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Chronic Disease Outcomes From Primary Care Population Health Program Implementation
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Insurance Switching and Mismatch Between the Costs and Benefits of New Technologies
David Cutler, PhD; Michael Ciarametaro, MBA; Genia Long, MPP; Noam Kirson, PhD; and Robert Dubois, MD, PhD
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Qian Shi, PhD, MPH; Thomas J. Yan, MS; Peter Lee, BS; Paul Murphree, MD, MHA; Xiaojing Yuan, MPH; Hui Shao, PhD, MHA; William H. Bestermann, MD; Selina Loupe, BS; Dawn Cantrell, BA; David Carmouche, MD; John Strapp, BA; and Lizheng Shi, PhD, MSPharm
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Rami J. Hosein, MD, MPH; Joan C. Lo, MD; Bruce Ettinger, MD; Bonnie H. Li, MS; Fang Niu, MS; Rita L. Hui, PharmD, MS; and Annette L. Adams, PhD, MPH
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Insurance Switching and Mismatch Between the Costs and Benefits of New Technologies

David Cutler, PhD; Michael Ciarametaro, MBA; Genia Long, MPP; Noam Kirson, PhD; and Robert Dubois, MD, PhD
Transformative therapies with high up-front costs will exacerbate the need to address gaps between payers when costs and benefits occur at different times.

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.

Am J Manag Care. 2017;23(12):750-757
Takeaway Points
  • Many therapies have financial costs and benefits at different times, creating gaps between initial and downstream payers when patients switch payers between the initial therapy payment and subsequent cost offsets.
  • In modeling the potential impacts of 5 hypothetical new transformative therapies representing diverse patient types, clinical intervention models, and disease burdens, we found substantial and varying gaps between initial and downstream payer impacts.
  • New creative mechanisms may be needed to ensure economic incentives for development of transformative therapies, appropriate patient access, and sustainable payer economics.
  • Manufacturers and payers should tailor solutions to specific disease and therapy characteristics to be effective.
Scientific progress has transformed patient outcomes in many disease areas, leading to economic gains.1 However, such therapies can challenge short-term payer budgets if benefits are not coincident with costs. Although this phenomenon is not new, recent and anticipated therapies may exacerbate these challenges. For example, sofosbuvir (Sovaldi), lauded as a breakthrough hepatitis C virus (HCV) treatment, has been restricted by some insurers concerned about short-term budget impacts, including by delaying access for patients with asymptomatic or milder disease whose costs would be paid later by Medicare.2 

Disconnects between the impacts on different payers can be large in the United States, where commercial insurers, state Medicaid programs, and the federal Medicare program pay most costs. Given insurance switching by patients over time, payers covering initial costs may not benefit from all, or any, downstream cost offsets. Moreover, patients and families may highly value better health and quality of life, improved functional status and productivity, and longer life, whereas insurers may value lower costs most. 

In 5 hypothetical examples, we modeled the mismatch between who pays for and who benefits from innovative therapies. Like others, we focused on patient movement over time across Medicaid, commercial insurance, and Medicare rather than contemporaneous switching among private insurers.3 Our aim was to explore a widely acknowledged feature of US healthcare, namely that the fragmented insurance system creates potential disincentives for coverage of therapies with up-front costs and long-lived or delayed benefits, and whether the situation may be exacerbated for new clinically effective therapies that are also high-priced relative to the current standard of care (SOC). Rather than precise numerical estimates for specific diseases, we explored whether substantial disconnects may be expected under credible, but by no means the only possible, assumptions (and therefore the extent to which some cost-effective therapies with potential to improve length and quality of life may face heightened coverage disincentives); whether they vary across examples; and the implications for policy. Some prior studies' results have illustrated payer disconnects for specific diseases; others have advocated specific policy responses. This study extends the literature by comparing disconnects across diseases subject to new transformative therapies and by exploring the implications for effectiveness of potential policy responses.

Disease Examples 

We examined 5 disease states for which transformative therapies have been discussed or recently launched: highly effective HCV therapy; curative gene therapy for beta-thalassemia (BT), a rare childhood genetic disorder; disease-modifying therapy for patients with mild Alzheimer disease (AD); and cardiovascular disease (CVD) therapy for patients with the rare genetic disorder familial hypercholesterolemia (FH) and those with prior CVD. Several resemble, but do not purport to be identical with, recently introduced on-market therapies (HCV and cardiovascular therapies); others reflect areas that may produce breakthrough therapies (disease-modifying therapy for AD and gene therapy). These examples, although not exhaustive, were selected to represent diverse patient types (ie, pediatric, adult, and senior populations), clinical intervention models (ie, 1-time curative and ongoing disease-modifying therapies), and disease burdens (ie, highly certain ongoing chronic health management costs, probabilistic catastrophic hospitalization costs, and custodial and other costs from function deterioration). Table 1 summarizes key characteristics across the examples. 


To assess the net effect by payer type of each therapy, we constructed disease-specific analytic models and compared the present discounted value (PDV) of an individual’s expected lifetime healthcare costs under the current SOC and the hypothetical new therapy, from the age an average patient initiates the latter (eg, gene therapy at age 2 years). We adopted this analytic frame to model payers’ budget impact considerations associated with covering the new therapy for a patient of expected age. We also calculated improvements in quality-adjusted length of life associated with the new therapy and the incremental cost per quality-adjusted life year (QALY). This cost-effectiveness metric is provided as an indicator of social desirability. For US commercial payers, cost-effectiveness analysis is typically not an established coverage determination constraint, but budget impact analyses are important considerations. Therefore, we focused on budget impact in our analysis.

