Incorporating Prescription Drugs Into Affordable Care Act Risk Adjustment.

BACKGROUND: The 2010 Patient Protection and Affordable Care Act reformed the individual and small group health insurance markets and established a risk adjustment program to create a level playing field for competition. A new set of predictive models for measuring enrollee risk across plans was developed for the Patient Protection and Affordable Care Act-reformed markets, referred to as the Department of Health and Human Services Hierarchical Condition Category (HHS-HCC) models. Beginning in 2018, selected prescription drug classes were added to the models as risk markers. OBJECTIVE: We describe the motivations, concerns, methodology, and results of adding prescription drug utilization to the HHS-HCC models. METHODS: Separate HHS-HCC models are estimated by enrollee age and plan actuarial value. We defined and added 10 prescription drug classes, called RXCs, to the HHS-HCC adult models. RESULTS: Using selected RXCs alongside demographic and diagnostic indicators yielded modest overall improvement in HHS-HCC models' predictive power. Also, adding RXCs captures the higher costs of enrollees taking certain expensive pharmaceuticals and allows imputation of diagnoses for enrollees utilizing a drug but lacking the associated diagnosis. CONCLUSIONS: Including selected drugs in risk adjustment improved the models' predictive power. In addition, inclusion of selected drugs may discourage insurers from using formulary and drug benefit design to avoid enrollment of patients taking high-cost drugs, such as for HIV, multiple sclerosis, and rheumatoid arthritis, and improve access for enrollees taking these drugs. Adding RXCs also may improve plan risk measurement for plans with less complete diagnosis reporting.

A Comparison of Health Risk and Costs Across Private Insurance Markets.

BACKGROUND: The Patient Protection and Affordable Care Act (PPACA) established new parameters for the individual and small group health insurance markets starting in 2014. We study these 2 reformed markets by comparing health risk and costs to the more mature large employer market. STUDY DATA: For 2017, claims data for all enrollees in PPACA-compliant individual and small group market plans as well as claims data from a sample of large employer market enrollees. VARIABLES AND METHODOLOGY: Risk scores and total (unadjusted and risk-adjusted) per-member-per-month (PMPM) allowed charges. Differences across markets in enrollment duration, age, and geographic distribution are addressed. The analysis is descriptive. RESULTS: Compared with large employer market enrollees, health risk was 3% lower among PPACA small group market enrollees and 20% higher among PPACA individual market enrollees. After adjusting for differences in health risk, enrollees in the PPACA individual market had 27% lower PMPM allowed charges than enrollees in the large employer market and enrollees in the PPACA small group market had 12% lower PMPM allowed charges than enrollees in the large employer market. CONCLUSIONS: On average, the PPACA individual market enrolls sicker individuals than the 2 group markets. But this does not translate to higher health costs; in fact, enrollees in the PPACA individual market accumulate lower allowed charges than enrollees in the large employer market. Lower-income enrollees particularly accumulate lower allowed charges. Narrower networks and increased enrollee cost-sharing among individual market plans, though they may reduce the value of coverage, likely significantly reduce allowed charges.

Underutilization and disparities in access to EGFR testing among Medicare patients with lung cancer from 2010 - 2013.

BACKGROUND: Tumor testing for mutations in the epidermal growth factor receptor (EGFR) gene is indicated for all newly diagnosed, metastatic lung cancer patients, who may be candidates for first-line treatment with an EGFR tyrosine kinase inhibitor. Few studies have analyzed population-level testing. METHODS: We identified clinical, demographic, and regional predictors of EGFR & KRAS testing among Medicare beneficiaries with a new diagnosis of lung cancer in 2011-2013 claims. The outcome variable was whether the patient underwent molecular, EGFR and KRAS testing. Independent variables included: patient demographics, Medicaid status, clinical characteristics, and region where the patient lived. We performed multivariate logistic regression to identify factors that predicted testing. RESULTS: From 2011 to 2013, there was a 19.7% increase in the rate of EGFR testing. Patient zip code had the greatest impact on odds to undergo testing; for example, patients who lived in the Boston, Massachusetts hospital referral region were the most likely to be tested (odds ratio (OR) of 4.94, with a 95% confidence interval (CI) of 1.67-14.62). Patient demographics also impacted odds to be tested. Asian/Pacific Islanders were most likely to be tested (OR 1.63, CI 1.53-1.79). Minorities and Medicaid patients were less likely to be tested. Medicaid recipients had an OR of 0.74 (CI 0.72-0.77). Hispanics and Blacks were also less likely to be tested (OR 0.97, CI 0.78-0.99 and 0.95, CI 0.92-0.99), respectively. Clinical procedures were also correlated with testing. Patients who underwent transcatheter biopsies were 2.54 times more likely to be tested (CI 2.49-2.60) than those who did not undergo this type of biopsy. CONCLUSIONS: Despite an overall increase in EGFR testing, there is widespread underutilization of guideline-recommended testing. We observed racial, income, and regional disparities in testing. Precision medicine has increased the complexity of cancer diagnosis and treatment. Targeted interventions and clinical decision support tools are needed to ensure that all patients are benefitting from advances in precision medicine. Without such interventions, precision medicine may exacerbate racial disparities in cancer care and health outcomes.

