Pain among US adults before, during, and after the COVID-19 pandemic: a study using the 2019 to 2023 National Health Interview Survey
Bibliographic record
Abstract
ABSTRACT: The unprecedented disruption of the COVID-19 pandemic raises crucial questions about its impact on chronic pain levels in the US population. We present a comprehensive analysis of chronic pain (CP), high-impact chronic pain (HICP), and site-specific pain prevalence before, during, and after the pandemic, and investigate key contributing factors. We analyze a nationally representative sample of 90,769 community-dwelling adults aged 18 years and older from 3 cross-sectional waves of the National Health Interview Survey (2019, 2021, and 2023). Outcomes are CP and HICP; we also present findings for 6 site-specific pain measures. We include an extensive range of covariates (demographics, socioeconomic status, health behaviors, health conditions, mental health, and health insurance type); additional analyses also explore the role of long COVID. Chronic pain prevalence increased from 20.5% (95% confidence interval: 19.9%-21.2%) in 2019 to 20.9% (20.3%-21.6%) in 2021 and 24.3% (23.7%-25.0%) in 2023, representing an 18% increase over the study period. High-impact chronic pain prevalence, which was 7.5% (7.1%-7.8%) in 2019, declined to 6.9% (6.6%-7.3%) in 2021 before rising to 8.5% (8.1%-8.9%) in 2023, a 13% overall increase. The 2023 pain increases were widespread: they occurred for all examined body sites except tooth/jaw pain and all population subgroups. Long COVID accounted for approximately 13% of the observed 2019 to 2023 increase in both CP and HICP. In 2023, an estimated 60 million Americans experienced CP and 21 million experienced HICP, the highest prevalence ever recorded in the National Health Interview Survey. These findings suggest a significant escalation in the population burden of pain, with crucial implications for public health policy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 0.001 |
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".