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Insurance Switching and Mismatch Between the Costs and Benefits of New Technologies
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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.
Slowing the progression of AD costs Medicare, both because it pays therapy costs and because patients live longer. However, it also reduces the need for nursing home care, thus saving Medicaid approximately $30,000 per patient. Families benefit from reduced needs for nursing home care, but experience additional caregiving burden during longer disease progression at home, for estimated increased net costs.

For patients initiating CVD therapy before age 65, commercial insurer and Medicaid costs are lower than the aggregate impact because many therapy costs occur after age 65. Although commercial insurers and Medicaid experience lower savings from avoided cardiovascular events, they also experience lower additional healthcare costs from longer life. For patients initiating therapy after age 65, Medicare experiences all therapy costs, healthcare cost offsets, and extra healthcare costs associated with extended life. For both populations, the net effect is negative, moreso for  patients with prior CVD than those with FH. Although the magnitude reflects the assumptions used by others (which have been critiqued11), the general pattern remains under other cost and disease transition assumptions.


Our research confirms that switching between payer types over time results in financial disconnects between initial and downstream payers across multiple hypothetical examples of highly effective new therapies with front-loaded costs and back-loaded benefits. Without mechanisms to monetize the downstream benefits of health improvements to others, returns from initial payers’ investments are understated. In particular, switching from commercial to Medicare coverage at age 65 may result in systematic disincentives for some new therapies by commercial payers, depending on specifics relating to age at initial treatment, up-front therapy cost, and morbidity and mortality impacts.

Medicare may be a financial “winner” or “loser,” depending on the balance between additional therapy cost, morbidity improvement savings, and extra healthcare costs from mortality gains. For HCV, we found (as have others) that Medicare benefits from initial payer coverage.3 For BT, Medicare impacts are far in the future and somewhat negative. For disease-modifying AD therapy and the CVD therapies as modeled, Medicare would pay more. Depending on the magnitude of the effects and the numbers of patients treated, downstream Medicare impacts of commercial insurer decisions could be an important additional form of “spillover,” documented in other contexts.12 Commercial insurers face negative financial impacts across the examples when they are the initial payers, suggesting all therapies could face coverage disincentives, overlooking downstream cost offsets. Yet, from an aggregate healthcare cost point of view, the incremental cost per QALY as modeled is within standard acceptable ranges and well below for some, and investment would be socially desirable. 

Absent direct social investment or subsidies, other approaches may address disconnects between privately incented and socially desirable outcomes. Two types of approaches, or a combination, may be relevant, depending on circumstances: mechanisms to align costs and benefits over time (for the same payer) and to help finance up-front therapy costs and mechanisms to share and align therapy costs and benefits across payers (eg, transfers between winners and losers). Several alternative financing proposals of the first type have been proposed, incorporating some form of cost amortizing to address challenges of high up-front costs. These include manufacturer­–payer financing mechanisms (eg, manufacturer-issued debt secured by dedicated streams of contractual payments from commercial payers)13,14; changes in accounting rules and/or insurance regulations to allow payers to amortize some costs over longer time periods14; monthly annuity payments or manufacturer service fees linked to clinical milestones and/or continued efficacy, rather than single up-front or per-dosage charges15; or consumer credit or debt programs.16,17 Such arrangements would be novel in biopharmaceutical reimbursement, but they are similar in some respects to financing expensive long-lived consumer medical devices, such as insulin pumps, that are used for chronic disease. Our results suggest that alternative financing mechanisms smoothing front-loaded costs over time could be relevant for 1-time curative gene therapy and highly effective HCV therapy, but they may be only partial solutions, as benefits and costs may still accrue to different payers. Disincentives for HCV therapies occur not only because initial costs for cure are high, creating short-term budget stress, but also because substantial downstream benefits accrue to others. Such mechanisms are likely less relevant for ongoing therapies, such as disease-modifying AD therapies or cardiovascular therapies, where costs are already spread over time.

The second types, cross-payer financial transfers and burden-sharing mechanisms between winners and losers, specifically address gaps between who pays and who benefits (rather than gaps in time between costs and benefits for the same payer). Transfers can address when costs to one payer type are offset by savings to another. For instance, up-front Medicaid cost burdens and future benefits to Medicare could be recognized by enhanced state Medicaid reimbursement rates or direct federal transfers. So-called burden-sharing proposals address when therapies are cost-effective but also cost-increasing, and a gap remains after transfers.

Transfers from one payer type to another theoretically could be appropriate for therapies such as those for BT, where Medicaid bears large up-front costs and commercial insurers experience substantial downstream benefits. However, proposals to smooth out therapy costs over time for the same payer will be easier to implement than proposals to transfer costs and benefits across payer types.18 More generally, future innovative therapies may benefit from proposals tailored to their specific circumstances, including mechanisms to amortize costs over time or to transfer value from downstream winners to initial losers, or a combination (see Table 4). Although we find disconnects between initial and downstream payers in all examples, some substantial, the magnitude and reasons vary. 


