Identifying undercompensated groups defined by multiple attributes in risk adjustment

Abstract

Objective: To identify undercompensated groups in plan payment risk adjustment that are defined by multiple attributes with a systematic new approach, improving on the arbitrary and inconsistent nature of existing evaluations.

Methods: Extending the concept of variable importance for single attributes, we construct a measure of 'group importance' in the random forests algorithm to identify groups with multiple attributes that are undercompensated by current risk adjustment formulas. Using 2016-2018 IBM MarketScan and 2015-2018 Medicare claims and enrolment data, we evaluate two risk adjustment scenarios: the risk adjustment formula used in the individual health insurance Marketplaces and the risk adjustment formula used in Medicare.

Results: A number of previously unidentified groups with multiple chronic conditions are undercompensated in the Marketplaces risk adjustment formula, while groups without chronic conditions tend to be overcompensated in the Marketplaces. The magnitude of undercompensation when defining groups with multiple attributes is many times larger than with single attributes. No complex groups were found to be consistently undercompensated or overcompensated in the Medicare risk adjustment formula.

Conclusions: Our method is effective at identifying complex undercompensated groups in health plan payment risk adjustment where undercompensation creates incentives for insurers to discriminate against these groups. This work provides policy-makers with new information on potential targets of discrimination in the healthcare system and a path towards more equitable health coverage.

Keywords: delivery of Health Care; health care sector; health equity.

Figure 1

Illustration of random forests for group importance.

Figure 2

Top undercompensated and overcompensated groups in the Marketplaces risk adjustment (minimum node size: 10 000, maximum nodes: 8). Unfilled circles indicate the lack of a condition. Only seven overcompensated groups were identified in this setting, after removing groups that appeared in less than 1% of trees across the 3 years.

Figure 3

Top undercompensated and overcompensated groups in the Marketplaces risk adjustment (minimum node size: 100, maximum nodes: 64). Unfilled circles indicate the lack of a condition.