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BACKGROUND: Identification of multiple sclerosis (MS) cases in routine healthcare data repositories remains challenging. MS can have a protracted diagnostic process and is rarely identified as a primary reason for admission to the hospital. Difficulties in identification are compounded in systems that do not include insurance or payer information concerning drug treatments or non-notifiable disease. AIM: To develop an algorithm to reliably identify MS cases within a national health data bank. METHOD: Retrospective analysis of the Secure Anonymised Information Linkage (SAIL) databank was used to identify MS cases using a novel algorithm. Sensitivity and specificity were tested using two existing independent MS datasets, one clinically validated and population-based and a second from a self-registered MS national registry. RESULTS: From 4 757 428 records, the algorithm identified 6194 living cases of MS within Wales on 31 December 2020 (prevalence 221.65 (95% CI 216.17 to 227.24) per 100 000). Case-finding sensitivity and specificity were 96.8% and 99.9% for the clinically validated population-based cohort and sensitivity was 96.7% for the self-declared registry population. DISCUSSION: The algorithm successfully identified MS cases within the SAIL databank with high sensitivity and specificity, verified by two independent populations and has important utility in large-scale epidemiological studies of MS.
RESUMEN
BACKGROUND: A contemporary understanding of disability evolution in multiple sclerosis (MS) is an essential tool for individual disease management and planning of interventional studies. We have used prospectively collected longitudinal data to analyse disability progression and variation in a British MS cohort. METHODS: Cox proportional hazards regression was used to estimate hazard of Expanded Disability Status Scale (EDSS) 4.0 and 6.0. A continuous Markov model was used to estimate transitional probabilities for individual EDSS scores. Models were adjusted for age at MS onset, sex and disease-modifying treatments (DMTs) exposure. RESULTS: 2135 patients were included (1487 (70%) female, 1922 (89%) relapsing onset). 865 (41%) had used DMTs. Median time to EDSS 4.0 and 6.0 was 18.2 years (95% CI 16.3 to 20.2) and 22.1 years (95% CI 20.5 to 24.5). In the Markov model, the median time spent at EDSS scores of <6 (0.40-0.98 year) was shorter than the time spent at EDSS scores of ≥6 (0.87-4.11 year). Hazard of change in EDSS was greatest at EDSS scores <6 (HR for increasing EDSS: 1.02-1.33; decreasing EDSS: 0.34-1.27) compared with EDSS scores ≥6 (HR for increasing EDSS: 0.08-0.61; decreasing EDSS: 0.18-0.54). CONCLUSIONS: These data provide a detailed contemporary model of disability outcomes in a representative population-based MS cohort. They support a trend of increasing time to disability milestones compared with historical reference populations, and document disability variation with the use of transitional matrices. In addition, they provide essential information for patient counselling, clinical trial design, service planning and offer a comparative baseline for assessment of therapeutic interventions.