P075 Evaluation of the impact of the COVID-19 pandemic on patients with rheumatic diseases in the UK: results from the REUMAVID study
Bibliographic record
Abstract
Abstract Background/Aims This study presents the impact of the COVID-19 pandemic on health care, access to treatment, daily activities, well-being and mental health and the role of patient organisations from the perspective of the patient with rheumatic disease in the UK. Methods REUMAVID is an international collaboration led by HTR of the University of Seville, together with a multidisciplinary team of rheumatologists and patient organisations from 7 European countries (in the UK: NASS, NRAS and Arthritis Action). The study consists of an online survey, including the following instruments: Self-Perceived Health, WHO-5 Well-Being Index and Hospital Anxiety and Depression Scale (HADS). Data are collected in two phases: the first wave of the pandemic (from May 14th to July 18th 2020) and the second wave (to be conducted in winter 2020). Results 558 patients with rheumatic diseases participated in REUMAVID UK. The most frequently reported diagnoses were axial spondyloarthritis (44.6%), rheumatoid arthritis (44.1%) and osteoarthritis (25.6%). The mean age was 58.5±13.4 years, 78.7% women, 70.8% married or in a relationship and 54.1% having university studies. 45.8% perceived their health status being "fair to very poor" with 38.4% reporting a worsening during lockdown. 48.8% had their rheumatology appointment cancelled. Of these, 46.9% were offered either online or telephone follow-up, while the remaining 50.6% were not given any alternative. 15.6% changed their medication, of which 66.3% were indicated to do so by the medical team and 21.7% did so out of concern with COVID-19 with the major fear being that their treatment would lead to serious illness if they contracted SARS-CoV-2, while the greater hope was to be able to continue with their treatment as usual. 10.3% smoked more than before, 59.4% quit smoking and 36.3% drank more alcohol than before. 20.7% were unable to physically exercise at home and 39.2% reported weight gain. According to the WHO-5 scale, 52.5% declared poor well-being (≤50). A total of 43.6% were at risk of anxiety and 33.6% at risk of depression according to the HADS scale. During the lockdown, 54.3% were able to continue their psychological therapy. The main source of COVID-19 information was the patient organisations (reaching 63.6% of members), compared to 45.3% of non-members who did not receive any information. Conclusion The REUMAVID study has allowed us to measure and quantify the experience of British patients with rheumatic disease during an unprecedented public health crisis. A reduction in healthcare access, concern about treatment, changes in daily life habits and worsening of well-being and mental health were reported during the first wave. Patient organisations were the main source of COVID-19-related information. Further data will be gathered during the second wave. Disclosure M. Garrido-Cumbrera: None. V. Navarro-Compán: Honoraria; Abbvie, BMS, Lilly, MSD, Novartis, Pfizer, Roche, UCB. D. Webb: Grants/research support; AbbVie, Biogen, Janssen, Lilly, Novartis, UCB. C. Jacklin: Grants/research support; Abbvie, Amgen, Biogen, Eli, Lilly, Gilead, Janssen, Pfizer, Roche, Sanofi, UCB. S. Irwin: Other; Coronavirus Community Support Fund. L. Christen: Other; Novartis employee. J. Correa-Fernández: None. S. Sanz-Gómez: None. H. Marzo-Ortega: Honoraria; Abbvie, Celgene, Eli-Lilly, Janssen, Novartis, Pfizer, Takeda, UCB. Grants/research support; Janssen, Novartis.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".