As with all modeling studies, different price, timing, and effectiveness assumptions yield different results for cumulative payer PDVs (see eAppendix sensitivity analyses). Moreover, not all relevant societal benefits have been included in the models relied upon for cost-effectiveness inputs. For instance, educational attainment and lifetime productivity impacts, important benefits of curing childhood genetic diseases, are not included for BT. Similarly, the value of reducing future transmission to others is not included for HCV and the benefits from sustained functioning and independence for patients and their families due to disease-modifying therapy are not included for AD. These benefits are not monetized but are real nonetheless, and including them could increase gaps between front-loaded costs and back-loaded benefits and/or improve cost-effectiveness. Second, for chronic therapies, we did not include changes in the new therapy’s net price over time. Third, given pre-existing health condition coverage exclusion prohibitions, we applied average population-level insurance coverage statistics and did not incorporate disease-specific insurance coverage or switching rates. We modeled at the aggregate payer type level, and this assumption may not hold true for all plans (eg, smaller payers may face greater temptations to free-ride on others’ prior coverage decisions) and patients (who may experience different switching rates post treatment). For simplicity, we assumed uniform therapy and medical costs across payer types; lower Medicaid prices could reduce Medicaid net PDVs relative to other payers. Fourth, our analyses reflect the limitations of the underlying Markov-type models (eg, constant age-specific mortality and transition rates over time), which may yield underestimated mortality benefits when extended over many years. To the degree that not all healthcare cost offsets from the new therapy are reflected in these underlying models, our calculations overstate net costs. For instance, cost offsets in heart failure and unstable angina are not included in the cardiovascular models and improvements in heart attack and stroke incidence may be understated, as they may reflect an assumed lower-risk treatment population than targeted.11 Because our focus is on general dynamics under plausible (but by no means the only possible) assumptions, our findings are representative rather than precise conclusions about specific disease–therapy combinations or forecasts of the impacts of specific therapies. Finally, our calculations reflect the assumption that, for chronic conditions, downstream payers also cover the therapy (this limitation is not relevant to 1-time therapies, such as gene therapy).


As scientific advances generate breakthrough therapies with varying profiles, creative thinking and flexible solutions by manufacturers, payers, and others will be needed to address barriers to realizing their benefits. Well-designed alternative financing or other mechanisms could help ensure economic incentives for future development, appropriate patient access, and sustainable payer economics for expensive but cost-effective transformative therapies. Although proposals have been suggested to address up-front cost barriers, proposed transfers from downstream winners to initial losers and burden-sharing mechanisms have received less attention. In some cases, both could be helpful, with the balance reflecting disease-specific circumstances. However, further research is needed to address practical and theoretical challenges, including defining why and under what circumstances Medicare would or would not incent private payer coverage, how to maintain incentives for private-sector coverage, and how and when to implement acceptable cross-payer transfers. The framework we used to disaggregate potential impacts on initial and downstream payers of new therapies, and to identify potential gaps between who pays and who benefits, may be a useful tool for manufacturers and others to map sources of potential coverage disincentives and develop and fine-tune such proposals before presenting them to payers. Failing to consider relevant disease-specific dynamics may mean the promise of new transformative therapies is not fully realized. 


The authors acknowledge and thank C.J. Enloe for data analysis and Kimberly Westrich and Howard Birnbaum for helpful suggestions.

Author Affiliations: Department of Economics, Harvard University (DC), Cambridge, MA; National Pharmaceutical Council (MC, RD), Washington, DC; Analysis Group, Inc (GL, NK), Boston, MA.

Source of Funding: Ms Long and Dr Kirson received funding for this research from the National Pharmaceutical Council. 

Author Disclosures: Mr Ciarametaro and Dr Dubois are employees of the National Pharmaceutical Council, an industry-funded health policy research group not involved in lobbying or advocacy. Ms Long and Dr Kirson are employees of Analysis Group, Inc, a consulting company that has provided services to biopharmaceutical manufacturers and insurers. Dr Cutler reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. 

Authorship Information: Concept and design (DC, MC, GL, NK, RD); acquisition of data (GL, NK); analysis and interpretation of data (DC, MC, GL, NK, RD); drafting of the manuscript (DC, MC, GL, NK, RD); critical revision of the manuscript for important intellectual content (DC, MC, GL, NK, RD); statistical analysis (DC, GL, NK); obtaining funding (GL); administrative, technical, or logistic support (DC); and supervision (DC, NK).

Address Correspondence to: David Cutler, PhD, Harvard University, 1805 Cambridge St, Littauer Ctr 230, Cambridge, MA 02138. E-mail:
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