Evidence map›Paper›PMID 42201733›Full record

ArticleJAMA network open2026

Long COVID Persistence and Surveillance Gaps Across 58 US Hospitals.

Jiazi Tian, Alaleh Azhir, Matthew Decaro, Ngan Chau, Jonas Hügel, Michele Morris, Jingya Cheng, Pedram Fard, Ingrid V Bassett, Douglas S Bell and 5 more

Abstract read
In one paragraph

Article in JAMA network open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Jiazi TianDepartment of Medicine, Massachusetts General Hospital, Boston.
Alaleh AzhirDepartment of Medicine, Massachusetts General Hospital, Boston.
Matthew DecaroD. Bradley McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston.
Ngan ChauCTSI/Biomedical Informatics Program, University of California, Los Angeles, Los Angeles.
Jonas HügelDepartment of Medicine, Massachusetts General Hospital, Boston.
Michele MorrisDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania.
Jingya ChengDepartment of Medicine, Massachusetts General Hospital, Boston.
Pedram FardDepartment of Medicine, Massachusetts General Hospital, Boston.
Ingrid V BassettDepartment of Medicine, Massachusetts General Hospital, Boston.
Douglas S BellDepartment of Medicine, University of California, Los Angeles, Los Angeles.
Elmer V BernstamD. Bradley McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston.
Shyam VisweswaranDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania.
Jeffrey G KlannDepartment of Medicine, Massachusetts General Hospital, Boston.
Shawn N MurphyDepartment of Neurology, Massachusetts General Hospital, Boston.
Hossein EstiriDepartment of Medicine, Massachusetts General Hospital, Boston.

Funding

ENACT: Translating Health Informatics Tools to Research and Clinical Decision MakingU24TR004111 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI STEVEN E REIS, SHYAM VISWESWARAN · 2022 to 2026
$23.3M
Temporal Phenotypes and Risk Models for the Post-COVID Syndrome and its sub-typesR01AI165535 · NIAID · MASSACHUSETTS GENERAL HOSPITAL · PI Hossein Estiri, SHAWN N MURPHY · 2022 to 2026
$4.1M
NCATS NIH HHS U24 TR004111NIAID NIH HHS R01 AI165535
6 · The paper itself

Abstract

Importance: Surveillance of postacute sequelae of SARS-CoV-2 infection (PASC) depends on diagnostic coding systems that capture fewer than one-half of affected individuals, rendering millions invisible to health systems and policymakers. Objective: To quantify the gap between true PASC burden and diagnostic code-based estimates, determine the proportion representing chronic disease, and characterize organ system heterogeneity and temporal trends across diverse populations. Design, Setting, and Participants: This retrospective cohort study used electronic health record data from 58 hospitals and affiliated clinics in 4 US regions, from 2017 to 2025. Adults (aged ≥18 years) with laboratory-confirmed SARS-CoV-2 infection or a COVID-19 diagnosis code were included. A custom artificial intelligence algorithm, the Precision Phenotyping for Research Cohorts (P2RC), was implemented using federated infrastructure. Exposure: Laboratory-confirmed SARS-CoV-2 infection or COVID-19 diagnosis code. Main Outcomes and Measures: The primary outcomes were PASC prevalence, the proportion classified as chronic conditions, organ system distribution, and temporal trends from 2020 to 2024. χ2 Tests were used to assess organ system heterogeneity across regions, and negative binomial regression was used to model quarterly temporal trends, yielding incidence rate ratios (IRRs) with 95% CIs. Results: In this cohort study of 457 950 COVID-19 cases (mean age, 52.05 years; 275 107 [60.07%] female), the P2RC algorithm identified 74 560 PASC cases (16.28% overall; 28 585 [18.58%] in New England, 978 [19.55%] in Southeast Texas, 10 534 [22.69%] in Southern California, and 34 463 [13.64%] in Western Pennsylvania), more than 2-fold higher than the proportion identified by code-based surveillance (<7%). Of 883 International Statistical Classification of Diseases, Tenth Revision, Clinical Modification codes associated with PASC, 594 (67.27%) represented chronic or potentially chronic conditions. Of 74 560 patients with PASC, 66 587 (89.31%) developed chronic conditions requiring ongoing clinical management; this represents 14.54% of the total number of 457 950 patients with COVID-19. Substantial organ system heterogeneity was observed (χ2 = 2504.73; P < .001): New England demonstrated thyroid-predominant endocrine patterns, while Southeast Texas, Southern California, and Western Pennsylvania showed metabolic-predominant profiles. Negative binomial regression revealed increasing PASC prevalence through mid-2024 (IRR per quarter, 1.01 [95% CI, 1.00-1.01; P < .001] in New England; 1.00 [95% CI, 1.00-1.01; P < .001] in Southern California; and 1.02 [95% CI, 1.01-1.02; P < .001] in Western Pennsylvania), indicating an accumulating rather than resolving burden. Conclusions and Relevance: In this cohort study, approximately 1 in 6 patients with COVID-19 developed PASC, and 89.31% of these patients had at least 1 chronic condition. Current diagnostic coding captured fewer than one-half of the cases, obscuring a substantial chronic disease burden. The persistently increasing prevalence through 2024 indicated an accumulating health care burden requiring investment in surveillance infrastructure and integrated care pathways.

Indexed as

COVID-19AdultAgedChronic DiseaseFemaleHospitalsHumansMaleMiddle AgedPopulation SurveillancePost-Acute COVID-19 SyndromePrevalenceRetrospective StudiesSARS-CoV-2United States

Identifiers

PMID42201733
PMCPMC13216987

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.