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Generation and validation of algorithms to identify subjects with dementia using administrative data

  • Jacopo C. DiFrancescoEmail author
  • Alessandra Pina
  • Giorgia Giussani
  • Laura Cortesi
  • Elisa Bianchi
  • Luca Cavalieri d’Oro
  • Emanuele Amodio
  • Alessandro Nobili
  • Lucio Tremolizzo
  • Valeria Isella
  • Ildebrando Appollonio
  • Carlo Ferrarese
  • Ettore Beghi
Original Article

Abstract

Objectives

To generate and validate algorithms for the identification of individuals with dementia in the community setting, by the interrogation of administrative records, an inexpensive and already available source of data.

Methods

We collected and anonymized information on demented individuals 65 years of age or older from ten general practitioners (GPs) in the district of Brianza (Northern Italy) and compared this with the administrative data of the local health protection agency (Agenzia per la Tutela della Salute). Indicators of the disease in the administrative database (diagnosis of dementia in the hospital discharge records; use of cholinesterase inhibitors/memantine; neuropsychological tests; brain CT/MRI; outpatient neurological visits) were used separately and in different combinations to generate algorithms for the detection of patients with dementia.

Results

When used individually, indicators of dementia showed good specificity, but low sensitivity. By their combination, we generated different algorithms: I-therapy with ChEI/memantine or diagnosis of dementia at discharge or neuropsychological tests (specificity 97.9%, sensitivity 52.5%); II-therapy with ChEI/memantine or diagnosis of dementia at discharge or neuropsychological tests or brain CT/MRI or neurological visit (sensitivity 90.8%, specificity 70.6%); III-therapy with ChEI/memantine or diagnosis of dementia at discharge or neuropsychological tests or brain CT/MRIMRI and neurological visit (specificity 89.3%, sensitivity 73.3%).

Conclusions

These results show that algorithms obtained from administrative data are not sufficiently accurate in classifying patients with dementia, whichever combination of variables is used for the identification of the disease. Studies in large patient cohorts are needed to develop further strategies for identifying patients with dementia in the community setting.

Keywords

Algorithm Dementia Administrative data Sensitivity Specificity 

Notes

Acknowledgments

The authors are extremely grateful to the General Practitioners who participated in the study.

Compliance with ethical standards

Ethical statements

All the data included in this research were managed according to the current Italian law on privacy and authorization was obtained from the ATS Brianza to obtain and use the administrative data for the purposes of this study.

No experiments on animals have been conducted for the present study.

Conflict of interest

The authors declare that they have no conflicts of interest.

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Copyright information

© Fondazione Società Italiana di Neurologia 2019

Authors and Affiliations

  • Jacopo C. DiFrancesco
    • 1
    Email author
  • Alessandra Pina
    • 1
  • Giorgia Giussani
    • 2
  • Laura Cortesi
    • 2
  • Elisa Bianchi
    • 2
  • Luca Cavalieri d’Oro
    • 3
  • Emanuele Amodio
    • 3
  • Alessandro Nobili
    • 2
  • Lucio Tremolizzo
    • 1
  • Valeria Isella
    • 1
  • Ildebrando Appollonio
    • 1
  • Carlo Ferrarese
    • 1
  • Ettore Beghi
    • 2
  1. 1.Department of Neurology, San Gerardo Hospital, Laboratory of Neurobiology, Milan Center for Neuroscience, School of Medicine and SurgeryUniversity of Milano-BicoccaMonzaItaly
  2. 2.Istituto di Ricerche Farmacologiche Mario Negri IRCCSMilanItaly
  3. 3.Epidemiology Unit, Health Protection Agency (Agenzia per la Tutela della Salute - ATS)MonzaItaly

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