A predictive algorithm to identify ever smoking in medical claims-based epidemiologic studies.

ABSTRACT:

Purpose

To develop and validate an algorithm to estimate probability of ever smoking using administrative claims.

Methods

Using population-based samples of Medicare-aged individuals (121,278 Behavioral Risk Factor Surveillance System survey respondents and 207,885 Medicare beneficiaries), we developed a logistic regression model to predict probability of ever smoking from demographic and claims data. We applied the model in 1,657,266 additional Medicare beneficiaries and calculated area under the receiver operating characteristic curve (AUC) using presence or absence of a tobacco-specific diagnosis or procedure code as our "gold standard." We used these "gold standard" and lung/laryngeal cancer codes to over-ride predicted probability as 100%. We calculated Spearman's rho be

SUBMITTER: Faust I 

PROVIDER: S-EPMC12757012 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature