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

Characterization of the short-term temporal variability of road dust chemical mixtures and meteorological profiles in a near-road urban site in British Columbia

Implications: Non-tailpipe emissions driven by springtime road dust in northern latitude communities is increasing in importance for air pollution control and improving our understanding of the health effects of chemical mixtures from particulate matter exposure. High-volume samples from a near-road site indicated that days affected by springtime road dust are substantively different from other days with respect to particulate matter mixture composition and meteorological drivers. The high load of trace elements in PM₁₀ on high road dust days has important implications for the acute toxicity of inhaled air and subsequent health effects. The complex relationships between road dust and weather identified in this study may facilitate further research on the health effects of chemical mixtures related to road dust while also highlighting potential changes in this unique form of air pollution as the climate changes.

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.

Association between lipid profiles and viral respiratory infections in human sputum samples

Background

Respiratory infections such as influenza account for significant global mortality each year. Generating lipid profiles is a novel and emerging research approach that may provide new insights regarding the development and progression of priority respiratory infections. We hypothesized that select clusters of lipids in human sputum would be associated with specific viral infections (Influenza (H1N1, H3N2) or Rhinovirus).

Methods

Lipid identification and semi-quantitation was determined with liquid chromatography and high-resolution mass spectrometry in induced sputum from individuals with confirmed respiratory infections (influenza (H1N1, H3N2) or rhinovirus). Clusters of lipid species and associations between lipid profiles and the type of respiratory viral agent was determined using Bayesian profile regression and multinomial logistic regression.

Results

More than 600 lipid compounds were identified across the sputum samples with the most abundant lipid classes identified as triglycerides (TG), phosphatidylethanolamines (PE), phosphatidylcholines (PC), Sphingomyelins (SM), ether-PC, and ether-PE. A total of 12 lipid species were significantly different when stratified by infection type and included acylcarnitine (AcCar) (10:1, 16:1, 18:2), diacylglycerols (DG) (16:0_18:0, 18:0_18:0), Lysophosphatidylcholine (LPC) (12:0, 20:5), PE (18:0_18:0), and TG (14:1_16:0_18:2, 15:0_17:0_19:0, 16:0_17:0_18:0, 19:0_19:0_19:0). Cluster analysis yielded three clusters of lipid profiles that were driven by just 10 lipid species (TGs and DGs). Cluster 1 had the highest levels of each lipid species and the highest prevalence of influenza A H3 infection (56%, n = 5) whereas cluster 3 had lower levels of each lipid species and the highest prevalence of rhinovirus (60%; n = 6). Using cluster 3 as the reference group, the crude odds of influenza A H3 infection compared to rhinovirus in cluster 1 was significantly (p = 0.047) higher (OR = 15.00 [95% CI: 1.03, 218.29]). After adjustment for confounders (smoking status and pulmonary comorbidities), the odds ratio (OR) became only marginally significant (p = 0.099), but the magnitude of the effect estimate was similar (OR = 16.00 [0.59, 433.03]).

Conclusions

In this study, human sputum lipid profiles were shown to be associated with distinct types of viral infection. Better understanding the relationship between respiratory infections of global importance and lipids contributes to advancing knowledge of pathogenesis of infections including identifying populations with increased susceptibility and developing effective therapeutics and biomarkers of health status.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02091-w.

Pre- and postnatal exposure to multiple ambient air pollutants and child behavioral problems at five years of age

Ambient air pollution is emerging as a risk factor for adverse neurological symptoms and early childhood diseases. This study aimed to evaluate the association between pre- and postnatal exposure to air pollutants and childhood behavior by using MOCEH prospective birth cohort data. In total, 353 mother-child pairs at birth, who completed child behavioral assessments using the Korean version of the Child Behavior Checklist at five years of age, were included in the study. Multivariate linear regression (MLR) for single pollutant and Bayesian kernel machine regression (BKMR) for multiple pollutants were conducted. MLR analysis showed that air pollutant exposures during the first trimester were significantly associated with the internalizing problems score after adjusting for covariates. The estimates were 0.19 (0.05-0.32) per 1 μg/m³ increase in PM_2.5, 0.13 (0.04-0.22) per 1 μg/m³ increase in PM₁₀, and 0.20 (0.02-0.37) per 1 ppb increase in NO₂. The BKMR model analysis revealed that the overall effects of multiple air pollutants during the first trimester of pregnancy and 0-6 months of the infantile period were significantly associated with behavioral problems. Boys showed a stronger associations than girls. Taken together, these results showed that the first trimester of pregnancy and 0-6 months of the infantile period were important for air pollutant exposure because exposure at these periods was associated with behavioral problems in 5-year-old children. Future efforts are required to control air pollution levels and reduce the health burden of vulnerable populations, including pregnant women and children.

