The Dollar or Disease Burden: Caps on Healthcare Spending May Save Money, but at What "Cost" to Patients?

OBJECTIVES: Various strategies to address healthcare spending and medical costs continue to be debated and implemented in the United States. To date, these efforts have failed to adequately contain the growth of healthcare cost. An alternative strategy that has elicited rising interest among policymakers is budget caps. As budget caps become more prevalent, it is important to identify which features are needed to ensure success, both in terms of cost reduction and health improvement. METHODS: We explored the impacts of different features of budget caps by comparing hypothetical service level and global budget caps across 3 annual budget cap growth strategies over a 10-year timeframe in 2005-2015 for 8 of the most commonly occurring conditions in the United States. Health was assessed by a measure of disease burden (disability-adjusted life years). RESULTS: The results indicate that budget caps have the potential for creating savings but can also result in patient harm if not designed well. As a result of these findings, 5 principles were developed for designing budget caps and should guide the use of budget caps to address medical spending. CONCLUSIONS: As public discussion grows about the use of budget caps to constrain health spending, it is critical to recognize that the budget cap design and the resulting healthcare provider behavior will determine whether there is potential harm to public health. Budget cap design should consider variability at the condition level, including patient population, improvements in health, treatment costs, and the innovations available, to both create savings and maximize patient health. In assessing the impact of healthcare spending caps on costs and disease burden, we demonstrate that budget cap design determines potential harm to public health.

A Comparison of Coverage Restrictions for Biopharmaceuticals and Medical Procedures.

BACKGROUND: Differences in payer evaluation and coverage of pharmaceuticals and medical procedures suggest that coverage may differ for medications and procedures independent of their clinical benefit. We hypothesized that coverage for medications is more restricted than corresponding coverage for nonmedication interventions. METHODS: We included top-selling medications and highly utilized procedures. For each intervention-indication pair, we classified value in terms of cost-effectiveness (incremental cost per quality-adjusted life-year), as reported by the Tufts Medical Center Cost-Effectiveness Analysis Registry. For each intervention-indication pair and for each of 10 large payers, we classified coverage, when available, as either "more restrictive" or as "not more restrictive," compared with a benchmark. The benchmark reflected the US Food and Drug Administration label information, when available, or pertinent clinical guidelines. We compared coverage policies and the benchmark in terms of step edits and clinical restrictions. Finally, we regressed coverage restrictiveness against intervention type (medication or nonmedication), controlling for value (cost-effectiveness more or less favorable than a designated threshold). RESULTS: We identified 392 medication and 185 procedure coverage decisions. A total of 26.3% of the medication coverage and 38.4% of the procedure coverage decisions were more restrictive than their corresponding benchmarks. After controlling for value, the odds of being more restrictive were 42% lower for medications than for procedures. Including unfavorable tier placement in the definition of "more restrictive" greatly increased the proportion of medication coverage decisions classified as "more restrictive" and reversed our findings. CONCLUSIONS: Therapy access depends on factors other than cost and clinical benefit, suggesting potential health care system inefficiency.

Concerns Around Budget Impact Thresholds: Not All Drugs Are the Same.

BACKGROUND: The use of budget thresholds is a recent development in the United States (e.g., the Institute for Clinical and Economic Review drug assessments). Budget thresholds establish limits that require some type of budgetary action if exceeded. This research focused on the advisability of using product-level budget thresholds as fixed spending caps by examining whether they are likely to improve or worsen market efficiency over status quo. OBJECTIVE: The aim of this study was to determine whether fixed product-level spending caps are advisable for biopharmaceuticals. METHODS: We systematically examined 5-year, postlaunch revenue for drugs that launched in the United States between 2003 and 2014 using the IMS MIDAS database. For products launched between 2011 and 2014, we used historical revenue as the baseline and trended out 60 months postlaunch based on exponential smoothing. Forecasted fifth-year revenue was compared to analyst reports. Fifth-year revenue was compared against a hypothetical $904 million spending cap to determine the amount of annual spending that might require reallocation. Descriptive statistics of 5-year, postlaunch revenue and annual spending requiring reallocation were calculated. RESULTS: Adhering to a $904 million product-level spending cap requires that approximately one-third of new drug spending be reallocated to other goods and services that have the potential to be less cost-effective due to significant barriers. CONCLUSION: Fixed product-level spending caps have the potential to reduce market efficiency due to their independence from value and the presence of important operational challenges.

