Benjamin L. Howell

Use of Medicare’s Diabetes Self-Management Training Benefit:

Medicare began reimbursing for outpatient diabetes self-management training (DSMT) in 2000; however, little is known about program utilization. Individuals diagnosed with diabetes in 2010 were identified from a 20% random selection of the Medicare fee-for-service population (N = 110,064). Medicare administrative and claims files were used to determine DSMT utilization. Multivariate logistic regression analyses evaluated the association of demographic, health status, and provider availability factors with DSMT utilization. Approximately 5% of Medicare beneficiaries with newly diagnosed diabetes used DSMT services. The adjusted odds of any utilization were lower among men compared with women, older individuals compared with younger, non-Whites compared with Whites, people dually eligible for Medicare and Medicaid compared with nondual eligibles, and patients with comorbidities compared with individuals without those conditions. Additionally, the adjusted odds of utilizing DSMT increased as the availability of providers who offered DSMT services increased and varied by Census region. Utilization of DSMT among Medicare beneficiaries with newly diagnosed diabetes is low. There appear to be marked disparities in access to DSMT by demographic and health status factors and availability of DSMT providers. In light of the increasing prevalence of diabetes, future research should identify barriers to DSMT access, describe DSMT providers, and explore the impact of DSMT services. With preventive services being increasingly covered by insurers, the low utilization of DSMT, a preventive service benefit that has existed for almost 15 years, highlights the challenges that may be encountered to achieve widespread dissemination and uptake of the new services.

Cost-related nonadherence to prescribed medication therapy among Medicare Part D beneficiaries with end-stage renal disease

PURPOSE Medication nonadherence due to cost issues among community-dwelling patients with end-stage renal disease (ESRD) enrolled in Medicare prescription drug plans was evaluated. METHODS Demographic and health status data were collected on 1329 patients with ESRD enrolled in Medicare Part D prescription drug plans who responded to a Centers for Medicare and Medicaid Services consumer survey in early 2007. The survey data were assessed for self-reported cost-related nonadherence (CRN), defined as delaying or not filling a prescription due to cost concerns. Multivariate logistic regression analysis was performed to evaluate CRN risk relative to patient demographic characteristics, socioeconomic status, other chronic conditions, health behaviors, and access to health care services. RESULTS Overall, survey respondents with ESRD were significantly more likely than those without ESRD to report CRN in the prior six months (unadjusted odds ratio [OR], 2.34; 95% confidence interval [CI], 2.00-2.75). After controlling for potential confounding factors such as other chronic conditions, the data analysis continued to show a significant association between ESRD and an increased risk of CRN (adjusted OR, 1.23; 95% CI, 1.07-1.41). Black race and receipt of Medicare Part D Low-Income Subsidy assistance were significant independent predictors of CRN for respondents with ESRD. CONCLUSION In early 2007, 31% of survey respondents with ESRD enrolled in Medicare Part D drug plans reported CRN in the preceding six months. After adjusting for potential confounders, respondents with ESRD remained 23% more likely than respondents without ESRD to report CRN in the preceding six months.

Utilization and Costs of Cardiovascular Disease Medications in Dialysis Patients in Medicare Part D

BACKGROUND Cardiovascular disease (CVD) is a major source of mortality and morbidity in dialysis patients. Population-level descriptions of CVD medication use are lacking in this population. STUDY DESIGN Retrospective cohort study. SETTING & PARTICIPANTS Adult dialysis patients in the United States, alive on December 31, 2006, with Medicare Parts A and B and enrollment in Medicare Part D continuously in 2007. PREDICTOR CVDs and demographic characteristics. OUTCOME ≥1 prescription fill during follow-up (2007). MEASUREMENTS Average out-of-pocket costs per user per month and average total drug costs per member per month were calculated. RESULTS Of 225,635 dialysis patients who met inclusion criteria during the entry period, 70% (n = 158,702) had continuous Part D coverage during follow-up. Of these, 76% received the low-income subsidy. β-Blockers were the most commonly used CVD medication (64%), followed by renin-angiotensin system inhibitors (52%), calcium channel blockers (51%), lipid-lowering agents (44%), and α-agonists (23%). Use varied by demographics, geographic region, and low-income subsidy status. For CVD medications, mean out-of-pocket costs per user per month were

Prescription Medication Cost-Related Non-adherence among Medicare CAHPS Respondents: Disparity by Hispanic Ethnicity

3.44 and

Guiding Principles for Center for Medicare & Medicaid Innovation Model Evaluations

49.59 and mean total costs per member per month were

Concordance between Self-Reports and Medicare Claims among Participants in a National Study of Chronic Disease Self-Management Program.

