Self-reports by persons with multiple sclerosis are an adequate surrogate for healthcare provider data on disease-modifying therapy and multiple sclerosis type

Introduction: Self-reports are a valuable and cost-effective method of data collection, though they can be influenced by bias. Limited evidence exists on the quality of self-reports by persons with multiple sclerosis (pwMS), particularly since more potent disease-modifying therapies (DMTs) have been introduced. This study aimed to assess the reliability and validity of self-reported DMT use and multiple sclerosis (MS) type in the Swiss Multiple Sclerosis Registry (SMSR) by comparing self-reports with reimbursement approval requests from the Swiss Association for Joint Tasks of Health Insurers.

Methods: The self-reported and reimbursement approval data were linked using privacy-preserving methods based on information available in both databases, i.e., date of birth, canton of residence, sex, and year of MS diagnosis. The SMSR baseline questionnaire data was utilized for the main analysis, while the SMSR follow-up survey data was utilized for the sensitivity analysis. For both analyses, we compared self-reported data with reimbursement approval data that corresponded to the respective periods of the SMSR data collection. Thus, the main analysis included the entirety of the data over the six-year period, while the sensitivity analysis captured a more recent snapshot of the data. To assess reliability, we estimated agreement using Cohen's kappa, and for validity, we estimated accuracy parameters using reimbursement approvals as the reference standard. Univariable and multivariable logistic regression models were employed to investigate factors associated with discordance between self-reports and reimbursement approvals in the main analysis.

Results: The main analysis included 446 participants, and the sensitivity analysis included 193 participants. The agreement between self-reported and reimbursement approval data for medication use was near-perfect in both analyses (κ = 0.87, 95% confidence interval (CI) 0.85, 0.90 and κ = 0.82, 95% CI 0.76, 0.88). However, the agreement between self-reported and reimbursement approval-documented MS types ranged from fair to moderate (κ = 0.37, 95% CI 0.25, 0.48 to κ = 0.61, 95% CI 0.46, 0.77). The accuracy estimates for self-reported DMT use were generally high (≥ 0.80) with narrow CIs, except for less frequently reported drugs. While the sensitivity and specificity for RRMS were high, there was a notable possibility of false-negative self-reports for RRMS (NPV = 0.33, 95% CI 0.22, 0.45), and false-positive reports for SPMS (PPV = 0.36, 95% CI 0.21, 0.54). Multivariable logistic regression models showed that age (OR = 1.07, 95% CI 1.04, 1.10 per year) and education level (OR = 0.27, 95% CI 0.11, 0.65) were associated with discordance in reported and documented MS types, whereas possession of Swiss citizenship (OR = 0.32, 95% CI 0.14, 0.72) was associated with discordance in DMT use.

Conclusion: Self-reported DMT use in pwMS is a reliable and valid information source, with near-perfect agreement and high accuracy. Self-reported MS types showed fair to moderate agreement and varying accuracy, likely reflecting the complexity of diagnosing progressive forms of MS and access to DMTs. In population-based MS research, self-reports of MS types, and particularly DMT use, can serve as a suitable surrogate for healthcare provider data.