Primary Prevention Using Cholesterol-Lowering Medications in Patients Meeting New Treatment Guidelines: A Retrospective Cohort Analysis.

BACKGROUND: The American College of Cardiology and American Heart Association (ACC/AHA) issued new cholesterol treatment guidelines in 2013. Two of the groups designated for primary prevention were analyzed: patients with a low-density lipoprotein cholesterol (LDL-C) level ≥ 190 mg per dL and diabetic patients aged 40-75 years. OBJECTIVE: To estimate the effects of primary prevention as specified in the 2013 guidelines on cardiovascular event risk and cost. METHODS: Primary prevention patients were identified using laboratory and diagnostic data for Humana members from 2007 to 2013. Potential study patients were classified into 3 risk groups: elevated LDL-C, diabetes, and elevated LDL-C and diabetes. Patients receiving cholesterol-lowering medications before their index date were excluded. Eligible patients were divided into 2 treatment groups: (1) primary prevention patients who initiated treatment before experiencing any cardiovascular disease (CVD)-related event, and (2) patients who either did not initiate treatment until after experiencing a CVD event or never initiated treatment. The associations between initiating cholesterol-lowering medications for primary prevention and the risk for acute myocardial infarction, stroke, coronary angioplasty, or coronary artery bypass graft surgery were estimated using Cox proportional hazards models. The effect of primary prevention on health care costs was estimated using generalized linear models. RESULTS: 91,066 patients met study selection criteria. Primary prevention rates were the lowest in diabetic patients (35%), who were newly designated for treatment in the 2013 guidelines. Primary prevention rates were higher for patients designated for treatment under earlier guidelines: 65% for patients with elevated LDL-C and 78% for the combined LDL-C and diabetes group. Primary prevention treatment was associated with significant reductions in cardiovascular event risk (up to 37%) and lower total all-cause costs (by $673) in the first post-index year. CONCLUSIONS: Initiating cholesterol-lowering medications for primary prevention, as specified in the ACC/AHA 2013 guidelines, for patients with high LDL-C and diabetes is associated with reduced CVD event risks and lower health care costs. DISCLOSURES: No outside funding supported this study. Han received fellowship support from the Pharmaceutical Research and Manufacturers Association Foundation (PhRMA) during the conduct of this study. Dougherty is employed by PhRMA. The authors have nothing to disclose.

Reducing out-of-pocket cost barriers to specialty drug use under Medicare Part D: addressing the problem of "too much too soon".

OBJECTIVES: Medicare Part D specialty drug users not qualifying for low-income subsidies (non-LIS beneficiaries) face high and variable cost sharing during the calendar year. We examined their out-of-pocket (OOP) cost patterns under the existing Part D cost-sharing policies and proposed changes to these policies. METHODS: Using 100% Medicare claims data from 2012, we examined mean annual and monthly OOP drug costs for Medicare Part D patients who were full-year users of Part D specialty drugs for rheumatoid arthritis (RA) (n = 1063), multiple sclerosis (MS) (n = 2256), or chronic myeloid leukemia (CML) (n = 1135) under existing policy. Using the same data, we simulated costs under both proposed Medicare Payment Advisory Commission (MedPAC) policy recommendations and our own recommendations. RESULTS: In 2012, our sample faced mean annual cumulative OOP drug costs (for all medications) of $3949 (RA), $5238 (MS), and $6322 (CML). Mean OOP costs were $977 (RA), $1613 (MS), and $2456 (CML) in January alone. A substantial proportion of total annual OOP prescription spending also occurred during the catastrophic coverage phase (RA: $1229 [31%]; MS: $2456 [47%]; CML: $3546 [56%]). Under proposed MedPAC changes, patients would have faced maximum annual OOP spending of $4700, but mean OOP costs in January and February would have been higher compared with the existing policy. Under our proposed strategy, OOP costs would have been spread evenly over 12 months (≤$392 per month). The potential incremental costs of our proposed strategy would have been $23.55 per non-LIS Part D beneficiary per year. CONCLUSIONS: The existing Part D cost-sharing structure creates a substantial financial burden for specialty drug users, especially early in the year. Implementing both annual and monthly OOP maximum spending limits would result in lower, more consistent OOP costs, potentially increasing patients' ability to access treatments for life-threatening, chronic, and rare diseases.

