Identification of Physician-Diagnosed Alzheimer’s Disease and Related Dementias in Population-Based Administrative Data: A Validation Study Using Family Physicians’ Electronic Medical Records

Article type: Research Article

Authors: Jaakkimainen, R. Liisa^{a; b; c; *} | Bronskill, Susan E.^b | Tierney, Mary C.^{a; d} | Herrmann, Nathan^e | Green, Diane^f | Young, Jacqueline^b | Ivers, Noah^{a; g} | Butt, Debra^{a; h} | Widdifield, Jessica^{c; i} | Tu, Karen^{a; c; j}

Affiliations: [a] Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada | [b] Institute for Clinical Evaluative Sciences, Toronto, ON, Canada | [c] Sunnybrook Academic Family Health Team, Toronto, ON, Canada | [d] Primary Care Research Unit, Sunnybrook Health Sciences Centre, Toronto, ON, Canada | [e] Division of Geriatric Psychiatry, Sunnybrook Health Sciences Centre, Toronto, ON, Canada | [f] Performance Management, Cancer Screening, Cancer Care Ontario, Toronto, ON, Canada | [g] Women’s College Hospital, Toronto, ON, Canada | [h] Scarborough Hospital, Toronto, ON, Canada | [i] McGill University Health Centre, Montreal, QC, Canada | [j] Toronto Western Family Health Team, Toronto, ON, Canada

Correspondence: [*] Correspondence to: R. Liisa Jaakkimainen, MSc, MD, CCFP, Institute for Clinical Evaluative Sciences, 2075 Bayview Ave, G wing, Toronto, ON M4N 3M5, Canada. Tel.: +1 416 480 4055/Ext. 1 3868; Fax: +1 416 480 6048; E-mail: [email protected].

Abstract: Background: Population-based surveillance of Alzheimer’s and related dementias (AD-RD) incidence and prevalence is important for chronic disease management and health system capacity planning. Algorithms based on health administrative data have been successfully developed for many chronic conditions. The increasing use of electronic medical records (EMRs) by family physicians (FPs) provides a novel reference standard by which to evaluate these algorithms as FPs are the first point of contact and providers of ongoing medical care for persons with AD-RD. Objective: We used FP EMR data as the reference standard to evaluate the accuracy of population-based health administrative data in identifying older adults with AD-RD over time. Methods: This retrospective chart abstraction study used a random sample of EMRs for 3,404 adults over 65 years of age from 83 community-based FPs in Ontario, Canada. AD-RD patients identified in the EMR were used as the reference standard against which algorithms identifying cases of AD-RD in administrative databases were compared. Results: The highest performing algorithm was “one hospitalization code OR (three physician claims codes at least 30 days apart in a two year period) OR a prescription filled for an AD-RD specific medication” with sensitivity 79.3% (confidence interval (CI) 72.9–85.8%), specificity 99.1% (CI 98.8–99.4%), positive predictive value 80.4% (CI 74.0–86.8%), and negative predictive value 99.0% (CI 98.7–99.4%). This resulted in an age- and sex-adjusted incidence of 18.1 per 1,000 persons and adjusted prevalence of 72.0 per 1,000 persons in 2010/11. Conclusion: Algorithms developed from health administrative data are sensitive and specific for identifying older adults with AD-RD.

Keywords: Dementia, electronic medical records, family physician, health administrative algorithm

DOI: 10.3233/JAD-160105

Journal: Journal of Alzheimer's Disease, vol. 54, no. 1, pp. 337-349, 2016