Evaluating the Importance of Heterogeneity of Treatment Effect: Variation in Patient Utilities Can Influence Choice of the "Optimal" Oral Anticoagulant for Atrial Fibrillation.

OBJECTIVES: To investigate heterogeneity of treatment effect (HTE) for anticoagulants in atrial fibrillation across subgroups defined by 1) clinical characteristics and 2) variation in patient utilities for benefits and harms of treatment. METHODS: We reanalyzed aggregate data from a published network meta-analysis that compared four anticoagulants for atrial fibrillation (apixaban, dabigatran, edoxaban, and rivaroxaban) as well as warfarin. Event rates for stroke/systemic embolism (SE) and major bleeding were generated for each agent across seven subgroups, and rankings were developed on the basis of clinical performance. Utilities were derived from a national catalog and then applied to generate summary measures of benefit. The choice between any two agents was examined across a range of plausible utility values, defined as the interquartile range for stroke/SE and major bleeding. RESULTS: Little HTE was apparent in clinical and utility-adjusted analyses. Dabigatran 150 mg produced the lowest rates of stroke/SE, and edoxaban 30 mg had the lowest rate of major bleeding. Greater HTE was observed when utilities were varied across a plausible utility range. For example, among patients 75 years and older, dabigatran 150 mg would be preferred over edoxaban 30 mg when mean utility estimates are used. The preferred agent, however, would change at plausible utility thresholds of 0.6 and 0.7 for major bleeding and stroke/SE, respectively. Nearly 25% of all possible comparisons would see a change in preferred treatment within the plausible utility range. CONCLUSIONS: The optimal choice of anticoagulant in atrial fibrillation differs across subgroups defined by clinical characteristics and reasonable ranges of utilities.

Medicaid best price reforms to enable innovative payment models for cell and gene therapies.

BACKGROUND: Cell and gene therapies promise durable benefits but face financial challenges from the uncertainty in their performance. Value-based purchasing arrangements (VBPAs) can address uncertainty but have been inhibited in the US by the Medicaid Drug Rebate Program (MDRP) approach to determining Medicaid Best Price (MBP) rebates. The likely effectiveness of MDRP reform proposals enabling VBPAs is examined in this study for durable cell and gene therapies. METHODS: Monte Carlo simulations examined three potential reforms (Multiple Best Prices, Bundled Sales, and National Pooling) to determine the impact on payment misalignment, the required payer size to participate, and the percentage of total lives covered by a VBPA. RESULTS: Simulation results suggest that 11% to 54% of commercial US lives would be feasibly covered by a VBPA depending on reform type and condition size. MPB reform achieved the highest commercial contracted percentage and lowest misalignment for commercial payers compared to National Pooling and Bundled Sales. State Medicaid plan results suggest lower extreme misalignment across all successfully contracted instances than commercial payers. CONCLUSIONS: The Multiple Best Prices will likely enable VBPAs for many durable cell and gene therapies and larger payers. Further reforms may be needed to extend VBPAs to ultra-orphan conditions.

Value-based performance arrangements for chronic conditions: an economic simulation of Medicaid Drug Rebate Program reforms.

OBJECTIVE: Changes to the Medicaid Drug Rebate Program (MDRP) determination of Medicaid Best Price (MBP) enables Value-based purchasing arrangements (VBPAs) to address financial uncertainty. This study estimates the likely effectiveness of MDRP-enabled VBPAs for chronically dosed medicines. METHODS: Monte Carlo simulations examined: Multiple Best Prices and Bundled Sales MBP approaches authorized under MDRP and a third National Pooling approach using payment misalignment; needed payer size for practical participation; and the resulting potential number of covered lives under a VBPA as evaluation metrics. RESULTS: Both Multiple Best Prices and National Pooling enable VBPAs for 95% of scenarios (including all 5i chronic products with ≥1,000 treated patients per year), with 75% of those with payment misalignment ≤9%. National pooling for retail drugs has less participation and worse misalignment (5i: 95% contracted, 75% ≤9% misalignment; retail: 71%, 66%). Bundled Sales performed worst (5i: 40%, 75% ≤9%; retail: 31%, 88%) due to rebate volatility risk of breaking best price and Average Manufacturer Price impact. Medicaid sees worse misalignment for the 60% drug performance scenarios because of comparison to the statutory rebate (23.1%). CONCLUSION: The Multiple Best Prices approach has the lowest misalignment and could be applied to most chronic therapies, even rare ones.

