학술논문
Identification of dementia and MCI cases in health information systems: An Italian validation study
Document Type
Report
Author
Bacigalupo, Ilaria; Lombardo, Flavia L.; Bargagli, Anna Maria; Cascini, Silvia; Agabiti, Nera; Davoli, Marina; Scalmana, Silvia; Di Palma, Annalisa; Greco, Annarita; Rinaldi, Marina; Giordana, Roberta; Imperiale, Daniele; Secreto, Piero; Golini, Natalia; Gnavi, Roberto; Lovaldi, Franca; Biagini, Carlo A.; Gualdani, Elisa; Francesconi, Paolo; Magliocchetti, Natalia; Di Fiandra, Teresa; Vanacore, Nicola
Source
Alzheimer’s & Dementia: Translational Research & Clinical Interventions. October 2022, Vol. 8 Issue 1
Subject
Language
English
Abstract
INTRODUCTION Dementia affects ≈50 million people worldwide, and this number will almost double every 20 years, reaching 82 million in 2030 and 152 million in 2050.[sup.1] Dementia is one of [...]
: Introduction: The identification of dementia cases through routinely collected health data represents an easily accessible and inexpensive method to estimate the prevalence of dementia. In Italy, a project aimed at the validation of an algorithm was conducted. Methods: The project included cases (patients with dementia or mild cognitive impairment [MCI]) recruited in centers for cognitive disorders and dementias and controls recruited in outpatient units of geriatrics and neurology. The algorithm based on pharmaceutical prescriptions, hospital discharge records, residential long‐term care records, and information on exemption from health‐care co‐payment, was applied to the validation population. Results: The main analysis was conducted on 1110 cases and 1114 controls. The sensitivity, specificity, and positive and negative predictive values in discerning cases of dementia were 74.5%, 96.0%, 94.9%, and 79.1%, respectively, whereas in detecting cases of MCI these values were 29.7%, 97.5%, 92.2%, and 58.1%, respectively. The variables associated with misclassification of cases were also identified. Discussion: This study provided a validated algorithm, based on administrative data, which can be used to identify cases with dementia and, with lower sensitivity, also early onset dementia but not cases with MCI.
: Introduction: The identification of dementia cases through routinely collected health data represents an easily accessible and inexpensive method to estimate the prevalence of dementia. In Italy, a project aimed at the validation of an algorithm was conducted. Methods: The project included cases (patients with dementia or mild cognitive impairment [MCI]) recruited in centers for cognitive disorders and dementias and controls recruited in outpatient units of geriatrics and neurology. The algorithm based on pharmaceutical prescriptions, hospital discharge records, residential long‐term care records, and information on exemption from health‐care co‐payment, was applied to the validation population. Results: The main analysis was conducted on 1110 cases and 1114 controls. The sensitivity, specificity, and positive and negative predictive values in discerning cases of dementia were 74.5%, 96.0%, 94.9%, and 79.1%, respectively, whereas in detecting cases of MCI these values were 29.7%, 97.5%, 92.2%, and 58.1%, respectively. The variables associated with misclassification of cases were also identified. Discussion: This study provided a validated algorithm, based on administrative data, which can be used to identify cases with dementia and, with lower sensitivity, also early onset dementia but not cases with MCI.