London (UK) 2002-2005. To better quantify the average health impact of these particles, we measured the same set of metrics in 2012, and we computed and compared the posterior predictive distributions of mortality under the exposure scenario in 2012 vs 2005.

RESULTS

The model resulted in a partition of the days into three clusters. We found a relative risk of 1.02 (95% credible intervals (CI): 1.00, 1.04) for respiratory mortality associated with days characterised by high posterior estimates of non-primary particles, especially nitrate and sulphate. We found a consistent reduction in the airborne particles in 2012 vs 2005 and the analysis of the posterior predictive distributions of respiratory mortality suggested an average annual decrease of -3.5% (95% CI: -0.12%, -5.74%).

CONCLUSIONS

We proposed an effective approach that enabled the better understanding of hidden structures in multipollutant health effects within time series analysis. It allowed the identification of exposure metrics associated with respiratory mortality and provided a tool to assess the changes in health effects from various policies to control the ambient particle matter mixtures.

PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes

PReMiuM is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership (Molitor, Papathomas, Jerrett, and Richardson 2010). The package allows binary, categorical, count and continuous response, as well as continuous and discrete covariates. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection.

Blood pressure differences associated with Optimal Macronutrient Intake Trial for Heart Health (OMNIHEART)-like diet compared with a typical American Diet

The Dietary Approaches to Stop Hypertension-Sodium (DASH-Sodium) trial demonstrated beneficial effects on blood pressure (BP) of the DASH diet with lower sodium intake when compared with typical American diet. The subsequent Optimal Macronutrient Intake Trial for Heart Health (OMNIHEART) trial reported additional BP benefits from replacing carbohydrate in the DASH diet with either protein or monounsaturated fats. The primary aim of this study is to assess possible BP benefits of an OMNIHEART-like diet in free-living Americans using cross-sectional US population data of the International Study of Macronutrients, Micronutrients and Blood Pressure (INTERMAP) study. The INTERMAP data include four 24-hour dietary recalls, 2 timed 24-hour urine collections, 8 BP readings for 2195 individuals aged 40 to 59 years from 8 US INTERMAP population samples. Analyses are conducted using 2 approaches: (1) regression of BP on a linear OMNIHEART nutrient score calculated for each individual and (2) a Bayesian approach comparing estimated BP levels of an OMNIHEART-like nutrient profile with a typical American nutrient profile. After adjustment for potential confounders, an OMNIHEART score higher by 1 point was associated with systolic/diastolic BP differences of -1.0/-0.5 mm Hg (both P<0.001). Mean systolic/diastolic BPs were 111.3/68.4 and 115.2/70.6 mm Hg for Bayesian OMNIHEART and Control profiles, respectively, after controlling for possible confounders, with BP differences of -3.9/-2.2 mm Hg, P(difference≤0)=0.98/0.96. Findings were comparable for men and women, for nonhypertensive participants, and with adjustment for antihypertensive treatment. Our findings from data on US population samples indicate broad generalizability of OMNIHEART results beyond the trial setting and support recommendations for an OMNIHEART-style diet for prevention/control of population-wide adverse BP levels.

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.

A semi-parametric approach to estimate risk functions associated with multi-dimensional exposure profiles: application to smoking and lung cancer

BACKGROUND

A common characteristic of environmental epidemiology is the multi-dimensional aspect of exposure patterns, frequently reduced to a cumulative exposure for simplicity of analysis. By adopting a flexible Bayesian clustering approach, we explore the risk function linking exposure history to disease. This approach is applied here to study the relationship between different smoking characteristics and lung cancer in the framework of a population based case control study.

METHODS

Our study includes 4658 males (1995 cases, 2663 controls) with full smoking history (intensity, duration, time since cessation, pack-years) from the ICARE multi-centre study conducted from 2001-2007. We extend Bayesian clustering techniques to explore predictive risk surfaces for covariate profiles of interest.

RESULTS

We were able to partition the population into 12 clusters with different smoking profiles and lung cancer risk. Our results confirm that when compared to intensity, duration is the predominant driver of risk. On the other hand, using pack-years of cigarette smoking as a single summary leads to a considerable loss of information.

CONCLUSIONS

Our method estimates a disease risk associated to a specific exposure profile by robustly accounting for the different dimensions of exposure and will be helpful in general to give further insight into the effect of exposures that are accumulated through different time patterns.

Orchestrated transcription of biological processes in the marine picoeukaryote Ostreococcus exposed to light/dark cycles

Background

Picoeukaryotes represent an important, yet poorly characterized component of marine phytoplankton. The recent genome availability for two species of Ostreococcus and Micromonas has led to the emergence of picophytoplankton comparative genomics. Sequencing has revealed many unexpected features about genome structure and led to several hypotheses on Ostreococcus biology and physiology. Despite the accumulation of genomic data, little is known about gene expression in eukaryotic picophytoplankton.

Results

We have conducted a genome-wide analysis of gene expression in Ostreococcus tauri cells exposed to light/dark cycles (L/D). A Bayesian Fourier Clustering method was implemented to cluster rhythmic genes according to their expression waveform. In a single L/D condition nearly all expressed genes displayed rhythmic patterns of expression. Clusters of genes were associated with the main biological processes such as transcription in the nucleus and the organelles, photosynthesis, DNA replication and mitosis.

Conclusions

Light/Dark time-dependent transcription of the genes involved in the main steps leading to protein synthesis (transcription basic machinery, ribosome biogenesis, translation and aminoacid synthesis) was observed, to an unprecedented extent in eukaryotes, suggesting a major input of transcriptional regulations in Ostreococcus. We propose that the diurnal co-regulation of genes involved in photoprotection, defence against oxidative stress and DNA repair might be an efficient mechanism, which protects cells against photo-damage thereby, contributing to the ability of O. tauri to grow under a wide range of light intensities.

[Preliminary results of findings on the use of suture threads in Italian hospitals]

This report describes the partial results of an investigation into preferences for various types of suture in Italy as a whole and in individual geographical areas, which identifies, where possible, differences in the use of such materials.

[Results of statistical studies on the use of disinfectant substances in Italy. Critical analysis]

A survey was conducted on the use of various disinfectants in Italy. Subsequently the data provided by the various hospitals appealed to (38.7% of which answered the questionnaire) was analysed. The disinfectants were divided into groups and the percentage used in various fields was calculated. A clear difference was revealed between the use of the various disinfectants in the North, Centre and South of Italy. The percentage of mistaken or improper use of the disinfectants was then calculated. Finally the relative consumption of commercial preparations using the disinfectants, the average cost per bed and the total expenditure on disinfectants in Italy were then calculated.