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Pregnancy is a critical window for long-term metabolic programming of fetal effects stemming from airborne particulate matter ≤2.5μm (PM 2.5 ) exposure. Yet, little is known about long-term metabolic effects of PM 2.5 exposure during and surrounding pregnancy in mothers. We assessed potential critical windows of PM 2.5 exposure during and surrounding pregnancy with maternal adiposity and lipid measures later in life. We included 517 pregnant women from the PROGRESS cohort with adiposity [body mass index (BMI), waist circumference (WC), % body fat] and lipids [total cholesterol, high-density-lipoprotein (HDL), low-density-lipoprotein (LDL)] measured repeatedly at 4, 6 and 8 years post-delivery. Monthly average PM 2.5 exposure was estimated at each participant's address using a validated spatiotemporal model. We employed distributed lag interaction models (DLIMs) adjusting for socio-demographics and clinical covariates. We found that a 1 μg/m 3 increase in PM 2.5 exposure throughout mid-/late-pregnancy was associated with higher WC at 6-years post-delivery, peaking at 6 months of gestation: 0.04 cm (95%CI: 0.01, 0.06). We also identified critical windows of PM 2.5 exposure during and surrounding pregnancy associated with higher LDL and lower HDL both measured at 4 years post-delivery with peaks at pre-conception for LDL [0.17 mg/dL (95%CI: 0.00, 0.34)] and at the 11 th month after conception for HDL [-0.07 mg/dL (95%CI: -0.11, -0.02)]. Stratified analyses by fetal sex indicated stronger associations with adiposity measures in mothers carrying a male, while with lipids in mothers carrying a female fetus. Stratified analyses also indicated potential stronger deleterious lagged effects in women with folic acid intake lower than 600mcg/day during pregnancy., Competing Interests: Declaration of Competing Interest ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025. Published by Elsevier Inc.)

The COVID-19 infection fatality rate (IFR) is the proportion of individuals infected with SARS-CoV-2 who subsequently die. As COVID-19 disproportionately affects older individuals, age-specific IFR estimates are imperative to facilitate comparisons of the impact of COVID-19 between locations and prioritize distribution of scarce resources. However, there lacks a coherent method to synthesize available data to create estimates of IFR and seroprevalence that vary continuously with age and adequately reflect uncertainties inherent in the underlying data. In this article, we introduce a novel Bayesian hierarchical model to estimate IFR as a continuous function of age that acknowledges heterogeneity in population age structure across locations and accounts for uncertainty in the estimates due to seroprevalence sampling variability and the imperfect serology test assays. Our approach simultaneously models test assay characteristics, serology, and death data, where the serology and death data are often available only for binned age groups. Information is shared across locations through hierarchical modeling to improve estimation of the parameters with limited data. Modeling data from 26 developing country locations during the first year of the COVID-19 pandemic, we found seroprevalence did not change dramatically with age, and the IFR at age 60 was above the high-income country estimate for most locations., (© 2024 John Wiley & Sons Ltd.)

Identifying the determinants of pregnancy loss is a critical public health concern. However, pregnancy loss is often not noticed, and even when it is, it is inconsistently recorded. Thus, past studies have been limited to medically-identified losses or small, highly selected cohorts, which can lead to biased or non-generalizable results. We show mathematically and through simulations a novel approach that overcomes this measurement challenge to infer effects about pregnancy loss by utilizing more available data: the number of conceptions that led to live births-i.e., live-birth-identified conceptions (LBICs). We simulated ten years of conceptions, pregnancies, losses, and births under several confounding patterns, and two NO2-pregnancy loss relationships (no effect, mid-gestation effect). We fitted distributed lag models (DLMs) adjusted for season, year, and temperature, and assessed model performance through bias and coverage. Our simulations showed that our models, across all scenarios, identified the two NO2-pregnancy loss relationships with appropriate coverage (>90% of confidence intervals captured the true effect) and low bias (never exceeded ±2%). In an applied example using NO2-a traffic emissions tracer-and live birth data from a large tertiary-care hospital in Massachusetts, USA, we found that higher prenatal NO2 was associated with more pregnancy losses. Our proposed approach based on LBICs provides an alternative way to study causes of pregnancy loss., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Pregnancy is a critical window for long-term metabolic programming of fetal effects stemming from airborne particulate matter ≤2.5μm (PM 2.5 ) exposure. Yet, little is known about long-term metabolic effects of PM 2.5 exposure during and surrounding pregnancy in mothers. We assessed potential critical windows of PM 2.5 exposure during and surrounding pregnancy with maternal adiposity and lipid measures later in life. We included 517 pregnant women from the PROGRESS cohort with adiposity [body mass index (BMI), waist circumference (WC), % body fat] and lipids [total cholesterol, high-density-lipoprotein (HDL), low-density-lipoprotein (LDL)] measured repeatedly at 4, 6 and 8 years post-delivery. Monthly average PM 2.5 exposure was estimated at each participant's address using a validated spatiotemporal model. We employed distributed lag interaction models (DLIMs) adjusting for socio-demographics and clinical covariates. We found that a 1 μg/m 3 increase in PM 2.5 exposure throughout mid-/late-pregnancy was associated with higher WC at 6-years post-delivery, peaking at 6 months of gestation: 0.04 cm (95%CI: 0.01, 0.06). We also identified critical windows of PM 2.5 exposure during and surrounding pregnancy associated with higher LDL and lower HDL both measured at 4 years post-delivery with peaks at pre-conception for LDL [0.17 mg/dL (95%CI: 0.00, 0.34)] and at the 11 th month after conception for HDL [-0.07 mg/dL (95%CI: -0.11, -0.02)]. Stratified analyses by fetal sex indicated stronger associations with adiposity measures in mothers carrying a male, whereas stronger associations were observed with lipids in mothers carrying a female fetus. Stratified analyses also indicated potential stronger deleterious lagged effects in women with folic acid intake lower than 600mcg/day during pregnancy.

Background: Prenatal air pollution (AP) exposure has been linked to postpartum psychological functioning, impacting health outcomes in both women and children. Extant studies primarily focused on individual pollutants., Objective: To assess the association between prenatal exposure to a mixture of seven AP components and postpartum psychological functioning using daily exposure data and data-driven statistical methods., Methods: Analyses included 981 women recruited at 24.0 ± 9.9 weeks gestation and followed longitudinally. We estimated prenatal daily exposure levels for constituents of fine particles [elemental carbon (EC), organic carbon (OC), nitrate (NO 3 - ), sulfate (SO 4 2- ), ammonium (NH 4 + )], nitrogen dioxide (NO 2 ), and ozone (O 3 ) using validated global 3-D chemical-transport models and satellite-based hybrid models based on residential addresses. Edinburgh Postnatal Depression Scale (EPDS) was administered to participants to derive a total EPDS score and the subconstruct scores for anhedonia and depressive symptoms. A distributed lag model (DLM) was employed within Bayesian Kernel Machine Regression (BKMR) to develop time-weighted exposure profile for each pollutant. These exposures were then input into a Weighted Quantile Sum (WQS) regression to estimate an overall mixture effect, adjusted for maternal age, education, race/ethnicity, season of delivery, and delivery year. Effect modification by race/ethnicity and fetal sex was also examined., Results: Women were primarily Hispanic (51%) and Black (32%) reporting ≤12 years of education (58%). Prenatal exposure to an AP mixture was significantly associated with increased anhedonia subscale z-scores, particularly in Hispanics (β = 0.07, 95%CI = 0.004-0.13, per unit increase in WQS index). It was also borderline associated with increased total EPDS (β = 0.11, 95%CI = 0.00-0.22) and depressive symptom subscale (β = 0.09, 95%CI = -0.02 to 0.19) z-scores, particularly among Hispanic women who gave birth to a male infant. Sulfate (SO 4 2- ), O 3 and OC were major contributors to these associations., Impact: This study utilizes an advanced data-driven approach to examine the temporally- and mixture-weighted effects of prenatal air pollution exposure on postpartum psychological functioning. We found that exposure to a prenatal air pollution mixture predicted poorer postpartum psychological functioning, particularly anhedonia symptoms in Hispanic women. Findings underscore the importance of considering both exposure mixtures as well as potential modifying factors to better help identify particular pollutants that drive effects and susceptible populations, which can inform more effective intervention strategies., Competing Interests: Competing interests: The authors declare no competing interests. Ethics approval and consent to participate: All study protocols were reviewed and approved by the Program for the Protection of Human Subjects at the Icahn School of Medicine at Mount Sinai for the ACCESS project (IRB#12-00661) and the PRISM project (IRB#12-00875). Informed written consent was obtained from all participants. The authors confirm that all procedures contributing to this work comply with the ethical standards as outlined in the Declaration of Helsinki., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background: Preeclampsia is a multi-system hypertensive disorder of pregnancy that is a leading cause of maternal and fetal morbidity and mortality. Prior studies disagree on the cause and even the presence of seasonal patterns in its incidence. Using unsuitable time windows for seasonal exposures can bias model results, potentially explaining these inconsistencies., Objectives: We aimed to investigate humidity and temperature as possible causes for seasonal trends in preeclampsia in Project Viva, a prebirth cohort in Boston, Massachusetts, considering only exposure windows that precede disease onset., Methods: Using the Parameter-elevation Relationships on Independent Slopes Model (PRISM) Climate Dataset, we estimated daily residential temperature and relative humidity (RH) exposures during pregnancy. Our primary multinomial regression adjusted for person-level covariates and season. Secondary analyses included distributed lag models (DLMs) and adjusted for ambient air pollutants including fine particulates (PM 2.5 ). We used Generalized Additive Mixed Models (GAMMs) for systolic blood pressure (SBP) trajectories across hypertensive disorder statuses to confirm exposure timing., Results: While preeclampsia is typically diagnosed late in pregnancy, GAMM-fitted SBP trajectories for preeclamptic and non-preeclamptic women began to diverge at around 20 weeks' gestation, confirming the need to only consider early exposures. In the primary analysis with 1776 women, RH in the early second trimester, weeks 14-20, was associated with significantly higher odds of preeclampsia (OR per IQR increase: 1.81, 95% CI: 1.10, 2.97). The DLM corroborated this window, finding a positive association from weeks 12-20. There were no other significant associations between RH or temperature and preeclampsia or gestational hypertension in any other time period., Discussion: The association between preeclampsia and RH in the early second trimester was robust to model choice, suggesting that RH may contribute to seasonal trends in preeclampsia incidence. Differences between these results and those of prior studies could be attributable to exposure timing differences., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The environmental justice literature demonstrates consistently that low-income and minority communities are disproportionately exposed to environmental hazards. In this case study, we examined cumulative multipollutant, multidomain, and multimatrix environmental exposures in Milwaukee County, Wisconsin for the year 2015. We identified spatial hot spots in Milwaukee County both individually (using local Moran's I) and through clusters (using K-means clustering) across a profile of environmental pollutants that span regulatory domains and matrices of exposure, as well as socioeconomic indicators. The cluster with the highest exposures within the urban area was largely characterized by low socioeconomic status and an overrepresentation of the Non-Hispanic Black population relative to the county as a whole. In this cluster, average pollutant concentrations were equivalent to the 78th percentile in county-level blood lead levels, 67th percentile in county-level NO 2 , 79th percentile in county-level CO, and 78th percentile in county-level air toxics. Simultaneously, this cluster had an average equivalent to the 62nd percentile in county-level unemployment, 70th percentile in county-level population rate lacking a high school diploma, 73rd percentile in county-level poverty rate, and 28th percentile in county-level median household income. The spatial patterns of pollutant exposure and SES indicators suggested that these disparities were not random but were instead structured by socioeconomic and racial factors. Our case study, which combines environmental pollutant exposures, sociodemographic data, and clustering analysis, provides a roadmap to identify and target overburdened communities for interventions that reduce environmental exposures and consequently improve public health., Competing Interests: The authors declare no conflicts of interest relevant to this study., (© 2024 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union.)