21-Gene recurrence score testing among Medicare beneficiaries with breast cancer in 2010-2013.

PURPOSE: We evaluated national patient-level utilization of the 21-gene recurrence score (21-gene RS) test among Medicare beneficiaries with breast cancer. We analyzed clinical, demographic, and regional factors that predict testing. METHODS: Using 2010-2013 Medicare claims, we conducted a retrospective study of breast cancer patients. The outcome variable was whether the patient underwent testing. Independent variables expected to predict testing were age, gender, race, Medicaid status, clinical characteristics, and hospital referral region (HRR). RESULTS: From 2010 to 2013, the number of test orders increased by 23.0%. Of the 256,818 patients identified in 2011-2012 claims, 25,352 (9.9%) underwent the 21-gene RS test. Estrogen receptor-positive status was the strongest positive predictor of testing (odds ratio (OR) 2.58, 95% confidence interval (CI) 2.48-2.69). White patients were more likely to be tested than minorities (OR 1.46, 95% CI 1.39-1.52). Secondary cancer was the strongest negative predictor. Medicaid recipients were less likely to be tested (OR 0.74, 95% CI 0.71-0.78). The likelihood of testing decreased with increasing age and comorbidities. CONCLUSIONS: Despite widespread implementation of the 21-gene RS test, minorities and Medicaid recipients had less access to testing. Many patients with serious comorbidities or advanced age were tested even though the risk algorithm may not have been applicable to them.Genet Med advance online publication 23 March 2017.

Utilization of genetic tests: analysis of gene-specific billing in Medicare claims data.

PURPOSE: We examined the utilization of precision medicine tests among Medicare beneficiaries through analysis of gene-specific tier 1 and 2 billing codes developed by the American Medical Association in 2012. METHODS: We conducted a retrospective cross-sectional study. The primary source of data was 2013 Medicare 100% fee-for-service claims. We identified claims billed for each laboratory test, the number of patients tested, expenditures, and the diagnostic codes indicated for testing. We analyzed variations in testing by patient demographics and region of the country. RESULTS: Pharmacogenetic tests were billed most frequently, accounting for 48% of the expenditures for new codes. The most common indications for testing were breast cancer, long-term use of medications, and disorders of lipid metabolism. There was underutilization of guideline-recommended tumor mutation tests (e.g., epidermal growth factor receptor) and substantial overutilization of a test discouraged by guidelines (methylenetetrahydrofolate reductase). Methodology-based tier 2 codes represented 15% of all claims billed with the new codes. The highest rate of testing per beneficiary was in Mississippi and the lowest rate was in Alaska. CONCLUSIONS: Gene-specific billing codes significantly improved our ability to conduct population-level research of precision medicine. Analysis of these data in conjunction with clinical records should be conducted to validate findings.Genet Med advance online publication 26 January 2017.

Competitive bidding for Medicare Part B clinical laboratory services.

The traditional Medicare fee-for-service program may be able to purchase clinical laboratory test services at a lower cost through competitive bidding. Demonstrations of competitive bidding for clinical laboratory tests have been twice mandated or authorized by Congress but never implemented. This article provides a summary and review of the final design of the laboratory competitive bidding demonstration mandated by the Medicare Modernization Act of 2003. The design was analogous to a sealed bid (first price), clearing price auction. Design elements presented include covered laboratory tests and beneficiaries, laboratory bidding and payment status under the demonstration, composite bids, determining bidding winners and the demonstration fee schedule, and quality under the demonstration. Expanded use of competitive bidding in Medicare, including specifically for clinical laboratory tests, has been recommended in some proposals for Medicare reform. The presented design may be a useful point of departure if Medicare clinical laboratory competitive bidding is revived in the future.