124.02 and

One-year Outcomes of Diabetes Self-management Training Among Medicare Beneficiaries Newly Diagnosed With Diabetes.

110.32 for patients with and without the low-income subsidy, respectively. LIMITATIONS Information was available for only filled prescriptions under the Part D benefit; information for clinical contraindications was lacking, information for over-the-counter medications was unavailable, and medication adherence and persistence were not examined. CONCLUSIONS Most Medicare dialysis patients in 2007 were enrolled in Part D, and most enrollees received the low-income subsidy. β-Blockers were the most used CVD medication. Total costs of CVD medications were modestly higher for low-income subsidy patients, but out-of-pocket costs were much higher for patients not receiving the subsidy. Further study is warranted to delineate sources of variation in the use and costs of CVD medications across subgroups.

Validity of a Claims-Based Diagnosis of Obesity Among Medicare Beneficiaries.

Purpose. We examined whether there was disparity in prescription medication cost-related non-adherence (CRN) by Hispanic ethnicity among Medicare enrollees. Methods. Multivariate logistic regression, adjusting for race, other socio-demographic variables, health status, health care utilization, and patient rating of their personal physician, was used to examine association of Hispanic ethnicity with CRN using cross-sectional data from Medicares Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey (data collected in Spring 2007). Results. Hispanic respondents constituted 6.9% (unweighted n=22,304) of the analytic sample (unweighted n=272,701; response rate 5 48%). Overall, 13.4% of respondents reported CRN; among Hispanics and non-Hispanics, 20.3% and 12.9% reported CRN, respectively, p<.0001. Adjusted odds ratio (95% CI) of reporting CRN in the past six months was 1.18 (1.08, 1.29) for Hispanic compared with non-Hispanic respondents. Conclusions. Hispanic ethnicity was significantly associated with CRN. More research is needed to understand interventions to eliminate the disparity for this minority group.

