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Updated: Sep 1, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Identification of Major Cognitive Disorders in Self-Reported versus Administrative Health Data: A Cohort Study in
Isabelle Dufour1, Isabelle Vedel2, Amélie Quesnel-Vallée1,3
1Department of Epidemiology, Biostatistics, and Occupational Health, Faculty of Medicine, McGill University, Montreal, Canada.
Background:
The first imperative in producing the relevant and needed knowledge about major neurocognitive disorder (MNCD) is to identify people presenting with the condition adequately. To document potential disparities between administrative health databases and population-based surveys could help identify specific challenges in this population and methodological shortfalls.
Objective:
To describe and compare the characteristics of community-dwelling older adults according to four groups: 1) No MNCD; 2) Self-reported MNCD only; 3) MNCD in administrative health data only; 4) MNCD in both self-reported and administrative health data.
Methods:
This retrospective cohort study used the Care Trajectories-Enriched Data (TorSaDE) cohort, a linkage between five waves of the Canadian Community Health Survey (CCHS) and health administrative health data. We included older adults living in the community who participated in at least one cycle of the CCHS. We reported on positive and negative MNCD in self-reported versus administrative health data. We then compared groups' characteristics using chi-square tests and ANOVA.
Results:
The study cohort was composed of 25,125 older adults, of which 784 (3.1%) had MNCD. About 70% of people with an MNCD identified in administrative health data did not report it in the CCHS. The four groups present specific challenges related to the importance of perception, timely diagnosis, and the caregivers' roles in reporting health information.
Conclusion:
To a certain degree, both data sources fail to consider subgroups experiencing issues related to MNCD; studies like ours provide insight to understand their characteristics and needs better.
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