Improvements in Medicare Part D risk adjustment: beneficiary access and payment accuracy.

INTRODUCTION: The continued success of the Medicare Part D program is contingent on appropriate Medicare payment adjustments for the projected drug costs of Part D plan enrollees. This article describes a major revision of these "risk adjustments," intended to more accurately match payments to costs, especially for high-cost, disadvantaged populations. METHODS: For the first time actual Part D data are used to calibrate risk adjustment. The sample is Medicare beneficiaries with fee-for-service enrollment in 2007 and Part D standalone prescription drug plan enrollment in 2008 (N = 14,224,301). Part D plan liability expenditures are predicted using demographic and diagnostic factors in a weighted least squares regression. Models for Medicare subpopulations are analyzed. The predictive accuracy of risk adjustment models is evaluated using R and predictive ratio statistics. RESULTS: Based on differences in both mean expenditures and incremental expenditures by diagnosis, separate Part D risk adjustment models are calibrated for 5 Medicare subpopulations: aged not low income; aged low income; nonaged not low income; nonaged low income; and institutionalized. The variation in plan liability drug expenditures (R) explained by these models ranges from 13% to 29%. The 5 separate models accurately predict mean plan liability expenditures ranging from $967 to $1762 across subpopulations and account for differences in incremental disease coefficients by subpopulation. CONCLUSIONS: The refined Part D risk adjustment model represents a significant improvement in the accuracy and fairness of payment to Part D plans. The new model provides greater incentives for drug plans to compete for low-income and institutionalized enrollees.

Minimum savings requirements in shared savings provider payment.

Payer (insurer) sharing of savings is a way of motivating providers of medical services to reduce cost growth. A Medicare shared savings program is established for accountable care organizations in the 2010 Patient Protection and Affordable Care Act. However, savings created by providers cannot be distinguished from the normal (random) variation in medical claims costs, setting up a classic principal-agent problem. To lessen the likelihood of paying undeserved bonuses, payers may pay bonuses only if observed savings exceed minimum levels. We study the trade-off between two types of errors in setting minimum savings requirements: paying bonuses when providers do not create savings and not paying bonuses when providers create savings.

Impact of multitiered copayments on the use and cost of prescription drugs among Medicare beneficiaries.

OBJECTIVES: To assess the impact of multitiered copayments on the cost and use of prescription drugs among Medicare beneficiaries. DATA SOURCES: Marketscan 2002 Medicare Supplemental and Coordination of Benefits database and Plan Benefit Design database. STUDY DESIGN: The study uses cross-sectional variation in copayment structures among firms with a self-insured retiree health plan to measure the impact of number of copayment tiers on total and enrollee drug payments, number of prescriptions filled, and generic substitution. The study also assesses the effect of enrollee cost sharing on the cost and use of prescription medications for the long-term treatment of chronic conditions. DATA COLLECTION METHODS: We linked plan enrollment and benefit data with medical and drug claims for 352,760 Medicare beneficiaries with employer-sponsored retiree drug coverage. PRIMARY FINDINGS: Medicare beneficiaries in three-tiered plans had 14.3 percent lower total drug expenditures, 14.6 percent fewer prescriptions filled, and 57.6 percent higher out-of-pocket costs than individuals in lower tiered plans. They also had fewer brand name and generic prescriptions filled, and a higher percentage of generics. The estimated price elasticity of demand for prescription drug expenditures was -0.23. Finally, for maintenance medications used for the long-term treatment of chronic conditions, members in three-tiered plans had 11.5 percent fewer prescriptions filled. CONCLUSIONS: Higher tiered drug plans reduce overall expenditures and the number of prescriptions purchased by Medicare beneficiaries. Beneficiaries are less responsive to cost sharing incentives when using drugs to treat chronic conditions.

Medicare risk adjustment for the frail elderly.

CMS has had a continuing interest in exploring ways to incorporate frailty adjustment into the CMS Hierarchical Condition Categories (CMS-HCC) risk adjustment methodology for Medicare Advantage and other Medicare private organizations. In this article we present research results for Medicare risk adjustment of the frail elderly since the adoption of frailty adjustment for Program of All-Inclusive Care for the Elderly (PACE) organizations in 2004. In particular, we present results on the revised frailty adjuster that is being phased in for PACE organizations between 2008 and 2012.

