Measuring Regional Variations in US Population-Level Health-Related Quality of Life During COVID-19 Using the EQ-5D-5L
Bibliographic record
Abstract
Abstract Regional variations in coronavirus disease 2019 (COVID-19) suggest non-uniform impacts on health-related quality-of-life (HRQoL) across the US. This study measured regional variations in US population-level HRQoL during COVID-19. HRQoL was measured by the EQ-5D-5L in a three-wave cross-sectional online survey (spring 2020, summer 2020, winter 2021). Adjusted likelihood of any problems in EQ-5D-5L domains and adjusted mean utility and EQ-VAS were estimated and compared between US Census Bureau-designated region-divisions and waves. Regional variations were significant (p < 0.05) in all domains except Pain/Discomfort in spring 2020, Mobility in summer 2020, and Anxiety/Depression in winter 2021. In spring 2020, East South Central (ESC) had the most Mobility (38%) and Usual Activities (66%) problems, while Self-Care problems were greatest in Mountain (53%), and Anxiety/Depression greatest in East North Central (ENC, 72%) and West North Central (80%). In summer 2020, Self-Care problems were again greatest in Mountain (62%), while ENC saw the most Usual Activities (69%), Pain/Discomfort (67%), and Anxiety/Depression (83%) problems. By winter 2021, ESC had the most problems in Mobility (52%), Self-Care (79%), and Pain/Discomfort (79%), with Usual Activities (68%) only second to Middle Atlantic (69%). Both mean utility and EQ-VAS were significantly lowest in ESC in spring 2020 and winter 2021. Otherwise, utility and EQ-VAS trends generally disagreed. HRQoL varied considerably across regions, often worst in ESC. Variation was likely driven by multiple factors including case rates, policies, and preexisting vulnerabilities; these relationships should be explored in future research. Findings support the need for region-specific health interventions.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".