Controlling for Frailty in Pharmacoepidemiologic Studies of Older Adults: Validation of an Existing Medicare Claims-based Algorithm. Issue 4 (July 2018)

• Record Type: Journal Article
• Authors: Cuthbertson, Carmen C.; Kucharska-Newton, Anna; Faurot, Keturah R.; Stürmer, Til; Jonsson Funk, Michele; Palta, Priya; Windham, B. Gwen; Thai, Sydney; Lund, Jennifer L.
• Is Part Of: Epidemiology. Volume 29:Issue 4(2018)
• Publication Date: 2018-07
• Subjects: comparative effectiveness research -- confounding -- frailty -- Medicare -- pharmacoepidemiology; Epidemiology -- Periodicals; Epidemiology -- Environmental aspects -- Periodicals; Epidemiology -- Periodicals; 614.405
• DOI: 10.1097/EDE.0000000000000833
• Languages: English
• ISSNs: 1044-3983

Abstract

Abstract : Background: Frailty is a geriatric syndrome characterized by weakness and weight loss and is associated with adverse health outcomes. It is often an unmeasured confounder in pharmacoepidemiologic and comparative effectiveness studies using administrative claims data. Methods: Among the Atherosclerosis Risk in Communities (ARIC) Study Visit 5 participants (2011–2013; n = 3, 146), we conducted a validation study to compare a Medicare claims-based algorithm of dependency in activities of daily living (or dependency) developed as a proxy for frailty with a reference standard measure of phenotypic frailty. We applied the algorithm to the ARIC participants' claims data to generate a predicted probability of dependency. Using the claims-based algorithm, we estimated the C-statistic for predicting phenotypic frailty. We further categorized participants by their predicted probability of dependency (<5%, 5% to <20%, and ≥20%) and estimated associations with difficulties in physical abilities, falls, and mortality. Results: The claims-based algorithm showed good discrimination of phenotypic frailty (C-statistic = 0.71; 95% confidence interval [CI] = 0.67, 0.74). Participants classified with a high predicted probability of dependency (≥20%) had higher prevalence of falls and difficulty in physical ability, and a greater risk of 1-year all-cause mortality (hazard ratio = 5.7 [95% CI = 2.5, 13]) than participants classified with a low predicted probability (<5%). Sensitivity and specificity varied across predicted probability of dependency thresholds. Conclusions: The Medicare claims-based algorithm showed good discrimination of phenotypic frailty and high predictive ability with adverse health outcomes. This algorithm can be used in future Medicare claims analyses to reduce confounding by frailty and improve study validity. Abstract : Supplemental Digital Content is available in the text.