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

Area of Science:

• Gerontology
• Public Health
• Health Informatics

Background:

• Dementia identification in community settings is crucial for timely intervention.
• Administrative records offer a cost-effective data source for health research.
• Current methods for dementia case-finding in the community require optimization.

Purpose of the Study:

• To develop and validate algorithms for identifying individuals with dementia using administrative data.
• To assess the accuracy of different combinations of administrative data indicators for dementia detection.

Main Methods:

• Retrospective analysis of anonymized data from general practitioners and a local health agency in Northern Italy.
• Inclusion of individuals aged 65 and older diagnosed with dementia.
• Development of algorithms based on administrative data indicators: dementia diagnosis, medication use (cholinesterase inhibitors/memantine), neuropsychological tests, brain imaging (CT/MRI), and neurological visits.

Main Results:

• Individual administrative data indicators demonstrated high specificity but low sensitivity for dementia identification.
• Algorithm I (medication, diagnosis, or neuropsychological tests) achieved 97.9% specificity and 52.5% sensitivity.
• Algorithm II (medication, diagnosis, tests, imaging, or visits) achieved 70.6% specificity and 90.8% sensitivity.
• Algorithm III (medication, diagnosis, tests, imaging, and visits) achieved 89.3% specificity and 73.3% sensitivity.

Conclusions:

• Algorithms derived from administrative data, even with combined variables, are not sufficiently accurate for classifying dementia patients in the community.
• The sensitivity and specificity trade-offs highlight limitations in using administrative data alone for dementia case-finding.
• Larger patient cohort studies are recommended to develop more effective strategies for community-based dementia identification.