Use of and spending on supportive care medications among Medicare beneficiaries with cancer.

PURPOSE: The study objective was to provide population-based estimates of supportive care medication (SCM) use among Medicare beneficiaries with cancer and determine factors related to SCM receipt. METHODS: This retrospective cohort study of community-based Medicare beneficiaries used the Medicare Current Beneficiary Survey (1997­2007). Dependent variables comprised use and spending on SCMs for three medication classes: opioids, antidepressants/sedative/hypnotics (ASH), and antiemetics. Independent variables of interest were supplemental insurance coverage, cancer site, and treatment. Multivariate models determined factors affecting receipt of, and spending on, SCMs. We also compared SCM use and spending among beneficiaries with and without cancer in order to understand what portion of SCM use and spending could be attributed to cancer as opposed to other comorbid conditions. RESULTS: A total of 1,836 Medicare beneficiaries with cancer and 9,898 beneficiaries without cancer were eligible for the study. Beneficiaries with cancer were more likely to receive opioids, ASH, and antiemetics compared to non-cancer beneficiaries. Adjusted annual payments for antiemetics were on average $637 higher in with cancer versus without cancer (p<0.01), while ASH payments were $184 lower (p<0.01). Opioid spending was similar among cancer and non-cancer users. Relative to colon cancer, beneficiaries with prostate cancer were least likely to receive any of the three SCM classes. Receipt of antineoplastic treatment increased the probability of use of all three classes of SCMs. Insurance coverage did not influence the use of or spending on opioids or antiemetics, but was associated with both outcomes for ASH. The use of all three SCM classes was significantly lower during years before Part D implementation of the new Medicare Part D prescription drug benefit and was higher after implementation of Part D. CONCLUSION: This study provides population-based information on SCM use among Medicare beneficiaries with cancer. Cancer site and treatment modality were important predictors of SCM use.

The effect of supplemental medical and prescription drug coverage on health care spending for Medicare beneficiaries with cancer.

OBJECTIVES: To examine whether patients with newly diagnosed cancer respond differently to supplemental coverage than the general Medicare population. METHODS: A cohort of newly diagnosed cancer patients (n = 1,799) from the 1997-2007 Medicare Current Beneficiary Survey and a noncancer cohort (n = 9,726) were identified and matched by panel year. Two-year total medical care spending was estimated by using generalized linear models with gamma distribution and log link-including endogeneity-corrected models. Interactions between cancer and type of insurance allowed testing for differential effects of a cancer diagnosis. RESULTS: The cancer cohort spent an adjusted $15,605 more over 2 years than did the noncancer comparison group. Relative to those without supplemental coverage, beneficiaries with employer-sponsored insurance, other private with prescription drug coverage, and public coverage had significantly higher total spending ($3,510, $2,823, and $4,065, respectively, for main models). For beneficiaries with cancer, supplemental insurance effects were similar in magnitude yet negative, suggesting little net effect of supplemental insurance for cancer patients. The endogeneity-corrected models produced implausibly large main effects of supplemental insurance, but the Cancer × Insurance interactions were similar in both models. CONCLUSIONS: Medicare beneficiaries with cancer are less responsive to the presence and type of supplemental insurance than are beneficiaries without cancer. Proposed restrictions on the availability of supplemental insurance intended to reduce Medicare spending would be unlikely to limit expenditures by beneficiaries with cancer, but would shift the financial burden to those beneficiaries. Policymakers should consider welfare effects associated with coverage restrictions.