Background: Rapidly developing tests for emerging diseases is critical for early disease monitoring. In the early stages of an epidemic, when low prevalences are expected, high specificity tests are desired to avoid numerous false positives. Selecting a cutoff to classify positive and negative test results that has the desired operating characteristics, such as specificity, is challenging for new tests because of limited validation data with known disease status. While there is ample statistical literature on estimating quantiles of a distribution, there is limited evidence on estimating extreme quantiles from limited validation data and the resulting test characteristics in the disease testing context., Methods: We propose using extreme value theory to select a cutoff with predetermined specificity by fitting a Pareto distribution to the upper tail of the negative controls. We compared this method to five previously proposed cutoff selection methods in a data analysis and simulation study. We analyzed COVID-19 enzyme linked immunosorbent assay antibody test results from long-term care facilities and skilled nursing staff in Colorado between May and December of 2020., Results: We found the extreme value approach had minimal bias when targeting a specificity of 0.995. Using the empirical quantile of the negative controls performed well when targeting a specificity of 0.95. The higher target specificity is preferred for overall test accuracy when prevalence is low, whereas the lower target specificity is preferred when prevalence is higher and resulted in less variable prevalence estimation., Discussion: While commonly used, the normal based methods showed considerable bias compared to the empirical and extreme value theory-based methods., Conclusions: When determining disease testing cutoffs from small training data samples, we recommend using the extreme value based-methods when targeting a high specificity and the empirical quantile when targeting a lower specificity., (© 2024. The Author(s).)

Introduction: Neurotoxicity resulting from air pollution is of increasing concern. Considering exposure timing effects on neurodevelopmental impairments may be as important as the exposure dose. We used distributed lag regression to determine the sensitive windows of prenatal exposure to fine particulate matter (PM 2.5 ) on children's cognition in a birth cohort in Mexico., Methods: Analysis included 553 full-term (≥37 weeks gestation) children. Prenatal daily PM 2.5 exposure was estimated using a validated satellite-based spatiotemporal model. McCarthy Scales of Children's Abilities (MSCA) were used to assess children's cognitive function at 4-5 years old (lower scores indicate poorer performance). To identify susceptibility windows, we used Bayesian distributed lag interaction models to examine associations between prenatal PM 2.5 levels and MSCA. This allowed us to estimate vulnerable windows while testing for effect modification., Results: After adjusting for maternal age, socioeconomic status, child age, and sex, Bayesian distributed lag interaction models showed significant associations between increased PM 2.5 levels and decreased general cognitive index scores at 31-35 gestation weeks, decreased quantitative scale scores at 30-36 weeks, decreased motor scale scores at 30-36 weeks, and decreased verbal scale scores at 37-38 weeks. Estimated cumulative effects (CE) of PM 2.5 across pregnancy showed significant associations with general cognitive index ( C E ^ = -0.35, 95% confidence interval [CI] = -0.68, -0.01), quantitative scale ( C E ^ = -0.27, 95% CI = -0.74, -0.02), motor scale ( C E ^ = -0.25, 95% CI = -0.44, -0.05), and verbal scale ( C E ^ = -0.2, 95% CI = -0.43, -0.02). No significant sex interactions were observed., Conclusions: Prenatal exposure to PM 2.5 , particularly late pregnancy, was inversely associated with subscales of MSCA. Using data-driven methods to identify sensitive window may provide insight into the mechanisms of neurodevelopmental impairment due to pollution., Competing Interests: The authors declare that they have no conflicts of interest with regard to the content of this report., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The Environmental Epidemiology. All rights reserved.)

Children's health studies support an association between maternal environmental exposures and children's birth outcomes. A common goal is to identify critical windows of susceptibility-periods during gestation with increased association between maternal exposures and a future outcome. The timing of the critical windows and magnitude of the associations are likely heterogeneous across different levels of individual, family, and neighborhood characteristics. Using an administrative Colorado birth cohort we estimate the individualized relationship between weekly exposures to fine particulate matter (PM 2.5 ) during gestation and birth weight. To achieve this goal, we propose a statistical learning method combining distributed lag models and Bayesian additive regression trees to estimate critical windows at the individual level and identify characteristics that induce heterogeneity from a high-dimensional set of potential modifying factors. We find evidence of heterogeneity in the PM 2.5 -birth weight relationship, with some mother-child dyads showing a 3 times larger decrease in birth weight for an IQR increase in exposure (5.9 to 8.5 PM 2.5 μg/m 3 ) compared to the population average. Specifically, we find increased vulnerabilitity for non-Hispanic mothers who are either younger, have higher body mass index or lower educational attainment. Our case study is the first precision health study of critical windows., Competing Interests: Conflict of interest No potential competing interest was reported by the authors.

Distributed lag models are useful in environmental epidemiology as they allow the user to investigate critical windows of exposure, defined as the time periods during which exposure to a pollutant adversely affects health outcomes. Recent studies have focused on estimating the health effects of a large number of environmental exposures, or an environmental mixture, on health outcomes. In such settings, it is important to understand which environmental exposures affect a particular outcome, while acknowledging the possibility that different exposures have different critical windows. Further, in studies of environmental mixtures, it is important to identify interactions among exposures and to account for the fact that this interaction may occur between two exposures having different critical windows. Exposure to one exposure early in time could cause an individual to be more or less susceptible to another exposure later in time. We propose a Bayesian model to estimate the temporal effects of a large number of exposures on an outcome. We use spike-and-slab priors and semiparametric distributed lag curves to identify important exposures and exposure interactions and discuss extensions with improved power to detect harmful exposures. We then apply these methods to estimate the effects of exposure to multiple air pollutants during pregnancy on birthweight from vital records in Colorado., (© The Author 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Gestational exposure to ambient air pollution has been associated with adverse health outcomes for mothers and newborns. The placenta is a central regulator of the in utero environment that orchestrates development and postnatal life via fetal programming. Ambient air pollution contaminants can reach the placenta and have been shown to alter bulk placental tissue DNA methylation patterns. Yet the effect of air pollution on placental cell-type composition has not been examined. We aimed to investigate whether the exposure to ambient air pollution during gestation is associated with placental cell types inferred from DNA methylation profiles., Methods: We leveraged data from 226 mother-infant pairs in the Programming of Intergenerational Stress Mechanisms (PRISM) longitudinal cohort in the Northeastern US. Daily concentrations of fine particulate matter (PM 2.5 ) at 1 km spatial resolution were estimated from a spatiotemporal model developed with satellite data and linked to womens' addresses during pregnancy and infants' date of birth. The proportions of six cell types [syncytiotrophoblasts, trophoblasts, stromal, endothelial, Hofbauer and nucleated red blood cells (nRBCs)] were derived from placental tissue 450K DNA methylation array. We applied compositional regression to examine overall changes in placenta cell-type composition related to PM 2.5 average by pregnancy trimester. We also investigated the association between PM 2.5 and individual cell types using beta regression. All analyses were performed in the overall sample and stratified by infant sex adjusted for covariates., Results: In male infants, first trimester (T1) PM 2.5 was associated with changes in placental cell composition (p = 0.03), driven by a decrease [per one PM 2.5 interquartile range (IQR)] of 0.037 in the syncytiotrophoblasts proportion (95% confidence interval (CI) [- 0.066, - 0.012]), accompanied by an increase in trophoblasts of 0.033 (95% CI: [0.009, 0.064]). In females, second and third trimester PM 2.5 were associated with overall changes in placental cell-type composition (T2: p = 0.040; T3: p = 0.049), with a decrease in the nRBC proportion. Individual cell-type analysis with beta regression showed similar results with an additional association found for third trimester PM 2.5 and stromal cells in females (decrease of 0.054, p = 0.024)., Conclusion: Gestational exposure to air pollution was associated with placenta cell composition. Further research is needed to corroborate these findings and evaluate their role in PM 2.5 -related impact in the placenta and consequent fetal programming., (© 2023. The Author(s).)

