Measuring Regional Variations in US Population-Level Health-Related Quality of Life During COVID-19 Using the EQ-5D-5L
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it