Trends in Prevalence and Correlates of Alcohol Use Disorder Diagnoses Among US Adult Cancer Survivors: Serial Cross-Sectional Analysis.

Background: Alcohol use disorder (AUD) is associated with cancer recurrence, new malignancies, and mortality among survivors of certain cancers. This study evaluated trends (2012-2021) in prevalence and correlates of AUD diagnoses among adult cancer survivors in the United States.

Methods: This retrospective, serial cross-sectional study used claims data (2011-2021) from a national sample of US individuals with employer-sponsored health insurance.

Bayesian nonparametric trees for principal causal effects.

Principal stratification analysis evaluates how causal effects of a treatment on a primary outcome vary across strata of units defined by their treatment effect on some intermediate quantity. This endeavor is substantially challenged when the intermediate variable is continuously scaled and there are infinitely many basic principal strata. We employ a Bayesian nonparametric approach to flexibly evaluate treatment effects across flexibly modeled principal strata.

Survivor Average Causal Effects for Continuous Time: A Principal Stratification Approach to Causal Inference With Semicompeting Risks.

In semicompeting risks problems, nonterminal time-to-event outcomes, such as time to hospital readmission, are subject to truncation by death. These settings are often modeled with illness-death models for the hazards of the terminal and nonterminal events, but evaluating causal treatment effects with hazard models is problematic due to conditioning on survival-a posttreatment outcome-that is embedded in the definition of a hazard. Extending an existing survivor average causal effect (SACE) estimand, we frame the evaluation of treatment effects in the context of semicompeting risks with principal stratification and introduce two new causal estimands: the time-varying survivor average causal effect (TV-SACE) and the restricted mean survivor average causal effect (RM-SACE).

Bipartite interference and air pollution transport: estimating health effects of power plant interventions.

Evaluating air quality interventions is confronted with the challenge of interference since interventions at a particular pollution source likely impact air quality and health at distant locations, and air quality and health at any given location are likely impacted by interventions at many sources. The structure of interference in this context is dictated by complex atmospheric processes governing how pollution emitted from a particular source is transformed and transported across space and can be cast with a bipartite structure reflecting the two distinct types of units: (i) interventional units on which treatments are applied or withheld to change pollution emissions; and (ii) outcome units on which outcomes of primary interest are measured. We propose new estimands for bipartite causal inference with interference that construe two components of treatment: a "key-associated" (or "individual") treatment and an "upwind" (or "neighborhood") treatment.

Alignment of Air Pollution Exposure Inequality Metrics with Environmental Justice and Equity Goals in the United States.

A growing literature within the field of air pollution exposure assessment addresses the issue of environmental justice. Leveraging the increasing availability of exposure datasets with broad spatial coverage and high spatial resolution, a number of works have assessed inequalities in exposure across racial/ethnic and other socioeconomic groupings. However, environmental justice research presents the additional need to evaluate exposure inequity-inequality that is systematic, unfair, and avoidable-which may be framed in several ways.

CAUSAL HEALTH IMPACTS OF POWER PLANT EMISSION CONTROLS UNDER MODELED AND UNCERTAIN PHYSICAL PROCESS INTERFERENCE.

Causal inference with spatial environmental data is often challenging due to the presence of interference: outcomes for observational units depend on some combination of local and nonlocal treatment. This is especially relevant when estimating the effect of power plant emissions controls on population health, as pollution exposure is dictated by: (i) the location of point-source emissions as well as (ii) the transport of pollutants across space via dynamic physical-chemical processes. In this work we estimate the effectiveness of air quality interventions at coal-fired power plants in reducing two adverse health outcomes in Texas in 2016: pediatric asthma ED visits and Medicare all-cause mortality.

Causal analysis of air pollution mixtures: estimands, positivity, and extrapolation.

Causal inference for air pollution mixtures is an increasingly important issue with appreciable challenges. When the exposure is a multivariate mixture, there are many exposure contrasts that may be of nominal interest for causal effect estimation, but the complex joint mixture distribution often renders observed data extremely limited in their ability to inform estimates of many commonly defined causal effects. We use potential outcomes to (1) define causal effects of air pollution mixtures, (2) formalize the key assumption of mixture positivity required for estimation, and (3) offer diagnostic metrics for positivity violations in the mixture setting that allow researchers to assess the extent to which data can actually support estimation of mixture effects of interest.

Neighborhood-level variability in asthma-related emergency department visits in Central Texas.

Background: The extent to which incidence rates of asthma-related emergency department (ED) visits vary from neighborhood to neighborhood and predictors of neighborhood-level asthma ED visit burden are not well understood.

Objective: We aimed to describe the census tract-level spatial distribution of asthma-related ED visits in Central Texas and identify neighborhood-level characteristics that explain variability in neighborhood-level asthma ED visit rates.

Methods: Conditional autoregressive models were used to examine the spatial distribution of asthma-related ED visit incidence rates across census tracts in Travis County, Texas, and assess the contribution of census tract characteristics to their distribution.

Pollen and viruses contribute to spatio-temporal variation in asthma-related emergency department visits.

Background: Asthma exacerbations are an important cause of emergency department visits but much remains unknown about the role of environmental triggers including viruses and allergenic pollen. A better understanding of spatio-temporal variation in exposure and risk posed by viruses and pollen types could help prioritize public health interventions.

Objective: Here we quantify the effects of regionally important Cupressaceae pollen, tree pollen, other pollen types, rhinovirus, seasonal coronavirus, respiratory syncytial virus, and influenza on asthma-related emergency department visits for people living near eight pollen monitoring stations in Texas.

Chronic household air pollution and exposure patterns among Himalayan nomads.