Background: Studies of prenatal air pollution (AP) exposure on child neurodevelopment have mostly focused on a single pollutant. We leveraged daily exposure data and implemented novel data-driven statistical approaches to assess effects of prenatal exposure to a mixture of seven air pollutants on cognitive functioning in school-age children from an urban pregnancy cohort., Methods: Analyses included 236 children born at ≥37 weeks gestation. Maternal prenatal daily exposure levels for nitrogen dioxide (NO 2 ), ozone (O 3 ), and constituents of fine particles [elemental carbon (EC), organic carbon (OC), nitrate (NO 3 - ), sulfate (SO 4 2- ), ammonium (NH 4 + )] were estimated based on residential addresses using validated satellite-based hybrid models or global 3-D chemical-transport models. Children completed Wide Range Assessment of Memory and Learning (WRAML-2) and Conners' Continuous Performance Test (CPT-II) at 6.5 ± 0.9 years of age. Time-weighted levels for mixture pollutants were estimated using Bayesian Kernel Machine Regression Distributed Lag Models (BKMR-DLMs), with which we also explored the interactions in the exposure-response functions among pollutants. Resulting time-weighted exposure levels were used in Weighted Quantile Sum (WQS) regressions to examine AP mixture effects on outcomes, adjusted for maternal age, education, child sex, and prenatal temperature., Results: Mothers were primarily ethnic minorities (81% Hispanic and/or black) reporting ≤12 years of education (68%). Prenatal AP mixture (per unit increase in WQS estimated AP index) was associated with decreased WRAML-2 general memory (GM; β = -0.64, 95%CI = -1.40, 0.00) and memory-related attention/concentration (AC; β = -1.03, 95%CI = -1.78, -0.27) indices, indicating poorer memory functioning, as well as increased CPT-II omission errors (OE; β = 1.55, 95%CI = 0.34, 2.77), indicating increased attention problems. When stratified by sex, association with AC index was significant among girls, while association with OE was significant among boys. Traffic-related pollutants (NO 2 , OC, EC) and SO 4 2- were major contributors to these associations. There was no significant evidence of interactions among mixture components., Conclusions: Prenatal exposure to an AP mixture was associated with child neurocognitive outcomes in a sex- and domain-specific manner., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Exposure to air pollution is associated with increased morbidity and mortality. Recent technological advancements permit the collection of time-resolved personal exposure data. Such data are often incomplete with missing observations and exposures below the limit of detection, which limit their use in health effects studies. In this paper, we develop an infinite hidden Markov model for multiple asynchronous multivariate time series with missing data. Our model is designed to include covariates that can inform transitions among hidden states. We implement beam sampling, a combination of slice sampling and dynamic programming, to sample the hidden states, and a Bayesian multiple imputation algorithm to impute missing data. In simulation studies, our model excels in estimating hidden states and state-specific means and imputing observations that are missing at random or below the limit of detection. We validate our imputation approach on data from the Fort Collins Commuter Study. We show that the estimated hidden states improve imputations for data that are missing at random compared to existing approaches. In a case study of the Fort Collins Commuter Study, we describe the inferential gains obtained from our model including improved imputation of missing data and the ability to identify shared patterns in activity and exposure among repeated sampling days for individuals and among distinct individuals., (© 2022 The Authors. Biometrics published by Wiley Periodicals LLC on behalf of International Biometric Society.)

A key goal of environmental health research is to assess the risk posed by mixtures of pollutants. As epidemiologic studies of mixtures can be expensive to conduct, it behooves researchers to incorporate prior knowledge about mixtures into their analyses. This work extends the Bayesian multiple index model (BMIM), which assumes the exposure-response function is a nonparametric function of a set of linear combinations of pollutants formed with a set of exposure-specific weights. The framework is attractive because it combines the flexibility of response-surface methods with the interpretability of linear index models. We propose three strategies to incorporate prior toxicological knowledge into construction of indices in a BMIM: (a) imposing directional homogeneity constraints on the weights, (b) structuring index weights by exposure transformations, and (c) placing informative priors on the index weights. We propose a novel prior specification that combines spike-and-slab variable selection with an informative Dirichlet distribution based on relative potency factors often derived from previous toxicological studies. In simulations we show that the proposed priors improve inferences when prior information is correct and can protect against misspecification suffered by naïve toxicological models when prior information is incorrect. Moreover, different strategies may be mixed-and-matched for different indices to suit available information (or lack thereof). We demonstrate the proposed methods on an analysis of data from the National Health and Nutrition Examination Survey and incorporate prior information on relative chemical potencies obtained from toxic equivalency factors available in the literature., (© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Background: In 2020, the American West faced two competing challenges: the COVID-19 pandemic and the worst wildfire season on record. Several studies have investigated the impact of wildfire smoke (WFS) on COVID-19 morbidity and mortality, but little is known about how these two public health challenges impact mortality risk for other causes., Objectives: Using a time-series design, we evaluated how daily risk of mortality due to WFS exposure differed for periods before and during the COVID-19 pandemic., Methods: Our study included daily data for 11 counties in the Front Range region of Colorado (2010-2020). We assessed WFS exposure using data from the National Oceanic and Atmospheric Administration and used mortality counts from the Colorado Department of Public Health and Environment. We estimated the interaction between WFS and the pandemic (an indicator variable) on mortality risk using generalized additive models adjusted for year, day of week, fine particulate matter, ozone, temperature, and a smoothed term for day of year., Results: WFS impacted the study area on 10% of county-days. We observed a positive association between the presence of WFS and all-cause mortality risk (incidence rate ratio (IRR) = 1.03, 95%CI: 1.01-1.04 for same-day exposures) during the period before the pandemic; however, WFS exposure during the pandemic resulted in decreased risk of all-cause mortality (IRR = 0.90, 95%CI: 0.87-0.93 for same-day exposures)., Discussion: We hypothesize that mitigation efforts during the first year of the pandemic, e.g., mask mandates, along with high ambient WFS levels encouraged health behaviors that reduced exposure to WFS and reduced risk of all-cause mortality. Our results suggest a need to examine how associations between WFS and mortality are impacted by pandemic-related factors and that there may be lessons from the pandemic that could be translated into health-protective policies during future wildfire events., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Sheryl Magzamen reports financial support was provided by A.J. Kauvar Foundation. Ander Wilson reports financial support was provided by A.J. Kauvar Foundation., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Distributed lag models (DLMs) are often used to estimate lagged associations and identify critical exposure windows. In a simulation study of prenatal nitrogen dioxide (NO2) exposure and birth weight, we demonstrate that bias amplification and variance inflation can manifest under certain combinations of DLM estimation approaches and time-trend adjustment methods when using low-spatial-resolution exposures with extended lags. Our simulations showed that when using high-spatial-resolution exposure data, any time-trend adjustment method produced low bias and nominal coverage for the distributed lag estimator. When using either low- or no-spatial-resolution exposures, bias due to time trends was amplified for all adjustment methods. Variance inflation was higher in low- or no-spatial-resolution DLMs when using a long-term spline to adjust for seasonality and long-term trends due to concurvity between a distributed lag function and secular function of time. NO2-birth weight analyses in a Massachusetts-based cohort showed that associations were negative for exposures experienced in gestational weeks 15-30 when using high-spatial-resolution DLMs; however, associations were null and positive for DLMs with low- and no-spatial-resolution exposures, respectively, which is likely due to bias amplification. DLM analyses should jointly consider the spatial resolution of exposure data and the parameterizations of the time trend adjustment and lag constraints., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Research linking prenatal ambient air pollution with childhood lung function has largely considered one pollutant at a time. Real-life exposure is to mixtures of pollutants and their chemical components; not considering joint effects/effect modification by co-exposures contributes to misleading results., Methods: Analyses included 198 mother-child dyads recruited from two hospitals and affiliated community health centers in Boston, Massachusetts, USA. Daily prenatal pollutant exposures were estimated using satellite-based hybrid chemical-transport models, including nitrogen dioxide(NO 2 ), ozone(O 3 ), and fine particle constituents (elemental carbon [EC], organic carbon [OC], nitrate [NO 3 - ], sulfate [SO 4 2- ], and ammonium [NH 4 + ]). Spirometry was performed at age 6.99 ± 0.89 years; forced expiratory volume in 1s (FEV 1 ), forced vital capacity (FVC), and forced mid-expiratory flow (FEF 25-75 ) z-scores accounted for age, sex, height, and race/ethnicity. We examined associations between weekly-averaged prenatal pollution mixture levels and outcomes using Bayesian Kernel Machine Regression-Distributed Lag Models (BKMR-DLMs) to identify susceptibility windows for each component and estimate a potentially complex mixture exposure-response relationship including nonlinear effects and interactions among exposures. We also performed linear regression models using time-weighted-mixture component levels derived by BKMR-DLMs adjusting for maternal age, education, perinatal smoking, and temperature., Results: Most mothers were Hispanic (63%) or Black (21%) with ≤12 years of education (67%). BKMR-DLMs identified a significant effect for O 3 exposure at 18-22 weeks gestation predicting lower FEV 1 /FVC. Linear regression identified significant associations for O 3, NH 4 + , and OC with decreased FEV 1 /FVC, FEV 1 , and FEF 25-75 , respectively. There was no evidence of interactions among pollutants., Conclusions: In this multi-pollutant model, prenatal O 3 , OC, and NH 4 + were most strongly associated with reduced early childhood lung function., Competing Interests: The authors declare that they have no conflicts of interest with regard to the content of this report., (Copyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The Environmental Epidemiology. All rights reserved.)

