Variability in Long COVID Definitions and Validation of Published Prevalence Rates Article Swipe
YOU?
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· 2025
· Open Access
·
· DOI: https://doi.org/10.1001/jamanetworkopen.2025.26506
Importance Long COVID definitions vary widely, and no consensus exists on how to accurately measure its prevalence, complicating both clinical care and research. Objective To assess long COVID prevalence using various definitions from published literature. Design, Setting, and Participants This prospective, multicenter cohort study used data from the longitudinal Innovative Support for Patients With SARS-CoV-2 Infections Registry (INSPIRE). Participants aged 18 years or older with symptoms suggestive of COVID-19 illness at the time of their index SARS-CoV-2 test enrolled at 8 sites across the US from December 11, 2020, through August 29, 2022, with follow-up surveys collected through February 28, 2023. Exposure Positive or negative SARS-CoV-2 test result at the time of acute symptoms. Main Outcomes and Measures Long COVID prevalence among INSPIRE participants with a positive vs negative index SARS-CoV-2 test, based on long COVID definitions in published literature. Secondary outcomes were sensitivity and specificity of published definitions compared with self-reported long COVID. Results A total of 4575 INSPIRE participants were included (mean [SD] age, 40.40 [14.58] years). Most were female (3013 of 4448 [67.7%]) and aged 18 to 49 years (3338 of 4541 [73.5%]). Applying 5 published definitions for long COVID yielded a prevalence that ranged from 30.84% (95% CI, 29.33%-32.40%) to 42.01% (95% CI, 40.37%-43.66%) at 3 months and 14.23% (95% CI, 13.01%-15.55%) to 21.94% (95% CI, 20.47%-23.47%) at 6 months postinfection; in the 5 comparator studies, reported prevalence of long COVID at 1 to 5 months postinfection ranged from 2.6% (≥84 days) to 47.4% (3-5 months) and at 6 or more months postinfection ranged from 10.0% (95% CI, 8.8%-11.0%) to 61.9% (6-11 months). Using participants’ self-reported long COVID as a criterion standard, existing published definitions had low-to-moderate sensitivity (up to 66.32% [95% CI, 62.59%-69.90%] at 3 months and 45.53% [95% CI, 41.51%-49.60%] at 6 months) and high specificity (up to 81.29% [95% CI, 79.32%-83.15%] at 3 months and 94.26% [95% CI, 92.98%-95.37%]) at 6 months. Conclusions and Relevance In this cohort study, variability in long COVID prevalence across published definitions highlights the need for a standardized, validated definition to improve clinical recognition and research comparability, ultimately guiding more accurate diagnosis and treatment strategies.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1001/jamanetworkopen.2025.26506
- OA Status
- gold
- Cited By
- 2
- References
- 39
- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413107997Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1001/jamanetworkopen.2025.26506Digital Object Identifier
- Title
-
Variability in Long COVID Definitions and Validation of Published Prevalence RatesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-12Full publication date if available
- Authors
-
Lauren E. Wisk, Michelle L’Hommedieu, Kate Diaz Roldan, Imtiaz Ebna Mannan, Erica S. Spatz, Robert A. Weinstein, Arjun K. Venkatesh, Michael Gottlieb, Ryan Huebinger, Kristin L. Rising, Juan Carlos C. Montoy, Kari A. Stephens, Robert M. Rodriguez, Mandy J. Hill, Kelli N. O’Laughlin, Nicole L. Gentile, Ahamed H. Idris, Shu‐Xia Li, Michelle Santangelo, Efrat R. Kean, Samuel McDonald, Kristyn Gatling, Joann G. Elmore, Katherine Koo, Antonia Derden, Zohaib Ahmed, Diego Cordero, Minna Hassaballa, Amro Kaadan, Jeremiah Kinsman, Caitlin Malicki, Zhenqiu Lin, Huihui Yu, Zimo Yang, Mengni Liu, Andrew Ulrich, Jocelyn Dorney, Senyte Pierce, Xavier Puente, W. Salah, Graham Nichol, Jill Anderson, Mary Schiffgens, Dana Morse, Karen Adams, Tracy Stober, Zenoura Maat, Rachel E. Geyer, Michael Willis, Zihan Zhang, Gary Chang, Victoria Lyon, Robin E. Klabbers, Luis Ruiz, Kerry Malone, Jasmine Park, Anna Marie Chang, Nicole Renzi, Phillip B. Watts, Morgan Kelly, Kevin Schaeffer, Dylan Grau, David Cheng, Carly Shutty, Alex Charlton, Lindsey Shughart, Hailey Shughart, Grace Amadio, Jessica Miao, Paavali Hannikainen, Chris Chandler, Megan Eguchi, Raul Moreno, Ralph C. Wang, Robin Kemball, Virginia Chan, Cecilia Lara Chavez, Angela Wong, Mireya Arreguin, Arun Kane, Peter Nikonowicz, Sarah G. H. Sapp, David Gallegos, Katherine Martin, Sharon Saydah, Ian D. Plumb, Aron J. Hall, Melissa Briggs‐HagenList of authors in order
