Publications by authors named "Elaine L Hill"

Background: Billions of dollars have been spent implementing regulations to reduce traffic-related air pollution (TRAP) from exhaust pipe emissions. However, few health studies have evaluated the change in TRAP emissions and associations with infant health outcomes. We hypothesize that the magnitude of association between vehicle exposure measures and adverse birth outcomes has decreased over time, parallelling regulatory improvements in exhaust pipe emissions.

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  • * Researchers analyzed data from the National COVID Cohort Collaborative to compare COVID-19 hospitalization rates among individuals with HIV (PWH) and without HIV (PWOH) based on racial and ethnic backgrounds, finding that hospitalization rates were higher for NH-Black PWH.
  • * The study revealed that certain county-level SDoH, such as household size, commute times, and health insurance coverage, influenced hospitalization risks differently among various racial and ethnic
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Previous research in the US has found negative health effects of contamination when it triggers regulatory violations. An important question is whether levels of contamination that do not trigger a health-based violation impact health. We study the impact of drinking water contamination in community water systems on birth outcomes using drinking water sampling results data in Pennsylvania.

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  • - Traffic-related air pollution negatively impacts perinatal health, making it hard to identify clear causal links due to overlapping socioeconomic factors and environmental exposures in high-traffic areas.
  • - A study of 3,570,272 births in Texas from 2007 to 2016 found that pregnant individuals living downwind from heavy traffic had lower term birth weights compared to those living upwind, with significant declines noted within 50 m of the road.
  • - Specifically, living downwind within 50 m was linked to a 36.3 g decrease in birth weight, while those 51-100 m away had an increased risk of very preterm birth, indicating significant health risks associated with traffic pollution, especially close to major roads.*
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COVID-19 vaccines have been shown to be effective in preventing severe illness, including among pregnant persons. The vaccines appear to be safe in pregnancy, supporting a continuously favorable overall risk/benefit profile, though supportive data for the U.S.

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Background: Although traffic-related air pollution is largely regulated at the federal level, congestion reduction projects may reduce local traffic and air pollution to levels that create positive co-benefits for population health. In recent years, many urban areas have implemented electronic tolling systems to improve traffic conditions.

Objective: Quantify associations between implementing electronic tolling and local changes in traffic and infant health.

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Background: More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). The objective is to identify risk factors associated with PASC/long-COVID diagnosis.

Methods: This was a retrospective case-control study including 31 health systems in the United States from the National COVID Cohort Collaborative (N3C).

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  • Traditional policies aiming to reduce low-acuity emergency department (ED) visits assume that patients use EDs as a backup when primary care (PC) is unavailable, but this can worsen ED overcrowding if patients are actually using them alongside their PC.
  • A study analyzing Medicaid managed care enrollees found that post-Medicaid expansion, EDs are generally substitutes for PC statewide, but in urban and poorer areas, especially at night and on weekends, they often serve as complements.
  • The findings suggest that strategies to reduce unnecessary ED use should prioritize increasing actual appointment availability with physicians in areas that lack sufficient PC resources.
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Objectives: To define pregnancy episodes and estimate gestational age within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C).

Materials And Methods: We developed a comprehensive approach, named Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS), and applied it to EHR data in the N3C (January 1, 2018-April 7, 2022). HIPPS combines: (1) an extension of a previously published pregnancy episode algorithm, (2) a novel algorithm to detect gestational age-specific signatures of a progressing pregnancy for further episode support, and (3) pregnancy start date inference.

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Importance: Air pollution presents clear environmental justice issues. However, few studies have specifically examined traffic-related air pollution (TRAP), a source driven by historically racist infrastructure policies, among pregnant individuals, a population susceptible to air pollution effects. How these disparities have changed over time is also unclear but has important policy implications.

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Traffic-related air pollution (TRAP) is an established health hazard, and roadway construction has the potential to affect TRAP by relieving congestion. The relationship between roadway construction and congestion is of policy importance, but few studies examine it using large samples of construction projects and detailed traffic and air pollution data. We create a dataset of construction projects in Texas and link them to data on air pollution and three variables operationalizing congestion: average annual daily traffic (AADT), AADT per lane, and delay in hours.

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More than 11 million Americans reside within 150 meters of a highway, an area of high air pollution exposure. Traffic congestion further contributes to environmental pollution (e.g.

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Objective: To define pregnancy episodes and estimate gestational aging within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C).

Materials And Methods: We developed a comprehensive approach, named H ierarchy and rule-based pregnancy episode I nference integrated with P regnancy P rogression S ignatures (HIPPS) and applied it to EHR data in the N3C from 1 January 2018 to 7 April 2022. HIPPS combines: 1) an extension of a previously published pregnancy episode algorithm, 2) a novel algorithm to detect gestational aging-specific signatures of a progressing pregnancy for further episode support, and 3) pregnancy start date inference.

