Cost of Caring for Medicare Beneficiaries with Parkinson's Disease: Impact of the CMS-HCC Risk-Adjustment Model

Inventory of real world data sources in Parkinson's disease

Background

Real world data have an important role to play in the evaluation of epidemiology and burden of disease; and in assisting health-care decision-makers, especially related to coverage and payment decisions. However, there is currently no overview of the existing longitudinal real world data sources in Parkinson’s disease (PD) in the USA. Such an assessment can be very helpful, to support a future effort to harmonize real world data collection and use the available resources in an optimal way.

Methods

The objective of this comprehensive literature review is to systematically identify and describe the longitudinal, real world data sources in PD in the USA, and to provide a summary of their measurements (categorized into 8 main dimensions: motor and neurological functions, cognition, psychiatry, activities of daily living, sleep, quality of life, autonomic symptoms and other). The literature search was performed using MEDLINE, EMBASE and internet key word search.

Results

Of the 53 data sources identified between May and August 2016, 16 were still ongoing. Current medications (81%) and comorbidities (79%) were frequently collected, in comparison to medical imaging (36%), genetic information (30%), caregiver burden (11%) and healthcare costs (2%). Many different measurements (n = 108) were performed and an interesting variability among used measurements was revealed.

Conclusions

Many longitudinal real world data sources on PD exist. Different types of measurements have been performed over time. To allow comparison and pooling of these multiple data sources, it will be essential to harmonize practices in terms of types of measurements.

Is there one measure-of-fit that fits all? A taxonomy and review of measures-of-fit for risk-equalization models

This study provides a taxonomy of measures-of-fit that have been used for evaluating risk-equalization models since 2000 and discusses important properties of these measures, including variations in analytic method. It is important to consider the properties of measures-of-fit and variations in analytic method, because they influence the outcomes of evaluations that eventually serve as a basis for policymaking. Analysis of 81 eligible studies resulted in the identification of 71 unique measures that were divided into 3 categories based on treatment of the prediction error: measured based on squared errors, untransformed errors, and absolute errors. We conclude that no single measure-of-fit is best across situations. The choice of a measure depends on preferences about the treatment of the prediction error and the analytic method. If the objective is measuring financial incentives for risk selection, the only adequate evaluation method is to assess the predictive performance for non-random groups.

Varied differences in the health status between Medicare advantage and fee-for-service enrollees

This article examines the differences in mortality measured health status between the Medicare Advantage (MA) program and Fee-for-Service (FFS) program from 1999 to 2007. At the national level, differences in mortality rates were associated with MA market share. In some counties, enrollees in the MA program were 40% less likely to die than their peers in the FFS program, but in other counties, they were 20% more likely to die. Cost shifting between the two programs could bias county classifications of average FFS spending, and enlarged disparities in health status could make it difficult to evaluate risk adjusters.

Incorporating new research into Medicare risk adjustment

BACKGROUND

The Medicare Advantage payment system underpays health plans that enroll beneficiaries with multiple and complex chronic conditions.

OBJECTIVES

This article addresses 3 major problems in the current payment system: (1) underreporting of chronic disease prevalence in fee-for-service (FFS) Medicare claims data, (2) overpayment of healthier and underpayment of sicker beneficiaries in the current payment system, and (3) underpayment for new beneficiaries in Medicare Advantage plans that require the beneficiaries to have at least one chronic disease to enroll.

RESEARCH DESIGN

We incorporate 2 years of data and a count of chronic diseases in the current Medicare payment model. We develop a separate payment adjustment for new enrollees.

SUBJECTS

A nationally representative sample of FFS beneficiaries in the 2004-2006 Medicare 5% claims data.

MEASURES

We use predictive ratios to evaluate whether our enhanced model improves the predictive accuracy over the current model overall and for subsets of beneficiaries.

RESULTS

The underreporting of chronic disease prevalence in Medicare FFS by 20% leads to systematic bias in the disease coefficients and demographic adjusters. The enhanced model reduces the level of payment for healthy beneficiaries and increases the payment for beneficiaries with multiple and complex chronic conditions. It improves payment for plans that enroll new enrollees with specific chronic conditions.

CONCLUSIONS

Our enhanced model reduces financial incentives for health plans to engage in risk selection against beneficiaries with multiple chronic conditions.