High Traffic Roads and Adverse Birth Outcomes: Comparing Births Upwind and Downwind of the Same Road.

Traffic related air pollution is a major concern for perinatal health. Determining causal associations, however, is difficult since high-traffic areas tend to correspond with lower socioeconomic neighborhoods and other environmental exposures. To overcome confounding, we compared pregnant individuals living downwind and upwind of the same high-traffic road. We leveraged vital statistics data for Texas from 2007-2016 (n=3,570,272 births) and computed hourly wind estimates for residential addresses within 500 m of high-traffic roads (i.e., annual average daily traffic greater than 25,000) (10.9% of births). We matched pregnant individuals predominantly upwind to pregnant neighbors downwind of the same road segment (n=37,631 pairs). Living downwind was associated with an 11.6 gram (95% CI: -18.01, -5.21) decrease in term birth weight. No associations were observed with low term birth weight, preterm birth, or very preterm birth. In distance-stratified models, living downwind within 50 m was associated with a -36.3 gram (95% CI: -67.74, -4.93) decrease in term birth weight and living 51-100m downwind was associated with an odds ratio of 3.68 (95% CI: 1.71, 7.90) for very preterm birth. These results suggest traffic air pollution is associated with adverse birth outcomes, with steep distance decay gradients around major roads.

Pregnancy Outcomes among Pregnant Persons after COVID-19 Vaccination: Assessing Vaccine Safety in Retrospective Cohort Analysis of U.S. National COVID Cohort Collaborative (N3C).

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. over different periods of variant predominance are lacking. We sought to analyze the association of adverse pregnancy outcomes with COVID-19 vaccinations in the pre-Delta, Delta, and Omicron SARS-CoV-2 variants' dominant periods (constituting 50% or more of each pregnancy) for pregnant persons in a large, nationally sampled electronic health record repository in the U.S. Our overall analysis included 311,057 pregnant persons from December 2020 to October 2023 at a time when there were approximately 3.6 million births per year. We compared rates of preterm births and stillbirths among pregnant persons who were vaccinated before or during pregnancy to persons vaccinated after pregnancy or those who were not vaccinated. We performed a multivariable Poisson regression with generalized estimated equations to address data site heterogeneity for preterm births and unadjusted exact models for stillbirths, stratified by the dominant variant period. We found lower rates of preterm birth in the majority of modeled periods (adjusted incidence rate ratio [aIRR] range: 0.42 to 0.85; p-value range: <0.001 to 0.06) and lower rates of stillbirth (IRR range: 0.53 to 1.82; p-value range: <0.001 to 0.976) in most periods among those who were vaccinated before or during pregnancy compared to those who were vaccinated after pregnancy or not vaccinated. We largely found no adverse associations between COVID-19 vaccination and preterm birth or stillbirth; these findings reinforce the safety of COVID-19 vaccination during pregnancy and bolster confidence for pregnant persons, providers, and policymakers in the importance of COVID-19 vaccination for this group despite the end of the public health emergency.

A population-based cohort study of electronic tolling, traffic congestion, and adverse birth outcomes.

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. METHODS: Using a population-based birth cohort (Texas, 1999-2016), we calculated residential proximity to the nearest tolled road segment within 5 km (n = 625,279) and examined changes in local traffic before and after toll implementation. Using a difference-in-differences design, we compared four markers of adverse birth outcomes (term birth weight, term low birth weight, preterm birth, very preterm birth) among infants from pregnant people residing < 0.5 km from a road segment before and after the tolls were implemented and compared them to a contemporaneous population of pregnant people residing at 2-5 km. RESULTS: We observed minimal changes in local traffic after the implementation of tolling. Among births within 500 m of a tolled road, we found little evidence of an association between the implementation of tolling and adverse birth outcomes (term birth weight [ß: -4.5, 95 % CI: -11.7, 2.6], term low birth weight [OR: 1.00, 95 % CI: 0.89, 1.13], preterm birth [OR: 0.99, 95 % CI: 0.92, 1.05], very preterm birth [OR: 1.00, 95 % CI: 0.84, 1.18]), compared to the contemporaneous control group of births at 2-5 km. In sub-analyses, we found some evidence of a reduced association between toll booth removal and preterm birth (OR: 0.84, 95 % CI: 0.70, 1.01) but not for other outcomes or tolling types. DISCUSSION: In this large population-based retrospective cohort study of births in Texas, we found little evidence that the implementation of tolling was consistently associated with improvements in local infant health outcomes.

Is the emergency department used as a substitute or a complement to primary care in Medicaid?