Good health policy requires high-quality evidence. let's remember that with drug pricing.

In the health spending debate, what policy makers need most is an honest, realistic, and evidence-based discussion. Unfortunately, many studies in the public arena fall far short.

Health spending expenditures for commercial plans are predominantly concentrated among a small population of high-intensity consumers across settings of care.

BACKGROUND: Rising health care spending has sparked new efforts to constrain health care expenditures. OBJECTIVE: To explore how health care spending is distributed across consumers and how utilization patterns compare across health care resource expenditures (eg, hospital, outpatient care). METHODS: Using the IQVIA PharMetrics Plus database, we conducted a retrospective claims analysis for the 2018 plan year to examine commercial health care spending and utilization across 5 settings of care: ambulatory services, inpatient services, office visits, pharmacy services, and additional services. RESULTS: Consistent with findings from previous analyses of total health spending, total health care spending for a large commercially insured population was largely concentrated within a small population of high-intensity consumers. These patterns persist when looking at individual segments of spending, including spending on prescription drugs and inpatient and ambulatory services. Inpatient spending was the most concentrated, with 97% of spending occurring within the top tenth percentile of patients. CONCLUSIONS: Our findings suggest that health care spending for commercial plans is predominantly concentrated within a small population of high-intensity consumers across all settings of care. Curbing rising health care spending will require systemwide evaluation of the value of spending within and across settings of care for a subset of high-resource-use patients. This is particularly important for health care settings with the highest concentration of spending, including inpatient care. DISCLOSURES: This study was funded by the National Pharmaceutical Council (NPC). Ciarametaro, Buelt, and Dubois are employed by the NPC. Kleinrock and Campbell are employed by IQVIA, which was contracted by the NPC for data analysis.

What might have happened: the impact of interrupting entry of innovative drugs on disease outcomes in the United States.

INTRODUCTION: The study estimated the extent to which drug innovations over the past 30 years may have improved outcomes for six diseases. AREAS COVERED: We analyzed six diseases (ischemic heart disease, lung cancer, breast cancer, human immunodeficiency virus [HIV] infection, type 2 diabetes mellitus, and rheumatoid arthritis [RA]) with significant mortality or morbidity for which there have been major drug innovations over the past 30 years. We used U.S. data from the Global Burden of Disease (GBD) database and a patient registry to perform counterfactual time-series analyses predicting the improved health outcomes that may have been associated with major drug innovations. For 5 conditions using data from the GBD study, years of life lost per individual with the condition could have been higher by 17.1% (breast cancer) to 660.6% (HIV infection) in 2017 had the major drug innovations not been introduced. For RA, using patient registry data, patients' functional status could have been 11.5% worse had biological therapies not been introduced. EXPERT OPINION: Policies targeting drug prices should be broadened to consider the price and value of all health-care services. The societal importance of the pharmaceutical industry's ability to respond rapidly to emerging diseases should be recognized.

Health Care Spending Effectiveness: Estimates Suggest That Spending Improved US Health From 1996 To 2016.

Health care spending effectiveness is the ratio of an increase in spending per case of illness or injury to an increase in disability-adjusted life-years (DALYs) averted per case. We report US spending-effectiveness ratios, using comprehensive estimates of health care spending from the Disease Expenditure Project and DALYs from the Global Burden of Disease Study 2017. We decomposed changes over time to estimate spending per case and DALYs averted per case, controlling for changes in population size, age-sex structure, and incidence or prevalence of cases. Across all causes of health care spending and disease burden, median spending was US$114,339 per DALY averted between 1996 and 2016. Twelve of thirty-four causes with the highest spending or highest burden had median spending that was less than $100,000 per DALY averted. Using decomposition results, we calculated an outcome-adjusted health care price index by assigning a dollar value to DALYs averted per case. When we used $100,000 as the dollar value per DALY averted, prices increased by 4 percent more than the broader economy; when we used $150,000 per DALY averted, relative prices fell by 13 percent, meaning that much of the growth in health care spending over time has purchased health improvements.

