Carl van Walraven1, Ian Colman2. 1. Department of Medicine/School of Epidemiology, Public Health, and Preventive Medicine, University of Ottawa, 451 Smyth Road, Ottawa ON K1N 6N5, Canada; Ottawa Hospital Research Institute; ICES uOttawa. Electronic address: carlv@ohri.ca. 2. Department of Medicine/School of Epidemiology, Public Health, and Preventive Medicine, University of Ottawa, 451 Smyth Road, Ottawa ON K1N 6N5, Canada.
Abstract
BACKGROUND: Migraine is a common and important source of pain and disability in society. Accurately identifying such people using routinely collected health data would be beneficial for health services research. OBJECTIVE: Externally validate a previously published method to identify migraineurs using health administrative data; and determine if a better model can be derived using data-mining techniques. METHODS: Migraine status was determined for Ontarians participating in a population-based, cross-sectional survey. Consenting participants were linked to population-based health administrative data to identify age, sex, and coded diagnoses. Discrimination and calibration measures were used to appraise the models. A de novo technique we term "double threshold analysis" was used to determine optimal lower and upper expected probabilities to identify migraine status in the newly derived model. RESULTS: A total of 1,01,114 people (mean age 46 years, 46% male) were included in the study, of which 11,314 (11.2%) had migraines. Using data-driven parameter estimates, the previous model to identify migraineurs had adequate discrimination (c-statistic 0.707 [95% CI 0.701-0.712]) and calibration (Hosmer-Lemeshow [H-L] statistic 20.8). A new model that included diagnostic code scores for physician visits, emergency visits, and hospitalizations with nonlinear terms for age and interactions significantly improved the model (c-statistic 0.724 [0.716-0.733], 16.4). Categorizing all people with a predicted migraine probability less than 10% or greater than 90% as without and having the disease, respectively, resulted in a sensitivity of 3.1%, a specificity of 99.96%, and a positive predictive value of 81.0% while capturing 57.0% of the cohort and 29.3% of migraineurs. CONCLUSION: A previously derived model to identify migraineurs was improved using data-mining techniques permitting accurate cohort identification using routinely collected health administrative data.
BACKGROUND:Migraine is a common and important source of pain and disability in society. Accurately identifying such people using routinely collected health data would be beneficial for health services research. OBJECTIVE: Externally validate a previously published method to identify migraineurs using health administrative data; and determine if a better model can be derived using data-mining techniques. METHODS:Migraine status was determined for Ontarians participating in a population-based, cross-sectional survey. Consenting participants were linked to population-based health administrative data to identify age, sex, and coded diagnoses. Discrimination and calibration measures were used to appraise the models. A de novo technique we term "double threshold analysis" was used to determine optimal lower and upper expected probabilities to identify migraine status in the newly derived model. RESULTS: A total of 1,01,114 people (mean age 46 years, 46% male) were included in the study, of which 11,314 (11.2%) had migraines. Using data-driven parameter estimates, the previous model to identify migraineurs had adequate discrimination (c-statistic 0.707 [95% CI 0.701-0.712]) and calibration (Hosmer-Lemeshow [H-L] statistic 20.8). A new model that included diagnostic code scores for physician visits, emergency visits, and hospitalizations with nonlinear terms for age and interactions significantly improved the model (c-statistic 0.724 [0.716-0.733], 16.4). Categorizing all people with a predicted migraine probability less than 10% or greater than 90% as without and having the disease, respectively, resulted in a sensitivity of 3.1%, a specificity of 99.96%, and a positive predictive value of 81.0% while capturing 57.0% of the cohort and 29.3% of migraineurs. CONCLUSION: A previously derived model to identify migraineurs was improved using data-mining techniques permitting accurate cohort identification using routinely collected health administrative data.
Authors: Anthony McKnight; Simone N Vigod; Cindy-Lee Dennis; Susitha Wanigaratne; Hilary K Brown Journal: Can J Psychiatry Date: 2020-12 Impact factor: 4.356
Authors: Erin B Graves; Brittany R Gerber; Patrick S Berrigan; Eileen Shaw; Tara M Cowling; Marie-Pier Ladouceur; Joanna K Bougie Journal: J Int Med Res Date: 2022-09 Impact factor: 1.573
Authors: Tetyana Kendzerska; Carl van Walraven; Daniel I McIsaac; Marcus Povitz; Sunita Mulpuru; Isac Lima; Robert Talarico; Shawn D Aaron; William Reisman; Andrea S Gershon Journal: Clin Epidemiol Date: 2021-06-17 Impact factor: 4.790