Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative.

Recent studies have investigated post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) using real-world patient data such as electronic health records (EHR). Prior studies have typically been conducted on patient cohorts with specific patient populations which makes their generalizability unclear. This study aims to characterize PASC using the EHR data warehouses from two large Patient-Centered Clinical Research Networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) area and 16.8 million patients in Florida respectively. With a high-throughput screening pipeline based on propensity score and inverse probability of treatment weighting, we identified a broad list of diagnoses and medications which exhibited significantly higher incidence risk for patients 30-180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients. We identified more PASC diagnoses in NYC than in Florida regarding our screening criteria, and conditions including dementia, hair loss, pressure ulcers, pulmonary fibrosis, dyspnea, pulmonary embolism, chest pain, abnormal heartbeat, malaise, and fatigue, were replicated across both cohorts. Our analyses highlight potentially heterogeneous risks of PASC in different populations.

Identifying environmental risk factors for post-acute sequelae of SARS-CoV-2 infection: An EHR-based cohort study from the recover program.

Post-acute sequelae of SARS-CoV-2 infection (PASC) affects a wide range of organ systems among a large proportion of patients with SARS-CoV-2 infection. Although studies have identified a broad set of patient-level risk factors for PASC, little is known about the association between "exposome"-the totality of environmental exposures and the risk of PASC. Using electronic health data of patients with COVID-19 from two large clinical research networks in New York City and Florida, we identified environmental risk factors for 23 PASC symptoms and conditions from nearly 200 exposome factors. The three domains of exposome include natural environment, built environment, and social environment. We conducted a two-phase environment-wide association study. In Phase 1, we ran a mixed effects logistic regression with 5-digit ZIP Code tabulation area (ZCTA5) random intercepts for each PASC outcome and each exposome factor, adjusting for a comprehensive set of patient-level confounders. In Phase 2, we ran a mixed effects logistic regression for each PASC outcome including all significant (false positive discovery adjusted p-value < 0.05) exposome characteristics identified from Phase I and adjusting for confounders. We identified air toxicants (e.g., methyl methacrylate), particulate matter (PM2.5) compositions (e.g., ammonium), neighborhood deprivation, and built environment (e.g., food access) that were associated with increased risk of PASC conditions related to nervous, blood, circulatory, endocrine, and other organ systems. Specific environmental risk factors for each PASC condition and symptom were different across the New York City area and Florida. Future research is warranted to extend the analyses to other regions and examine more granular exposome characteristics to inform public health efforts to help patients recover from SARS-CoV-2 infection.

Peripartum women's perspectives on research study participation in the OneFlorida Clinical Research Consortium during COVID-19 pandemic.

Introduction: The COVID-19 pandemic created an unprecedented need for population-level clinical trials focused on the discovery of life-saving therapies and treatments. However, there is limited information on perception of research participation among perinatal populations, a population of particular interest during the pandemic. Methods: Eligible respondents were 18 years or older, were currently pregnant or had an infant (≤12 months old), and lived in Florida within 50 miles of sites participating in the OneFlorida Clinical Research Consortium. Respondents were recruited via Qualtrics panels between April and September 2020. Respondents completed survey items about barriers and facilitators to participation and answered sociodemographic questions. Results: Of 533 respondents, most were between 25 and 34 years of age (n = 259, 49%) and identified as White (n = 303, 47%) and non-Hispanic (n = 344, 65%). Facebook was the most popular social media platform among our respondents. The most common barriers to research participation included poor explanation of study goals, discomforts to the infant, and time commitment. Recruitment through healthcare providers was perceived as the best way to learn about clinical research studies. When considering research participation, "myself" had the greatest influence, followed by familial ties. Noninvasive biological samples were highly acceptable. Hispanics had higher positive perspectives on willingness to participate in a randomized study (p = 0.009). Education (p = 0.007) had significant effects on willingness to release personal health information. Conclusion: When recruiting women during the pregnancy and postpartum periods for perinatal studies, investigators should consider protocols that account for common barriers and preferred study information sources. Social media-based recruitment is worthy of adoption.

Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes.

The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated or newly incident in the period after acute SARS-CoV-2 infection. Most studies have examined these conditions individually without providing evidence on co-occurring conditions. In this study, we leveraged the electronic health record data of two large cohorts, INSIGHT and OneFlorida+, from the national Patient-Centered Clinical Research Network. We created a development cohort from INSIGHT and a validation cohort from OneFlorida+ including 20,881 and 13,724 patients, respectively, who were SARS-CoV-2 infected, and we investigated their newly incident diagnoses 30-180 days after a documented SARS-CoV-2 infection. Through machine learning analysis of over 137 symptoms and conditions, we identified four reproducible PASC subphenotypes, dominated by cardiac and renal (including 33.75% and 25.43% of the patients in the development and validation cohorts); respiratory, sleep and anxiety (32.75% and 38.48%); musculoskeletal and nervous system (23.37% and 23.35%); and digestive and respiratory system (10.14% and 12.74%) sequelae. These subphenotypes were associated with distinct patient demographics, underlying conditions before SARS-CoV-2 infection and acute infection phase severity. Our study provides insights into the heterogeneity of PASC and may inform stratified decision-making in the management of PASC conditions.

Incidence Trends of New-Onset Diabetes in Children and Adolescents Before and During the COVID-19 Pandemic: Findings From Florida.

This study examined the incidence trends of new-onset type 1 and type 2 diabetes in children and adolescents in Florida before and during the coronavirus disease 2019 (COVID-19) pandemic. In this observational descriptive cohort study, we used a validated computable phenotype to identify incident diabetes cases among individuals <18 years of age in the OneFlorida+ network of the national Patient-Centered Clinical Research Network between January 2017 and June 2021. We conducted an interrupted time series analysis based on the autoregressive integrated moving average model to compare changes in age-adjusted incidence rates of type 1 and type 2 diabetes before and after March 2020, when COVID-19 was declared a national health emergency in the U.S. The age-adjusted incidence rates of both type 1 and type 2 diabetes increased post-COVID-19 for children and adolescents. These results highlight the need for longitudinal cohort studies to examine how the pandemic might influence subsequent diabetes onset in young individuals.

An external exposome-wide association study of COVID-19 mortality in the United States.

The risk factors for severe COVID-19 beyond older age and certain underlying health conditions are largely unknown. Recent studies suggested that long-term environmental exposures may be important determinants of severe COVID-19. However, very few environmental factors have been studied, often separately, without considering the totality of the external environment (i.e., the external exposome). We conducted an external exposome-wide association study (ExWAS) using the nationwide county-level COVID-19 mortality data in the contiguous US. A total of 337 variables characterizing the external exposome from 8 data sources were integrated, harmonized, and spatiotemporally linked to each county. A two-phase procedure was used: (1) in Phase 1, a random 50:50 split divided the data into a discovery set and a replication set, and associations between COVID-19 mortality and individual factors were examined using mixed-effect negative binomial regression models, with multiple comparisons addressed, and (2) in Phase 2, a multivariable regression model including all variables that are significant from both the discovery and replication sets in Phase 1 was fitted. A total of 13 and 22 variables were significant in the discovery and replication sets in Phase 1, respectively. All the 4 variables that were significant in both sets in Phase 1 remained statistically significant in Phase 2, including two air toxicants (i.e., nitrogen dioxide or NO2, and benzidine), one vacant land measure, and one food environment measure. This is the first external exposome study of COVID-19 mortality. It confirmed some of the previously reported environmental factors associated with COVID-19 mortality, but also generated unexpected predictors that may warrant more focused evaluation.