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Background In recent years, there has been a growing interest in developing methods based on health administrative databases to estimate prevalence of chronic diseases, hoping that this would lead to more precise and reliable estimates. The capture-recapture method can be used to improve the prevalence of a disease reducing the bias of case underestimation. Objective To assess the prevalence of multiple sclerosis (MS) in the Liguria region in Italy using routinely collected healthcare data. Methods Data from three sources collected in Liguria Health Authority (A.Li.Sa.) databases - hospital discharge records, drug-dispensing records, disease-specific payment exemptions from copayment to health care - and a capture-recapture method with log-linear models were used to estimate the prevalence of MS. The accuracy of the case-finding algorithm was tested using a sample of people residents in Liguria Region with a definite diagnosis of MS extracted from clinical records of Liguria AISM Rehabilitation Service as reference standard. Results The case-finding algorithm to capture people with MS from routinely collected healthcare data used in our study found an observed crude prevalence of MS in Liguria region on 31 December 2017, of 202.4 cases per 100,000 inhabitants (95% CI: 195.3-209.5). After linkage to clinical data, the algorithm showed a sensitivity of 82.7%, with 22.1% of MS cases undetected on capture-recapture models. Consequently, the capture-recapture method prevalence estimate of 259.7 per 100,000 inhabitants (95% CI: 251.7-267.8) suggest that this region constitutes a high-risk area for MS. Conclusions Our initial study of crude MS prevalence showed an overall regional prevalence of MS of 202.4 cases per 100,000, capture-recapture analysis would add another 20%, to those figures. This method could be considered a good alternative to the population-based study design for estimating the prevalence of MS. Key messages Studies based on routinely collected healthcare data could help estimate the prevalence of chronic disease. To reduce the bias of case underestimation, the capture–recapture method can be useful.