Revealing the racial and spatial disparity in pediatric asthma: A Kansas City case study

Black and other socially disadvantaged children are disproportionately burdened by high rates of pediatric asthma. Intraurban variation in environmental risk factors and limited access to high-resolution health data make it difficult to identify vulnerable patients, communities, or the immediate exposures that may contribute to pediatric asthma exacerbation. This article presents a novel, interdisciplinary health disparities research and intervention strategy applied to the problem of pediatric asthma in Kansas City. First, address-level electronic health records from a major children's hospital in the Kansas City region are used to map the distribution of asthma encounters in 2012 at a high spatial resolution. Census tract Environmental Justice Screening Method (EJSM) indicators are then developed to scan for patterns in both the population health risks and vulnerabilities that may contribute to the burden of asthma in different communities. A Bayesian Profile Regression cluster analysis is used to systematically explore the complex relationships between census tract EJSM indicators and pediatric asthma incidence rates, helping to identify population characteristics and risk factors associated with both high and low rates of pediatric asthma throughout the region. The EJSM scanning exercise and BPR analysis demonstrate that each community faces a distinct set of risks and vulnerabilities that can contribute to the rate of acute pediatric asthma acute care encounters, providing targets for research and intervention. It is clear, however, that different forms of social disadvantage are driving high rates of pediatric asthma, which is closely tied to uneven development patterns and racial residential segregation. The results provide a starting point for designing place-based health disparities research and intervention strategies catered to the unique needs of vulnerable patients and communities; disparities-focused research and intervention strategies that leverage local knowledge and resources through community-based practices.

Multiple air pollutant exposure and lung cancer in Tehran, Iran

Lung cancer is the most rapidly increasing malignancy worldwide with an estimated 2.1 million cancer cases in the latest, 2018 World Health Organization (WHO) report. The objective of this study was to investigate the association of air pollution and lung cancer, in Tehran, Iran. Residential area information of the latest registered lung cancer cases that were diagnosed between 2014 and 2016 (N = 1,850) were inquired from the population-based cancer registry of Tehran. Long-term average exposure to PM₁₀, SO₂, NO, NO₂, NO_X, benzene, toluene, ethylbenzene, m-xylene, p-xylene, o-xylene (BTEX), and BTEX in 22 districts of Tehran were estimated using land use regression models. Latent profile analysis (LPA) was used to generate multi-pollutant exposure profiles. Negative binomial regression analysis was used to examine the association between air pollutants and lung cancer incidence. The districts with higher concentrations for all pollutants were mostly in downtown and around the railway station. Districts with a higher concentration for NOx (IRR = 1.05, for each 10 unit increase in air pollutant), benzene (IRR = 3.86), toluene (IRR = 1.50), ethylbenzene (IRR = 5.16), p-xylene (IRR = 9.41), o-xylene (IRR = 7.93), m-xylene (IRR = 2.63) and TBTEX (IRR = 1.21) were significantly associated with higher lung cancer incidence. Districts with a higher multiple air-pollution profile were also associated with more lung cancer incidence (IRR = 1.01). Our study shows a positive association between air pollution and lung cancer incidence. This association was stronger for, respectively, p-xylene, o-xylene, ethylbenzene, benzene, m-xylene and toluene.

The joint effect of ambient air pollution and agricultural pesticide exposures on lung function among children with asthma

BACKGROUND

Ambient environmental pollutants have been shown to adversely affect respiratory health in susceptible populations. However, the role of simultaneous exposure to multiple diverse environmental pollutants is poorly understood.

OBJECTIVE

We applied a multidomain, multipollutant approach to assess the association between pediatric lung function measures and selected ambient air pollutants and pesticides.

METHODS

Using data from the US EPA and California Pesticide Use Registry, we reconstructed three months prior exposure to ambient air pollutants ((ozone (O₃), nitrogen dioxide (NO₂), particulate matter with a median aerodynamic diameter < 2.5 μm (PM_2.5) and <10 μm (PM₁₀)) and pesticides (organophosphates (OP), carbamates (C) and methyl bromide (MeBr)) for 153 children with mild intermittent or mild persistent asthma from the San Joaquin Valley of California, USA. We implemented Bayesian kernel machine regression (BKMR) to estimate the association between simultaneous exposures to air pollutants and pesticides and lung function measures (forced expiratory volume in 1 s (FEV₁), forced vital capacity (FVC), and forced expiratory flow between 25% and 75% of vital capacity (FEF_25-75)).

