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.

Roadway construction as a natural experiment to examine air pollution impacts on infant health.

Traffic-related air pollution (TRAP) poses a significant public health risk that is associated with adverse birth outcomes. Large roadway infrastructure projects present a natural experiment to examine how resulting congestion change is associated with adverse birth outcomes for nearby populations. This study is designed to examine the influence of living close to a roadway before, during, and after a construction project using a difference-in-differences design. We integrated data on all large roadway construction projects (defined as widening of existing roads, building new roads, improving bridges, installing intelligent transportation systems, improving intersections, and installing or upgrading traffic signals) in Texas from 2007 to 2016 with Vital Statistic data for all births with residential addresses within 1 km of construction projects. Our outcomes included term low birth weight, term birth weight, preterm birth, and very preterm birth. Using a difference-in-differences design, we included births within 3 years of construction start and 2 years of construction end. In our main model, the exposed group is limited to pregnant individuals residing within 300 m of a construction project, and the control group includes those living within 300-1000 m from a project. We used regression models to estimate the influence of construction on infant health. We included 1,360 large roadway construction projects linked to 408,979 births. During construction, we found that the odds of term low birth weight increased by 19% (95% CI: 1.05, 1.36). However, we saw little evidence of an association for other birth outcomes. Contrary to our hypothesis of decreased TRAP after construction ends, we did not observe consistent improvements post-construction for pregnant individuals living within 300 m. Continued consideration of the influence of traffic congestion programs on birth outcomes is necessary to inform future policy decisions.

The role of medical support workers during the COVID-19 pandemic in the NHS in the UK: A qualitative service evaluation at the Oxford University Hospitals NHS Foundation Trust.

We conducted a service evaluation of the medical support worker (MSW) role at Oxford University Hospitals NHS Foundation Trust following the Coronavirus 2019 (COVID-19) pandemic. The aim was to explore the roles of MSWs, their contributions to the NHS, factors influencing their career choices, and the goals of the MSW position, to inform quality improvement in relation to their integration into the Trust. The perspectives of MSWs, their supervisors and recruiters were analysed through nine semi-structured interviews and two focus group discussions involving 18 participants. Results were categorised into micro-, meso-, and macro-levels of the health system. At the micro-level, MSWs were recognised as a diverse group of highly qualified international medical graduates (IMGs) who had a crucial role during the pandemic. At the meso-level, participants emphasised the importance of a comprehensive induction by the hospital, to clarify responsibilities and familiarise MSWs with the health system. At the macro-level, the role facilitated MSW integration within the NHS, with the aim of practising as doctors. The importance of comprehensive hospital induction, with role clarity for both MSWs and their teams, supportive supervision and assistance with applying for registration with the General Medical Council, were highlighted as key quality improvement areas.

Multiple Dimensions of Environmental Justice and Oil and Gas Development in Pennsylvania.

Background: Community socioeconomic deprivation (CSD) may be related to higher oil and natural gas development (OGD) exposure. We tested for distributive and benefit-sharing environmental injustice in Pennsylvania's Marcellus Shale by examining (1) whether OGD and waste disposal occurred disproportionately in more deprived communities and (2) discordance between the location of land leased for OGD and where oil and gas rights owners resided. Materials and Methods: Analyses took place at the county subdivision level and considered OGD wells, waste disposal, and land lease agreement locations from 2005 to 2019. Using 2005-2009 American Community Survey data, we created a CSD index relevant to community vulnerability in suburban/rural areas. Results: In adjusted regression models accounting for spatial dependence, we observed no association between the CSD index and conventional or unconventional drilled well presence. However, a higher CSD index was linearly associated with odds of a subdivision having an OGD waste disposal site and receiving a larger volume of waste. A higher percentage of oil and gas rights owners lived in the same county subdivision as leased land when the community was least versus most deprived (66% vs. 56% in same county subdivision), suggesting that individuals in more deprived communities were less likely to financially benefit from OGD exposure. Discussion and Conclusions: We observed distributive environmental injustice with respect to well waste disposal and benefit-sharing environmental injustice related to oil and rights owner's residential locations across Pennsylvania's Marcellus Shale. These results add evidence of a disparity between exposure and benefits resulting from OGD.

Influence of Community Determinants on Barriers and Facilitators to COVID-19 Vaccine Uptake: Regional Stakeholders' Perspectives.