An important goal of environmental health research is to assess the risk posed by mixtures of environmental exposures. Two popular classes of models for mixtures analyses are response-surface methods and exposure-index methods. Response-surface methods estimate high-dimensional surfaces and are thus highly flexible but difficult to interpret. In contrast, exposure-index methods decompose coefficients from a linear model into an overall mixture effect and individual index weights; these models yield easily interpretable effect estimates and efficient inferences when model assumptions hold, but, like most parsimonious models, incur bias when these assumptions do not hold. In this paper, we propose a Bayesian multiple index model framework that combines the strengths of each, allowing for non-linear and non-additive relationships between exposure indices and a health outcome, while reducing the dimensionality of the exposure vector and estimating index weights with variable selection. This framework contains response-surface and exposure-index models as special cases, thereby unifying the two analysis strategies. This unification increases the range of models possible for analysing environmental mixtures and health, allowing one to select an appropriate analysis from a spectrum of models varying in flexibility and interpretability. In an analysis of the association between telomere length and 18 organic pollutants in the National Health and Nutrition Examination Survey (NHANES), the proposed approach fits the data as well as more complex response-surface methods and yields more interpretable results., (© 2021 The International Biometric Society.)

Maternal exposure to environmental chemicals during pregnancy can alter birth and children's health outcomes. Research seeks to identify critical windows, time periods when exposures can change future health outcomes, and estimate the exposure-response relationship. Existing statistical approaches focus on estimation of the association between maternal exposure to a single environmental chemical observed at high temporal resolution (e.g., weekly throughout pregnancy) and children's health outcomes. Extending to multiple chemicals observed at high temporal resolution poses a dimensionality problem and statistical methods are lacking. We propose a regression tree-based model for mixtures of exposures observed at high temporal resolution. The proposed approach uses an additive ensemble of tree pairs that defines structured main effects and interactions between time-resolved predictors and performs variable selection to select out of the model predictors not correlated with the outcome. In simulation, we show that the tree-based approach performs better than existing methods for a single exposure and can accurately estimate critical windows in the exposure-response relation for mixtures. We apply our method to estimate the relationship between five exposures measured weekly throughout pregnancy and birth weight in a Denver, Colorado, birth cohort. We identified critical windows during which fine particulate matter, sulfur dioxide, and temperature are negatively associated with birth weight and an interaction between fine particulate matter and temperature. Software is made available in the R package dlmtree., (© 2021 The International Biometric Society.)

Background: Both environmental and social factors have been linked to birth weight and adiposity at birth, but few studies consider the effects of exposure mixtures. Our objective was to identify which components of a mixture of neighborhood-level environmental and social exposures were driving associations with birth weight and adiposity at birth in the Healthy Start cohort., Methods: Exposures were assessed at the census tract level and included air pollution, built environment characteristics, and socioeconomic status. Prenatal exposures were assigned based on address at enrollment. Birth weight was measured at delivery and adiposity was measured using air displacement plethysmography within three days. We used non-parametric Bayes shrinkage (NPB) to identify exposures that were associated with our outcomes of interest. NPB models were compared to single-predictor linear regression. We also included generalized additive models (GAM) to assess nonlinear relationships. All regression models were adjusted for individual-level covariates, including maternal age, pre-pregnancy BMI, and smoking., Results: Results from NPB models showed most exposures were negatively associated with birth weight, though credible intervals were wide and generally contained zero. However, the NPB model identified an interaction between ozone and temperature on birth weight, and the GAM suggested potential non-linear relationships. For associations between ozone or temperature with birth weight, we observed effect modification by maternal race/ethnicity, where effects were stronger for mothers who identified as a race or ethnicity other than non-Hispanic White. No associations with adiposity at birth were observed., Conclusions: NPB identified prenatal exposures to ozone and temperature as predictors of birth weight, and mothers who identify as a race or ethnicity other than non-Hispanic White might be disproportionately impacted. However, NPB models may have limited applicability when non-linear effects are present. Future work should consider a two-stage approach where NPB is used to reduce dimensionality and alternative approaches examine non-linear effects., (© 2022. The Author(s).)

Recent ecological analyses suggest air pollution exposure may increase susceptibility to and severity of coronavirus disease 2019 (COVID-19). Individual-level studies are needed to clarify the relationship between air pollution exposure and COVID-19 outcomes. We conduct an individual-level analysis of long-term exposure to air pollution and weather on peak COVID-19 severity. We develop a Bayesian multinomial logistic regression model with a multiple imputation approach to impute partially missing health outcomes. Our approach is based on the stick-breaking representation of the multinomial distribution, which offers computational advantages, but presents challenges in interpreting regression coefficients. We propose a novel inferential approach to address these challenges. In a simulation study, we demonstrate our method's ability to impute missing outcome data and improve estimation of regression coefficients compared to a complete case analysis. In our analysis of 55,273 COVID-19 cases in Denver, Colorado, increased annual exposure to fine particulate matter in the year prior to the pandemic was associated with increased risk of severe COVID-19 outcomes. We also found COVID-19 disease severity to be associated with interactions between exposures. Our individual-level analysis fills a gap in the literature and helps to elucidate the association between long-term exposure to air pollution and COVID-19 outcomes., (© 2022 The Authors. Environmetrics published by John Wiley & Sons Ltd.)

In studies of maternal exposure to air pollution, a children's health outcome is regressed on exposures observed during pregnancy. The distributed lag nonlinear model (DLNM) is a statistical method commonly implemented to estimate an exposure-time-response function when it is postulated the exposure effect is nonlinear. Previous implementations of the DLNM estimate an exposure-time-response surface parameterized with a bivariate basis expansion. However, basis functions such as splines assume smoothness across the entire exposure-time-response surface, which may be unrealistic in settings where the exposure is associated with the outcome only in a specific time window. We propose a framework for estimating the DLNM based on Bayesian additive regression trees. Our method operates using a set of regression trees that each assume piecewise constant relationships across the exposure-time space. In a simulation, we show that our model outperforms spline-based models when the exposure-time surface is not smooth, while both methods perform similarly in settings where the true surface is smooth. Importantly, the proposed approach is lower variance and more precisely identifies critical windows during which exposure is associated with a future health outcome. We apply our method to estimate the association between maternal exposures to PM$_{2.5}$ and birth weight in a Colorado, USA birth cohort., (© The Author 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Exposures to environmental chemicals during gestation can alter health status later in life. Most studies of maternal exposure to chemicals during pregnancy have focused on a single chemical exposure observed at high temporal resolution. Recent research has turned to focus on exposure to mixtures of multiple chemicals, generally observed at a single time point. We consider statistical methods for analyzing data on chemical mixtures that are observed at a high temporal resolution. As motivation, we analyze the association between exposure to four ambient air pollutants observed weekly throughout gestation and birth weight in a Boston-area prospective birth cohort. To explore patterns in the data, we first apply methods for analyzing data on (1) a single chemical observed at high temporal resolution, and (2) a mixture measured at a single point in time. We highlight the shortcomings of these approaches for temporally-resolved data on exposure to chemical mixtures. Second, we propose a novel method, a Bayesian kernel machine regression distributed lag model (BKMR-DLM), that simultaneously accounts for nonlinear associations and interactions among time-varying measures of exposure to mixtures. BKMR-DLM uses a functional weight for each exposure that parameterizes the window of susceptibility corresponding to that exposure within a kernel machine framework that captures non-linear and interaction effects of the multivariate exposure on the outcome. In a simulation study, we show that the proposed method can better estimate the exposure-response function and, in high signal settings, can identify critical windows in time during which exposure has an increased association with the outcome. Applying the proposed method to the Boston birth cohort data, we find evidence of a negative association between organic carbon and birth weight and that nitrate modifies the organic carbon, elemental carbon, and sulfate exposure-response functions.