RESULTS

In BKMR analysis, the overall effect of mixtures (pollutants and pesticides) was associated with reduced FEV₁ and FVC, particularly when all the environmental exposures were above their 60th percentile. For example, the effect of the overall mixture at the 70th percentile (compared to the median) was a -0.12SD (-50 mL, 95% CI: -180 mL, 90 mL) change in the FEV₁ and a -0.18SD (-90 mL, 95% CI: -240 mL, 60 mL) change in the FVC. However, 95% credible intervals around all of the joint effect estimates contained the null value.

CONCLUSION

At this agricultural-urban interface, we observed results from multipollutant analyses, suggestive of adverse effects on some pediatric lung function measures following a cumulative increase in ambient air pollutants and agricultural pesticides. Given the uncertainty in effect estimates, this approach should be explored in larger studies.

Bayesian Profile Regression to Deal With Multiple Highly Correlated Exposures and a Censored Survival Outcome. First Application in Ionizing Radiation Epidemiology

As multifactorial and chronic diseases, cancers are among these pathologies for which the exposome concept is essential to gain more insight into the associated etiology and, ultimately, lead to better primary prevention strategies for public health. Indeed, cancers result from the combined influence of many genetic, environmental and behavioral stressors that may occur simultaneously and interact. It is thus important to properly account for multifactorial exposure patterns when estimating specific cancer risks at individual or population level. Nevertheless, the risk factors, especially environmental, are still too often considered in isolation in epidemiological studies. Moreover, major statistical difficulties occur when exposures to several factors are highly correlated due, for instance, to common sources shared by several pollutants. Suitable statistical methods must then be used to deal with these multicollinearity issues. In this work, we focused on the specific problem of estimating a disease risk from highly correlated environmental exposure covariates and a censored survival outcome. We extended Bayesian profile regression mixture (PRM) models to this context by assuming an instantaneous excess hazard ratio disease sub-model. The proposed hierarchical model incorporates an underlying truncated Dirichlet process mixture as an attribution sub-model. A specific adaptive Metropolis-Within-Gibbs algorithm-including label switching moves-was implemented to infer the model. This allows simultaneously clustering individuals with similar risks and similar exposure characteristics and estimating the associated risk for each group. Our Bayesian PRM model was applied to the estimation of the risk of death by lung cancer in a cohort of French uranium miners who were chronically and occupationally exposed to multiple and correlated sources of ionizing radiation. Several groups of uranium miners with high risk and low risk of death by lung cancer were identified and characterized by specific exposure profiles. Interestingly, our case study illustrates a limit of MCMC algorithms to fit full Bayesian PRM models even if the updating schemes for the cluster labels incorporate label-switching moves. Then, although this paper shows that Bayesian PRM models are promising tools for exposome research, it also opens new avenues for methodological research in this class of probabilistic models.

Using Latent Class Modeling to Jointly Characterize Economic Stress and Multipollutant Exposure

BACKGROUND

Work is needed to better understand how joint exposure to environmental and economic factors influence cancer. We hypothesize that environmental exposures vary with socioeconomic status (SES) and urban/rural locations, and areas with minority populations coincide with high economic disadvantage and pollution.

METHODS

To model joint exposure to pollution and SES, we develop a latent class mixture model (LCMM) with three latent variables (SES Advantage, SES Disadvantage, and Air Pollution) and compare the LCMM fit with K-means clustering. We ran an ANOVA to test for high exposure levels in non-Hispanic black populations. The analysis is at the census tract level for the state of North Carolina.

RESULTS

The LCMM was a better and more nuanced fit to the data than K-means clustering. Our LCMM had two sublevels (low, high) within each latent class. The worst levels of exposure (high SES disadvantage, low SES advantage, high pollution) are found in 22% of census tracts, while the best levels (low SES disadvantage, high SES advantage, low pollution) are found in 5.7%. Overall, 34.1% of the census tracts exhibit high disadvantage, 66.3% have low advantage, and 59.2% have high mixtures of toxic pollutants. Areas with higher SES disadvantage had significantly higher non-Hispanic black population density (NHBPD; P < 0.001), and NHBPD was higher in areas with higher pollution (P < 0.001).

CONCLUSIONS

Joint exposure to air toxins and SES varies with rural/urban location and coincides with minority populations.

IMPACT

Our model can be extended to provide a holistic modeling framework for estimating disparities in cancer survival.See all articles in this CEBP Focus section, "Environmental Carcinogenesis: Pathways to Prevention."

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 PM_2.5. 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 PM_2.5 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.

Complex Mixtures, Complex Analyses: an Emphasis on Interpretable Results

PURPOSE OF REVIEW

The purpose of this review is to outline the main questions in environmental mixtures research and provide a non-technical explanation of novel or advanced methods to answer these questions.