Profiling efficiency and quality of physician organizations in Medicare.

This article presents a methodology for profiling the cost efficiency and quality of care of physician organizations (POs). The method is implemented for the Boston metropolitan area using 2002 Medicare claims. After adjustments for case mix and other factors, 4 of 30 organizations are identified with different than average efficiency Twenty-one of 30 organizations are identified with a different composite quality of care than average. Without changes in PO behavior, the gains from redirecting patients from lower to higher efficiency and quality providers are likely to be limited.

Medicare physician group practice demonstration design: quality and efficiency pay-for-performance.

The Medicare Physician Group Practice (PGP) demonstration is Medicare's first physician pay-for-performance (P4P) initiative. The demonstration, which is legislatively mandated, establishes incentives for quality improvement (QI) and cost efficiency at the level of the PGP Ten large physician groups are participating in the demonstration, which started on April 1, 2005, and will run for 3 years. In this article the authors provide an overview of the PGP demonstration's key design elements, including the selection process for PGP participants; beneficiary assignment; comparison population; measurement of demonstration savings; performance payments; and quality measurement and reporting. A summary of early case study findings is also provided.

Medicare preferred provider organization demonstration: plan offerings and beneficiary enrollment.

As preferred provider organizations (PPOs) become the dominant model of managed health care in the private sector, policymakers have increasingly viewed PPOs as an attractive option for Medicare. In part to understand how PPOs might operate under the Medicare Program, CMS launched the Medicare PPO demonstration in January 2003. In this article, we examine how PPOs have operated so far under the demonstration, including PPO availability and market entry; premiums, benefits, and beneficiary cost sharing; and enrollment, market share, enrollee characteristics, and disenrollment to date.

Impact of nonresponse on Medicare Current Beneficiary Survey estimates.

The Medicare Current Beneficiary Survey (MCBS) has been used by policymakers and research analysts to provide information on a wide array of topics about the Medicare Program. Nonresponse bias is potentially one of the most important threats to the validity of the estimates from the MCBS. In this article we present results of our methodological study that analyzes the impact of nonresponse on MCBS estimates, including initial round unit nonresponse, panel attrition, and item nonresponse. Our findings indicate that for most of the measures studied, the bias caused by differences between nonrespondents and respondents in the MCBS was substantially reduced or eliminated by the nonresponse procedures currently employed.

CMS Frailty Adjustment Model.

The authors document the development of the CMS frailty adjustment model, a Medicare payment approach that adjusts payments to a Medicare managed care organization (MCO) according to the functional impairment of its community-residing enrollees. Beginning in 2004, this approach is being applied to certain organizations, such as Program of All-Inclusive Care for the Elderly (PACE), that specialize in providing care to the community-residing frail elderly. In the future, frailty adjustment could be extended to more Medicare managed care organizations.

Risk adjustment of Medicare capitation payments using the CMS-HCC model.

This article describes the CMS hierarchical condition categories (HCC) model implemented in 2004 to adjust Medicare capitation payments to private health care plans for the health expenditure risk of their enrollees. We explain the model's principles, elements, organization, calibration, and performance. Modifications to reduce plan data reporting burden and adaptations for disabled, institutionalized, newly enrolled, and secondary payer subpopulations are discussed.

Affordable Care Act risk adjustment: overview, context, and challenges.

Beginning in 2014, individuals and small businesses will be able to purchase private health insurance through competitive marketplaces. The Affordable Care Act (ACA) provides for a program of risk adjustment in the individual and small group markets in 2014 as Marketplaces are implemented and new market reforms take effect. The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge and the incentive for plans to avoid sicker enrollees. This article--the first of three in the Medicare & Medicaid Research Review--describes the key program goal and issues in the Department of Health and Human Services (HHS) developed risk adjustment methodology, and identifies key choices in how the methodology responds to these issues. The goal of the HHS risk adjustment methodology is to compensate health insurance plans for differences in enrollee health mix so that plan premiums reflect differences in scope of coverage and other plan factors, but not differences in health status. The methodology includes a risk adjustment model and a risk transfer formula that together address this program goal as well as three issues specific to ACA risk adjustment: 1) new population; 2) cost and rating factors; and 3) balanced transfers within state/market. The risk adjustment model, described in the second article, estimates differences in health risks taking into account the new population and scope of coverage (actuarial value level). The transfer formula, described in the third article, calculates balanced transfers that are intended to account for health risk differences while preserving permissible premium differences.

