Generation and validation of algorithms to identify subjects with dementia using administrative data
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.
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.
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%).
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.
KeywordsAlgorithm Dementia Administrative data Sensitivity Specificity
The authors are extremely grateful to the General Practitioners who participated in the study.
Compliance with ethical standards
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.
- 3.Collaborators GD (2018) Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet NeurolGoogle Scholar
- 8.Ritchie C, Smailagic N, Noel-Storr AH, Ukoumunne O, Ladds EC, Martin S (2017) CSF tau and the CSF tau/Abeta ratio for the diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev 3:CD010803Google Scholar
- 16.Jaakkimainen RL, Bronskill SE, Tierney MC, Herrmann N, Green D, Young J et al (2016) Identification of physician-diagnosed Alzheimer’s disease and related dementias in population-based administrative data: a validation study using family physicians’ electronic medical records. J Alzheimers Dis 54(1):337–349CrossRefGoogle Scholar
- 21.American Medical Association Hospital (2005) International classification of diseases, 9th revisionGoogle Scholar
- 24.Albrecht JS, Hanna M, Kim D, Perfetto EM (2018) Predicting diagnosis of Alzheimer’s disease and related dementias using administrative claims. J Manag Care Spec Pharm 24(11):1138–1145Google Scholar
- 25.Mar J, Arrospide A, Soto-Gordoa M, Machón M, Iruin Á, Martinez-Lage P et al (2018) Validity of a computerized population registry of dementia based on clinical databases. NeurologiaGoogle Scholar