Our study objective was to explore possible pathways by which neighborhood-level characteristics drive COVID-19 vaccination among communities experiencing health inequity and disparities. To do so, we conducted semi-structured qualitative interviews with stakeholders in the Finger Lakes region of New York between November 2022 through January 2023. Using a pre-developed interview guide informed by the Health Belief Model, we elicited county health commissioner, medical professional and community-health partner's perspectives regarding the impact community determinants have on shaping barriers to and facilitators of COVID-19 vaccine uptake, as well as recommended solutions for ensuring health equity in future vaccination efforts. Fifteen stakeholders were interviewed, of which 40% were county health commissioners, 27% hospital executives and 33% community-health organization partners. We identified diverse perceived and logistical barriers to COVID-19 vaccination that emerged as sub-themes including perceived risks outweighing the benefits of vaccination, concerns of vaccine safety, distrust stemming from changing scientific knowledge surrounding the vaccines, mistrust of the medical system, and challenges with transportation to and registering for vaccine appointments. Stakeholders reported these barriers were linked to the racial diversity, poverty, political conservatism, and availability of health care of the communities where they reported these were experienced. Notable solutions for improving equitable vaccination included facilitating structural access to vaccination and engaging community trusted messengers. Perceived and experienced barriers to COVID-19 vaccination may differ based upon the social, racial and economic composition of neighboring communities. Strategies for ensuring health equity in future vaccination campaigns should specifically target prevalent barriers based upon the community composition.

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.

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.

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.

Effect of Nirmatrelvir/Ritonavir (Paxlovid) on Hospitalization among Adults with COVID-19: an EHR-based Target Trial Emulation from N3C.

This study leverages electronic health record data in the National COVID Cohort Collaborative's (N3C) repository to investigate disparities in Paxlovid treatment and to emulate a target trial assessing its effectiveness in reducing COVID-19 hospitalization rates. From an eligible population of 632,822 COVID-19 patients seen at 33 clinical sites across the United States between December 23, 2021 and December 31, 2022, patients were matched across observed treatment groups, yielding an analytical sample of 410,642 patients. We estimate a 65% reduced odds of hospitalization among Paxlovid-treated patients within a 28-day follow-up period, and this effect did not vary by patient vaccination status. Notably, we observe disparities in Paxlovid treatment, with lower rates among Black and Hispanic or Latino patients, and within socially vulnerable communities. Ours is the largest study of Paxlovid's real-world effectiveness to date, and our primary findings are consistent with previous randomized control trials and real-world studies.

Coding long COVID: characterizing a new disease through an ICD-10 lens.

BACKGROUND: Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of long COVID are still in flux, and the deployment of an ICD-10-CM code for long COVID in the USA took nearly 2 years after patients had begun to describe their condition. Here, we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." METHODS: We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 33,782), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. RESULTS: We established the diagnoses most commonly co-occurring with U09.9 and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty and low unemployment. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. CONCLUSIONS: This work offers insight into potential subtypes and current practice patterns around long COVID and speaks to the existence of disparities in the diagnosis of patients with long COVID. This latter finding in particular requires further research and urgent remediation.

SARS-CoV-2 Reinfection is Preceded by Unique Biomarkers and Related to Initial Infection Timing and Severity: an N3C RECOVER EHR-Based Cohort Study.

Although the COVID-19 pandemic has persisted for over 2 years, reinfections with SARS-CoV-2 are not well understood. We use the electronic health record (EHR)-based study cohort from the National COVID Cohort Collaborative (N3C) as part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative to characterize reinfection, understand development of Long COVID after reinfection, and compare severity of reinfection with initial infection. We validate previous findings of reinfection incidence (5.9%), the occurrence of most reinfections during the Omicron epoch, and evidence of multiple reinfections. We present novel findings that Long COVID diagnoses occur closer to the index date for infection or reinfection in the Omicron BA epoch. We report lower albumin levels leading up to reinfection and a statistically significant association of severity between first infection and reinfection (chi-squared value: 9446.2, p-value: 0) with a medium effect size (Cramer's V: 0.18, DoF = 4).

The Geographical Correlation Between Historical Preterm Birth Disparities and COVID-19 Burden.

Similar to obstetric outcomes, rates of SARS-CoV-2 (COVID-19) infection are not homogeneously distributed among populations; risk factors accumulate in discrete locations. This study aimed to investigate the geographical correlation between pre-COVID-19 regional preterm birth (PTB) disparities and subsequent COVID-19 disease burden. We performed a retrospective, ecological cohort study of an upstate New York birth certificate database from 2004 to 2018, merged with publicly available community resource data. COVID-19 rates from 2020 were used to allocate ZIP codes to "low-," "moderate-," and "high-prevalence" groups, defined by median COVID-19 diagnosis rates. COVID-19 cohorts were associated with poverty and educational attainment data from the US Census Bureau. The dataset was analyzed for the primary outcome of PTB using ANOVA. GIS mapping visualized PTB rates and COVID-19 disease rates by ZIP code. Within 38 ZIP codes, 123,909 births were included. The median COVID-19 infection rate was 616.5 (per 100 K). PTB (all) and COVID-19 were positively correlated, with high- prevalence COVID-19 ZIP codes also being the areas with the highest prevalence of PTB (F = 11.06, P = .0002); significance was also reached for PTB < 28 weeks (F = 15.87, P < .0001) and periviable birth (F = 16.28, P < .0001). Odds of PTB < 28 weeks were significantly higher in the "high-prevalence" COVID-19 cohort compared to the "low-prevalence" COVID 19 cohort (OR 3.27 (95% CI 2.42-4.42)). COVID-19 prevalence was directly associated with number of individuals below poverty level and indirectly associated with median household income and educational attainment. GIS mapping demonstrated ZIP code clustering in the urban center with the highest rates of PTB < 28 weeks overlapping with high COVID-19 disease burden. Historical disparities in social determinants of health, exemplified by PTB outcomes, map community distribution of COVID-19 disease burden. These data should inspire socioeconomic policies supporting economic vibrancy to promote optimal health outcomes across all communities.