Background: While evidence suggests that daily ambient temperature exposure influences stroke risk, little is known about the potential triggering role of ultra short-term temperature., Methods: We examined the association between hourly temperature and ischemic and hemorrhagic stroke, separately, and identified any relevant lags of exposure among adult New York State residents from 2000 to 2015. Cases were identified via ICD-9 codes from the New York Department of Health Statewide Planning and Reearch Cooperative System. We estimated ambient temperature up to 36 h prior to estimated stroke onset based on patient residential ZIP Code. We applied a time-stratified case-crossover study design; control periods were matched to case periods by year, month, day of week, and hour of day. Additionally, we assessed effect modification by leading stroke risk factors hypertension and atrial fibrillation., Results: We observed 578,181 ischemic and 164,755 hemorrhagic strokes. Among ischemic and hemorrhagic strokes respectively, the mean (standard deviation; SD) patient age was 71.8 (14.6) and 66.8 (17.4) years, with 55% and 49% female. Temperature ranged from -29.5 °C to 39.2 °C, with mean (SD) 10.9 °C (10.3 °C). We found linear relationships for both stroke types. Higher temperature was associated with ischemic stroke over the 7 h following exposure; a 10 °C increase over 7 h was associated with 5.1% (95% Confidence Interval [CI]: 3.8, 6.4%) increase in hourly stroke rate. In contrast, temperature was negatively associated with hemorrhagic stroke over 5 h, with a 5-h cumulative association of -6.2% (95% CI: 8.6, -3.7%). We observed suggestive evidence of a larger association with hemorrhagic stroke among patients with hypertension and a smaller association with ischemic stroke among those with atrial fibrillation., Conclusion: Hourly temperature was positively associated with ischemic stroke and negatively associated with hemorrhagic stroke. Our results suggest that ultra short-term weather influences stroke risk and hypertension may confer vulnerability., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Prenatal exposure to fine particulate matter with a diameter of ≤2.5 μm (PM 2.5 ) has been linked to adverse neurodevelopmental outcomes in later childhood, while research on early infant behavior remains sparse., Objectives: We examined associations between prenatal PM 2.5 exposure and infant negative affectivity, a stable temperamental trait associated with longer-term behavioral and mental health outcomes. We also examined sex-specific effects., Methods: Analyses included 559 mother-infant pairs enrolled in the PRogramming of Intergenerational Stress Mechanisms (PRISM) cohort. Daily PM 2.5 exposure based on geocoded residential address during pregnancy was estimated using a satellite-based spatiotemporal model. Domains of negative affectivity (Sadness, Distress to Limitations, Fear, Falling Reactivity) were assessed using the Infant Behavior Questionnaire-Revised (IBQ-R) when infants were 6 months old. Subscale scores were calculated as the mean of item-specific responses; the global Negative Affectivity (NA) score was derived by averaging the mean of the four subscale scores. Bayesian distributed lag interaction models (BDLIMs) were used to identify sensitive windows for prenatal PM 2.5 exposure on global NA and its subscales, and to examine effect modification by sex., Results: Mothers were primarily racial/ethnic minorities (38% Black, 37% Hispanic), 40% had ≤12 years of education; most did not smoke during pregnancy (87%). In the overall sample, BDLIMs revealed that increased PM 2.5 at mid-pregnancy was associated with higher global NA, Sadness, and Fear scores, after adjusting for covariates (maternal age, education, race/ethnicity, sex). Among boys, increased PM 2.5 at early pregnancy was associated with decreased Fear scores, while exposure during late pregnancy was associated with increased Fear scores (cumulative effect estimate = 0.57, 95% CI: 0.03-1.41). Among girls, increased PM 2.5 during mid-pregnancy was associated with higher Fear scores (cumulative effect estimate = 0.82, 95% CI: 0.05-1.91)., Conclusions: Prenatal PM 2.5 exposure was associated with negative affectivity at age 6 months, and the sensitive windows may vary by subdomains and infant sex., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Household air pollution from solid fuel combustion was estimated to cause 2.31 million deaths worldwide in 2019; cardiovascular disease is a substantial contributor to the global burden. We evaluated the cross-sectional association between household air pollution (24-h gravimetric kitchen and personal particulate matter (PM 2.5 ) and black carbon (BC)) and C-reactive protein (CRP) measured in dried blood spots among 107 women in rural Honduras using wood-burning traditional or Justa (an engineered combustion chamber) stoves. A suite of 6 additional markers of systemic injury and inflammation were considered in secondary analyses. We adjusted for potential confounders and assessed effect modification of several cardiovascular-disease risk factors. The median (25th, 75th percentiles) 24-h-average personal PM 2.5 concentration was 115 μg/m 3 (65,154 μg/m 3 ) for traditional stove users and 52 μg/m 3 (39, 81 μg/m 3 ) for Justa stove users; kitchen PM 2.5 and BC had similar patterns. Higher concentrations of PM 2.5 and BC were associated with higher levels of CRP (e.g., a 25% increase in personal PM 2.5 was associated with a 10.5% increase in CRP [95% CI: 1.2-20.6]). In secondary analyses, results were generally consistent with a null association. Evidence for effect modification between pollutant measures and four different cardiovascular risk factors (e.g., high blood pressure) was inconsistent. These results support the growing evidence linking household air pollution and cardiovascular disease., (Copyright © 2022 Elsevier GmbH. All rights reserved.)

Household air pollution is a leading risk factor for morbidity and premature mortality. Numerous cookstoves have been developed to reduce household air pollution, but it is unclear whether such cookstoves meaningfully improve health. In a controlled exposure study with a crossover design, we assessed the effect of pollution emitted from multiple cookstoves on acute differences in blood lipids and inflammatory biomarkers. Participants (n = 48) were assigned to treatment sequences of exposure to air pollution emitted from five cookstoves and a filtered-air control. Blood lipids and inflammatory biomarkers were measured before and 0, 3, and 24 hours after treatments. Many of the measured outcomes had inconsistent results. However, compared to control, intercellular adhesion molecule-1 was higher 3 hours after all treatments, and C-reactive protein and serum amyloid-A were higher 24 hours after the highest treatment. Our results suggest that short-term exposure to cookstove air pollution can increase inflammatory biomarkers within 24 hours.

Fine particulate matter (PM 2.5 ) potentiates in utero oxidative stress influencing fetal development while antioxidants have potential protective effects. We examined associations among prenatal PM 2.5 , maternal antioxidant intake, and childhood wheeze in an urban pregnancy cohort ( n = 530). Daily PM 2.5 exposure over gestation was estimated using a satellite-based spatiotemporally resolved model. Mothers completed the modified Block98 food frequency questionnaire. Average energy-adjusted percentile intake of β-carotene, vitamins (A, C, E), and trace minerals (zinc, magnesium, selenium) constituted an antioxidant index (AI). Maternal-reported child wheeze was ascertained up to 4.1 ± 2.8 years. Bayesian distributed lag interaction models (BDLIMs) were used to examine time-varying associations between prenatal PM 2.5 and repeated wheeze (≥2 episodes) and effect modification by AI, race/ethnicity, and child sex. Covariates included maternal age, education, asthma, and temperature. Women were 39% Black and 33% Hispanic, 36% with ≤high school education; 21% of children had repeated wheeze. Higher AI was associated with decreased wheeze in Blacks (OR = 0.37 (0.19-0.73), per IQR increase). BDLIMs identified a sensitive window for PM 2.5 effects on wheeze among boys born to Black mothers with low AI (at 33-40 weeks gestation; OR = 1.74 (1.19-2.54), per µg/m 3 increase in PM 2.5 ). Relationships among prenatal PM 2.5 , antioxidant intake, and child wheeze were modified by race/ethnicity and sex.

Americans spend most of their time indoors at home, but comprehensive characterization of in-home air pollution is limited by the cost and size of reference-quality monitors. We assembled small "Home Health Boxes" (HHBs) to measure indoor PM 2.5 , PM 10 , CO 2 , CO, NO 2 , and O 3 concentrations using filter samplers and low-cost sensors. Nine HHBs were collocated with reference monitors in the kitchen of an occupied home in Fort Collins, Colorado, USA for 168 h while wildfire smoke impacted local air quality. When HHB data were interpreted using gas sensor manufacturers' calibrations, HHBs and reference monitors (a) categorized the level of each gaseous pollutant similarly (as either low, elevated, or high relative to air quality standards) and (b) both indicated that gas cooking burners were the dominant source of CO and NO 2 pollution; however, HHB and reference O 3 data were not correlated. When HHB gas sensor data were interpreted using linear mixed calibration models derived via collocation with reference monitors, root-mean-square error decreased for CO 2 (from 408 to 58 ppm), CO (645 to 572 ppb), NO 2 (22 to 14 ppb), and O 3 (21 to 7 ppb); additionally, correlation between HHB and reference O 3 data improved (Pearson's r increased from 0.02 to 0.75). Mean 168-h PM 2.5 and PM 10 concentrations derived from nine filter samples were 19.4 μg m -3 (6.1% relative standard deviation [RSD]) and 40.1 μg m -3 (7.6% RSD). The 168-h PM 2.5 concentration was overestimated by PMS5003 sensors (median sensor/filter ratio = 1.7) and underestimated slightly by SPS30 sensors (median sensor/filter ratio = 0.91)., Competing Interests: CONFLICT OF INTEREST John Volckens is a scientific founder of Access Sensor Technologies, LLC and has an equity interest in the company. The terms of this arrangement have been reviewed and approved by Colorado State University in accordance with its conflict-of-interest policies. Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Background: Although injuries experienced during hurricanes and other tropical cyclones have been relatively well-characterized through traditional surveillance, less is known about tropical cyclones' impacts on noninjury morbidity, which can be triggered through pathways that include psychosocial stress or interruption in medical treatment., Methods: We investigated daily emergency Medicare hospitalizations (1999-2010) in 180 US counties, drawing on an existing cohort of high-population counties. We classified counties as exposed to tropical cyclones when storm-associated peak sustained winds were ≥21 m/s at the county center; secondary analyses considered other wind thresholds and hazards. We matched storm-exposed days to unexposed days by county and seasonality. We estimated change in tropical cyclone-associated hospitalizations over a storm period from 2 days before to 7 days after the storm's closest approach, compared to unexposed days, using generalized linear mixed-effect models., Results: For 1999-2010, 175 study counties had at least one tropical cyclone exposure. Cardiovascular hospitalizations decreased on the storm day, then increased following the storm, while respiratory hospitalizations were elevated throughout the storm period. Over the 10-day storm period, cardiovascular hospitalizations increased 3% (95% confidence interval = 2%, 5%) and respiratory hospitalizations increased 16% (95% confidence interval = 13%, 20%) compared to matched unexposed periods. Relative risks varied across tropical cyclone exposures, with strongest association for the most restrictive wind-based exposure metric., Conclusions: In this study, tropical cyclone exposures were associated with a short-term increase in cardiorespiratory hospitalization risk among the elderly, based on a multi-year/multi-site investigation of US Medicare beneficiaries ≥65 years., Competing Interests: The authors report no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Challenges arise in researching health effects associated with chemical mixtures. Several methods have recently been proposed for estimating the association between health outcomes and exposure to chemical mixtures, but a formal simulation study comparing broad-ranging methods is lacking. We select five recently developed methods and evaluate their performance in estimating the exposure-response function, identifying active mixture components, and identifying interactions in a simulation study. Bayesian kernel machine regression (BKMR) and nonparametric Bayes shrinkage (NPB) were top-performing methods in our simulation study. BKMR and NPB outperformed other contemporary methods and traditional linear models in estimating the exposure-response function and identifying active mixture components. BKMR and NPB produced similar results in a data analysis of the effects of multipollutant exposure on lung function in children with asthma., Competing Interests: The authors have declared that no competing interests exist.