Policies to decrease low-acuity emergency department (ED) use have traditionally assumed that EDs are a substitute for unavailable primary care (PC). However, such policies can exacerbate ED overcrowding, rather than ameliorate it, if patients use EDs to complement, rather than substitute, their PC use. We tested whether Medicaid managed care enrolees visit the ED for nonemergent and PC treatable conditions to substitute for or to complement PC. Based on consumer choice theory, we modelled county-level monthly ED visit rate as a function of PC supply and used 2012-2015 New York Statewide Planning and Research Cooperative System (SPARCS) outpatient data and non-linear least squares method to test substitution vs complementarity. In the post-Medicaid expansion period (2014-2015), ED and PC are substitutes state-wide, but are complements in highly urban and poorer counties during nights and weekends. There is no evidence of complementarity before the expansion (2012-2013). Analyses by PC provider demonstrate that the relationship between ED and PC differs depending on whether PC is provided by physicians or advanced practice providers. Policies to reduce low-acuity ED use via improved PC access in Medicaid are likely to be most effective if they focus on increasing actual appointment availability, ideally by physicians, in areas with low PC provider supply. Different aspects of PC access may be differently related to low-acuity ED use.

Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study.

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). 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system and COVID index date within ± 45 days of the corresponding case's earliest COVID index date. Measurements of risk factors included demographics, comorbidities, treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. RESULTS: Among 8,325 individuals with PASC, the majority were > 50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30 + days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls. CONCLUSIONS: This national study identified important risk factors for PASC diagnosis such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.

Who is pregnant? Defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C).

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. Clinicians performed validation of HIPPS on a subset of episodes. We then generated pregnancy cohorts based on gestational age precision and pregnancy outcomes for assessment of accuracy and comparison of COVID-19 and other characteristics. Results: We identified 628 165 pregnant persons with 816 471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, abortions), and 23.3% had unknown outcomes. Clinician validation agreed 98.8% with HIPPS-identified episodes. We were able to estimate start dates within 1 week of precision for 475 433 (58.2%) episodes. 62 540 (7.7%) episodes had incident COVID-19 during pregnancy. Discussion: HIPPS provides measures of support for pregnancy-related variables such as gestational age and pregnancy outcomes based on N3C data. Gestational age precision allows researchers to find time to events with reasonable confidence. Conclusion: We have developed a novel and robust approach for inferring pregnancy episodes and gestational age that addresses data inconsistency and missingness in EHR data.

Changes in Socioeconomic Disparities for Traffic-Related Air Pollution Exposure During Pregnancy Over a 20-Year Period in Texas.

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. Objective: To examine changes in TRAP exposure by sociodemographic characteristics among recorded pregnancies over a 20-year period. Design, Setting, and Participants: This population-based birth cohort study used descriptive analysis among pregnant individuals in Texas from 1996 to 2016. All pregnant individuals with valid residential address, socioeconomic, and demographic data were included. Individual-level race and ethnicity, education, and maternal birthplace data were extracted from birth certificates and neighborhood-level household income and historical neighborhood disinvestment (ie, redlining) data were assessed via residential addresses. Data analysis occurred between June 2022 and June 2023. Main Outcomes and Measures: The main outcome, TRAP exposure at residential addresses, was assessed via traffic levels, represented by total and truck-specific vehicle miles traveled (VMT) within 500 m; nitrogen dioxide (no2) concentrations from a spatial-temporal land use regression model (ie, vehicle tailpipe emissions); and National Air Toxic Agency cancer risk index from on-road vehicle emissions. TRAP exposure differences were assessed by sociodemographic indicators over the 1996 to 2016 period. Results: Among 7 043 598 pregnant people (mean [SD] maternal age, 26.8 [6.1] years) in Texas from 1996 to 2016, 48% identified as Hispanic or Latinx, 4% identified as non-Hispanic Asian or Pacific Islander, 12% identified as non-Hispanic Black, and 36% identified as non-Hispanic White. There were differences in TRAP for pregnant people by all sociodemographic variables examined. The absolute level of these disparities decreased from 1996 to 2016, but the relative level of these disparities increased: for example, in 1996, non-Hispanic Black pregnant individuals were exposed to a mean (SD) 15.3 (4.1) ppb of no2 vs 13.5 (4.4) ppb of no2 for non-Hispanic White pregnant individuals, compared with 2016 levels of 6.7 (2.4) ppb no2 for Black pregnant individuals and 5.2 (2.4) ppb of no2 for White pregnant individuals. Large absolute and relative differences in traffic levels were observed for all sociodemographic characteristics, increasing over time. For example, non-Hispanic Black pregnant individuals were exposed to a mean (SD) of 22 836 (32 844) VMT within 500 m of their homes, compared with 12 478 (22 870) VMT within 500 m of the homes of non-Hispanic White pregnant individuals in 2016, a difference of 83%. Conclusions and Relevance: This birth cohort study found that while levels of air pollution disparities decreased in absolute terms over the 20 years of the study, relative disparities persisted and large differences in traffic levels remained, requiring renewed policy attention.

