bneR: A collaborative workflow for air pollution exposure modeling and uncertainty characterization using the Bayesian Nonparametric Ensemble.

Background: Air pollution is a major public health threat globally. Health studies, regulatory actions, and policy evaluations typically rely on air pollutant concentrations from single exposure models, assuming accurate estimations and ignoring related uncertainty. We developed a modeling framework, bneR, to apply the Bayesian Nonparametric Ensemble (BNE) prediction model that combines existing exposure models as inputs to provide air pollution estimates and their spatio-temporal uncertainty.

High-resolution national radon maps based on massive indoor measurements in the United States.

Radon, a common radioactive indoor air pollutant, is the second leading cause of lung cancer in the United States. Knowledge about its distribution is essential for risk assessment and designing efficient protective regulations. However, the three current radon maps for the United States are unable to provide the up-to-date, high-resolution, and time-varying radon concentrations.

Associations of maternal per- and polyfluoroalkyl substance plasma concentrations during pregnancy with offspring polycystic ovary syndrome and related characteristics in project viva.

Background: Per- and polyfluoroalkyl substances (PFAS) may impact ovarian folliculogenesis and steroidogenesis, but whether prenatal exposure may impact offspring reproductive health is unknown. This study examines the extent to which maternal PFAS plasma concentrations during pregnancy are associated with polycystic ovary syndrome (PCOS) and related characteristics in female offspring.

Methods: We studied 322 mother-daughter pairs in Project Viva, a Boston-area longitudinal pre-birth cohort enrolled 1999-2002.

Environmental Mixtures Analysis (E-MIX) Workflow and Methods Repository.

Human exposure to complex, changing, and variably correlated mixtures of environmental chemicals has presented analytical challenges to epidemiologists and human health researchers. There have been a wide variety of recent advances in statistical methods for analyzing mixtures data, with most of these methods having open-source software for implementation. However, there is no one-size-fits-all method for analyzing mixtures data given the considerable heterogeneity in scientific focus and study design.

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.