We estimated cardiopulmonary morbidity and mortality associated with wildfire smoke (WFS) fine particulate matter (PM 2.5 ) in the Front Range of Colorado from 2010 to 2015. To estimate WFS PM 2.5 , we developed a daily kriged PM 2.5 surface at a 15  × 15 km resolution based on the Environmental Protection Agency Air Quality System monitors for the western United States; we subtracted out local seasonal-average PM 2.5 of nonsmoky days, identified using satellite-based smoke plume estimates, from the local daily estimated PM 2.5 if smoke was identified by National Oceanic and Atmospheric Administration's Hazard Mapping System. We implemented time-stratified case-crossover analyses to estimate the effect of a 10 µg/m 3 increase in WFS PM 2.5 with cardiopulmonary hospitalizations and deaths using single and distributed lag models for lags 0-5 and distinct annual impacts based on local and long-range smoke during 2012, and long-range transport of smoke in 2015. A 10 µg/m 3 increase in WFS was associated with all respiratory, asthma, and chronic obstructive pulmonary disease hospitalizations for lag day 3 and hospitalizations for ischemic heart disease at lag days 2 and 3. Cardiac arrest deaths were associated with WFS PM 2.5 at lag day 0. For 2012 local wildfires, asthma hospitalizations had an inverse association with WFS PM 2.5 (OR: 0.716, 95% CI: 0.517-0.993), but a positive association with WFS PM 2.5 during the 2015 long-range transport event (OR: 1.455, 95% CI: 1.093-1.939). Cardiovascular mortality was associated with the 2012 long-range transport event (OR: 1.478, 95% CI: 1.124-1.944)., Competing Interests: The authors declare no conflicts of interest relevant to this study., (© 2021. The Authors.)

Background: Exposure to household air pollution from solid fuel combustion for cooking and heating is an important risk factor for premature death and disability worldwide. Current evidence supports an association of ambient air pollution with cardiovascular disease but is limited for household air pollution and for cardiac function. Controlled exposure studies can complement evidence provided by field studies., Objectives: To investigate effects of short-term, controlled exposures to emissions from five cookstoves on measures of cardiac function., Methods: Forty-eight healthy adults (46% female; 20-36 years) participated in six, 2-h exposures ('treatments'), including emissions from five cookstoves and a filtered-air control. Target fine particulate matter (PM 2.5 ) exposure-concentrations per treatment were: control, 0 µg/m 3 ; liquefied petroleum gas, 10 µg/m 3 ; gasifier, 35 µg/m 3 ; fan rocket, 100 µg/m 3 ; rocket elbow, 250 µg/m 3 ; and three stone fire, 500 µg/m 3 . Participants were treated in a set (pre-randomized) sequence as groups of 4 to minimize order bias and time-varying confounders. Heart rate variability (HRV) and cardiac repolarization metrics were calculated as 5-min means immediately and at 3 h following treatment, for analysis in linear mixed-effects models comparing cookstove to control., Results: Short-term differences in SDNN (standard deviation of duration of all NN intervals) and VLF (very-low frequency power) existed for several cookstoves compared to control. While all cookstoves compared to control followed a similar trend for SDNN, the greatest effect was seen immediately following three stone fire (β = -0.13 ms {%}; 95% confidence interval = -0.22, -0.03%), which reversed in direction at 3 h (0.03%; -0.06, 0.13%). VLF results were similar in direction and timing to SDNN; however, other HRV or cardiac repolarization results were not similar to those for SDNN., Discussion: We observed some evidence of short-term, effects on HRV immediately following cookstove treatments compared to control. Our results suggest that cookstoves with lower PM 2.5 emissions are potentially capable of affecting cardiac function, similar to stoves emitting higher PM 2.5 emissions., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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 (O 3 ), nitrogen dioxide (NO 2 ), particulate matter with a median aerodynamic diameter < 2.5 μm (PM 2.5 ) and <10 μm (PM 10 )) 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 (FEV 1 ), 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 FEV 1 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 FEV 1 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., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: Evidence links gestational exposure to particulate matter with an aerodynamic diameter of less than 2.5 μm (PM 2.5 ) with changes in leukocyte telomere length in cord blood with some studies showing sex-specific effects. PM 2.5 exposure in utero increases oxidative stress, which can impact telomere biology. Thus, maternal antioxidant intakes may also modify the particulate air pollution effects., Methods: We examined associations among prenatal PM 2.5 exposure and newborn relative leukocyte telomere length (rLTL), and the modifying effects of maternal antioxidant intake and infant sex. We estimated daily PM 2.5 exposures over gestation using a validated spatiotemporally resolved satellite-based model. Maternal dietary and supplemental antioxidant intakes over the prior three months were ascertained during the second trimester using the modified Block98 food frequency questionnaire; high and low antioxidant intakes were categorized based on a median split. We employed Bayesian distributed lag interaction models (BDLIMs) to identify both sensitive windows of exposure and cumulative effect estimates for prenatal PM 2.5 exposure on newborn rLTL, and to examine effect modification by maternal antioxidant intakes. A 3-way interaction between PM 2.5, maternal antioxidant intake and infant sex was also explored., Results: For the main effect of PM 2.5, BDLIMs identified a sensitive window at 12-20 weeks gestation for the association between increased prenatal PM 2.5 exposure and shorter newborn rLTL and a cumulative effect of PM 2.5 over gestation on newborn telomere length [cumulative effect estimate (CEE) = -0.29 (95% CI -0.49 to -0.10) per 1μg/m 3 increase in PM 2.5 ]. In models examining maternal antioxidant intake effects, BDLIMs found that children born to mothers reporting low antioxidant intakes were most vulnerable [CEE of low maternal antioxidant intake = -0.31 (95% CI -0.55 to -0.06) vs high maternal antioxidant intake = -0.07 (95% CI -0.34 to 0.17) per 1μg/m 3 increase in PM 2.5 ]. In exploratory models examining effect modification by both maternal antioxidant intakes and infant sex, the cumulative effect remained significant only in boys whose mothers reported low antioxidant intakes [CEE = -0.38 (95% CI -0.80 to -0.004)]; no sensitive windows were identified in any group., Conclusions: Prenatal PM 2.5 exposure in mid-gestation was associated with reduced infant telomere length. Higher maternal antioxidant intakes mitigated these effects., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Wildfire smoke (WFS) increases the risk of respiratory hospitalizations. We evaluated the association between WFS and asthma healthcare utilization (AHCU) during the 2013 wildfire season in Oregon. WFS particulate matter ≤ 2.5 μm in diameter (PM 2.5 ) was estimated using a blended model of in situ monitoring, chemical transport models, and satellite-based data. Asthma claims and place of service were identified from Oregon All Payer All Claims data from 1 May 2013 to 30 September 2013. The association with WFS PM 2.5 was evaluated using time-stratified case-crossover designs. The maximum WFS PM 2.5 concentration during the study period was 172 µg/m 3 . A 10 µg/m 3 increase in WFS increased risk in asthma diagnosis at emergency departments (odds ratio [OR]: 1.089, 95% confidence interval [CI]: 1.043-1.136), office visit (OR: 1.050, 95% CI: 1.038-1.063), and outpatient visits (OR: 1.065, 95% CI: 1.029-1.103); an association was observed with asthma rescue inhaler medication fills (OR: 1.077, 95% CI: 1.065-1.088). WFS increased the risk for asthma morbidity during the 2013 wildfire season in Oregon. Communities impacted by WFS could see increases in AHCU for tertiary, secondary, and primary care.

In many applications there is interest in estimating the relation between a predictor and an outcome when the relation is known to be monotone or otherwise constrained due to the physical processes involved. We consider one such application-inferring time-resolved aerosol concentration from a low-cost differential pressure sensor. The objective is to estimate a monotone function and make inference on the scaled first derivative of the function. We proposed Bayesian nonparametric monotone regression which uses a Bernstein polynomial basis to construct the regression function and puts a Dirichlet process prior on the regression coefficients. The base measure of the Dirichlet process is a finite mixture of a mass point at zero and a truncated normal. This construction imposes monotonicity while clustering the basis functions. Clustering the basis functions reduces the parameter space and allows the estimated regression function to be linear. With the proposed approach we can make closed-formed inference on the derivative of the estimated function including full quantification of uncertainty. In a simulation study the proposed method performs similar to other monotone regression approaches when the true function is wavy but performs better when the true function is linear. We apply the method to estimate time-resolved aerosol concentration with a newly-developed portable aerosol monitor. The R package bnmr is made available to implement the method.

Objective: Limited studies have examined effects of bioaerosols on the respiratory health of dairy workers; previous findings have been inconsistent across populations., Methods: Using a repeated measures design, exposures to dust, bioaerosols, and ozone were assessed and pre- and post-shift spirometry was performed for dairy workers (n = 36). Workers completed 1 to 8 visits. Linear mixed effect models estimated associations between air pollutant constituents and changes in spirometry., Results: There was an association between higher dust exposures and increased peak expiratory flow rate. However, for all other outcomes there was no association with the exposures considered., Conclusions: Relationships between bioaerosol exposures and respiratory health in dairy workers remain unclear. Future studies should increase sample sizes, include repeated measures designs, vary the timing of spirometry measurements, and include markers for Gram positive bacteria such as muramic acid or peptidoglycan.

