Tracking the transmission dynamics of COVID-19 with a time-varying coefficient state-space model.

The spread of COVID-19 has been greatly impacted by regulatory policies and behavior patterns that vary across counties, states, and countries. Population-level dynamics of COVID-19 can generally be described using a set of ordinary differential equations, but these deterministic equations are insufficient for modeling the observed case rates, which can vary due to local testing and case reporting policies and nonhomogeneous behavior among individuals. To assess the impact of population mobility on the spread of COVID-19, we have developed a novel Bayesian time-varying coefficient state-space model for infectious disease transmission. The foundation of this model is a time-varying coefficient compartment model to recapitulate the dynamics among susceptible, exposed, undetected infectious, detected infectious, undetected removed, hospitalized, detected recovered, and detected deceased individuals. The infectiousness and detection parameters are modeled to vary by time, and the infectiousness component in the model incorporates information on multiple sources of population mobility. Along with this compartment model, a multiplicative process model is introduced to allow for deviation from the deterministic dynamics. We apply this model to observed COVID-19 cases and deaths in several U.S. states and Colorado counties. We find that population mobility measures are highly correlated with transmission rates and can explain complicated temporal variation in infectiousness in these regions. Additionally, the inferred connections between mobility and epidemiological parameters, varying across locations, have revealed the heterogeneous effects of different policies on the dynamics of COVID-19.

Effects of household and participant characteristics on personal exposure and kitchen concentration of fine particulate matter and black carbon in rural Honduras.

Traditional cooking with solid fuels (biomass, animal dung, charcoals, coal) creates household air pollution that leads to millions of premature deaths and disability worldwide each year. Exposure to household air pollution is highest in low- and middle-income countries. Using data from a stepped-wedge randomized controlled trial of a cookstove intervention among 230 households in Honduras, we analyzed the impact of household and personal variables on repeated 24-h measurements of fine particulate matter (PM2.5) and black carbon (BC) exposure. Six measurements were collected approximately six-months apart over the course of the three-year study. Multivariable mixed models explained 37% of variation in personal PM2.5 exposure and 49% of variation in kitchen PM2.5 concentrations. Additionally, multivariable models explained 37% and 47% of variation in personal and kitchen BC concentrations, respectively. Stove type, season, presence of electricity, primary stove location, kitchen enclosure type, stove use time, and presence of kerosene for lighting were all associated with differences in geometric mean exposures. Stove type explained the most variability of the included variables. In future studies of household air pollution, tracking the cooking behaviors and daily activities of participants, including outdoor exposures, may explain exposure variation beyond the household and personal variables considered here.

Assessing the health estimation capacity of air pollution exposure prediction models.

BACKGROUND: The era of big data has enabled sophisticated models to predict air pollution concentrations over space and time. Historically these models have been evaluated using overall metrics that measure how close predictions are to monitoring data. However, overall methods are not designed to distinguish error at timescales most relevant for epidemiologic studies, such as day-to-day errors that impact studies of short-term health associations. METHODS: We introduce frequency band model performance, which quantifies health estimation capacity of air quality prediction models for time series studies of air pollution and health. Frequency band model performance uses a discrete Fourier transform to evaluate prediction models at timescales of interest. We simulated fine particulate matter (PM2.5), with errors at timescales varying from acute to seasonal, and health time series data. To compare evaluation approaches, we use correlations and root mean squared error (RMSE). Additionally, we assess health estimation capacity through bias and RMSE in estimated health associations. We apply frequency band model performance to PM2.5 predictions at 17 monitors in 8 US cities. RESULTS: In simulations, frequency band model performance rates predictions better (lower RMSE, higher correlation) when there is no error at a particular timescale (e.g., acute) and worse when error is added to that timescale, compared to overall approaches. Further, frequency band model performance is more strongly associated (R2 = 0.95) with health association bias compared to overall approaches (R2 = 0.57). For PM2.5 predictions in Salt Lake City, UT, frequency band model performance better identifies acute error that may impact estimated short-term health associations. CONCLUSIONS: For epidemiologic studies, frequency band model performance provides an improvement over existing approaches because it evaluates models at the timescale of interest and is more strongly associated with bias in estimated health associations. Evaluating prediction models at timescales relevant for health studies is critical to determining whether model error will impact estimated health associations.

