Using capture-recapture methodologies to estimate the prevalence of undetected dementia in Ontario

Project Summary

The rising prevalence of dementia is a prominent population and public health challenge. Evidence indicates that a significant number of dementia cases go undetected, which may downwardly bias public surveillance estimates of dementia prevalence and incidence. Inaccurate surveillance limits effective public health planning, resource allocation, and policy and program evaluation. We will use data from the Canadian Longitudinal Study on Aging (CLSA) linked to population-based administrative data in Ontario to estimate the extent of undetected dementia.

We will employ a CLSA-based neurocognitive disorder algorithm that was developed by our team to identify individuals who may be living with dementia among Ontario CLSA participants at baseline, follow-up 1, and follow-up 2. This algorithm is primarily based on the four cognitive tests common to the tracking and comprehensive cohort in CLSA but also includes data on self-reported diagnoses and use of dementia medication. We will compare the agreement between the CLSA-based algorithm and the health administrative-based algorithm, which will yield an alternate estimate of the extent of dementia that is undetected by health administrative sources. We will also compare the average cognitive performance of individuals based on their dementia status as determined by health administrative data.