Background: On 21-22 July 2012, Beijing, China, suffered its heaviest rainfall in 60 years. Two studies have estimated the fatality toll of this disaster using a traditional surveillance approach. However, traditional surveillance can miss disaster-related deaths, including a substantial number of deaths from natural causes triggered by disaster exposure. Here, we investigated community-wide mortality risk during this flood compared with rates in unexposed reference periods., Methods: We compared community-wide mortality rates on the peak flood day and the four following days to seasonally matched nonflood days in previous years (2008-2011), controlling for potential confounders, to estimate the relative risks (RRs) of daily mortality among Beijing residents associated with this flood., Results: On 21 July 2012, the flood-associated RRs were 1.34 (95% confidence interval = 1.11, 1.61) for all-cause, 1.37 (1.01, 1.85) for circulatory, and 4.40 (2.98, 6.51) for accidental mortality, compared with unexposed periods. We observed no evidence of increased risk of respiratory mortality. For the flood period of 21-22 July 2012, we estimated a total of 79 excess deaths among Beijing residents; by contrast, only 34 deaths were reported among Beijing residents in a study using a traditional surveillance approach., Conclusions: To our knowledge, this is the first study analyzing community-wide changes in mortality rates during the 2012 flood in Beijing and one of the first to do so for any major flood worldwide. This study offers critical evidence on flood-related health impacts, as urban flooding is expected to become more frequent and severe in China.

Cooking and heating with solid fuels results in high levels of household air pollutants, including particulate matter (PM); however, limited data exist for size fractions smaller than PM 2.5 (diameter less than 2.5 μm). We collected 24-h time-resolved measurements of PM 2.5 (n = 27) and particle number concentrations (PNC, average diameter 10-700 nm) (n = 44; 24 with paired PM 2.5 and PNC) in homes with wood-burning traditional and Justa (i.e., with an engineered combustion chamber and chimney) cookstoves in rural Honduras. The median 24-h PM 2.5 concentration (n = 27) was 79 μg/m 3 (interquartile range [IQR]: 44-174 μg/m 3 ); traditional (n = 15): 130 μg/m 3 (IQR: 48-250 μg/m 3 ); Justa (n = 12): 66 μg/m 3 (IQR: 44-97 μg/m 3 ). The median 24-h PNC (n = 44) was 8.5 × 10 4 particles (pt)/cm 3 (IQR: 3.8 × 10 4 -1.8 × 10 5  pt/cm 3 ); traditional (n = 27): 1.3 × 10 5  pt/cm 3 (IQR: 3.3 × 10 4 -2.0 × 10 5  pt/cm 3 ); Justa (n = 17): 6.3 × 10 4  pt/cm 3 (IQR: 4.0 × 10 4 -1.2 × 10 5  pt/cm 3 ). The 24-h average PM 2.5 and particle number concentrations were correlated for the full sample of cookstoves (n = 24, Spearman ρ: 0.83); correlations between PM 2.5 and PNC were higher in traditional stove kitchens (n = 12, ρ: 0.93) than in Justa stove kitchens (n = 12, ρ: 0.67). The 24-h average concentrations of PM 2.5 and PNC were also correlated with the maximum average concentrations during shorter-term averaging windows of one-, five-, 15-, and 60-min, respectively (Spearman ρ: PM 2.5 [0.65, 0.85, 0.82, 0.71], PNC [0.74, 0.86, 0.88, 0.86]). Given the moderate correlations observed between 24-h PM 2.5 and PNC and between 24-h and the shorter-term averaging windows within size fractions, investigators may need to consider cost-effectiveness and information gained by measuring both size fractions for the study objective. Further evaluations of other stove and fuel combinations are needed., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Background: Exposure to household air pollution generated as a result of cooking and heating is a leading contributor to global disease. The effects of cookstove-generated air pollution on adult lung function, however, remain uncertain. Objectives: We investigated acute responses in lung function following controlled exposures to cookstove-generated air pollution. Methods: We recruited 48 healthy adult volunteers to undergo six two-hour treatments: a filtered-air control and emissions from five different stoves with fine particulate matter (PM 2.5 ) targets from 10 to 500 µg/m 3 . Spirometry was conducted prior to exposure and immediately, and three and 24 h post-exposure. Mixed-effect models were used to estimate differences in post-exposure lung function for stove treatments versus control. Results: Immediately post-exposure, lung function was lower compared to the control for the three highest PM 2.5 -level stoves. The largest differences were for the fan rocket stove (target 250 µg/m 3 ; forced vital capacity (FVC): -60 mL, 95% confidence interval (95% CI) -135, 15; forced expiratory volume (FEV 1 ): -51 mL, 95% CI -117, 16; mid-expiratory flow (FEF 25-75 ): -116 mL/s, 95% CI -239, 8). At 3 h post-exposure, lung function was lower compared to the control for all stove treatments; effects were of similar magnitude for all stoves. At 24 h post-exposure, results were consistent with a null association for FVC and FEV 1 ; FEF 25-75 was lower relative to the control for the gasifier, fan rocket, and three stone fire. Conclusions: Patterns suggesting short-term decreases in lung function follow from exposure to cookstove air pollution even for stove exposures with low PM 2.5 levels.

Household air pollution emitted from solid-fuel cookstoves used for domestic cooking is a leading risk factor for morbidity and premature mortality globally. There have been attempts to design and distribute lower emission cookstoves, yet it is unclear if they meaningfully improve health. Using a crossover design, we assessed differences in central aortic hemodynamics and arterial stiffness following controlled exposures to air pollution emitted from five different cookstove technologies compared to a filtered air control. Forty-eight young, healthy participants were assigned to six 2-h controlled treatments of pollution from five different cookstoves and a filtered air control. Each treatment had a target concentration for fine particulate matter: filtered air control = 0 μg/m 3 , liquefied petroleum gas = 10 μg/m 3 , gasifier = 35 μg/m 3 , fan rocket = 100 μg/m 3 , rocket elbow = 250 μg/m 3 , three stone fire = 500 μg/m 3 . Pulse wave velocity (PWV), central augmentation index (AIx), and central pulse pressure (CPP) were measured before and at three time points after each treatment (0, 3, and 24 h). Linear mixed models were used to assess differences in the outcomes for each cookstove treatment compared to control. PWV and CPP were marginally higher 24 h after all cookstove treatments compared to control. For example, PWV was 0.15 m/s higher (95% confidence interval: -0.02, 0.31) and CPP was 0.6 mmHg higher (95% confidence interval: -0.8, 2.1) 24 h after the three stone fire treatment compared to control. The magnitude of the differences compared to control was similar across all cookstove treatments. PWV and CPP had no consistent trends at the other post-treatment time points (0 and 3 h). No consistent trends were observed for AIx at any post-treatment time point. Our findings suggest higher levels of PWV and CPP within 24 h after 2-h controlled treatments of pollution from five different cookstove technologies. The similar magnitude of the differences following each cookstove treatment compared to control may indicate that acute exposures from even the cleanest cookstove technologies can adversely impact these subclinical markers of cardiovascular health, although differences were small and may not be clinically meaningful., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: Violence is a leading cause of death and an important public health threat, particularly among adolescents and young adults. However, the environmental causes of violent behavior are not well understood. Emerging evidence suggests exposure to air pollution may be associated with aggressive or impulsive reactions in people., Methods: We applied a two-stage hierarchical time-series model to estimate change in risk of violent and nonviolent criminal behavior associated with short-term air pollution in U.S. counties (2000-2013). We used daily monitoring data for ozone and fine particulate matter (PM2.5) from the Environmental Protection Agency and daily crime counts from the Federal Bureau of Investigation. We evaluated the exposure-response relation and assessed differences in risk by community characteristics of poverty, urbanicity, race, and age., Results: Our analysis spans 301 counties in 34 states, representing 86.1 million people and 721,674 days. Each 10 µg/m change in daily PM2.5 was associated with a 1.17% (95% confidence interval [CI] = 0.90, 1.43) and a 10 ppb change in ozone with a 0.59% (95% CI = 0.41, 0.78) relative risk increase (RRI) for violent crime. However, we observed no risk increase for nonviolent property crime due to PM2.5 (RRI: 0.11%; 95% CI = -0.09, 0.31) or ozone (RRI: -0.05%; 95% CI = -0.22, 0.12). Our results were robust across all community types, except rural regions. Exposure-response curves indicated increased violent crime risk at concentrations below regulatory standards., Conclusions: Our results suggest that short-term changes in ambient air pollution may be associated with a greater risk of violent behavior, regardless of community type.

