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Published on: May 10, 2024
Epidemiology of eosinophilic granulomatosis with polyangiitis in Northern Italy: a population-based capture-recapture
Francesco Muratore1, Chiara Marvisi2, Pamela Mancuso3
1Rheumatology Unit, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy; University of Modena and Reggio Emilia, Modena, Italy.
Objectives:
To estimate the prevalence and incidence of eosinophilic granulomatosis with polyangiitis (EGPA) in Northern Italy using multi-source capture-recapture methodology.
Methods:
We conducted a population-based study in Reggio Emilia area from 1991 to 2022. Cases fulfilling the 2022 ACR/EULAR classification criteria were identified through seven independent sources: outpatient vasculitis and asthma clinic records, centralized laboratory databases for ANCA testing, drug prescription records for IL-5 inhibitors, hospital discharge data, day service admissions database and the regional rare disease registry. Capture-recapture models were fitted to estimate the total number of cases, including those not identified by any source.
Results:
Forty patients with EGPA were identified (mean age 55 years, 75% female). On January 1, 2023, 36 prevalent cases were alive and resident. The best-fitting capture-recapture model estimated 0 missed cases (95% CI 0 to 2), yielding a crude prevalence of 68 per million inhabitants (95% CI 68-72). Between 2001 and 2022, 37 incident cases were identified. Capture-recapture analysis estimated complete case ascertainment in both periods 2001-2011 and 2012-2022 (0 missed cases in both periods, 95% CI 0-3 and 0-2, respectively). The age- and sex-standardized incidence was 3.31 per million inhabitants (95% CI 2.24-4.37), increasing from 2.83 (95% CI 1.39-4.27) in 2001-2011 to 3.59 (95% CI 2.08-5.09) in 2012-2022. Hospital discharge data identified only 25% of prevalent cases, while the exemption registry captured 86%.
Conclusions:
EGPA prevalence in Northern Italy is among the highest reported in Europe, likely reflecting near-complete case ascertainment through integrated health data sources rather than true geographic variation.