Background: Household air pollution (HAP) is a major risk factor of non-communicable diseases, causing millions of premature deaths each year in developing nations. Populations living at high altitudes are particularly vulnerable to HAP and associated health outcomes.

Objectives: This study aims to explore the relationships between activity patterns, HAP, and an HAP biomarker among 100 Himalayan nomadic households during both cooking and heating-only periods.

The Role of Neighborhood Air Pollution in Disparate Racial and Ethnic Asthma Acute Care Use.

The share of Black or Latinx residents in a census tract remains associated with asthma-related emergency department (ED) visit rates after controlling for socioeconomic factors. The extent to which evident disparities relate to the within-city heterogeneity of long-term air pollution exposure remains unclear. To investigate the role of intraurban spatial variability of air pollution in asthma acute care use disparity.

Statistical inference for complete and incomplete mobility trajectories under the flight-pause model.

We formulate a statistical flight-pause model (FPM) for human mobility, represented by a collection of random objects, called motions, appropriate for mobile phone tracking (MPT) data. We develop the statistical machinery for parameter inference and trajectory imputation under various forms of missing data. We show that common assumptions about the missing data mechanism for MPT are not valid for the mechanism governing the random motions underlying the FPM, representing an understudied missing data phenomenon.

Local exposure misclassification in national models: relationships with urban infrastructure and demographics.

Background: National-scale linear regression-based modeling may mischaracterize localized patterns, including hyperlocal peaks and neighborhood- to regional-scale gradients. For studies focused on within-city differences, this mischaracterization poses a risk of exposure misclassification, affecting epidemiological and environmental justice conclusions.

Objective: Characterize the difference between intraurban pollution patterns predicted by national-scale land use regression modeling and observation-based estimates within a localized domain and examine the relationship between that difference and urban infrastructure and demographics.

Mortality risk from United States coal electricity generation.

Policy-makers seeking to limit the impact of coal electricity-generating units (EGUs, also known as power plants) on air quality and climate justify regulations by quantifying the health burden attributable to exposure from these sources. We defined "coal PM" as fine particulate matter associated with coal EGU sulfur dioxide emissions and estimated annual exposure to coal PM from 480 EGUs in the US. We estimated the number of deaths attributable to coal PM from 1999 to 2020 using individual-level Medicare death records representing 650 million person-years.

Evaluating Federal Policies Using Bayesian Time Series Models: Estimating the Causal Impact of the Hospital Readmissions Reduction Program.

Researchers are often faced with evaluating the effect of a policy or program that was simultaneously initiated across an entire population of units at a single point in time, and its effects over the targeted population can manifest at any time period afterwards. In the presence of data measured over time, Bayesian time series models have been used to impute what would have happened after the policy was initiated, had the policy not taken place, in order to estimate causal effects. However, the considerations regarding the definition of the target estimands, the underlying assumptions, the plausibility of such assumptions, and the choice of an appropriate model have not been thoroughly investigated.

Inequitable Exposures to U.S. Coal Power Plant-Related : 22 Years and Counting.

Background: Emissions from coal power plants have decreased over recent decades due to regulations and economics affecting costs of providing electricity generated by coal vis-à-vis its alternatives. These changes have improved regional air quality, but questions remain about whether benefits have accrued equitably across population groups.

Objectives: We aimed to quantify nationwide long-term changes in exposure to particulate matter (PM) with an aerodynamic diameter () associated with coal power plant emissions.

Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions.

Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations.

Optimal mode of delivery in pregnancy: Individualized predictions using national vital statistics data.

Child birth via Cesarean section accounts for approximately 32% of all births each year in the United States. A variety of risk factors and complications can lead caregivers and patients to plan for a Cesarean delivery in advance before onset of labor. However, a non-trivial subset of Cesarean sections (∼25%) are unplanned and occur after an initial trial of labor is attempted.

Bayesian nonparametric adjustment of confounding.

Analysis of observational studies increasingly confronts the challenge of determining which of a possibly high-dimensional set of available covariates are required to satisfy the assumption of ignorable treatment assignment for estimation of causal effects. We propose a Bayesian nonparametric approach that simultaneously (1) prioritizes inclusion of adjustment variables in accordance with existing principles of confounder selection; (2) estimates causal effects in a manner that permits complex relationships among confounders, exposures, and outcomes; and (3) provides causal estimates that account for uncertainty in the nature of confounding. The proposal relies on specification of multiple Bayesian additive regression trees models, linked together with a common prior distribution that accrues posterior selection probability to covariates on the basis of association with both the exposure and the outcome of interest.

Impacts of wind power on air quality, premature mortality, and exposure disparities in the United States.

Understanding impacts of renewable energy on air quality and associated human exposures is essential for informing future policy. We estimate the impacts of U.S.

A Mechanistic Model of Annual Sulfate Concentrations in the United States.

Understanding how individual pollution sources contribute to ambient sulfate pollution is critical for assessing past and future air quality regulations. Since attribution to specific sources is typically not encoded in spatial air pollution data, we develop a mechanistic model which we use to estimate, with uncertainty, the contribution of ambient sulfate concentrations attributable specifically to sulfur dioxide (SO) emissions from individual coal-fired power plants in the central United States. We propose a multivariate Ornstein-Uhlenbeck (OU) process approximation to the dynamics of the underlying space-time chemical transport process, and its distributional properties are leveraged to specify novel probability models for spatial data that are viewed as either a snapshot or time-averaged observation of the OU process.

Association between county-level coal-fired power plant pollution and racial disparities in preterm births from 2000 to 2018.

Coal has historically been a primary energy source in the United States. The byproducts of coal combustion, such as fine particulate matter (PM), have increasingly been associated with adverse birth outcomes. The goal of this study was to leverage the current progressive transition away from coal in the United States (U.