Changes in traffic congestion and air pollution due to major roadway infrastructure improvements in Texas.

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. We use difference-in-difference methods to estimate the effect of widening and intersection improvements on congestion and air pollution. On average over the period during construction, we find that widening increases delay by 42% (95% CI: 30, 56%), but intersection projects do not affect delay. On average and over the first three years post-construction, we find that widening reduces delay by 33% (95% CI: -41, -24%) and reduces NO2 levels within 500 m by 13% (95% CI: -22, -2%), and intersection projects reduce delay by 52% (95% CI: -65, -35%) and reduce NO2 levels within 500 m by 12% (95% CI: -18, -5%). These short-term impacts are relevant for understanding the impact of roadway construction on human health.

A population-based cohort study of traffic congestion and infant growth using connected vehicle data.

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., air and noise), but its unique importance for population health is unclear. We hypothesized that degraded environmental quality specifically from traffic congestion has harmful impacts on fetal growth. Using a population-based cohort of births in Texas (2015-2016), we leveraged connected vehicle data to calculate traffic congestion metrics around each maternal address at delivery. Among 579,122 births, we found consistent adverse associations between traffic congestion and reduced term birth weight (8.9 grams), even after accounting for sociodemographic characteristics, typical traffic volume, and diverse environmental coexposures. We estimated that up to 1.2 million pregnancies annually may be exposed to traffic congestion (27% of births in the United States), with ~256,000 in the highest congestion zones. Therefore, improvements to traffic congestion may yield positive cobenefits for infant health.

Who is pregnant? defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C).

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. Clinicians performed validation of HIPPS on a subset of episodes. We then generated three types of pregnancy cohorts based on the level of precision for gestational aging and pregnancy outcomes for comparison of COVID-19 and other characteristics. Results: We identified 628,165 pregnant persons with 816,471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, spontaneous abortions), and 23.3% had unknown outcomes. We were able to estimate start dates within one week of precision for 431,173 (52.8%) episodes. 66,019 (8.1%) episodes had incident COVID-19 during pregnancy. Across varying COVID-19 cohorts, patient characteristics were generally similar though pregnancy outcomes differed. Discussion: HIPPS provides support for pregnancy-related variables based on EHR data for researchers to define pregnancy cohorts. Our approach performed well based on clinician validation. Conclusion: We have developed a novel and robust approach for inferring pregnancy episodes and gestational aging that addresses data inconsistency and missingness in EHR data.

Coroner county systems are associated with a higher likelihood of unclassified drug overdoses compared to medical examiner county systems.

Background: 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.Objectives: We sought to understand the role of medicolegal death investigation (MDI) systems in unclassified drug overdose mortality.Methods: This is an observational study of 2014 and 2018 fatal drug overdoses and U.S. county-level MDI system type (coroner vs medical examiner). Mortality data are from the CDC's National Center for Health Statistics. We estimated multivariable logistic regressions to quantify associations between MDI system type and several outcome variables: whether the drug overdose was unclassified and whether involvement of any opioid, synthetic opioid, methadone, and heroin was recorded (vs unclassified), for 2014 (N = 46,996) and 2018 (N = 67,359).Results: In 2018, drug overdose deaths occurring in coroner counties were almost four times more likely to be unclassified (OR 3.87, 95% CI 2.32, 6.46) compared to medical examiner counties. These odds ratios are twice as large as in 2014 (difference statistically significant, P < .001), indicating that medical examiner counties are improving identification of opioids in drug overdoses faster than coroner counties.Conclusions: Accurate reporting of drug overdose deaths depends on MDI systems. When developing state policies and local interventions aimed to decrease opioid overdose mortality, decision-makers should understand the role their MDI system is playing in underestimating the extent of the opioid overdose crisis. Improvements to state and county MDI systems are desirable if accurate reporting and appropriate policy response are to be achieved.

Drinking water, fracking, and infant health.

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. These results indicate large social costs of water pollution and provide impetus for re-visiting the regulation of public drinking water.

Associations between residential proximity to oil and gas extraction and hypertensive conditions during pregnancy: a difference-in-differences analysis in Texas, 1996-2009.