A Spatiotemporal Prediction Model for Black Carbon in the Denver Metropolitan Area, 2009-2020.

Studies on health effects of air pollution from local sources require exposure assessments that capture spatial and temporal trends. To facilitate intraurban studies in Denver, Colorado, we developed a spatiotemporal prediction model for black carbon (BC). To inform our model, we collected more than 700 weekly BC samples using personal air samplers from 2018 to 2020. The model incorporated spatial and spatiotemporal predictors and smoothed time trends to generate point-level weekly predictions of BC concentrations for the years 2009-2020. Our results indicate that our model reliably predicted weekly BC concentrations across the region during the year in which we collected data. We achieved a 10-fold cross-validation R2 of 0.83 and a root-mean-square error of 0.15 µg/m3 for weekly BC concentrations predicted at our sampling locations. Predicted concentrations displayed expected temporal trends, with the highest concentrations predicted during winter months. Thus, our prediction model improves on typical land use regression models that generally only capture spatial gradients. However, our model is limited by a lack of long-term BC monitoring data for full validation of historical predictions. BC predictions from the weekly spatiotemporal model will be used in traffic-related air pollution exposure-disease associations more precisely than previous models for the region have allowed.

Long-Term Coarse Particulate Matter Exposure Is Associated with Asthma among Children in Medicaid.

RATIONALE: Short- and long-term fine particulate matter (particulate matter ≤2.5 µm in aerodynamic diameter [PM2.5]) pollution is associated with asthma development and morbidity, but there are few data on the effects of long-term exposure to coarse PM (PM10-2.5) on respiratory health. OBJECTIVES: To understand the relationship between long-term fine and coarse PM exposure and asthma prevalence and morbidity among children. METHODS: A semiparametric regression model that incorporated PM2.5 and PM10 monitor data and geographic characteristics was developed to predict 2-year average PM2.5 and PM10-2.5 exposure during the period 2009 to 2010 at the zip-code tabulation area level. Data from 7,810,025 children aged 5 to 20 years enrolled in Medicaid from 2009 to 2010 were used in a log-linear regression model with predicted PM levels to estimate the association between PM exposure and asthma prevalence and morbidity, adjusting for race/ethnicity, sex, age, area-level urbanicity, poverty, education, and unmeasured spatial confounding. MEASUREMENTS AND MAIN RESULTS: Exposure to coarse PM was associated with increased asthma diagnosis prevalence (rate ratio [RR] for 1-µg/m3 increase in coarse PM level, 1.006; 95% confidence interval [CI], 1.001-1.011), hospitalizations (RR, 1.023; 95% CI, 1.003-1.042), and emergency department visits (RR, 1.017; 95% CI, 1.001-1.033) when adjusting for fine PM. Fine PM exposure was more strongly associated with increased asthma prevalence and morbidity than coarse PM. The estimates remained elevated across different levels of spatial confounding adjustment. CONCLUSIONS: Among children enrolled in Medicaid, exposure to higher average coarse PM levels is associated with increased asthma prevalence and morbidity. These results suggest the need for direct monitoring of coarse PM and reconsideration of limits on long-term average coarse PM pollution levels.

Study protocol for a stepped-wedge randomized cookstove intervention in rural Honduras: household air pollution and cardiometabolic health.