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There is a striking geographic variation in drug overdose deaths without a specific drug recorded, many of which likely involve opioids. Knowledge of the reasons underlying this variation is limited. We sought to understand the role of medicolegal death investigation (MDI) systems in unclassified drug overdose mortality.

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This study assesses the health risks associated with drinking water contamination using variation in the timing and location of shale gas development (SGD). Our novel dataset, linking health and drinking water outcomes to shale gas activity through water sources, enables us to provide new estimates of the causal effects of water pollution on health and to isolate drinking water as a specific mechanism of exposure for SGD. We find consistent and robust evidence that drilling shale gas wells negatively impacts both drinking water quality and infant health.

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Background: Oil and gas extraction produces air pollutants that are associated with increased risks of hypertension. To date, no study has examined residential proximity to oil and gas extraction and hypertensive conditions during pregnancy. This study quantifies associations between residential proximity to oil and gas development on gestational hypertension and eclampsia.

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Background: Oil and natural gas extraction may produce environmental pollution at levels that affect reproductive health of nearby populations. Available studies have primarily focused on unconventional gas drilling and have not accounted for local population changes that can coincide with drilling activity.

Objective: Our study sought to examine associations between residential proximity to oil and gas drilling and adverse term birth outcomes using a difference-in-differences study design.

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  • - The National COVID Cohort Collaborative (N3C) is a massive electronic health record database that provides valuable insights into COVID-19, supporting the development of better diagnostic tools and clinical practices.
  • - This study analyzed data from nearly 2 million adults across 34 medical centers to evaluate the severity of COVID-19 and its risk factors over time, using advanced machine learning techniques to predict severe outcomes.
  • - Among the 174,568 adults infected with SARS-CoV-2, a significant portion experienced severe illness, highlighting the need for continuous monitoring and adjustment of treatment approaches based on demographic characteristics and disease severity.
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Background: Whereas it is plausible that unconventional natural gas development (UNGD) may adversely affect cardiovascular health, little is currently known. We investigate whether UNGD is associated with acute myocardial infarction (AMI).

Methods: In this observational study leveraging the natural experiment generated by New York's ban on hydraulic fracturing, we analyzed the relationship between age- and sex-specific county-level AMI hospitalization and mortality rates and three UNGD drilling measures.

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Background: Since the 1990s, extensive regulations to reduce traffic-related air pollution (TRAP) have been implemented, yet the effectiveness of these regulations has not been assessed with respect to improving infant health. In this study, we evaluate how infant health risks associated with maternal residences near highways during pregnancy have changed over time.

Methods: We created a population-based retrospective birth cohort with geocoded residential addresses in Texan metropolitan areas from 1996 through 2009 (n = 2 259 411).

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  • The National COVID Cohort Collaborative (N3C) is the largest U.S. COVID-19 patient database, created to provide a comprehensive analysis of clinical characteristics, disease progression, and treatment outcomes across multiple health centers, enhancing predictive and diagnostic tools for COVID-19.
  • A study involving over 1.9 million patients from 34 medical centers found significant clinical data, showing that certain factors like age, sex, and underlying conditions affect disease severity, with a notable decrease in mortality rates among hospitalized patients over time.
  • The N3C dataset was utilized in machine learning models to successfully predict severe outcomes in COVID-19 patients, achieving high accuracy rates and demonstrating the potential of using electronic health
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  • - Social distancing measures in the U.S. aimed at controlling COVID-19 significantly reduced the rate at which confirmed cases doubled across most states, with the average doubling time dropping from about 0.302 days to 0.010 days.
  • - The study found a statistically significant reduction in the doubling rate due to social distancing, except in Nebraska, North Dakota, and South Dakota, indicating varied effectiveness by state.
  • - While social distancing stabilized the spread of COVID-19, it did not reduce it to a subcritical level, suggesting ongoing risks and important policy considerations to address the pandemic's impact.
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Background And Aims: A substantial share of fatal drug overdoses is missing information on specific drug involvement, leading to under-reporting of opioid-related death rates and a misrepresentation of the extent of the opioid epidemic. We aimed to compare methodological approaches to predicting opioid involvement in unclassified drug overdoses in US death records and to estimate the number of fatal opioid overdoses from 1999 to 2016 using the best-performing method.

Design: This was a secondary data analysis of the universe of drug overdoses in 1999-2016 obtained from the National Center for Health Statistics Detailed Multiple Cause of Death records.

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