Background Exposure to air pollution from solid fuel used in residential cookstoves is considered a leading environmental risk factor for disease globally, but evidence for this relationship is largely extrapolated from literature on smoking, secondhand smoke, and ambient fine particulate matter ( PM 2.5 ). Methods and Results We conducted a controlled human-exposure study (STOVES [the Subclinical Tests on Volunteers Exposed to Smoke] Study) to investigate acute responses in blood pressure following exposure to air pollution emissions from cookstove technologies. Forty-eight healthy adults received 2-hour exposures to 5 cookstove treatments (three stone fire, rocket elbow, fan rocket elbow, gasifier, and liquefied petroleum gas), spanning PM 2.5 concentrations from 10 to 500 μg/m 3 , and a filtered air control (0 μg/m 3 ). Thirty minutes after exposure, systolic pressure was lower for the three stone fire treatment (500 μg/m 3 PM 2.5 ) compared with the control (-2.3 mm Hg; 95% CI, -4.5 to -0.1) and suggestively lower for the gasifier (35 μg/m 3 PM 2.5 ; -1.8 mm Hg; 95% CI , -4.0 to 0.4). No differences were observed at 3 hours after exposure; however, at 24 hours after exposure, mean systolic pressure was 2 to 3 mm Hg higher for all treatments compared with control except for the rocket elbow stove. No differences were observed in diastolic pressure for any time point or treatment. Conclusions Short-term exposure to air pollution from cookstoves can elicit an increase in systolic pressure within 24 hours. This response occurred across a range of stove types and PM 2.5 concentrations, raising concern that even low-level exposures to cookstove air pollution may pose adverse cardiovascular effects.

Many portable monitors for quantifying mass concentrations of particulate matter air pollution rely on aerosol light scattering as the measurement method; however, the relationship between scattered light (what is measured) and aerosol mass concentration (the metric of interest) is a complex function of the refractive index, size distribution, and shape of the particles. In this study, we compared 33-h personal PM 2.5 concentrations measured simultaneously using nephelometry (personal DataRAM pDR-1200) and gravimetric filter sampling for working adults (44 participants, 249 samples). Nephelometer- and filter-derived 33-h average PM 2.5 concentrations were correlated (Spearman's ρ = 0.77); however, the nephelometer-derived concentration was within 20% of the filter-derived concentration for only 13% of samples. The nephelometer/filter ratio, which is used to correct light-scattering measurements to a gravimetric sample, had a median value of 0.52 and varied by over a factor of three (10th percentile = 0.35, 90 th percentile = 1.1). When 33-h samples with >50% of 10-s average nephelometer readings below the nephelometer limit of detection were removed from the dataset during sensitivity analyses, the fraction of nephelometer-derived concentrations that were within 20% of the filter-derived concentration increased to 25%. We also evaluated how much the accuracy of nephelometer-derived concentrations improved after applying: (1) a median correction factor derived from a subset of 44 gravimetric samples, (2) participant-specific correction factors derived from one same from each subject, and (3) correction factors predicted using linear models based on other variables recorded during the study. Each approach independently increased the fraction of nephelometer-derived concentrations that were within 20% of the filter-derived concentration to approximately 45%. These results illustrate the challenges with using light scattering (without correction to a concurrent gravimetric sample) to estimate personal exposure to PM 2.5 mass among mobile adults exposed to low daily average concentrations (median = 8 μg m -3 in this study)., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Cookstoves emit many pollutants that are harmful to human health and the environment. However, most of the existing scientific literature focuses on fine particulate matter (PM 2.5 ) and carbon monoxide (CO). We present an extensive data set of speciated air pollution emissions from wood, charcoal, kerosene, and liquefied petroleum gas (LPG) cookstoves. One-hundred and twenty gas- and particle-phase constituents-including organic carbon, elemental carbon (EC), ultrafine particles (10-100 nm), inorganic ions, carbohydrates, and volatile/semivolatile organic compounds (e.g., alkanes, alkenes, alkynes, aromatics, carbonyls, and polycyclic aromatic hydrocarbons (PAHs))-were measured in the exhaust from 26 stove/fuel combinations. We find that improved biomass stoves tend to reduce PM 2.5 emissions; however, certain design features (e.g., insulation or a fan) tend to increase relative levels of other coemitted pollutants (e.g., EC ultrafine particles, carbonyls, or PAHs, depending on stove type). In contrast, the pressurized kerosene and LPG stoves reduced all pollutants relative to a traditional three-stone fire (≥93% and ≥79%, respectively). Finally, we find that PM 2.5 and CO are not strong predictors of coemitted pollutants, which is problematic because these pollutants may not be indicators of other cookstove smoke constituents (such as formaldehyde and acetaldehyde) that may be emitted at concentrations that are harmful to human health.

Background: Studies found approximately linear short-term associations between particulate matter (PM) and mortality in Western communities. However, in China, where the urban PM levels are typically considerably higher than in Western communities, some studies suggest nonlinearity in this association. Health impact assessments (HIA) of PM in China have generally not incorporated nonlinearity in the concentration-response (C-R) association, which could result in large discrepancies in estimates of excess deaths if the true association is nonlinear., Objectives: We investigated nonlinearity in the C-R associations between with PM with aerodynamic diameter [Formula: see text] ([Formula: see text]) and mortality in Beijing, China, and the sensitivity of HIA to linearity assumptions., Methods: We modeled the C-R association between [Formula: see text] and cause-specific mortality in Beijing, China (2009-2012), using generalized linear models (GLM). [Formula: see text] was included through either linear, piecewise-linear, or spline functions to investigate evidence of nonlinearity. To determine the sensitivity of HIA to linearity assumptions, we estimated [Formula: see text]-attributable deaths using both linear- and nonlinear-based C-R associations between [Formula: see text] and mortality., Results: We found some evidence that, for nonaccidental and circulatory mortality, the shape of the C-R association was relatively flat at lower concentrations of [Formula: see text], but then had a positive slope at higher concentrations, indicating nonlinearity. Conversely, the shape for respiratory mortality was positive and linear at lower concentrations of [Formula: see text], but then leveled off at the higher concentrations. Estimates of excess deaths attributable to short-term [Formula: see text] exposure were, in some cases, very sensitive to the linearity assumption in the association, but in other cases robust to this assumption., Conclusions: Our results demonstrate some evidence of nonlinearity in [Formula: see text]-mortality associations and that an assumption of linearity in this association can influence HIAs, highlighting the importance of understanding potential nonlinearity in the [Formula: see text]-mortality association at the high concentrations of [Formula: see text] in developing megacities like Beijing. https://doi.org/10.1289/EHP4464.

Introduction: In utero particulate matter exposure produces oxidative stress that impacts cellular processes that include telomere biology. Newborn telomere length is likely critical to an individual's telomere biology; reduction in this initial telomere setting may signal increased susceptibility to adverse outcomes later in life. We examined associations between prenatal particulate matter with diameter ≤2.5 µm (PM 2.5 ) and relative leukocyte telomere length (LTL) measured in cord blood using a data-driven approach to characterize sensitive windows of prenatal PM 2.5 effects and explore sex differences., Methods: Women who were residents of Mexico City and affiliated with the Mexican Social Security System were recruited during pregnancy (n = 423 for analyses). Mothers' prenatal exposure to PM 2.5 was estimated based on residence during pregnancy using a validated satellite-based spatio-temporally resolved prediction model. Leukocyte DNA was extracted from cord blood obtained at delivery. Duplex quantitative polymerase chain reaction was used to compare the relative amplification of the telomere repeat copy number to single gene (albumin) copy number. A distributed lag model incorporating weekly averages for PM 2.5 over gestation was used in order to explore sensitive windows. Sex-specific associations were examined using Bayesian distributed lag interaction models., Results: In models that included child's sex, mother's age at delivery, prenatal environmental tobacco smoke exposure, pre-pregnancy BMI, gestational age, birth season and assay batch, we found significant associations between higher PM 2.5 exposure during early pregnancy (4-9 weeks) and shorter LTL in cord blood. We also identified two more windows at 14-19 and 34-36 weeks in which increased PM 2.5 exposure was associated with longer LTL. In stratified analyses, the mean and cumulative associations between PM 2.5 and shortened LTL were stronger in girls when compared to boys., Conclusions: Increased PM 2.5 during specific prenatal windows was associated with shorter LTL and longer LTL. PM 2.5 was more strongly associated with shortened LTL in girls when compared to boys. Understanding sex and temporal differences in response to air pollution may provide unique insight into mechanisms., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Introduction: Lead (Pb) crosses the placenta and can cause oxidative stress, reduced fetal growth and neurological problems. The principal source of oxidative stress in human cells is mitochondria. Therefore, disruption of normal mitochondrial function during pregnancy may represent a primary mechanism behind the adverse effects of lead. We sought to assess the association of Pb exposure during pregnancy with mitochondrial DNA (mtDNA) content, a sensitive marker of mitochondrial function, in cord blood., Materials and Methods: This study comprised mother-infant pairs from the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) study, a prospective birth-cohort that enrolled 1050 pregnant women from Mexico City who were receiving prenatal care between December 2007 and July 2011. Quantitative PCR was used to calculate relative MtDNA content (mitochondrial-to-nuclear DNA ratio (mtDNA/nDNA)) in cord blood. Lead concentrations in both maternal blood (2nd and 3rd trimester and at delivery day) and in cord blood were measured by ICP-MS. Multivariable regression models adjusting for multiple confounders were fitted with 410 mother-infant pairs for whom complete data for mtDNA content, lead levels, and covariates were available., Results: Maternal blood Pb measured in the second (mean 3.79 μg/dL, SD 2.63; β = 0.059, 95% CI 0.008, 0.111) and third trimester (mean 3.90 μg/dL; SD 2.84; β = 0.054, 95% CI 0.002, 0.107) during pregnancy and PB in cord blood (mean 3.50 μg/dL, SD 2.59; β = 0.050, 95% CI 0.004; 0.096) were associated with increased cord blood mtDNA content (mean 1.46, SD 0.44). In two-way interaction analyses, cord blood Pb marginally interacted with gestational age leading to an increase in mtDNA content for pre-term births (Benjamini-Hochberg False Discovery Rate correction; BH-FDR = 0.08)., Conclusion: This study shows that lead exposure in pregnancy alters mtDNA content in cord blood; therefore, alteration of mtDNA content might be a mechanism underlying the toxicity of lead., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)