Pain Among US Adults Before, During, and After the COVID-19 Pandemic: A Repeated Cross-Sectional Study using the 2019-2023 National Health Interview Survey
Bibliographic record
Abstract
ABSTRACT Importance Chronic pain (CP) is a major public health problem in the US. The COVID-19 pandemic led to widespread disruptions in the US and it is important to monitor changes in pain during and after the pandemic. Objective To determine prevalence of chronic pain (CP) and high-impact chronic pain (HICP) before, during, and after the COVID-19 pandemic and identify potential contributing factors. Methods We analyze a nationally representative sample of 88,469 community-dwelling Americans aged 18 and older from three cross-sectional waves of the National Health Interview Survey before (2019), during (2021), and after (2023) the COVID-19 pandemic. Year of interview is the exposure. All regression models control for age and sex; fully controlled models include 19 additional covariates (demographics, socioeconomic status, health behaviors, health conditions, mental health, and health insurance type); analyses also explore the role of long COVID. Outcomes are CP and HICP using measures proposed by the US National Pain Strategy; we also present findings for six site-specific pain measures. Results Between 2019 and 2023, CP and HICP prevalence increased by 18% and 13%, respectively. Specifically, CP prevalence was 20.6% (95%CI: 19.9-21.2%) in 2019, 20.9%(20.3-21.6%) in 2021, and 24.3% (23.7-25.0%) in 2023. HICP prevalence declined from 7.5% (7.1-7.8%) in 2019 to 6.9% (6.6-7.3%) in 2021, before rising sharply to 8.5% (8.1-8.9%) in 2023. The increases occurred in all examined body sites except for tooth/jaw pain, and in all major population groups. Approximately 13% of the increase in CP and HICP was attributable to long COVID. Conclusions and Relevance Pain among US adults was high before and during the pandemic but has surged substantially since. In 2023, an unprecedented 60 million Americans had chronic pain and 21 million had high-impact chronic pain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".