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Appendix A: Criteria for Selecting Risk Factors Reviewed by the Committee
Pages 104-109

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From page 104...
... -HCC risk adjustment model for individual and small group markets under the Affordable Care Act (Kautter et al., 2014) ; and • The National Quality Forum 2014 report Risk Adjustment for Socioeconomic Status or Other Sociodemographic Factors.
From page 105...
... Given the extreme skewness of medical expenditure data, the data cannot reliably determine the expected cost of extremely rare diagnostic categories. Principle 4 -- In creating an individual's clinical profile, hierarchies should be used to characterize the person's illness level within each disease process, while the effects of unrelated disease processes accumulate.
From page 106...
... By combining both predictive power and heterogeneity into a single measure, the impact factor is more informative than purely predictive measures such as R2; it approximates the magnitude of the incremental adjustments due to adding a variable to the case-mix model (O'Malley et al., 2005)
From page 107...
... Criterion 4 -- Identify chronic, predictable, or other conditions that are subject to insurer risk selection, risk segmentation, or provider network selection, rather than random acute events that represent insurance risk. Following an extensive review process, we selected 127 HHS-HCCs to be included in the HHS risk adjustment model … Finally, to balance the competing goals of improving predictive power and limiting the influence of discretionary coding, a subset of HHS-HCCs in the risk adjustment model were grouped into larger aggregates, in other words "grouping" clusters of HCCs together as a single condition with a single coefficient that can only be counted once.
From page 108...
... Accurate data that can be reliably Data limitations often represent a   and feasibly captured practical constraint to what factors are included in risk models Contribution of unique variation in Prevent overfitting and unstable   the outcome (i.e., not redundant or estimates, or coefficients that appear highly correlated with another risk to be in the wrong direction; reduce factor) data collection burden Potentially, improvement of the Change in R-squared or C-statistic   risk model (e.g., risk model may not be significant, but metrics of discrimination -- i.e., calibration at different deciles of risk sensitivity/specificity, calibration)
From page 109...
... b Examples of sociodemographic factors include income; education; English language proficiency, etc.

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