COVID-19 infodemic and health-related quality of life in patients with chronic respiratory diseases: A multicentre, observational study
Bibliographic record
Abstract
Background: The explosion of information, misinformation and disinformation (the "infodemic") related to the coronavirus disease 2019 (COVID-19) pandemic on digital and social media is reported to affect mental health and quality of life. However, reports assessing the COVID-19 infodemic on health-related quality of life (HRQL) in patients with chronic diseases are scarce. In this study, we investigated the associations between the infodemic and HRQL in uninfected individuals with pre-existing chronic respiratory diseases (CRDs) such as asthma, chronic obstructive pulmonary disease (COPD) and other CRDs. Methods: We conducted a multi-national, cross-sectional, observational study in Canada, India, New Zealand and the United Kingdom where we distributed a set of digitised questionnaires among 1018 participants with chronic respiratory diseases who were not infected with the SARS-CoV-2 virus at least three months prior to the study. We collected information about the infodemic such as news watching or social media use more than usual during the pandemic. HRQL was assessed using the short form of the chronic respiratory questionnaire (SF-CRQ). Demographic information, comorbidities, compliance, mental health, behavioural function, and social support were also recorded. We analysed the direct and indirect relationships between infodemic and HRQL using structural equation models (SEM). Results: Of all participants, 54% were females and had a mean (standard deviation (SD)) age of 53 (17) years. We found that higher infodemic was associated with worse emotional function (regression coefficient β = -0.08; 95% confidence interval (CI) = -0.14 to -0.01), which means a one SD change of the higher infodemic latent variable was associated with a 0.08 SD change of emotional function level. The association between higher infodemic and worse emotional function was mediated by worse mental health and behavioural functions but is marginally ameliorated by improved social support. In stratification analysis, we found significant disease and country-wise variations in the associations between infodemic and SF-CRQ domain scores. Conclusions: These results provide new evidence that the COVID-19 infodemic significantly influences the HRQL in patients with CRDs through a complex interplay between mental health, behavioural function, and social support. This new dimension of research also opens avenues for further research on infodemic-related health effects in other chronic diseases.
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".