Development and validation of a predictive algorithm to identify adult asthmatics from medical services and pharmacy claims databases

Research

Related Publications

Smolina K, Weymann D, Morgan S, Ross C, Carleton B. Association between regulatory advisories and codeine prescribing to postpartum women. Journal of the American Medical Association. 2015;313(18):1861-2.
Suter E, Misfeldt R, Mallinson S, Wilhelm A, Boakye O, Marchildon G, et al. Comparative Review of the Policy Landscape of Team-based Primary Health Care Service Delivery in Western Canada. Alberta Health Services; 2014.
Laberge M, Pang J, Walker K, Wong ST, Hogg W, Wodchis WP. QUALICOPC (Quality and Costs of Primary Care) Canada: A focus on the aspects of primary care most highly rated by current patients of primary care practices. Ottawa, ON: Canadian Foundation for Healthcare Improvement; 2014.
McGregor MJ, Abu-Laban RB, Ronald L, McGrail KM, Andrusiek D, Baumbusch J, et al. Nursing Home Characteristics Associated with Resident Transfers to Emergency Department. Canadian Journal on Aging. 2012;33(1):38-48.

Publication Topics

Development and validation of a predictive algorithm to identify adult asthmatics from medical services and pharmacy claims databases

Title
Publication TypeJournal Article
Year of Publication2011
AuthorsKawasumi Y, Abrahamowicz M, Ernst P, Tamblyn R
JournalHealth Services Research
Volume46
Issue3
Pages939 - 963
Date Published2011
KeywordsAdult, Aged, Algorithms, Asthma/epidemiology/prevention & control, Drug Prescriptions/statistics & numerical data, Female, Health Status Indicators, Humans, Insurance Claim Review/statistics & numerical data, Logistic Models, Male, Management Information Systems/statistics & numerical data, Mass Screening/methods, Middle Aged, Multivariate Analysis, Quebec/epidemiology, Reproducibility of Results, Sensitivity and Specificity, Severity of Illness Index
AbstractOBJECTIVE: 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.
Citation Key371