Risk transfer formula for individual and small group markets under the Affordable Care Act.

The Affordable Care Act provides for a program of risk adjustment in the individual and small group health insurance markets in 2014 as Marketplaces are implemented and new market reforms take effect. The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge. The risk adjustment methodology includes the risk adjustment model and the risk transfer formula. This article is the third of three in this issue of the Medicare & Medicaid Research Review that describe the ACA risk adjustment methodology and focuses on the risk transfer formula. In our first companion article, we discussed the key issues and choices in developing the methodology. In our second companion paper, we described the risk adjustment model that is used to calculate risk scores. In this article we present the risk transfer formula. We first describe how the plan risk score is combined with factors for the plan allowable premium rating, actuarial value, induced demand, geographic cost, and the statewide average premium in a formula that calculates transfers among plans. We then show how each plan factor is determined, as well as how the factors relate to each other in the risk transfer formula. The goal of risk transfers is to offset the effects of risk selection on plan costs while preserving premium differences due to factors such as actuarial value differences. Illustrative numerical simulations show the risk transfer formula operating as anticipated in hypothetical scenarios.

The HHS-HCC risk adjustment model for individual and small group markets under the Affordable Care Act.

Beginning in 2014, individuals and small businesses are able to purchase private health insurance through competitive Marketplaces. The Affordable Care Act (ACA) provides for a program of risk adjustment in the individual and small group markets in 2014 as Marketplaces are implemented and new market reforms take effect. The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge. The risk adjustment methodology includes the risk adjustment model and the risk transfer formula. This article is the second of three in this issue of the Review that describe the Department of Health and Human Services (HHS) risk adjustment methodology and focuses on the risk adjustment model. In our first companion article, we discuss the key issues and choices in developing the methodology. In this article, we present the risk adjustment model, which is named the HHS-Hierarchical Condition Categories (HHS-HCC) risk adjustment model. We first summarize the HHS-HCC diagnostic classification, which is the key element of the risk adjustment model. Then the data and methods, results, and evaluation of the risk adjustment model are presented. Fifteen separate models are developed. For each age group (adult, child, and infant), a model is developed for each cost sharing level (platinum, gold, silver, and bronze metal levels, as well as catastrophic plans). Evaluation of the risk adjustment models shows good predictive accuracy, both for individuals and for groups. Lastly, this article provides examples of how the model output is used to calculate risk scores, which are an input into the risk transfer formula. Our third companion paper describes the risk transfer formula.

Financial and quality impacts of the Medicare physician group practice demonstration.

OBJECTIVE: To examine the impact of the Medicare Physician Group Practice (PGP) demonstration on expenditure, utilization, and quality outcomes. DATA SOURCE: Secondary data analysis of 2001-2010 Medicare claims for 1,776,387 person years assigned to the ten participating provider organizations and 1,579,080 person years in the corresponding local comparison groups. STUDY DESIGN: We used a pre-post comparison group observational design consisting of four pre-demonstration years (1/01-12/04) and five demonstration years (4/05-3/10). We employed a propensity-weighted difference-in-differences regression model to estimate demonstration effects, adjusting for demographics, health status, geographic area, and secular trends. PRINCIPAL FINDINGS: The ten demonstration sites combined saved $171 (2.0%) per assigned beneficiary person year (p<0.001) during the five-year demonstration period. Medicare paid performance bonuses to the participating PGPs that averaged $102 per person year. The net savings to the Medicare program were $69 (0.8%) per person year. Demonstration savings were achieved primarily from the inpatient setting. The demonstration improved quality of care as measured by six of seven claims-based process quality indicators. CONCLUSIONS: The PGP demonstration, which used a payment model similar to the Medicare Accountable Care Organization (ACO) program, resulted in small reductions in Medicare expenditures and inpatient utilization, and improvements in process quality indicators. Judging from this demonstration experience, it is unlikely that Medicare ACOs will initially achieve large savings. Nevertheless, ACOs paid through shared savings may be an important first step toward greater efficiency and quality in the Medicare fee-for-service program.