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Development and Validation of a Predictive Algorithm to Identify Adult Asthmatics from Medical Services and Pharmacy Claims Databases

Authors

  • Yuko Kawasumi,

    1. Centre for Health Services and Policy Research, University of British Columbia, 201-2206 East Mall, Vancouver, BC, Canada V6T 1Z3
    2. Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada
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    • Address correspondence to Yuko Kawasumi, Ph.D., Centre for Health Services and Policy Research, University of British Columbia, 201-2206 East Mall, Vancouver, BC, Canada V6T 1Z3; e-mail: ykawasumi@chspr.ubc.ca. Yuko Kawasumi, Ph.D., Michal Abrahamowicz, Ph.D., and Robyn Tamblyn, Ph.D., are with the Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada. Michal Abrahamowicz, Ph.D., and Robyn Tamblyn, Ph.D., are with the Department of Medicine, McGill University Health Centre, Montreal, QC, Canada. Pierre Ernst, M.D., M.Sc., F.R.C.P.(c), is with the Center for Clinical Epidemiology, Lady Davis Research Institute, Jewish General Hospital, Montreal, QC, Canada. Pierre Ernst, M.D., M.Sc., F.R.C.P.(c), is with the Department of Medicine, Division of Pulmonary Diseases, Jewish General Hospital, Montreal, QC, Canada.

  • Michal Abrahamowicz,

    1. Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada
    2. Department of Medicine, McGill University Health Centre, Montreal, QC, Canada
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  • Pierre Ernst,

    1. Center for Clinical Epidemiology, Lady Davis Research Institute, Jewish General Hospital, Montreal, QC, Canada
    2. Department of Medicine, Division of Pulmonary Diseases, Jewish General Hospital, Montreal, QC, Canada
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  • Robyn Tamblyn

    1. Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada
    2. Department of Medicine, McGill University Health Centre, Montreal, QC, Canada
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Abstract

Objective. To develop and validate the accuracy of a predictive model to identify adult asthmatics from administrative health care databases.

Study Setting. An existing electronic medical record project in Montreal, Quebec.

Study Design. One thousand four hundred and thirty-one patients with confirmed asthma status were identified from primary care physician's electronic medical record.

Data Collection/Extraction Methods. Therapeutic indication of asthma in an electronic prescription and/or confirmed asthma from an automated problem list were used as the gold standard. Five groups of asthma-specific markers were identified from administrative health care databases to estimate the probability of the presence of asthma. Cross-validation evaluated the diagnostic ability of each predictive model using 50 percent of sample.

Principal Findings. The best performance in discriminating between the patients with asthma and those without it included indicators from medical service and prescription claims databases. The best-fitting algorithm had a sensitivity of 70 percent, a specificity of 94 percent, and positive predictive value of 65 percent. The prescriptions claims–specific algorithm demonstrated a nearly equal performance to the model with medical services and prescription claims combined.

Conclusions. Our algorithm using asthma-specific markers from administrative claims databases provided moderate sensitivity and high specificity.

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