TRADE-OFFS IN COGNITIVE TESTING IN PRIMARY CARE IN THE UNITED STATES
Author(s)
Sigal Maya, MS1, James G. Kahn, MD, MPH1, Huong Q. Nguyen, PhD, RN2, Kelly Atkins, PhD3, Men T. Hoang, MPhil, MSc4, Cyprian M. Mostert, MSc, PhD5, Kate Possin, PhD6;
1University of California San Francisco, Philip R. Lee Institute for Health Policy Studies, San Francisco, CA, USA, 2Kaiser Permanente Southern California, Department of Research & Evaluation, Pasadena, CA, USA, 3Monash University, Melbourne, Australia, 4Trinity College Dublin, Dublin, Ireland, 5Global Brain Health Institute, Dublin, Ireland, 6Global Brain Health Institute, San Francisco, CA, USA
1University of California San Francisco, Philip R. Lee Institute for Health Policy Studies, San Francisco, CA, USA, 2Kaiser Permanente Southern California, Department of Research & Evaluation, Pasadena, CA, USA, 3Monash University, Melbourne, Australia, 4Trinity College Dublin, Dublin, Ireland, 5Global Brain Health Institute, Dublin, Ireland, 6Global Brain Health Institute, San Francisco, CA, USA
OBJECTIVES: Cognitive testing in primary care is used for diagnosing mild cognitive impairment and dementia. Evaluating trade-offs between accurate diagnoses and false positives are important for resource-constrained health services wishing to implement cognitive testing.
METHODS: We defined three testing strategies. “Reactive” refers patients for cognitive testing if memory concerns are organically brought up in a primary care visit. “Selective” refers patients who endorse cognitive concerns during systematic inquiries at annual visits. “Inclusive” administers a cognitive test to all patients. We used published data on the prevalence of undiagnosed cognitive impairment and test performance for the TabCAT Brain Health Assessment Global Score, and pilot data on reactive and selective testing to estimate results.
RESULTS: For 1000 70-year-olds without diagnosed cognitive impairment, reactive, selective, and inclusive strategies test 29, 130, and 1000 patients, and accurately classify (true positive or negative) 929, 957, and 888, respectively. Of 92 people with cognitive impairment, reactive identifies 22 (24%), selective 56 (61%), and inclusive 76 (83%), with <1, 7, and 97 false positives, respectively. Thus, expanding from reactive to selective adds 34 people correctly classified as impaired and 6 false positives (6:1 ratio); from selective to inclusive adds 21 and 90 (1:4). The reactive strategy has a positive predictive value of 98%, selective 89%, and inclusive 44%. Negative predictive values are 93%, 96%, and 98%. The number needed to test to identify 10 cognitively impaired cases are 13.6 (0.2 false positives) in reactive, 23.4 (1.2 false positives) in selective, and 131.6 (12.7 false positives) in inclusive.
CONCLUSIONS: The strategy used to initiate cognitive testing in primary care has considerable trade-offs in the identification of cases, workforce capacity requirements, and false positives. Inclusive approaches identify more cases at the expense of greater workforce resources and false positives; more restrictive strategies prevent false positives and require fewer resources but miss more cases.
METHODS: We defined three testing strategies. “Reactive” refers patients for cognitive testing if memory concerns are organically brought up in a primary care visit. “Selective” refers patients who endorse cognitive concerns during systematic inquiries at annual visits. “Inclusive” administers a cognitive test to all patients. We used published data on the prevalence of undiagnosed cognitive impairment and test performance for the TabCAT Brain Health Assessment Global Score, and pilot data on reactive and selective testing to estimate results.
RESULTS: For 1000 70-year-olds without diagnosed cognitive impairment, reactive, selective, and inclusive strategies test 29, 130, and 1000 patients, and accurately classify (true positive or negative) 929, 957, and 888, respectively. Of 92 people with cognitive impairment, reactive identifies 22 (24%), selective 56 (61%), and inclusive 76 (83%), with <1, 7, and 97 false positives, respectively. Thus, expanding from reactive to selective adds 34 people correctly classified as impaired and 6 false positives (6:1 ratio); from selective to inclusive adds 21 and 90 (1:4). The reactive strategy has a positive predictive value of 98%, selective 89%, and inclusive 44%. Negative predictive values are 93%, 96%, and 98%. The number needed to test to identify 10 cognitively impaired cases are 13.6 (0.2 false positives) in reactive, 23.4 (1.2 false positives) in selective, and 131.6 (12.7 false positives) in inclusive.
CONCLUSIONS: The strategy used to initiate cognitive testing in primary care has considerable trade-offs in the identification of cases, workforce capacity requirements, and false positives. Inclusive approaches identify more cases at the expense of greater workforce resources and false positives; more restrictive strategies prevent false positives and require fewer resources but miss more cases.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
CO99
Topic
Clinical Outcomes
Topic Subcategory
Clinical Outcomes Assessment
Disease
SDC: Neurological Disorders