with the need for a common vocabulary and understanding of the definitions and, hence, the rule. The importance of the choice between prospective and concurrent risk adjustment. The rule appears to leave flexibility for the risk-adjustment methodology to be concurrent (i.e., risk adjustment for a given year is based on the health status of insureds during the same year) or prospective (i.e., the health status for one year affects the subsequent year’s risk adjustment, such as Medicare Advantage). Medicare Advantage risk adjustment is used for payments from CMS to issuers and Medicaid managed care risk adjustment is used for payments from states to issuers for relatively stable populations using capitation rates established by the government agencies. Risk adjustment under ACA, however, is an unprecedented transfer between issuers in a relatively unknown market in which significant transfers are anticipated in an unprecedented guaranteed issue environment with different premiums expected to be set by each issuer. The choice between prospective and concurrent risk adjustment affects data timing, risk adjustment transfer predictability, the degree to which an issuer’s risks in that year are recognized, issuers’ pricing and financial forecasting, and issuers’ approaches to developing care-management monitoring targets. We discuss the implications of each of the two approaches in this letter. The necessary role of premiums and rating factors in risk-adjustment methodologies. Because different issuers will set different premiums for different health plans (benefit levels or “metal” product tiers within a market), for benefits beyond the essential health benefits, and for certain insured demographic factors (e.g., age, family tier, smoking status, and geographic area), premium variations and rating factors must be accounted for in the riskadjustment methodology. The preamble to the proposed rule asks for guidance on how premiums and rating factors may be built into risk-adjustment methodologies. We have provided a high-level discussion on pages 21-24 of the conceptual trade-offs between the various options. The necessary links between methodology, data, and timing. The risk-adjustment methodology determines what data must be collected. The available data, particularly in the first year of risk adjustment, similarly constrains the choices with respect to risk-adjustment methodology. Appropriate timeframes for risk-adjustment settlement are, in turn, a function of risk-adjustment methodology and data collection. As a result, methodology, data collection, and time frames must be synchronized. The complexity of modeling risk-adjustment methodologies. In this letter, the work group has provided a high-level perspective of risk-adjustment methodologies. A detailed analysis of risk-adjustment methodologies, inclusive of accounting for premium variations and rating factors, requires modeling that is beyond the scope of this discussion. If requested, we could pursue a joint project with the Society of Actuaries (SOA) and actuaries with expertise in this area to identify and assemble resources to assist with such modeling. State alternate methodology evaluation with consideration for local markets, policy goals, and data availability. The work group suggests that, in the interests of clarity, the rule should provide guidance on the criteria CMS will use to evaluate states’ deviations from the federal standard. The work group also suggests that the rule not require a similar or better statistical fit5 for an alternate state methodology to be acceptable. Statistical fit should be included as only one consideration by which an alternate state methodology can be evaluated.
5
Statistical fit primarily refers to the R-squared statistic of the risk-adjustment regression model. It also is used sometimes to refer to the mean absolute percentage error between the regression data and the regression model’s estimated values. It sometimes is used loosely to describe the general performance of a model in its application via predictive ratios, that is, the ratios of the model’s predictions and the actual values.
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