RECENT FINDINGS

Machine learning techniques are now being incorporated into environmental mixture research to overcome issues with traditional methods. Though some methods perform well on specific tasks, no method consistently outperforms all others in complex mixture analyses, largely because different methods were developed to answer different research questions. We discuss four main questions in environmental mixtures research: (1) Are there specific exposure patterns in the study population? (2) Which are the toxic agents in the mixture? (3) Are mixture members acting synergistically? And, (4) what is the overall effect of the mixture? We emphasize the importance of robust methods and interpretable results over predictive accuracy. We encourage collaboration with computer scientists, data scientists, and biostatisticians in future mixture method development.

Selection of nonlinear interactions by a forward stepwise algorithm: Application to identifying environmental chemical mixtures affecting health outcomes

In this paper, we propose a stepwise forward selection algorithm for detecting the effects of a set of correlated exposures and their interactions on a health outcome of interest when the underlying relationship could potentially be nonlinear. Though the proposed method is very general, our application in this paper remains to be on analysis of multiple pollutants and their interactions. Simultaneous exposure to multiple environmental pollutants could affect human health in a multitude of complex ways. For understanding the health effects of multiple environmental exposures, it is often important to identify and estimate complex interactions among exposures. However, this issue becomes analytically challenging in the presence of potential nonlinearity in the outcome-exposure response surface and a set of correlated exposures. Through simulation studies and analyses of test datasets that were simulated as a part of a data challenge in multipollutant modeling organized by the National Institute of Environmental Health Sciences (http://www.niehs.nih.gov/about/events/pastmtg/2015/statistical/), we illustrate the advantages of our proposed method in comparison with existing alternative approaches. A particular strength of our method is that it demonstrates very low false positives across empirical studies. Our method is also used to analyze a dataset that was released from the Health Outcomes and Measurement of the Environment Study as a benchmark beta-tester dataset as a part of the same workshop.

Associations between multipollutant day types and select cardiorespiratory outcomes in Columbia, South Carolina, 2002 to 2013

Background

Health studies of air pollution are increasingly aiming to study associations between air pollutant mixtures and health.

Objective

Estimate associations between observed combinations of ambient air pollutants and select cardiorespiratory outcomes in Columbia, SC during 2002 to 2013.

Methods

We estimate associations using a two-stage approach. First, we identified a collection of observed pollutant combinations, which we define as multipollutant day types (MDTs), by applying a self-organizing map (SOM) to daily measures of nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and particulate matter ≤ 2.5 microns (PM_2.5). Then, overdispersed Poisson time-series models were used to estimate associations between MDTs and each outcome using a 'clean' MDT referent and controlling for long-term, seasonal, and day-of-the-week trends and meteorology. Outcomes included daily emergency department visits for asthma and upper respiratory infection (URI), and hospital admissions for congestive heart failure (CHF) and ischemic heart disease (IHD).

Results

We found that a number of MDTs were significantly and positively associated (point estimates ranged from~2-5%) with cardiorespiratory outcomes in Columbia when compared to days with low pollution. Estimated associations revealed that outcomes for asthma, URIs, and IHD increased 2-4% on warm, dry days experiencing elevated levels of O₃ and PM_2.5. We also found that cooler days with higher NO₂ pollution associated with increased asthma, CHF, and IHD outcomes (2-5%).

Conclusion

Our analysis continues support for using self-organizing maps to develop multipollutant exposure metrics and further illustrates how such metrics can be applied to explore associations between pertinent pollutant combinations and health.

Small-Scale Variations in Urban Air Pollution Levels Are Significantly Associated with Premature Births: A Case Study in São Paulo, Brazil

Premature birth is the result of a complex interaction among genetic, epigenetic, behavioral, socioeconomic, and environmental factors. We evaluated the possible associations between air pollution and the incidence of prematurity in spatial clusters of high and low prevalence in the municipality of São Paulo. It is a spatial case-control study. The residential addresses of mothers with live births that occurred in 2012 and 2013 were geo-coded. A spatial scan statistical test performed to identify possible low-prevalence and high-prevalence clusters of premature births. After identifying, the spatial clusters were drawn samples of cases and controls in each cluster. Mothers were interviewed face-to-face using questionnaires. Air pollution exposure was assessed by passive tubes (NO₂ and O₃) as well as by the determination of trace elements' concentration in tree bark. Binary logistic regression models were applied to determine the significance of the risk of premature birth. Later prenatal care, urinary infection, and hypertension were individual risk factors for prematurity. Particles produced by traffic emissions (estimated by tree bark accumulation) and photochemical pollutants involved in the photochemical cycle (estimated by O₃ and NO₂ passive tubes) also exhibited significant and robust risks for premature births. The results indicate that air pollution is an independent risk factor for prematurity.