ArticleEnvironmental advances2023
Identifying environmental risk factors for post-acute sequelae of SARS-CoV-2 infection: An EHR-based cohort study from the recover program.
Article in Environmental advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Omics Landscape of Long COVID-A Comprehensive Systematic Review to Advance Biomarker, Target and Drug Discovery.Allergy · 2025Pooled it
- Ambient Air Pollution and COVID-19 in California.Research report (Health Effects Institute) · 2026Article
- Social Determinants of Health and Risk for Long COVID in the U.S. RECOVER-Adult Cohort.Annals of internal medicine · 2025Observational
- Long COVID after SARS-CoV-2 during pregnancy in the United States.Nature communications · 2025Article
- Long-term outcomes of post-acute sequelae of SARS-CoV-2 infection: a cohort study protocol.Frontiers in public health · 2025Article
- Japan's State of Emergency: How Political Decisions Affected Post-COVID-19 Syndrome.JMA journal · 2024Article
- Identification of risk factors of Long COVID and predictive modeling in the RECOVER EHR cohorts.Communications medicine · 2024Article
- The impact of COVID-19 on accelerating of immunosenescence and brain aging.Frontiers in cellular neuroscience · 2024Review
- Pathogenesis Underlying Neurological Manifestations of Long COVID Syndrome and Potential Therapeutics.Cells · 2023Review
- A roadmap to advance exposomics through federation of data.Exposome · 2023Article
Corrections and comments
- Update of
Authors and funding
17 authors.
Funding
Abstract
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 (PM
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.