Author
Listed:
- Marcello Moccia
(Multiple Sclerosis Clinical Care and Research Centre, Department of Neuroscience, Reproductive Science and Odontostomatology, Federico II University, Via Sergio Pansini 5, 80131 Naples, Italy)
- Vincenzo Brescia Morra
(Multiple Sclerosis Clinical Care and Research Centre, Department of Neuroscience, Reproductive Science and Odontostomatology, Federico II University, Via Sergio Pansini 5, 80131 Naples, Italy)
- Roberta Lanzillo
(Multiple Sclerosis Clinical Care and Research Centre, Department of Neuroscience, Reproductive Science and Odontostomatology, Federico II University, Via Sergio Pansini 5, 80131 Naples, Italy)
- Ilaria Loperto
(Department of Public Health, Federico II University, 80131 Naples, Italy)
- Roberta Giordana
(Campania Region Healthcare System Commissioner Office, 80131 Naples, Italy)
- Maria Grazia Fumo
(Regional Healthcare Society (So.Re.Sa), 80131 Naples, Italy)
- Martina Petruzzo
(Multiple Sclerosis Clinical Care and Research Centre, Department of Neuroscience, Reproductive Science and Odontostomatology, Federico II University, Via Sergio Pansini 5, 80131 Naples, Italy)
- Nicola Capasso
(Multiple Sclerosis Clinical Care and Research Centre, Department of Neuroscience, Reproductive Science and Odontostomatology, Federico II University, Via Sergio Pansini 5, 80131 Naples, Italy)
- Maria Triassi
(Department of Public Health, Federico II University, 80131 Naples, Italy)
- Maria Pia Sormani
(Biostatistics Unit, Department of Health Sciences, University of Genoa, 16121 Genoa, Italy)
- Raffaele Palladino
(Department of Public Health, Federico II University, 80131 Naples, Italy
Department of Primary Care and Public Health, Imperial College, London SW7 2AZ, UK)
Abstract
We aim to validate a case-finding algorithm to detect individuals with multiple sclerosis (MS) using routinely collected healthcare data, and to assess the prevalence of MS in the Campania Region (South Italy). To identify individuals with MS living in the Campania Region, we employed an algorithm using different routinely collected healthcare administrative databases (hospital discharges, drug prescriptions, outpatient consultations with payment exemptions), from 1 January 2015 to 31 December 2017. The algorithm was validated towards the clinical registry from the largest regional MS centre (n = 1460). We used the direct method to standardise the prevalence rate and the capture-recapture method to estimate the proportion of undetected cases. The case-finding algorithm including individuals with at least one MS record during the study period captured 5362 MS patients (females = 64.4%; age = 44.6 ± 12.9 years), with 99.0% sensitivity (95% CI = 98.3%, 99.4%). Standardised prevalence rate per 100,000 people was 89.8 (95% CI = 87.4, 92.2) (111.8 for females [95% CI = 108.1, 115.6] and 66.2 for males [95% CI = 63.2, 69.2]). The number of expected MS cases was 2.7% higher than cases we detected. We developed a case-finding algorithm for MS using routinely collected healthcare data from the Campania Region, which was validated towards a clinical dataset, with high sensitivity and low proportion of undetected cases. Our prevalence estimates are in line with those reported by international studies conducted using similar methods. In the future, this cohort could be used for studies with high granularity of clinical, environmental, healthcare resource utilisation, and pharmacoeconomic variables.
Suggested Citation
Marcello Moccia & Vincenzo Brescia Morra & Roberta Lanzillo & Ilaria Loperto & Roberta Giordana & Maria Grazia Fumo & Martina Petruzzo & Nicola Capasso & Maria Triassi & Maria Pia Sormani & Raffaele P, 2020.
"Multiple Sclerosis in the Campania Region (South Italy): Algorithm Validation and 2015–2017 Prevalence,"
IJERPH, MDPI, vol. 17(10), pages 1-10, May.
Handle:
RePEc:gam:jijerp:v:17:y:2020:i:10:p:3388-:d:357392
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