Novel application of approaches to predicting medication adherence using medical claims data. (20th August 2019)

• Record Type: Journal Article
• Authors: Zullig, Leah L.; Jazowski, Shelley A.; Wang, Tracy Y.; Hellkamp, Anne; Wojdyla, Daniel; Thomas, Laine; Egbuonu‐Davis, Lisa; Beal, Anne; Bosworth, Hayden B.
• Is Part Of: Health services research. Volume 54:Number 6(2019)
• Page Start: 1255
• Page End: 1262
• Publication Date: 2019-08-20
• Subjects: biostatistical methods -- chronic disease -- medicare; Medical care -- Periodicals; Medical care -- Evaluation -- Periodicals; Hospital care -- Periodicals; Health services administration -- Periodicals; 362
• DOI: 10.1111/1475-6773.13200
• Languages: English
• ISSNs: 0017-9124

Abstract

Abstract: Objective: To compare predictive analytic approaches to characterize medication nonadherence and determine under which circumstances each method may be best applied. Data Sources/Study Setting: Medicare Parts A, B, and D claims from 2007 to 2013. Study Design: We evaluated three statistical techniques to predict statin adherence (proportion of days covered [PDC ≥ 80 percent]) in the year following discharge: standard logistic regression with backward selection of covariates, least absolute shrinkage and selection operator (LASSO), and random forest. We used the C‐index to assess model discrimination and decile plots comparing predicted values to observed event rates to evaluate model performance. Data Extraction: We identified 11 969 beneficiaries with an acute myocardial infarction (MI)‐related admission from 2007 to 2012, who filled a statin prescription at, or shortly after, discharge. Principal Findings: In all models, prior statin use was the most important predictor of future adherence (OR = 3.65, 95% CI: 3.34‐3.98; OR = 3.55). Although the LASSO regression model selected nearly 90 percent of all candidate predictors, all three analytic approaches had moderate discrimination (C‐index ranging from 0.664 to 0.673). Conclusions: Although none of the models emerged as clearly superior, predictive analytics could proactively determine which patients are at risk of nonadherence, thus allowing for timely engagement in adherence‐improving interventions.