BACKGROUND: Growing evidence links household air pollution exposure from biomass-burning cookstoves to cardiometabolic disease risk. Few randomized controlled interventions of cookstoves (biomass or otherwise) have quantitatively characterized changes in exposure and indicators of cardiometabolic health, a growing and understudied burden in low- and middle-income countries (LMICs). Ideally, the solution is to transition households to clean cooking, such as with electric or liquefied petroleum gas stoves; however, those unable to afford or to access these options will continue to burn biomass for the foreseeable future. Wood-burning cookstove designs such as the Justa (incorporating an engineered combustion zone and chimney) have the potential to substantially reduce air pollution exposures. Previous cookstove intervention studies have been limited by stove types that did not substantially reduce exposures and/or by low cookstove adoption and sustained use, and few studies have incorporated community-engaged approaches to enhance the intervention. METHODS/DESIGN: We conducted an individual-level, stepped-wedge randomized controlled trial with the Justa cookstove intervention in rural Honduras. We enrolled 230 female primary cooks who were not pregnant, non-smoking, aged 24-59 years old, and used traditional wood-burning cookstoves at baseline. A community advisory board guided survey development and communication with participants, including recruitment and retention strategies. Over a 3-year study period, participants completed 6 study visits approximately 6 months apart. Half of the women received the Justa after visit 2 and half after visit 4. At each visit, we measured 24-h gravimetric personal and kitchen fine particulate matter (PM2.5) concentrations, qualitative and quantitative cookstove use and adoption metrics, and indicators of cardiometabolic health. The primary health endpoints were blood pressure, C-reactive protein, and glycated hemoglobin. Overall study goals are to explore barriers and enablers of new cookstove adoption and sustained use, compare health endpoints by assigned cookstove type, and explore the exposure-response associations between PM2.5 and indicators of cardiometabolic health. DISCUSSION: This trial, utilizing an economically feasible, community-vetted cookstove and evaluating endpoints relevant for the major causes of morbidity and mortality in LMICs, will provide critical information for household air pollution stakeholders globally. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT02658383 , posted January 18, 2016, field work completed May 2018. Official title, "Community-Based Participatory Research: A Tool to Advance Cookstove Interventions." Principal Investigator Maggie L. Clark, Ph.D. Last update posted July 12, 2018.

Selecting Shrinkage Parameters for Effect Estimation: The Multi-Ethnic Study of Atherosclerosis.

We present a method for improving estimation in linear regression models in samples of moderate size, using shrinkage techniques. Our work connects the theory of causal inference, which describes how variable adjustment should be performed with large samples, with shrinkage estimators such as ridge regression and the least absolute shrinkage and selection operator (LASSO), which can perform better in sample sizes seen in epidemiologic practice. Shrinkage methods reduce mean squared error by trading off some amount of bias for a reduction in variance. However, when inference is the goal, there are no standard methods for choosing the penalty "tuning" parameters that govern these tradeoffs. We propose selecting the penalty parameters for these shrinkage estimators by minimizing bias and variance in future similar data sets drawn from the posterior predictive distribution. Our method provides both the point estimate of interest and corresponding standard error estimates. Through simulations, we demonstrate that it can achieve better mean squared error than using cross-validation for penalty parameter selection. We apply our method to a cross-sectional analysis of the association between smoking and carotid intima-media thickness in the Multi-Ethnic Study of Atherosclerosis (multiple US locations, 2000-2002) and compare it with similar analyses of these data.

Measurement Error Correction for Predicted Spatiotemporal Air Pollution Exposures.

BACKGROUND: Air pollution cohort studies are frequently analyzed in two stages, first modeling exposure then using predicted exposures to estimate health effects in a second regression model. The difference between predicted and unobserved true exposures introduces a form of measurement error in the second stage health model. Recent methods for spatial data correct for measurement error with a bootstrap and by requiring the study design ensure spatial compatibility, that is, monitor and subject locations are drawn from the same spatial distribution. These methods have not previously been applied to spatiotemporal exposure data. METHODS: We analyzed the association between fine particulate matter (PM2.5) and birth weight in the US state of Georgia using records with estimated date of conception during 2002-2005 (n = 403,881). We predicted trimester-specific PM2.5 exposure using a complex spatiotemporal exposure model. To improve spatial compatibility, we restricted to mothers residing in counties with a PM2.5 monitor (n = 180,440). We accounted for additional measurement error via a nonparametric bootstrap. RESULTS: Third trimester PM2.5 exposure was associated with lower birth weight in the uncorrected (-2.4 g per 1 µg/m difference in exposure; 95% confidence interval [CI]: -3.9, -0.8) and bootstrap-corrected (-2.5 g, 95% CI: -4.2, -0.8) analyses. Results for the unrestricted analysis were attenuated (-0.66 g, 95% CI: -1.7, 0.35). CONCLUSIONS: This study presents a novel application of measurement error correction for spatiotemporal air pollution exposures. Our results demonstrate the importance of spatial compatibility between monitor and subject locations and provide evidence of the association between air pollution exposure and birth weight.

Combining Land-Use Regression and Chemical Transport Modeling in a Spatiotemporal Geostatistical Model for Ozone and PM2.5.

Assessments of long-term air pollution exposure in population studies have commonly employed land-use regression (LUR) or chemical transport modeling (CTM) techniques. Attempts to incorporate both approaches in one modeling framework are challenging. We present a novel geostatistical modeling framework, incorporating CTM predictions into a spatiotemporal LUR model with spatial smoothing to estimate spatiotemporal variability of ozone (O3) and particulate matter with diameter less than 2.5 µm (PM2.5) from 2000 to 2008 in the Los Angeles Basin. The observations include over 9 years' data from more than 20 routine monitoring sites and specific monitoring data at over 100 locations to provide more comprehensive spatial coverage of air pollutants. Our composite modeling approach outperforms separate CTM and LUR models in terms of root-mean-square error (RMSE) assessed by 10-fold cross-validation in both temporal and spatial dimensions, with larger improvement in the accuracy of predictions for O3 (RMSE [ppb] for CTM, 6.6; LUR, 4.6; composite, 3.6) than for PM2.5 (RMSE [µg/m(3)] CTM: 13.7, LUR: 3.2, composite: 3.1). Our study highlights the opportunity for future exposure assessment to make use of readily available spatiotemporal modeling methods and auxiliary gridded data that takes chemical reaction processes into account to improve the accuracy of predictions in a single spatiotemporal modeling framework.

Development of Long-term Spatiotemporal Models for Ambient Ozone in Six Metropolitan regions of the United States: The MESA Air Study.

BACKGROUND: Current epidemiologic studies rely on simple ozone metrics which may not appropriately capture population ozone exposure. For understanding health effects of long-term ozone exposure in population studies, it is advantageous for exposure estimation to incorporate the complex spatiotemporal pattern of ozone concentrations at fine scales. OBJECTIVE: To develop a geo-statistical exposure prediction model that predicts fine scale spatiotemporal variations of ambient ozone in six United States metropolitan regions. METHODS: We developed a modeling framework that estimates temporal trends from regulatory agency and cohort-specific monitoring data from MESA Air measurement campaigns and incorporates land use regression with universal kriging using predictor variables from a large geographic database. The cohort-specific data were measured at home and community locations. The framework was applied in estimating two-week average ozone concentrations from 1999 to 2013 in models of each of the six MESA Air metropolitan regions. RESULTS: Ozone models perform well in both spatial and temporal dimensions at the agency monitoring sites in terms of prediction accuracy. City-specific leave-one (site)-out cross-validation R2 accounting for temporal and spatial variability ranged from 0.65 to 0.88 in the six regions. For predictions at the home sites, the R2 is between 0.60 and 0.91 for cross-validation that left out 10% of home sites in turn. The predicted ozone concentrations vary substantially over space and time in all the metropolitan regions. CONCLUSION: Using the available data, our spatiotemporal models are able to accurately predict long-term ozone concentrations at fine spatial scales in multiple regions. The model predictions will allow for investigation of the long-term health effects of ambient ozone concentrations in future epidemiological studies.

Estimating long-term average household air pollution concentrations from repeated short-term measurements in the presence of seasonal trends and crossover.

Estimating long-term exposure to household air pollution is essential for quantifying health effects of chronic exposure and the benefits of intervention strategies. However, typically only a small number of short-term measurements are made. We compare different statistical models for combining these short-term measurements into predictions of a long-term average, with emphasis on the impact of temporal trends in concentrations and crossover in study design. We demonstrate that a linear mixed model that includes time adjustment provides the best predictions of long-term average, which have lower error than using household averages or mixed models without time, for a variety of different study designs and underlying temporal trends. In a case study of a cookstove intervention study in Honduras, we further demonstrate how, in the presence of strong seasonal variation, long-term average predictions from the mixed model approach based on only two or three measurements can have less error than predictions based on an average of up to six measurements. These results have important implications for the efficiency of designs and analyses in studies assessing the chronic health impacts of long-term exposure to household air pollution.

Aerosol emissions from wind instruments: effects of performer age, sex, sound pressure level, and bell covers.

