Developing two separate measures may resolve the problem that medical care insurance benefits are not fungible—that is, they cannot be used for other necessary expenses, such as housing and food. But this approach really pushes many of the difficulties presented by the measurement of medical care need into the domain of the second measure.
Review of Existing Measurement Strategies
Before identifying strategies for moving forward, Meier briefly reviewed some of the work done to date toward development of a measure of MCER. Two analyses warrant particular emphasis: the first is a 1995 analysis by Short and Banthin, which estimates underinsurance among privately insured adults under age 65. It focuses on economic circumstances in the case of a catastrophic event, in which individuals are assigned to a risk group on the basis of expected expenditures and a catastrophic event is defined for each risk group. An individual is underinsured if the catastrophic event exceeds 10 percent of income.
A second analysis, by Banthin and Bernard (2006), also examines insurance adequacy. It covers the broader population, including the underinsured among both publicly and privately insured. However, this analysis focuses on actual medical expenditures over 10 and 20 percent of family income, an ex post concept. So it omits the risk aspect that we are interested in talking about today.
Another measurement strategy is the empirical model developed by Handel (2010). Meier observed that because a major objective of the framework she and Wolfe suggest is to identify an empirical strategy that enables more robust treatment of medical care risk and its implications, they relied on Handel’s model to develop an MCER-relevant strategy to model and quantify risk.
Briefly, Handel’s method takes a base sample of individuals and applies their claims information into a risk adjustment software model— specifically, the Johns Hopkins Adjusted Clinical Groups (ACG) Case-Mix System model. Based on claims experience—essentially prior diagnosis information—and an individual’s demographic characteristics (age and gender), this software comes up with a risk score that is an indicator of the relative risk of individuals if one compares their scores.
Individuals are assigned to a risk cell for each claim type (four categories, including pharmaceutical claims, mental illness–related claims, physician claims, and hospital claims). Each cell includes similarly risky individuals as determined by the Johns Hopkins ACG software. Taking each claim type separately and the risk cells within the claim type, expenditure distributions are fit to the risk cell/claim type combinations, using actual claims experience.