BACKGROUND: Multiple Sclerosis (MS) epidemiology in Italy is mainly based on population-based prevalence studies. Administrative data are an additional source of information, when available, in prevalence studies of chronic diseases such as MS. The aim of our study is to update the prevalence rate of MS in Tuscany (central Italy) as at 2011 using a validated case-finding algorithm based on administrative data. METHODS: The prevalence was calculated using an algorithm based on the following administrative data: hospital discharge records, drug-dispensing records, disease-specific exemptions from copayment to health care, home and residential long-term care and inhabitant registry. To test algorithm sensitivity, we used a true-positive reference cohort of MS patients from the Tuscan MS register. To test algorithm specificity, we used another cohort of individuals who were presumably not affected by MS. RESULTS: As at December 31, 2011, we identified 6,890 cases (4,738 females and 2,152 males) with a prevalence of 187.9 per 100,000. The sensitivity of algorithm was 98% and the specificity was 99.99%. CONCLUSIONS: We found a prevalence higher than the rates present in literature. Our algorithm, based on administrative data, can accurately identify MS patients; moreover, the resulting cohort is suitable to monitor disease care pathways.
BACKGROUND: Multiple Sclerosis (MS) epidemiology in Italy is mainly based on population-based prevalence studies. Administrative data are an additional source of information, when available, in prevalence studies of chronic diseases such as MS. The aim of our study is to update the prevalence rate of MS in Tuscany (central Italy) as at 2011 using a validated case-finding algorithm based on administrative data. METHODS: The prevalence was calculated using an algorithm based on the following administrative data: hospital discharge records, drug-dispensing records, disease-specific exemptions from copayment to health care, home and residential long-term care and inhabitant registry. To test algorithm sensitivity, we used a true-positive reference cohort of MSpatients from the Tuscan MS register. To test algorithm specificity, we used another cohort of individuals who were presumably not affected by MS. RESULTS: As at December 31, 2011, we identified 6,890 cases (4,738 females and 2,152 males) with a prevalence of 187.9 per 100,000. The sensitivity of algorithm was 98% and the specificity was 99.99%. CONCLUSIONS: We found a prevalence higher than the rates present in literature. Our algorithm, based on administrative data, can accurately identify MSpatients; moreover, the resulting cohort is suitable to monitor disease care pathways.
Authors: Daiana Bezzini; L Policardo; F Profili; G Meucci; M Ulivelli; S Bartalini; P Francesconi; M A Battaglia Journal: Neurol Sci Date: 2018-08-08 Impact factor: 3.307
Authors: Anna Iljicsov; Dániel Milanovich; András Ajtay; Ferenc Oberfrank; Mónika Bálint; Balázs Dobi; Dániel Bereczki; Magdolna Simó Journal: PLoS One Date: 2020-07-27 Impact factor: 3.240
Authors: Marcello Moccia; Vincenzo Brescia Morra; Roberta Lanzillo; Ilaria Loperto; Roberta Giordana; Maria Grazia Fumo; Martina Petruzzo; Nicola Capasso; Maria Triassi; Maria Pia Sormani; Raffaele Palladino Journal: Int J Environ Res Public Health Date: 2020-05-13 Impact factor: 3.390