Aerosol emissions from wind instruments are a suspected route of transmission for airborne infectious diseases, such as SARS-CoV-2. We evaluated aerosol number emissions (from 0.25 to 35.15 µm) from 81 volunteer performers of both sexes and varied age (12 to 63 years) while playing wind instruments (bassoon, clarinet, flute, French horn, oboe, piccolo, saxophone, trombone, trumpet, and tuba) or singing. Measured emissions spanned more than two orders of magnitude, ranging in rate from < 8 to 1,815 particles s-1, with brass instruments, on average, producing 191% (95% CI 81-367%) more aerosol than woodwinds. Being male was associated with a 70% increase in emissions (vs. female; 95% CI 9-166%). Each 1 dBA increase in sound pressure level was associated with a 28% increase (95% CI 10-40%) in emissions from brass instruments; sound pressure level was not associated with woodwind emissions. Age was not a significant predictor of emissions. The use of bell covers reduced aerosol emissions from three brass instruments tested (trombone, tuba, and trumpet), with average reductions ranging from 53 to 73%, but not for the two woodwind instruments tested (oboe and clarinet). Results from this work can facilitate infectious disease risk management for the performing arts.

Reduced Black Carbon Concentrations following a Three-Year Stepped-Wedge Randomized Trial of the Wood-Burning Justa Cookstove in Rural Honduras.

Introduction: Household air pollution from cooking-related biomass combustion remains a leading risk factor for global health. Black carbon (BC) is an important component of particulate matter (PM) in household air pollution. We evaluated the impact of the engineered, wood-burning Justa stove intervention on BC concentrations. Methods: We conducted a 3-year stepped-wedge randomized controlled trial with 6 repeated visits among 230 female primary cooks in rural Honduras. Participants used traditional stoves at baseline and were randomized to receive the Justa after visit 2 or after visit 4. At each visit, we measured 24-hour gravimetric personal and kitchen fine PM (PM2.5) concentrations and estimated BC mass concentrations (Sootscan Transmissometer). We conducted intent-to-treat analyses using linear mixed models with natural log-transformed 24-hour personal and kitchen BC. Results: BC concentrations were reduced for households assigned to the Justa vs. traditional stoves: e.g., personal BC geometric mean (GSD), 3.6 µg/m3 (6.4) vs. 11.5 µg/m3 (4.6), respectively. Following the intervention, we observed 53% (95% CI: 35-65%) lower geometric mean personal BC concentrations and 76% (95% CI: 66-83%) lower geometric mean kitchen BC concentrations. Conclusions: The Justa stove intervention substantially reduced BC concentrations, mitigating household air pollution and potentially benefitting human and climate health.

Fine-Scale Air Pollution Models for Epidemiologic Research: Insights From Approaches Developed in the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air).

PURPOSE OF REVIEW: Epidemiological studies of short- and long-term health impacts of ambient air pollutants require accurate exposure estimates. We describe the evolution in exposure assessment and assignment in air pollution epidemiology, with a focus on spatiotemporal techniques first developed to meet the needs of the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Initially designed to capture the substantial variation in pollutant levels and potential health impacts that can occur over small spatial and temporal scales in metropolitan areas, these methods have now matured to permit fine-scale exposure characterization across the contiguous USA and can be used for understanding long- and short-term health effects of exposure across the lifespan. For context, we highlight how the MESA Air models compare to other available exposure models. RECENT FINDINGS: Newer model-based exposure assessment techniques provide predictions of pollutant concentrations with fine spatial and temporal resolution. These validated models can predict concentrations of several pollutants, including particulate matter less than 2.5 µm in diameter (PM2.5), oxides of nitrogen, and ozone, at specific locations (such as at residential addresses) over short time intervals (such as 2 weeks) across the contiguous USA between 1980 and the present. Advances in statistical methods, incorporation of supplemental pollutant monitoring campaigns, improved geographic information systems, and integration of more complete satellite and chemical transport model outputs have contributed to the increasing validity and refined spatiotemporal spans of available models. Modern models for predicting levels of outdoor concentrations of air pollutants can explain a substantial amount of the spatiotemporal variation in observations and are being used to provide critical insights into effects of air pollutants on the prevalence, incidence, progression, and prognosis of diseases across the lifespan. Additional enhancements in model inputs and model design, such as incorporation of better traffic data, novel monitoring platforms, and deployment of machine learning techniques, will allow even further improvements in the performance of pollutant prediction models.

Impact of the wood-burning Justa cookstove on fine particulate matter exposure: A stepped-wedge randomized trial in rural Honduras.

