Objective: We aim to validate an algorithm based on routinely-collected healthcare data to detect incidence of multiple sclerosis (MS) in the Campania Region (South Italy) and to explore its spatial and temporal variations.
Methods: We included individuals resident in the Campania Region who had at least one MS record in administrative datasets (drug prescriptions, hospital discharge, outpatients), from 2015 to 2020. We merged administrative to the clinical datasets to ascertain the actual date of diagnosis, and validated the minimum interval from our study baseline (Jan 1, 2015) to first MS records in administrative datasets to detect incident cases. We used Bayesian approach to explore geographical distribution, also including deprivation index as a covariate in the estimation model. We used the capture-recapture method to estimate the proportion of undetected cases.
Results: The best performance was achieved by the 12-month interval algorithm, detecting 2,150 incident MS cases, with 74.4% sensitivity (95%CI = 64.1%, 85.9%) and 95.3% specificity (95%CI = 90.7%, 99.8%). The cumulative incidence was 36.68 (95%CI = 35.15, 38.26) per 100,000 from 2016 to 2020. The mean annual incidence was 7.34 (95%CI = 7.03, 7.65) per 100,000 people-year. The geographical distribution of MS relative risk shows a decreasing east-west incidence gradient. The number of expected MS cases was 11% higher than the detected cases.
Conclusions: We validated a case-finding algorithm based on administrative data to estimate MS incidence, and its spatial/temporal variations. This algorithm provides up-to-date estimates of MS incidence, and will be used in future studies to evaluate changes in MS incidence in relation to different risk factors.
Keywords: Bayes; Epidemiology; Multiple sclerosis; Neurology.
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