Smooth and shape-constrained quantile distributed lag models.

Exposure to environmental pollutants during the gestational period can significantly impact infant health outcomes, such as birth weight and neurological development. Identifying critical windows of susceptibility, which are specific periods during pregnancy when exposure has the most profound effects, is essential for developing targeted interventions. Distributed lag models (DLMs) are widely used in environmental epidemiology to analyze the temporal patterns of exposure and their impact on health outcomes.

Structured Bayesian Regression Tree Models for Estimating Distributed Lag Effects: The R Package dlmtree.

When examining the relationship between an exposure and an outcome, there is often a time lag between exposure and the observed effect on the outcome. A common statistical approach for estimating the relationship between the outcome and lagged measurements of exposure is a distributed lag model (DLM). Because repeated measurements are often autocorrelated, the lagged effects are typically constrained to vary smoothly over time.

Distributed lag interaction model with index modification.

Epidemiological evidence supports an association between exposure to air pollution during pregnancy and birth and child health outcomes. Typically, such associations are estimated by regressing an outcome on daily or weekly measures of exposure during pregnancy using a distributed lag model. However, these associations may be modified by multiple factors.

Windows of susceptibility to air pollution during and surrounding pregnancy in relation to longitudinal maternal measures of adiposity and lipid profiles.

Pregnancy is a critical window for long-term metabolic programming of fetal effects stemming from airborne particulate matter ≤2.5 μm (PM) exposure. Yet, little is known about long-term metabolic effects of PM exposure during and surrounding pregnancy in mothers.

A novel approach for inferring effects on pregnancy loss.

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.

Windows of Susceptibility to Air Pollution During and Surrounding Pregnancy in Relation to Longitudinal Maternal Measures of Adiposity and Lipid Profiles.

Pregnancy is a critical window for long-term metabolic programming of fetal effects stemming from airborne particulate matter ≤2.5μm (PM ) exposure. Yet, little is known about long-term metabolic effects of PM exposure during and surrounding pregnancy in mothers.

Air pollution mixture exposure during pregnancy and postpartum psychological functioning: racial/ethnic- and fetal sex-specific associations.

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.

A Hierarchical Bayesian Model for Estimating Age-Specific COVID-19 Infection Fatality Rates in Developing Countries.

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.

Heterogeneous Distributed Lag Models to Estimate Personalized Effects of Maternal Exposures to Air Pollution.

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.

Penalized distributed lag interaction model: Air pollution, birth weight, and neighborhood vulnerability.

Maternal exposure to air pollution during pregnancy has a substantial public health impact. Epidemiological evidence supports an association between maternal exposure to air pollution and low birth weight. A popular method to estimate this association while identifying windows of susceptibility is a distributed lag model (DLM), which regresses an outcome onto exposure history observed at multiple time points.

Relative humidity, temperature, and hypertensive disorders of pregnancy: Findings from the Project Viva cohort.

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.

Cumulative Exposures to Environmental and Socioeconomic Risk Factors in Milwaukee County, Wisconsin.

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.

Sensitive development windows of prenatal air pollution and cognitive functioning in preschool age Mexican children.

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) on children's cognition in a birth cohort in Mexico.

Estimating cutoff values for diagnostic tests to achieve target specificity using extreme value theory.

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.

DNA methylation profiles reveal sex-specific associations between gestational exposure to ambient air pollution and placenta cell-type composition in the PRISM cohort study.

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.

Prenatal ambient air pollutant mixture exposure and neurodevelopment in urban children in the Northeastern United States.

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.

Incorporating biological knowledge in analyses of environmental mixtures and health.

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.

Prenatal Ambient Air Pollutant Mixture Exposure and Early School-age Lung Function.

Unlabelled: 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.

Bias Amplification and Variance Inflation in Distributed Lag Models Using Low-Spatial-Resolution Data.

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.

Using non-parametric Bayes shrinkage to assess relationships between multiple environmental and social stressors and neonatal size and body composition in the Healthy Start cohort.

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.

KERNEL MACHINE AND DISTRIBUTED LAG MODELS FOR ASSESSING WINDOWS OF SUSCEPTIBILITY TO ENVIRONMENTAL MIXTURES IN CHILDREN'S HEALTH STUDIES.

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.

Multiple exposure distributed lag models with variable selection.

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.

Association between air pollution and COVID-19 disease severity via Bayesian multinomial logistic regression with partially missing outcomes.

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.

Infinite hidden Markov models for multiple multivariate time series with missing data.

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.