TRIAL DESIGN: We evaluated the impact of a biomass stove intervention on fine particulate matter (PM2.5) concentrations using an individual-level, stepped-wedge randomized trial. METHODS: We enrolled 230 women in rural Honduran households using traditional biomass stoves and randomly allocated them to one of two study arms. The Justa stove, the study intervention, was locally-sourced, wood-burning, and included an engineered combustion chamber and chimney. At each of 6 visits over 3 years, we measured 24-hour gravimetric personal and kitchen PM2.5 concentrations. Half of the households received the intervention after Visit 2 and half after Visit 4. We conducted intent-to-treat analyses to evaluate the intervention effect using linear mixed models with log-transformed kitchen or personal PM2.5 (separately) as the dependent variable, adjusting for time. We also compared PM2.5 concentrations to World Health Organization (WHO) guidelines. RESULTS: Arms 1 and 2 each had 115 participants with 664 and 632 completed visits, respectively. Median 24-hour average personal PM2.5 exposures were 81 µg/m3 (25th-75th percentile: 50-141 µg/m3) for the traditional stove condition (n=622) and 43 µg/m3 (25th-75th percentile: 27-73 µg/m3) for the Justa stove condition (n=585). Median 24-hour average kitchen concentrations were 178 µg/m3 (25th-75th percentile: 69-440 µg/m3; n=629) and 53 µg/m3 (25th-75th percentile: 29-103 µg/m3; n=578) for the traditional and Justa stove conditions, respectively. The Justa intervention resulted in a 32% reduction in geometric mean personal PM2.5 (95% confidence interval [CI]: 20-43%) and a 56% reduction (95% CI: 46-65%) in geometric mean kitchen PM2.5. During rainy and dry seasons, 53% and 41% of participants with the Justa intervention had 24-hour average personal PM2.5 exposures below the WHO interim target-3 guideline (37.5 µg/m3), respectively. CONCLUSION: The Justa stove intervention substantially lowered personal and kitchen PM2.5 and may be a provisional solution that is feasible for Latin American communities where cleaner fuels may not be available, affordable, or acceptable for some time. Clinicaltrials.gov: NCT02658383.

Selecting a Scale for Spatial Confounding Adjustment.

Unmeasured, spatially-structured factors can confound associations between spatial environmental exposures and health outcomes. Adding flexible splines to a regression model is a simple approach for spatial confounding adjustment, but the spline degrees of freedom do not provide an easily interpretable spatial scale. We describe a method for quantifying the extent of spatial confounding adjustment in terms of the Euclidean distance at which variation is removed. We develop this approach for confounding adjustment with splines and using Fourier and wavelet filtering. We demonstrate differences in the spatial scales these bases can represent and provide a comparison of methods for selecting the amount of confounding adjustment. We find the best performance for selecting the amount of adjustment using an information criterion evaluated on an outcome model without exposure. We apply this method to spatial adjustment in an analysis of fine particulate matter and blood pressure in a cohort of United States women.

A hierarchical model for estimating the exposure-response curve by combining multiple studies of acute lower respiratory infections in children and household fine particulate matter air pollution.

Adverse health effects of household air pollution, including acute lower respiratory infections (ALRIs), pose a major health burden around the world, particularly in settings where indoor combustion stoves are used for cooking. Individual studies have limited exposure ranges and sample sizes, while pooling studies together can improve statistical power. METHODS: We present hierarchical models for estimating long-term exposure concentrations and estimating a common exposure-response curve. The exposure concentration model combines temporally sparse, clustered longitudinal observations to estimate household-specific long-term average concentrations. The exposure-response model provides a flexible, semiparametric estimate of the exposure-response relationship while accommodating heterogeneous clustered data from multiple studies. We apply these models to three studies of fine particulate matter (PM2.5) and ALRIs in children in Nepal: a case-control study in Bhaktapur, a stepped-wedge trial in Sarlahi, and a parallel trial in Sarlahi. For each study, we estimate household-level long-term PM2.5 concentrations. We apply the exposure-response model separately to each study and jointly to the pooled data. RESULTS: The estimated long-term PM2.5 concentrations were lower for households using electric and gas fuel sources compared with households using biomass fuel. The exposure-response curve shows an estimated ALRI odds ratio of 3.39 (95% credible interval = 1.89, 6.10) comparing PM2.5 concentrations of 50 and 150 µg/m3 and a flattening of the curve for higher concentrations. CONCLUSIONS: These flexible models can accommodate additional studies and be applied to other exposures and outcomes. The studies from Nepal provides evidence of a nonlinear exposure-response curve that flattens at higher concentrations.

