The Association Between Physical Distancing Behaviors to Avoid COVID-19 and Health-Related Quality of Life in Immunocompromised and Nonimmunocompromised Individuals: Patient-Informed Protocol for the Observational, Cross-Sectional EAGLE Study
Bibliographic record
Abstract
BACKGROUND: Immunocompromised individuals are known to respond inadequately to SARS-CoV-2 vaccines, placing them at high risk of severe or fatal COVID-19. Thus, immunocompromised individuals and their caregivers may still practice varying degrees of social or physical distancing to avoid COVID-19. However, the association between physical distancing to avoid COVID-19 and quality of life has not been comprehensively evaluated in any study. OBJECTIVE: We aim to measure physical distancing behaviors among immunocompromised individuals and the association between those behaviors and person-centric outcomes, including health-related quality of life (HRQoL) measures, health state utilities, anxiety and depression, and work and school productivity impairment. METHODS: A patient-informed protocol was developed to conduct the EAGLE Study, a large cross-sectional, observational study, and this paper describes that protocol. EAGLE is designed to measure distancing behaviors and outcomes in immunocompromised individuals, including children (aged ≥6 mo) and their caregivers, and nonimmunocompromised adults in the United States and United Kingdom who report no receipt of passive immunization against COVID-19. We previously developed a novel self- and observer-reported instrument, the Physical Distancing Scale for COVID-19 Avoidance (PDS-C19), to measure physical distancing behavior levels cross-sectionally and retrospectively. Using an interim or a randomly selected subset of the study population, the PDS-C19 psychometric properties will be assessed, including structural validity, internal consistency, known-group validity, and convergent validity. Associations (correlations) will be assessed between the PDS-C19 and validated HRQoL-related measures and utilities. Structural equation modeling and regression will be used to assess these associations, adjusting for potential confounders. Participant recruitment and data collection took place from December 2022 to June 2023 using direct-to-patient channels, including panels, clinician referral, patient advocacy groups, and social media, with immunocompromising diagnosis confirmation collected and assessed for a randomly selected 25% of immunocompromised participants. The planned total sample size is 3718 participants and participant-caregiver pairs. Results will be reported by immunocompromised status, immunocompromising condition category, country, age group, and other subgroups. RESULTS: All data analyses and reporting were planned to be completed by December 2023. Results are planned to be submitted for publication in peer-reviewed journals in 2024-2025. CONCLUSIONS: This study will quantify immunocompromised individuals' physical distancing behaviors to avoid COVID-19 and their association with HRQoL as well as health state utilities. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/52643.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".