Sources of Drug Coverage among Medicare Beneficiaries with ESRD

The Center for Medicare & Medicaid Innovation (Innovation Center) was created by the Affordable Care Act to test innovative payment and service delivery models to reduceexpenditureswhilepreservingorenhancingthequality of care for beneficiaries of Medicare, Medicaid, and the Children’s Health Insurance Program. Evaluating these innovative payment and service delivery models to determine whether they are appropriate for expansion and which characteristics were associated with success should helpinformthepoliciesandregulationsnecessarytotransform the US health care delivery system from one that rewards volume of care to one that provides coordinated, patient-centered, and outcome-driven care. This Viewpoint outlines the approach the US Centers for Medicare & Medicaid Services (CMS) is taking to evaluate these new payment and delivery models by describingthecontextinwhichthesemodelsoperate;theunderlying principles that govern model evaluation; and examples of how these principles are being implemented. Four operational features affect the design and evaluation of new CMS payment and delivery models. First, for most models, participation by hospitals, physicians and otherclinicians,andcommunity-basedorganizationsisvoluntary. As such, the design, scope, and implementation of the models are influenced by the need to engage willing partners.Second,manymodelsaredesignedtofostermultipayer participation. CMS finances only a portion of healthcaredeliveryintheUnitedStates.Toachieveitsaims, CMSmustworkwithotherpayerstoalignincentives.1 Third, CMSmodelsarenotstatic.Everymodelisdesignedwiththe intentthatCMSwillmakechangesincrementallyandrefine interventions and incentive structures as more is learned about the performance of models. That is, these models have feedback and learning systems embedded into their design to allow them to adapt to better meet their core objectives.Fourth,theinstitutional, local,andmarketcontext in which model participants operate can vary substantially. It is important to determine how and why model participants succeed or fail to provide an accurate sense of what willhappenifmodelsareexpandedandtoinformhowbest to structure potential expansion activities. Collectively, these 4 operational features make CMS models complex to evaluate. Many traditional research designs that may provide a high degree of rigor in evaluating “conceptually neat components of clinical practice,”2 such as randomized clinical trials, provide minimal insight when applied to complex, multicomponent interventions. In short, the optimal evaluation approach varies according to both what information needs to be learned and what evidentiarythresholdwillbeacceptableforactiononevidence. To manage this complexity, CMS applies 3 guiding principles when evaluating models: examine model progress frequently and in a timely manner so that both CMS and model participants can improve their performance over the life of the model; apply the most rigorous evaluation methods possible to provide policy makers with the best information available; and evaluate comprehensively to determine whether a model is successful and to describe the drivers of that success. CMS contracts with external evaluators for all Innovation Center models to ensure the independence of evaluation results. CMS generates frequent reports, typically on a quarterly basis, that track progress on a set of core measures3 and report findings on model implementation and operations from site visits, stakeholder interviews, patient focus groups, and other qualitative assessments. These evaluation feedback reports are designed to support both decision making at CMS and rapid-cycle quality improvement among model participants. Although these reports are typically internal to a model program, their content is summarized and disseminated publicly in annual reports that add more in-depth analyses and integrated syntheses of findings to form a more comprehensive assessment of model progress to date. Providing policy makers and model participants with accurate information on model performance requires methodologically rigorous evaluation. One central issue in study design is developing a valid counterfactual comparison to each of the models—that is, how a model performs relative to what would have happened in its absence. Because CMS models are often complex, multipronged, and involve participation by multiple stakeholders, finding appropriate comparison groups in this context is challenging. Moreover, model participants often differ substantially fromnonparticipants,andotherhealthsystemreformscan sometimes overlap with model programs. Randomization is a powerful tool for constructing an experimental control group. When testing discrete, “conceptually neat”2 interventions (eg, testing various forms of a letter to communicate information to beneficiaries), CMS uses randomized designs. However, for interventions that are constantly evolving or multimodal, randomization mightnotbefeasibleorappropriate.Forexample,randomization can discourage potential applicants to the model because of concern about being assigned to a comparison group; participants assigned to control groups may be less willing to report key data to the evaluation once the model has started; and randomizing beneficiaries to different levels of care or benefits under a model raises both legal and ethical concerns, particularly when working with vulnerable and at-risk populations. When a randomized approach proves inappropriate, CMS uses a variety of statistical methods to construct comparisongroupstotrytoensurethatmodelparticipantsand matched nonparticipants are as similar as possible on a variety of key characteristics, including potential overlap with VIEWPOINT

Effects of a Community-Based Fall Management Program on Medicare Cost Savings

Objectives To evaluate the concordance between self-reported data and variables obtained from Medicare administrative data in terms of chronic conditions and health care utilization. Design Retrospective observational study. Participants We analyzed data from a sample of Medicare beneficiaries who were part of the National Study of Chronic Disease Self-Management Program (CDSMP) and were eligible for the Centers for Medicare and Medicaid Services (CMS) pilot evaluation of CDSMP (n = 119). Methods Self-reported and Medicare claims-based chronic conditions and health care utilization were examined. Percent of consistent numbers, kappa statistic (κ), and Pearson’s correlation coefficient were used to evaluate concordance. Results The two data sources had substantial agreement for diabetes and chronic obstructive pulmonary disease (COPD) (κ = 0.75 and κ = 0.60, respectively), moderate agreement for cancer and heart disease (κ = 0.50 and κ = 0.47, respectively), and fair agreement for depression (κ = 0.26). With respect to health care utilization, the two data sources had almost perfect or substantial concordance for number of hospitalizations (κ = 0.69–0.79), moderate concordance for ED care utilization (κ = 0.45–0.61), and generally low agreement for number of physician visits (κ ≤ 0.31). Conclusion Either self-reports or claim-based administrative data for diabetes, COPD, and hospitalizations can be used to analyze Medicare beneficiaries in the US. Yet, caution must be taken when only one data source is available for other types of chronic conditions and health care utilization.