We first calculated the aggregate budgetary impact on all payers of the new therapy, including healthcare offsets due to morbidity improvements and additional healthcare costs due to extended life. We also incorporated the impact on elder care in the case of disease-modifying therapy for AD, including the net impact on both nursing home care and family caregiving. Second, we disaggregated these effects into those on a representative initial payer in the 3 main payer types and those on a representative downstream payer, restricting analysis to the most relevant payers (eg, 2-year-olds are generally covered by commercial insurance or Medicaid, not Medicare).

We modeled the impact of patients switching payer types as they age, rather than switching commercial insurance plans contemporaneously. Whereas approximately 1 in 8 nonelderly Americans with employer coverage switched health plans in 2010 (approximately 1 in 13 due to reasons other than job change), nearly all will transition to Medicare at age 65 years.4 If recent and expected therapy breakthroughs suggest a continuing shift toward front-loaded costs and back-loaded benefits, the implications for both commercial insurance and Medicare may be far reaching. We explored the reasonableness of this modeling choice via several anonymized interviews with medical directors at large commercial payers who confirmed that, given prohibitions on pre-existing condition exclusions and the nature of geographic competition where leading plans may tend toward similar coverage, they generally expect that short-run losses from a therapy for patients who “switch out” roughly offset gains from those patients who have “switched in” and whose therapy costs were covered by other commercial payers. However, for the effects of switching over time as patients age, similar assumptions do not apply. Respondents were not interviewed about the effects of potential Affordable Care Act repeal or about actions that could affect the balance between commercial coverage and state exchanges. 

Our analyses rely on best-available Markov-type models published by others, incorporating rates of patient transition from one health state to another and healthcare costs and patient utilities for each state. In order to compare the hypothetical new treatment with the current SOC, we adapted these models by varying parameters related to efficacy, cost, and age at therapy initiation, specific to the hypothesized intervention. We applied shared assumptions across the models for the percentage distribution of insurance type by age and sex from the literature. In calculating the total impact of the new therapy, we valued a QALY at $100,000; the impact on payers excludes this value, as there is no market to monetize the value of additional QALYs.5 (Cost-effectiveness calculations exclude the value of additional QALYs, by definition.) Throughout, the value of all costs and savings was discounted at a 3% annual rate. For additional relevant disease-specific and shared assumptions, see the eAppendix (eAppendices available at 


Table 2 summarizes the aggregate impact of the different therapies. For the 2 CVD examples, the model relied on recently released calculations for patients aged 35 to 74 years (rather than a single age) with FH and a history of CVD.6 Without direct access to the authors’ health state-specific model, we calculated incremental cost per QALY from these figures (after adjusting for a modeled average 20% net price discount). Figures reported for the 2 CVD therapies in Tables 2 and 37 and the Figure reflect these cost-effectiveness figures (rather than higher figures reflected in a PCSK9 inhibitor manufacturer’s technology appraisal submission to the United Kingdom’s National Institute for Health and Care Excellence).8 Regardless, we focused on the difference between the impacts on the initial and downstream payers rather than their absolute levels.

Under our assumptions, all 5 therapies would increase discounted net healthcare costs. The magnitude of additional QALYs and the healthcare costs in additional years of life would vary, depending on patient and disease dynamics. Under the assumptions used, 3 therapies were highly cost-effective, with an incremental cost per QALY of $55,000 or less; the 2 CVD therapies were cost-effective at a value of about $250,000 per QALY (less, under manufacturers’ estimates; translated from pounds to dollars without any other adjustment for differences in utilization or unit prices, the corresponding figures would be incremental costs per QALY of $33,703 for FH and $67,701 for prior CVD). Our focus, however, was on disconnects across payers, and the Figure disaggregates the overall payer impact into impacts on initial and downstream payers. For HCV, BT, and AD, the financial impact on the initial payer was negative and the impact on at least 1 downstream payer type was positive. Table 3 reports these figures in dollar terms and, to allow for direct comparison, per dollar of aggregate payer impact. 

Under our assumptions, treating BT costs the healthcare system nearly $180,000. The impact by payer varies depending on the initial insurer. When commercial payers are the initial insurers, they face slightly lower financial impacts relative to aggregate healthcare costs, the difference being additional downstream Medicare costs from children now surviving to age 65. That said, most costs are paid by commercial payers. Children initially covered by Medicaid, however, are covered by private insurance when older (modeled at age 21). Thus, Medicaid pays all treatment costs and commercial insurers realize a gain, the net effect of more likely survival and lower per-patient costs. For every patient whose treatment at age 2 is paid by Medicaid, commercial insurers benefit by a cumulative PDV of $120,661.

Others also have analyzed the tension between long-term cost-effectiveness and the immediate budget impact of highly effective therapies for HCV and similarly find a disincentive for commercial insurer coverage, with results borne by Medicare and other downstream payers7,9,10; 1 study estimated roughly a 15-year payback period for private payer coverage.3 Under our assumptions, initial commercial payers experience a cumulative PDV net cost of about $15,000 per patient. Medicare benefits from commercial payer coverage because patients avoid later expensive catastrophic events, such as liver cancer and transplants. These savings are greater than the additional costs incurred from patients living longer, for a gain of nearly $3000 per patient.

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