Air Pollution, Clustering of Particulate Matter Components, and Breast Cancer in the Sister Study: A U.S.-Wide Cohort.

BACKGROUND: Particulate matter (PM) is a complex mixture. Geographic variations in PM may explain the lack of consistent associations with breast cancer. OBJECTIVE: We aimed to evaluate the relationship between air pollution, PM components, and breast cancer risk in a United States-wide prospective cohort. METHODS: We estimated annual average ambient residential levels of particulate matter <2.5 µm and <10 µm in aerodynamic diameter (PM2.5 and PM10, respectively) and nitrogen dioxide (NO2) using land-use regression for 47,433 Sister Study participants (breast cancer-free women with a sister with breast cancer) living in the contiguous United States. Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for risk associated with an interquartile range (IQR) increase in pollutants. Predictive k-means were used to assign participants to clusters derived from PM2.5 component profiles to evaluate the impact of heterogeneity in the PM2.5 mixture. For PM2.5, we investigated effect measure modification by component cluster membership and by geographic region without regard to air pollution mixture. RESULTS: During follow-up (mean=8.4 y), 2,225 invasive and 623 ductal carcinoma in situ (DCIS) cases were identified. PM2.5 and NO2 were associated with breast cancer overall [HR=1.05 (95% CI:0.99, 1.11) and 1.06 (95% CI:1.02, 1.11), respectively] and with DCIS but not with invasive cancer. Invasive breast cancer was associated with PM2.5 only in the Western United States [HR=1.14 (95% CI:1.02, 1.27)] and NO2 only in the Southern United States [HR=1.16 (95% CI:1.01, 1.33)]. PM2.5 was associated with a higher risk of invasive breast cancer among two of seven identified composition-based clusters. A higher risk was observed [HR=1.25 (95% CI: 0.97, 1.60)] in a California-based cluster characterized by low S and high Na and nitrate (NO3-) fractions and for another Western United States cluster [HR=1.60 (95% CI: 0.90, 2.85)], characterized by high fractions of Si, Ca, K, and Al. CONCLUSION: Air pollution measures were related to both invasive breast cancer and DCIS within certain geographic regions and PM component clusters. https://doi.org/10.1289/EHP5131.

Air pollution, particulate matter composition and methylation-based biologic age.

BACKGROUND: Epigenetic age, as defined by DNA methylation, may be influenced by air pollution exposure. OBJECTIVE: To evaluate the relationship between NO2, particulate matter (PM), PM components and accelerated epigenetic age. METHODS: In a sample of non-Hispanic white women living in the contiguous U.S. (n = 2747), we estimated residential exposure to PM2.5, PM10 and NO2 using a model incorporating land-use regression and kriging. Predictive k-means was used to assign participants to clusters representing different PM2.5 component profiles. We measured DNA methylation (DNAm) in blood using the Illumina's Infinium HumanMethylation450 BeadChip and calculated DNAm age using the Hannum, Horvath and Levine epigenetic clocks. Age acceleration was defined based on residuals after regressing DNAm age on chronological age. We estimated associations between interquartile range (IQR) increases in pollutants and age acceleration using linear regression. For PM2.5, we stratified by cluster membership. We examined epigenome-wide associations using robust linear regression models corrected with false discovery rate q-values. RESULTS: NO2 was inversely associated with age acceleration using the Hannum clock (ß = -0.24, 95% CI: -0.47, -0.02). No associations were observed for PM10. For PM2.5, the association with age acceleration varied by PM2.5 component cluster. For example, with the Levine clock, an IQR increase in PM2.5 was associated with an over 6-year age acceleration in a cluster that has relatively high fractions of crustal elements relative to overall PM2.5 (ß = 6.57, 95% CI: 2.68, 10.47), and an almost 2-year acceleration in a cluster characterized by relatively low sulfur fractions (ß = 1.88, 95% CI: 0.51, 3.25). In a cluster distinguished by lower relative nitrate concentrations, PM2.5 was inversely associated with age acceleration (ß = -1.33, 95% CI: -2.43, -0.23). Across the epigenome, NO2 was associated with methylation at 2 CpG sites. CONCLUSION: Air pollution was associated with epigenetic age, a marker of mortality and disease risk, among certain PM2.5 component profiles.