Do Patient Preferences Align With Value Frameworks? A Discrete-Choice Experiment of Patients With Breast Cancer.

Purpose. Assess patient preferences for aspects of breast cancer treatments to evaluate and inform the usual assumptions in scoring rubrics for value frameworks. Methods. A discrete-choice experiment (DCE) was designed and implemented to collect quantitative evidence on preferences from 100 adult female patients with a self-reported physician diagnosis of stage 3 or stage 4 breast cancer. Respondents were asked to evaluate some of the treatment aspects currently considered in value frameworks. Respondents' choices were analyzed using logit-based regression models that produced preference weights for each treatment aspect considered. Aggregate- and individual-level preferences were used to assess the relative importance of treatment aspects and their variability across respondents. Results. As expected, better clinical outcomes were associated with higher preference weights. While life extensions with treatment were considered to be most important, respondents assigned great value to out-of-pocket cost of treatment, treatment route of administration, and the availability of reliable tests to help gauge treatment efficacy. Two respondent classes were identified in the sample. Differences in class-specific preferences were primarily associated with route of administration, out-of-pocket treatment cost, and the availability of a test to gauge treatment efficacy. Only patient cancer stage was found to be correlated with class assignment (P = 0.035). Given the distribution of individual-level preference estimates, preference for survival benefits are unlikely to be adequately described with two sets of preference weights. Conclusions. Although value frameworks are an important step in the systematic evaluation of medications in the context of a complex treatment landscape, the frameworks are still largely driven by expert judgment. Our results illustrate issues with this approach as patient preferences can be heterogeneous and different from the scoring weights currently provided by the frameworks.

The Effect of Medical Technology Innovations on Patient Outcomes, 1990-2015: Results of a Physician Survey.

BACKGROUND: Developments in diagnostics, medical devices, procedures, and prescription drugs have increased life expectancy and quality of life after diagnosis for many diseases. Previous research has shown that, overall, increased investment in medical technology has led to increased health outcomes. In addition, the value of investment in specific innovations, particularly in new pharmaceuticals or biopharmaceuticals, has frequently been shown through an evaluation of the associated health outcomes and costs. Value assessments for all medical technologies and interventions are an important consideration in current debates on access and affordability of health care in the United States. OBJECTIVE: To identify practicing physician impressions of the historical effect of postdiagnosis innovations in medical technology on patient outcomes within the 8 health conditions that have the largest effect on health in the United States. METHODS: National statistics were used to identify the 8 conditions responsible for the most mortality and morbidity within the United States between 1990 and 2014. A physician survey was developed for each major condition to obtain physician opinion on the extent to which pharmaceuticals and biopharmaceuticals, medical devices, diagnostics, and surgical procedures contributed to improvements in postdiagnosis mortality and morbidity outcomes over the evaluated period. Respondents were provided with a fifth category, "cannot allocate," to account for postdiagnosis outcome gains resulting from other factors such as public health interventions. RESULTS: The conditions identified as having the greatest effect on morbidity and mortality since 1990 were breast cancer, ischemic heart disease, human immunodeficiency virus infection, diabetes, unipolar depression, chronic obstructive pulmonary disease, cerebrovascular disease, and lung cancer. After excluding other factors, physicians specializing in these conditions, with a mean of 21.4 years in practice, considered pharmaceuticals and biopharmaceuticals as having the greatest postdiagnosis effect across all 8 conditions, with 56% of outcome gains attributed to this innovation category. Diagnostics was the second biggest contributor at 20%. CONCLUSIONS: Physician perceptions indicated that attention should be paid to value assessments of innovative diagnostics, devices, and surgical procedures, as well as to pharmaceuticals and biopharmaceuticals, before goals for allocating health care expenditures among the different innovations are determined. DISCLOSURES: Funding for this study was provided by the National Pharmaceutical Council, a health policy research group that receives its funding from biopharmaceutical manufacturers. Wamble is employed by RTI Health Solutions, which received funding from the National Pharmaceutical Council to conduct this research. Ciarametaro and Dubois are employed by the National Pharmaceutical Council.