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.

Post-Acute Care Use Associated with Medicare Shared Savings Program and Disparities.

OBJECTIVES: Medicare Shared Savings Program (MSSP) was implemented in 2012, but the impact of the MSSP on institutional post-acute care (PAC) use, and by race/ethnicity and payer status is less studied. We studied the impact of hospital participation in the MSSP on institutional PAC use and variations by race/ethnicity and payer status among 3 Medicare patient groups: ischemic stroke, hip fracture, and elective total joint arthroplasty (TJA). DESIGN: A retrospective analysis of 2010-2016 Medicare Provider Analysis and Review files. SETTING AND PARTICIPANTS: Medicare fee-for-service patients originally admitted for ischemic stroke, hip fracture, or elective TJA in MSSP-participating hospitals or nonparticipating hospitals. METHODS: Patient-level linear probability models with difference-in-differences approach were used to compare the changes in institutional PAC use in MSSP-participating hospitals with nonparticipating hospitals as well as to compare the changes in differences by race/ethnicity and payer status in institutional PAC use over time. RESULTS: Hospital participation in MSSP was significantly associated with increased institutional PAC use for the ischemic stroke cohort by 1.5 percentage points [95% confidence interval (CI) 0.00-0.3, P < .05] compared with non-MSSP participating hospitals. Regarding variations by race/ethnicity and payer status, for the elective TJA patients, racial minority patients in MSSP-participating hospitals had 3.8 percentage points greater (95% CI 0.01-0.06, P < .01) in institutional PAC use than white patients. Also, for ischemic stroke cohort, dual-eligible patients in MSSP-participating hospitals had 2.0 percentage points greater (95% CI 0.00-0.04, P < .10) in institutional PAC use than Medicare-only patients. CONCLUSIONS AND IMPLICATIONS: This study found that hospital participation in the MSSP was associated with slightly increased institutional PAC use for ischemic stroke Medicare patients. Also, compared to non-MSSP participating hospitals, MSSP-participating hospitals were more likely to discharge racial minority patients for elective TJA and dual-eligible patients for ischemic stroke to institutional PAC.

Coding Long COVID: Characterizing a new disease through an ICD-10 lens.

Background: Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes Long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of Long COVID are still in flux, and the deployment of an ICD-10-CM code for Long COVID in the US took nearly two years after patients had begun to describe their condition. Here we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." Methods: We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code ( n = 21,072), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. Results: We established the diagnoses most commonly co-occurring with U09.9, and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty, high education, and high access to medical care. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. Conclusions: This work offers insight into potential subtypes and current practice patterns around Long COVID, and speaks to the existence of disparities in the diagnosis of patients with Long COVID. This latter finding in particular requires further research and urgent remediation.

The roles of physician associates and advanced nurse practitioners in the National Health Service in the UK: a scoping review and narrative synthesis.

BACKGROUND: Mid-level practitioners (MLPs), including physician associates (PAs) and advanced nurse practitioners (ANPs), have emerged to address workforce shortages in the UK and perform specific roles in relation to population needs. This has resulted in new ways of working and changes to established professional hierarchies. We conducted a study to investigate the career development, competencies, effectiveness, perceptions, and regulation of PAs and ANPs, with the aim of understanding ways to effectively integrate MLPs into the NHS workforce. METHODS: We conducted a systematic scoping review following PRISMA guidelines. Embase, Medline, the Cochrane database, Pubmed, and CINAHL databases were searched, using terms relating to PAs and ANPs in the UK. A total of 128 studies (60 on PAs and 68 on ANPs) were included in the final analysis. A narrative synthesis, guided by the pre-defined themes and emerging themes, was conducted to bring together the findings. RESULTS: PAs are educated on a medical model with basic medical skills but lack formal professional regulation and do not have prescribing rights. ANPs are educated on a nurse model with enhanced skills that depend on roles within specific specialities, and their governance is mostly employer-led. PAs are primarily employed in secondary care. ANPs are employed widely in both primary and secondary care. No defined career progression exists for PAs. In contrast, becoming an ANP is a form of career progression within nursing. Both roles were regarded as cost-effective in comparison to doctors performing simple tasks. PAs were less understood compared to ANPs and received a mixed reception from colleagues, which sometimes undermined their professional identity, whereas ANPs were mostly welcomed by colleagues. CONCLUSIONS: Potential ways to better integrate PAs and ANPs into the NHS workforce include further initiatives by regulatory bodies and the NHS to create more awareness and clearer role definitions for MLPs, outline potential for career progression, offer transparency with regard to remuneration, and introduction of prescribing rights. Future research might include more cadres of MLPs and explore the international literature.