Effects of COVID-19 Infection on Symptom Severity and Disability in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Infections, including infection with SARS-CoV-2 (COVID-19), could alter the course of multiple sclerosis (MS). Previous studies assessing the effects of COVID-19 on MS outcomes were small and had discordant findings. The study objective was to evaluate the association of COVID-19 infection with changes in the trajectory of MS symptoms and disability. METHODS: We used a controlled interrupted time series (ITS), a quasiexperimental study design using longitudinal data from the North American Research Committee on Multiple Sclerosis Registry. Participants who completed at least 3 surveys before and after their index survey were identified. Exposure of interest was COVID-19 infection based on confirmed diagnosis using an at-home or laboratory test, as reported by the participant. Symptoms were measured using the SymptoMScreen (SMSS), a self-report measure of symptom severity across 12 domains common in MS. Disability was measured using PDDS. Segmented regression was used to compare changes in outcomes over time between cohorts, before and after the exposure (COVID-19 infection). RESULTS: The spring 2023 survey response rate was 67.3%, and 4,787 participants completed the COVID-19 questions. Of those participants, 2,106 (44.0%) reported ever having confirmed COVID-19. The COVID-19 infection cohort included 796 participants with ≥3 surveys both before and after index survey. The uninfected cohort included 1,534 participants (32.0%) who reported never having COVID-19 nor other infections in the previous 6 months, of whom 1,336 had the requisite number of presurveys and postsurveys. After adjusting for participant characteristics, the SMSS score increased nominally over time in the COVID-19 and uninfected cohorts and this change over time did not differ between cohorts either before (0.005, 95% CI -0.025 to 0.035) or after (-0.0002, 95% CI -0.014 to 0.014) COVID-19 infection. The immediate effect of COVID-19 infection on the SMSS total score was minimal within the COVID-19 cohort and did not differ between cohorts (0.41, 95% CI -0.13 to 0.94). Findings were similar for disability. DISCUSSION: Our study using an ITS design found that COVID-19 infection was not associated with immediate changes in symptom severity or disability, nor did it change the trajectories of these outcomes over a median follow-up of 18 months. Although results may not generalize to younger people with MS, these findings enhance our general understanding of the consequences of infection in people with MS.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".