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. METHODS: We utilized a population-based retrospective birth cohort in Texas (1996-2009), where mothers reside <10 km from an active or future drilling site (n = 2 845 144.) Using full-address data, we linked each maternal residence at delivery to assign exposure and evaluate this exposure with respect to gestational hypertension and eclampsia. In a difference-in-differences framework, we model the interaction between maternal health before (unexposed) or after (exposed) the start of drilling activity (exposed) and residential proximity near (0-1, >1-2 or >2-3 km) or far (≥3-10 km) from an active or future drilling site. RESULTS: Among pregnant women residing 0-1 km from an active oil or gas extraction site, we estimate 5% increased odds of gestational hypertension [95% confidence interval (CI): 1.00, 1.10] and 26% increased odds of eclampsia (95% CI: 1.05, 1.51) in adjusted models. This association dissipates in the 1- to 3-km buffer zones. In restricted models, we find elevated odds ratios among maternal ages ≤35 years at delivery, maternal non-Hispanic White race, ≥30 lbs gained during pregnancy, nulliparous mothers and maternal educational attainment beyond high school. CONCLUSIONS: Living within 1 km of an oil or gas extraction site during pregnancy is associated with increased odds of hypertensive conditions during pregnancy.

Associations between Residential Proximity to Oil and Gas Drilling and Term Birth Weight and Small-for-Gestational-Age Infants in Texas: A Difference-in-Differences Analysis.

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. METHODS: We created a retrospective population-based term birth cohort in Texas between 1996 and 2009 composed of mother-infant dyads (n=2,598,025) living <10km from an oil or gas site. We implemented a difference-in-differences approach to estimate associations between drilling activities and infant health: term birth weight and term small for gestational age (SGA). Using linear and logistic regression, we modeled interactions between births before (unexposed) or during (exposed) drilling activity and residential proximity near (0-1, 1-2, or 2-3km) or far (3-10km) from an active or future drilling site, adjusting for individual- and neighborhood-level characteristics. RESULTS: The adjusted mean difference in term birth weight for mothers living 0-1 vs. 3-10km from a current or future drilling site was -7.3g [95% confidence interval (CI): -11.6, -3.0] for births during active vs. future drilling. The corresponding adjusted odds ratio for SGA was 1.02 (95% CI: 0.98, 1.06). Negative associations with term birth weight were observed for the 1-2 and 2-3km near groups, and no consistent differences were identified by type of drilling activity. Larger, though imprecise, adverse associations were found for infants born to Hispanic women, women with the lowest educational attainment, and women living in cities. CONCLUSIONS: Residing near oil and gas drilling sites during pregnancy was associated with a small reduction in term birth weight but not SGA, with some evidence of environmental injustices. Additional work is needed to investigate specific drilling-related exposures that might explain these associations. https://doi.org/10.1289/EHP7678.

Clinical Characterization and Prediction of Clinical Severity of SARS-CoV-2 Infection Among US Adults Using Data From the US National COVID Cohort Collaborative.

Importance: The National COVID Cohort Collaborative (N3C) is a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy. Objectives: To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. Design, Setting, and Participants: In a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). Main Outcomes and Measures: Patient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. Results: The cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, age (odds ratio [OR], 1.03 per year; 95% CI, 1.03-1.04), male sex (OR, 1.60; 95% CI, 1.51-1.69), liver disease (OR, 1.20; 95% CI, 1.08-1.34), dementia (OR, 1.26; 95% CI, 1.13-1.41), African American (OR, 1.12; 95% CI, 1.05-1.20) and Asian (OR, 1.33; 95% CI, 1.12-1.57) race, and obesity (OR, 1.36; 95% CI, 1.27-1.46) were independently associated with higher clinical severity. Conclusions and Relevance: This cohort study found that COVID-19 mortality decreased over time during 2020 and that patient demographic characteristics and comorbidities were associated with higher clinical severity. The machine learning models accurately predicted ultimate clinical severity using commonly collected clinical data from the first 24 hours of a hospital admission.

Acute myocardial infarction associated with unconventional natural gas development: A natural experiment.

