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Published on: July 27, 2018
Validating chronic disease ascertainment algorithms for use in the Canadian longitudinal study on aging
Mark Oremus1, Ronald Postuma, Lauren Griffith
1Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada.
Summary
Seven chronic disease algorithms were validated for the Canadian Longitudinal Study on Aging, showing high accuracy. These tools effectively identify individuals with diabetes mellitus type 2, parkinsonism, and heart disease.
Area of Science:
- Gerontology
- Epidemiology
- Biostatistics
Background:
- Chronic diseases are a major concern in aging populations.
- Accurate disease ascertainment is crucial for longitudinal studies like the Canadian Longitudinal Study on Aging (CLOSA).
- Existing algorithms require validation for specific cohorts and research objectives.
Purpose of the Study:
- To validate seven chronic disease ascertainment algorithms for the CLOSA.
- To assess the performance of algorithms for diabetes mellitus type 2, parkinsonism, chronic airflow obstruction (CAO), hand osteoarthritis (OA), hip OA, knee OA, and ischemic heart disease.
Main Methods:
- Algorithm validation using interviewer-administered questionnaires, biochemical tests (fasting glucose), and pulmonary function tests (spirometry).
- Sensitivity and specificity were calculated against physician diagnosis as the reference standard.
- Recruitment of 176 participants across three Canadian cities (2009-2011).
Main Results:
- Most validated algorithms demonstrated high sensitivity and specificity, generally 80% or greater.
- The algorithms accurately classified participants' disease status for the targeted chronic conditions.
- Successful identification of cases and controls for multiple chronic diseases.
Conclusions:
- The seven validated algorithms are suitable for chronic disease ascertainment in the CLOSA.
- These algorithms provide reliable tools for epidemiological research on aging and chronic diseases.
- High performance indicates the utility of these algorithms in identifying individuals with specific chronic conditions.
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