What's Been The Bang For The Buck? Cost-Effectiveness Of Health Care Spending Across Selected Conditions In The US.

The continued rise in health care spending has led to an intense debate among policy makers and other health care stakeholders on how to best manage increasing costs, leading to a focus on cost increases with little consideration of the associated change in outcomes. We identified the extent to which increased medical intervention spending on seven prevalent chronic conditions in the US over a twenty-year period has been a good investment. The results provide disease-level cost-effectiveness ratios for comparing changes in medical care spending to changes in health outcomes for patients diagnosed with one of the conditions. This study has two key findings: First, dollars spent on medical care can be a source of high value creation, and such investment should continue. Second, significant variability in value exists across diseases, which highlights the need for disease-specific spending approaches.

Utilization of High-Cost Interventions for Targeted Clinical Conditions During the Early Stages of ACO Development in a Commercially Insured Population.

This study compared utilization patterns of high-cost services and medications for patients receiving care from Accountable Care Organization (ACO)-participating physicians and those receiving care from non-ACO physicians during the initial phases of ACO development in a commercially insured environment. Patients ≥18 years (≥40 years for chronic obstructive pulmonary disease [COPD]) with prevalent rheumatoid arthritis, inflammatory bowel disease, multiple sclerosis, type 2 diabetes, COPD, or chronic low back pain between January 1, 2012, and August 31, 2014 were identified in the HealthCore Integrated Research DatabaseSM. Patients were assigned to the ACO cohort if their primary treating physician was contracted to the health plan through an ACO agreement. Each clinical condition was stratified for severity of illness. Cohort utilization patterns were compared for the 12-month period following the index encounter. The primary outcome measures show that there was no statistically significant utilization difference between the ACO and non-ACO cohorts for 90% of the 82 comparisons made. It is expected that some measures will achieve significant difference simply because of having this many comparisons, but no clear pattern was identified. This study did not observe statistically significant differences in utilization of high-cost services and medications between ACO and non-ACO cohorts with limited experience in the ACO model. Future analyses with longer study durations, at later stages of ACO development, tracking a more granular level of physician organizational structure, and with designs that integrate clinical and administrative data are essential to better understand the impact of payment innovation strategies using an ACO structure.

Impact of formulary restrictions on medication intensification in diabetes treatment.

OBJECTIVES: To explore formulary restrictions on noninsulin antihyperglycemic drugs (NIADs) in Medicare Part D plans and to estimate the impact of formulary restrictions on use of NIADs among low-income subsidy (LIS) recipient enrollees with type 2 diabetes (T2D) undergoing treatment intensification. STUDY DESIGN: Retrospective cohort study. METHODS: A cohort of 2919 LIS enrollees with T2D receiving metformin monotherapy during the first quarter of 2012 who intensified treatment later in the year was tracked to assess selection of and days' supply with sulfonylureas, dipeptidyl peptidase-4 (DPP-4) inhibitors, and other NIADs. We tested whether being enrolled in a Part D plan with significant formulary restrictions on sole-source brand name NIADs reduced the likelihood of receiving such agents and, if so, what the impact was on days of therapy with the second agent. A 2-part regression model was estimated with explanatory variables for plan-level restrictions and individual covariates. RESULTS: We found that 63% of study subjects initiated a sulfonylurea, 25% a DPP-4 inhibitor, and 12% another NIAD. Greater restrictions on DPP-4 inhibitors as a class were associated with small reductions in initiation of DPP-4 inhibitors and a concomitant increase in use of sulfonylureas, but neither effect was statistically significant. For individual DPP-4 inhibitors, step therapy requirements on sitagliptin and formulary exclusion of saxagliptin resulted in significant reductions in uptake of the specific drugs but had no significant impact on total days' supply of antihyperglycemic therapy. CONCLUSIONS: Part D formulary restrictions on sole-source brand name NIADs had little impact on patterns of treatment intensification for T2D among LIS recipients enrolled in Medicare Part D plans in 2012.

