The Epidemiology of Long Coronavirus Disease in US Adults
Bibliographic record
Abstract
BACKGROUND: We estimated the prevalence of long COVID and impact on daily living among a representative sample of adults in the United States. METHODS: We conducted a population-representative survey, 30 June-2 July 2022, of a random sample of 3042 US adults aged 18 years or older and weighted to the 2020 US population. Using questions developed by the UK's Office of National Statistics, we estimated the prevalence of long COVID, by sociodemographics, adjusting for gender and age. RESULTS: An estimated 7.3% (95% confidence interval: 6.1-8.5%) of all respondents reported long COVID, corresponding to approximately 18 828 696 adults. One-quarter (25.3% [18.2-32.4%]) of respondents with long COVID reported their day-to-day activities were impacted "a lot" and 28.9% had severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection more than 12 months ago. The prevalence of long COVID was higher among respondents who were female (adjusted prevalence ratio [aPR]: 1.84 [1.40-2.42]), had comorbidities (aPR: 1.55 [1.19-2.00]), or were not (vs were) boosted (aPR: 1.67 [1.19-2.34]) or not vaccinated (vs boosted) (aPR: 1.41 [1.05-1.91]). CONCLUSIONS: We observed a high burden of long COVID, substantial variability in prevalence of SARS-CoV-2, and risk factors unique from SARS-CoV-2 risk, suggesting areas for future research. Population-based surveys are an important surveillance tool and supplement to ongoing efforts to monitor long COVID.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".