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

Traditional cooking with solid fuels (biomass, animal dung, charcoals, coal) creates household air pollution that leads to millions of premature deaths and disability worldwide each year. Exposure to household air pollution is highest in low- and middle-income countries. Using data from a stepped-wedge randomized controlled trial of a cookstove intervention among 230 households in Honduras, we analyzed the impact of household and personal variables on repeated 24-h measurements of fine particulate matter (PM) and black carbon (BC) exposure.

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

Aerosol emissions from wind instruments are a suspected route of transmission for airborne infectious diseases, such as SARS-CoV-2. We evaluated aerosol number emissions (from 0.25 to 35.

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

Introduction: Household air pollution from cooking-related biomass combustion remains a leading risk factor for global health. Black carbon (BC) is an important component of particulate matter (PM) in household air pollution. We evaluated the impact of the engineered, wood-burning stove intervention on BC concentrations.

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

The spread of COVID-19 has been greatly impacted by regulatory policies and behavior patterns that vary across counties, states, and countries. Population-level dynamics of COVID-19 can generally be described using a set of ordinary differential equations, but these deterministic equations are insufficient for modeling the observed case rates, which can vary due to local testing and case reporting policies and nonhomogeneous behavior among individuals. To assess the impact of population mobility on the spread of COVID-19, we have developed a novel Bayesian time-varying coefficient state-space model for infectious disease transmission.

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

Background: The era of big data has enabled sophisticated models to predict air pollution concentrations over space and time. Historically these models have been evaluated using overall metrics that measure how close predictions are to monitoring data. However, overall methods are not designed to distinguish error at timescales most relevant for epidemiologic studies, such as day-to-day errors that impact studies of short-term health associations.

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

Estimating long-term exposure to household air pollution is essential for quantifying health effects of chronic exposure and the benefits of intervention strategies. However, typically only a small number of short-term measurements are made. We compare different statistical models for combining these short-term measurements into predictions of a long-term average, with emphasis on the impact of temporal trends in concentrations and crossover in study design.

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

Unlabelled: Adverse health effects of household air pollution, including acute lower respiratory infections (ALRIs), pose a major health burden around the world, particularly in settings where indoor combustion stoves are used for cooking. Individual studies have limited exposure ranges and sample sizes, while pooling studies together can improve statistical power.

Methods: We present hierarchical models for estimating long-term exposure concentrations and estimating a common exposure-response curve.

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

Studies on health effects of air pollution from local sources require exposure assessments that capture spatial and temporal trends. To facilitate intraurban studies in Denver, Colorado, we developed a spatiotemporal prediction model for black carbon (BC). To inform our model, we collected more than 700 weekly BC samples using personal air samplers from 2018 to 2020.

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

Trial Design: We evaluated the impact of a biomass stove intervention on fine particulate matter (PM) concentrations using an individual-level, stepped-wedge randomized trial.

Methods: We enrolled 230 women in rural Honduran households using traditional biomass stoves and randomly allocated them to one of two study arms. The Justa stove, the study intervention, was locally-sourced, wood-burning, and included an engineered combustion chamber and chimney.

Selecting a Scale for Spatial Confounding Adjustment.

Unmeasured, spatially-structured factors can confound associations between spatial environmental exposures and health outcomes. Adding flexible splines to a regression model is a simple approach for spatial confounding adjustment, but the spline degrees of freedom do not provide an easily interpretable spatial scale. We describe a method for quantifying the extent of spatial confounding adjustment in terms of the Euclidean distance at which variation is removed.

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

Background: Particulate matter (PM) is a complex mixture. Geographic variations in PM may explain the lack of consistent associations with breast cancer.

Objective: We aimed to evaluate the relationship between air pollution, PM components, and breast cancer risk in a United States-wide prospective cohort.

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

Background: Epigenetic age, as defined by DNA methylation, may be influenced by air pollution exposure.

Objective: To evaluate the relationship between NO, particulate matter (PM), PM components and accelerated epigenetic age.

Methods: In a sample of non-Hispanic white women living in the contiguous U.

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

Background: Growing evidence links household air pollution exposure from biomass-burning cookstoves to cardiometabolic disease risk. Few randomized controlled interventions of cookstoves (biomass or otherwise) have quantitatively characterized changes in exposure and indicators of cardiometabolic health, a growing and understudied burden in low- and middle-income countries (LMICs). Ideally, the solution is to transition households to clean cooking, such as with electric or liquefied petroleum gas stoves; however, those unable to afford or to access these options will continue to burn biomass for the foreseeable future.

Pollutant composition modification of the effect of air pollution on progression of coronary artery calcium: the Multi-Ethnic Study of Atherosclerosis.

Background: Differences in traffic-related air pollution (TRAP) composition may cause heterogeneity in associations between air pollution exposure and cardiovascular health outcomes. Clustering multi-pollutant measurements allows investigation of effect modification by TRAP profiles.

Methods: We measured TRAP components with fixed-site and on-road instruments for two two-week periods in Baltimore, Maryland.

Ambient Air Pollution and Chronic Bronchitis in a Cohort of U.S. Women.

Background: Limited evidence links air pollution exposure to chronic cough and sputum production. Few reports have investigated the association between long-term exposure to air pollution and classically defined chronic bronchitis.

Objectives: Our objective was to estimate the association between long-term exposure to particulate matter (diameter <10 μm, PM; <2.

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

Rationale: Short- and long-term fine particulate matter (particulate matter ≤2.5 μm in aerodynamic diameter [PM]) pollution is associated with asthma development and morbidity, but there are few data on the effects of long-term exposure to coarse PM (PM) on respiratory health.

Objectives: To understand the relationship between long-term fine and coarse PM exposure and asthma prevalence and morbidity among children.

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

We present a method for improving estimation in linear regression models in samples of moderate size, using shrinkage techniques. Our work connects the theory of causal inference, which describes how variable adjustment should be performed with large samples, with shrinkage estimators such as ridge regression and the least absolute shrinkage and selection operator (LASSO), which can perform better in sample sizes seen in epidemiologic practice. Shrinkage methods reduce mean squared error by trading off some amount of bias for a reduction in variance.

COVARIATE-ADAPTIVE CLUSTERING OF EXPOSURES FOR AIR POLLUTION EPIDEMIOLOGY COHORTS.

Cohort studies in air pollution epidemiology aim to establish associations between health outcomes and air pollution exposures. Statistical analysis of such associations is complicated by the multivariate nature of the pollutant exposure data as well as the spatial misalignment that arises from the fact that exposure data are collected at regulatory monitoring network locations distinct from cohort locations. We present a novel clustering approach for addressing this challenge.

Measurement Error Correction for Predicted Spatiotemporal Air Pollution Exposures.

Background: Air pollution cohort studies are frequently analyzed in two stages, first modeling exposure then using predicted exposures to estimate health effects in a second regression model. The difference between predicted and unobserved true exposures introduces a form of measurement error in the second stage health model. Recent methods for spatial data correct for measurement error with a bootstrap and by requiring the study design ensure spatial compatibility, that is, monitor and subject locations are drawn from the same spatial distribution.