Insurance switching and mismatch between the costs and benefits of new technologies.

OBJECTIVES: Many therapies have immediate costs but delayed benefits. Recent and anticipated transformative therapies may exacerbate these challenges. This study explored whether disconnects between short-term budget impacts and long-term costs and benefits, and among impacts on initial payers, downstream payers, and society, are expected for a range of such therapies and whether they are likely consistent or variable, with implications for potential policy responses. STUDY DESIGN: Modeling. METHODS: We modeled the impacts of 5 hypothetical therapies affecting different patient types: curative gene therapy for a childhood disorder, highly effective hepatitis C virus therapy, disease-modifying Alzheimer disease therapy, and cardiovascular disease therapy for both rare genetic and higher-risk prior cardiovascular event populations. We constructed disease-specific models, modifying best-available Markov analysis estimates for standard-of-care state transition rates, utilities, and costs. We disaggregated total healthcare impacts into impacts on initial versus downstream payers, dividing payers into 3 types: commercial insurers, Medicaid, and Medicare. RESULTS: Although we found gaps between the impacts on initial and downstream payers in all examples, some substantial, the magnitude and reasons vary. CONCLUSIONS: As scientific advances generate transformative therapies with substantial structural disconnects between "who pays" and "who benefits," creative approaches may be needed by manufacturers, payers, and others to ensure appropriate access to cost-effective therapies, adequate economic incentives for future development, and sustainable payer economics. Mechanisms may amortize high up-front costs over time, provide for transfers among payers, or a combination. Our research suggests that approaches should be tailored to specific disease and therapy characteristics to be effective.

No improvement in the reporting of clinical trial subgroup effects in high-impact general medical journals.

BACKGROUND: When subgroup analyses are not correctly analyzed and reported, incorrect conclusions may be drawn, and inappropriate treatments provided. Despite the increased recognition of the importance of subgroup analysis, little information exists regarding the prevalence, appropriateness, and study characteristics that influence subgroup analysis. The objective of this study is to determine (1) if the use of subgroup analyses and multivariable risk indices has increased, (2) whether statistical methodology has improved over time, and (3) which study characteristics predict subgroup analysis. METHODS: We randomly selected randomized controlled trials (RCTs) from five high-impact general medical journals during three time periods. Data from these articles were abstracted in duplicate using standard forms and a standard protocol. Subgroup analysis was defined as reporting any subgroup effect. Appropriate methods for subgroup analysis included a formal test for heterogeneity or interaction across treatment-by-covariate groups. We used logistic regression to determine the variables significantly associated with any subgroup analysis or, among RCTs reporting subgroup analyses, using appropriate methodology. RESULTS: The final sample of 416 articles reported 437 RCTs, of which 270 (62 %) reported subgroup analysis. Among these, 185 (69 %) used appropriate methods to conduct such analyses. Subgroup analysis was reported in 62, 55, and 67 % of the articles from 2007, 2010, and 2013, respectively. The percentage using appropriate methods decreased over the three time points from 77 % in 2007 to 63 % in 2013 (p < 0.05). Significant predictors of reporting subgroup analysis included industry funding (OR 1.94 (95 % CI 1.17, 3.21)), sample size (OR 1.98 per quintile (1.64, 2.40), and a significant primary outcome (OR 0.55 (0.33, 0.92)). The use of appropriate methods to conduct subgroup analysis decreased by year (OR 0.88 (0.76, 1.00)) and was less common with industry funding (OR 0.35 (0.18, 0.70)). Only 33 (18 %) of the RCTs examined subgroup effects using a multivariable risk index. CONCLUSIONS: While we found no significant increase in the reporting of subgroup analysis over time, our results show a significant decrease in the reporting of subgroup analyses using appropriate methods during recent years. Industry-sponsored trials may more commonly report subgroup analyses, but without utilizing appropriate methods. Suboptimal reporting of subgroup effects may impact optimal physician-patient decision-making.