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. This longitudinal panel analysis compares Pennsylvania and New York counties on the Marcellus Shale observed over 2005-2014 (N = 2840 county-year-quarters). RESULTS: A hundred cumulative wells is associated with 0.26 more hospitalizations per 10,000 males 45-54y.o. (95% CI 0.07,0.46), 0.40 more hospitalizations per 10,000 males 65-74y.o. (95% CI 0.09,0.71), 0.47 more hospitalizations per 10,000 females 65-74y.o. (95% CI 0.18,0.77) and 1.11 more hospitalizations per 10,000 females 75y.o.+ (95% CI 0.39,1.82), translating into 1.4-2.8% increases. One additional well per square mile is associated with 2.63 more hospitalizations per 10,000 males 45-54y.o. (95% CI 0.67,4.59) and 9.7 hospitalizations per 10,000 females 75y.o.+ (95% CI 1.92,17.42), 25.8% and 24.2% increases, respectively. As for mortality rates, a hundred cumulative wells is associated with an increase of 0.09 deaths per 10,000 males 45-54y.o. (95% CI 0.02,0.16), a 5.3% increase. CONCLUSIONS: Cumulative UNGD is associated with increased AMI hospitalization rates among middle-aged men, older men and older women as well as with increased AMI mortality among middle-aged men. Our findings lend support for increased awareness about cardiovascular risks of UNGD and scaled-up AMI prevention as well as suggest that bans on hydraulic fracturing can be protective for public health.

Assessing the effectiveness of vehicle emission regulations on improving perinatal health: a population-based accountability study.

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). We compared term birthweight (37-42 weeks of gestation) among maternal residences <300 m from a highway (high TRAP exposure) (n = 394 346) and 500-3500 m from a highway (comparison group) (n = 1 865 065). We implemented linear regressions to evaluate interactions between high TRAP exposure and birth year, adjusting for demographics, socioeconomic status and neighbourhood context. In addition, we used propensity score matching to further reduce residual confounding. RESULTS: From 1996 to 2009, outdoor NO2 decreased by 51.3%, based on regulatory monitoring data in Texas. Among pregnant women who resided in the high TRAP zone during pregnancy, interaction terms between residential location and birth year show that birthweight increased by 1.1 g [95% confidence interval CI): 0.7, 1.5) in unadjusted models and 0.3 g (95% CI: 0.0, 0.6) in matched models. Time-stratified models also show decreasing impacts of living in high TRAP areas on birthweight when comparing infants born in 1996-97 with 2008-09. Sensitivity analyses with alternative exposure and control groups show consistent results. CONCLUSIONS: Infant health risks associated with maternal residence near highways have reduced over time, paralleling regulatory measures to improve exhaust pipe emissions.

The National COVID Cohort Collaborative: Clinical Characterization and Early Severity Prediction.

Background: The majority of U.S. reports of COVID-19 clinical characteristics, disease course, and treatments are from single health systems or focused on one domain. Here we report the creation of the National COVID Cohort Collaborative (N3C), a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative U.S. cohort of COVID-19 cases and controls to date. This multi-center dataset supports robust evidence-based development of predictive and diagnostic tools and informs critical care and policy. Methods and Findings: In a retrospective cohort study of 1,926,526 patients from 34 medical centers nationwide, we stratified patients using a World Health Organization COVID-19 severity scale and demographics; we then evaluated differences between groups over time using multivariable logistic regression. We established vital signs and laboratory values among COVID-19 patients with different severities, providing the foundation for predictive analytics. The cohort included 174,568 adults with severe acute respiratory syndrome associated with SARS-CoV-2 (PCR >99% or antigen <1%) as well as 1,133,848 adult patients that served as lab-negative controls. Among 32,472 hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March/April 2020 to 8.6% in September/October 2020 (p = 0.002 monthly trend). In a multivariable logistic regression model, age, male sex, liver disease, dementia, African-American and Asian race, and obesity were independently associated with higher clinical severity. To demonstrate the utility of the N3C cohort for analytics, we used machine learning (ML) to predict clinical severity and risk factors over time. Using 64 inputs available on the first hospital day, we predicted a severe clinical course (death, discharge to hospice, invasive ventilation, or extracorporeal membrane oxygenation) using random forest and XGBoost models (AUROC 0.86 and 0.87 respectively) that were stable over time. The most powerful predictors in these models are patient age and widely available vital sign and laboratory values. The established expected trajectories for many vital signs and laboratory values among patients with different clinical severities validates observations from smaller studies, and provides comprehensive insight into COVID-19 characterization in U.S. patients. Conclusions: This is the first description of an ongoing longitudinal observational study of patients seen in diverse clinical settings and geographical regions and is the largest COVID-19 cohort in the United States. Such data are the foundation for ML models that can be the basis for generalizable clinical decision support tools. The N3C Data Enclave is unique in providing transparent, reproducible, easily shared, versioned, and fully auditable data and analytic provenance for national-scale patient-level EHR data. The N3C is built for intensive ML analyses by academic, industry, and citizen scientists internationally. Many observational correlations can inform trial designs and care guidelines for this new disease.