- Landing page
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https://doi.org/10.1001/jamanetworkopen.2025.26506Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1001/jamanetworkopen.2025.26506Direct OA link when available
- Concepts
-
Coronavirus disease 2019 (COVID-19), Medicine, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 2019-20 coronavirus outbreak, Prospective cohort study, Cohort study, Demography, Pediatrics, Internal medicine, Pathology, Outbreak, Disease, Infectious disease (medical specialty), SociologyTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
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39Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.research | 347 |
| abstract_inverted_index.studies, | 229 |
| abstract_inverted_index.symptoms | 65 |
| abstract_inverted_index.Objective | 23 |
| abstract_inverted_index.Relevance | 321 |
| abstract_inverted_index.Secondary | 140 |
| abstract_inverted_index.[73.5%]). | 185 |
| abstract_inverted_index.collected | 96 |
| abstract_inverted_index.consensus | 8 |
| abstract_inverted_index.criterion | 274 |
| abstract_inverted_index.diagnosis | 353 |
| abstract_inverted_index.follow-up | 94 |
| abstract_inverted_index.published | 33, 138, 147, 188, 277, 332 |
| abstract_inverted_index.research. | 22 |
| abstract_inverted_index.standard, | 275 |
| abstract_inverted_index.symptoms. | 113 |
| abstract_inverted_index.treatment | 355 |
| abstract_inverted_index.validated | 340 |
| abstract_inverted_index.(INSPIRE). | 57 |
| abstract_inverted_index.Importance | 0 |
| abstract_inverted_index.Infections | 55 |
| abstract_inverted_index.Innovative | 49 |
| abstract_inverted_index.SARS-CoV-2 | 54, 76, 105, 130 |
| abstract_inverted_index.accurately | 13 |
| abstract_inverted_index.comparator | 228 |
| abstract_inverted_index.definition | 341 |
| abstract_inverted_index.highlights | 334 |
| abstract_inverted_index.prevalence | 28, 120, 195, 231, 330 |
| abstract_inverted_index.suggestive | 66 |
| abstract_inverted_index.ultimately | 349 |
| abstract_inverted_index.8.8%-11.0%) | 262 |
| abstract_inverted_index.Conclusions | 319 |
| abstract_inverted_index.definitions | 3, 31, 136, 148, 189, 278, 333 |
| abstract_inverted_index.literature. | 34, 139 |
| abstract_inverted_index.multicenter | 41 |
| abstract_inverted_index.prevalence, | 16 |
| abstract_inverted_index.recognition | 345 |
| abstract_inverted_index.sensitivity | 143, 281 |
| abstract_inverted_index.specificity | 145, 301 |
| abstract_inverted_index.strategies. | 356 |
| abstract_inverted_index.variability | 326 |
| abstract_inverted_index.Participants | 38, 58 |
| abstract_inverted_index.complicating | 17 |
| abstract_inverted_index.longitudinal | 48 |
| abstract_inverted_index.participants | 123, 160 |
| abstract_inverted_index.prospective, | 40 |
| abstract_inverted_index.postinfection | 240, 256 |
| abstract_inverted_index.self-reported | 151, 269 |
| abstract_inverted_index.standardized, | 339 |
| abstract_inverted_index.13.01%-15.55%) | 215 |
| abstract_inverted_index.20.47%-23.47%) | 220 |
| abstract_inverted_index.29.33%-32.40%) | 202 |
| abstract_inverted_index.40.37%-43.66%) | 207 |
| abstract_inverted_index.41.51%-49.60%] | 295 |
| abstract_inverted_index.62.59%-69.90%] | 287 |
| abstract_inverted_index.79.32%-83.15%] | 307 |
| abstract_inverted_index.comparability, | 348 |
| abstract_inverted_index.postinfection; | 224 |
| abstract_inverted_index.92.98%-95.37%]) | 315 |
| abstract_inverted_index.low-to-moderate | 280 |
| abstract_inverted_index.participants’ | 268 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| corresponding_author_ids | https://openalex.org/A5057733848 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 88 |
| corresponding_institution_ids | https://openalex.org/I161318765 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.94896831 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |