TY - JOUR AU - Ericson, Keith Marzilli AU - Geissler, Kimberley AU - Lubin, Benjamin TI - The Impact of Partial-Year Enrollment on the Accuracy of Risk Adjustment Systems: A Framework and Evidence JF - National Bureau of Economic Research Working Paper Series VL - No. 23765 PY - 2017 Y2 - September 2017 DO - 10.3386/w23765 UR - http://www.nber.org/papers/w23765 L1 - http://www.nber.org/papers/w23765.pdf N1 - Author contact info: Keith Marzilli Ericson Boston University Questrom School of Business 595 Commonwealth Avenue Boston, MA 02215 Tel: 617/353-4553 E-Mail: kericson@bu.edu Kimberley Geissler University of Massachusetts at Amherst School of Public Health and Health Sciences 715 N Pleasant St 325 Arnold Hall Amherst, MA 01003 E-Mail: kgeissler@umass.edu Benjamin Lubin 595 Commonwealth Avenue Boston, MA 02215 USA E-Mail: blubin@bu.edu AB - Accurate risk adjustment facilitates healthcare market competition. Risk adjustment typically aims to predict annual costs of individuals enrolled in an insurance plan for a full year. However, partial-year enrollment is common and poses a challenge to risk adjustment, since diagnoses are observed with lower probability when individual is observed for a shorter time. Due to missed diagnoses, risk adjustment systems will underpay for partial-year enrollees, as compared to full-year enrollees with similar underlying health status and usage patterns. We derive a new adjustment for partial-year enrollment in which payments are scaled up for partial-year enrollees’ observed diagnoses, which improves upon existing methods. We simulate the role of missed diagnoses using a sample of commercially insured individuals and the 2014 Marketplace risk adjustment algorithm, and find the expected spending of six-month enrollees is underpredicted by 19%. We then examine whether there are systematically different care usage patterns for partial-year enrollees in this data, which can offset or amplify underprediction due to missed diagnoses. Accounting for differential spending patterns of partial-year enrollees does not substantially change the underprediction for six-month enrollees. However, one-month enrollees use systematically less than one-twelfth the care of full-year enrollees, partially offsetting the missed diagnosis effect. ER -