Government Risk Modeling & Assessment A FRAMEWORK TO DETERMINE THE COMMERCIAL RISKS AND OPPORTUNITIES OF US HEALTHCARE REFORM By Stephen Lu, Mengran Wang, and Matthew Shindel The implementation of the Affordable Care Act in the United States is creating massive shifts in reimbursement channels within patient populations. These shifts bring with them potential upsides and downsides for manufacturers of biopharmaceutical products. Potential upside could result from the uninsured population becoming insured through Medicaid or moving into the commercial market or Public Exchanges. There is also a potential upside from patients transitioning from Patient Assistance Programs into Medicaid as a result of Medicaid expansion. Meanwhile, a potential downside exists as patient populations move from high-margin employer-sponsored plans to low-margin Public Exchange plans. The potential prohibition of co-pay assistance programs in the Public Exchanges could also impact certain therapeutic categories (e.g., specialty drugs). Campbell Alliance has developed a methodology to estimate the percent of a given disease population that will move from the uninsured channel to Public Exchanges, Medicaid, or the commercial channel as well as the percent moving from commercial to Medicaid and Public Exchanges as a result of the ACA. This population breakdown can be extrapolated, by channel, across the next five years. Then, by applying company data, the rebate level, and pricing data, it is possible to predict the performance metrics for a brand in a given therapeutic category based on the movement within the various channels. Manufacturers need to understand the potential impact these shifts will have on the top and bottom line what channels are increasing or decreasing in terms of the number of patients, product revenue, or rebates in order to guide their investment in resources in the various channels. To predict the channel dynamics that could result from the changes brought by healthcare reform, This article will outline the methodology we use to estimate how patients move from uninsured to commercial, Medicaid, and Public Exchanges and show how it can be applied to a hypothetical brand. By leveraging this model, manufacturers can gain insights into the channel dynamics to guide their resourcing efforts in those channels.
PATIENT OUTFLOW FROM UNINSURED POPULATION Our model first predicts patient dynamics among channels, detailing the patient shift from the uninsured population to the commercial market, Medicaid, or Public Exchanges. This is done in five steps: Estimate the total outflow Allocate the outflow in 2014 Allocate the outflow at steady state Apply ramp-up curve Add on natural channel dynamics Total US Medicare Medicaid Public Exchanges (Cash) Low-Income Subsidy Non-Low-Income Subsidy Federally Facilitated Exchanges State-Based Exchanges 2
Harnessing Risks and Opportunities the Power of US of Experience Healthcare Reform and Insights 1 Estimate the total outflow In the first step we break down the total US population by channel and predict total outflow from the uninsured population in 2014 (Figure 1). We begin by calculating the percent of change in the uninsured rate pre- and post-aca by taking the average of estimates from a variety of sources, including RAND, the Commonwealth Fund, Gallup, Urban Institute, the CDC, and WalletHub. We determined a percent change of about 21%, representing a decrease in the uninsured population of about 8.5 million people. 2 Allocate the outflow in 2014 In the second step, we allocate the outflow from the uninsured pool to the commercial, Medicaid, and Public Exchange channels in 2014. After reviewing other sources (e.g., McKinsey, RAND), we selected Kaiser Family Foundation as the major source to estimate the allocation of outflow from the uninsured population. We determined that 20% (1.7 million people) moved to the commercial channel, 23.5% (2 million people) moved to the Medicaid channel, and 56.5% (4.8 million people) moved to Public Exchanges. Figure 1 Estimate the Total Outflow Comparison of Rate (Pre-ACA vs. Post-ACA) Sources Pre-ACA Post-ACA % of Change RAND 20.5% 15.8% 22.9% Commonweath Fund 20.0% 15.0% 25.0% Gallup 18.0% 13.0% 27.8% Urban Institute 17.9% 14.0% 21.8% CDC 20.0% 18.0% 10.0% WalletHub 17.9% 14.2% 20.4% Average 21.3% Key Issue: All private surveys are under representative of total US population, with a margin of sampling error (MOSE) of about plus or minus 5-10 percentage points. Solution w/o ACA.1M 1 X 21.3% Decreased (2014) 8.5M 1 CPS-ASEC interviews over 85,000 households each year and is the most widely used source for counts of the uninsured population. 3
3 Allocate the outflow at steady state Assuming that all eligible populations will shift to their corresponding channels, we next use a steady-state approach to predict the channel dynamics in the future (Figure 2). Of the total uninsured population pre-aca in 2014, 25% are eligible for federal subsidies, and at steady state we predict this entire segment will move to Public Exchanges. Meanwhile, the 31% of the population that is eligible for Medicaid is predicted to move to Medicaid. For the 24% who are illegible for federal subsidies because their income is too high, we predict 7.5% will move to Public Exchanges and another 7.5% will move to the commercial channel. The remaining 20% are either illegible for coverage due to immigration status or because they fall into the coverage gap, and we predict they will remain with their current uninsured status. At a steady state, 3.2 million people will move to commercial,.8 million to Medicaid, and 13.8 million to Public Exchanges. The outflow population in 2014 is deducted from the total at steady state, resulting in 1.5 million for commercial, 10.8 million for Medicaid, and 9 million for Public Exchanges. Figure 2 Allocate the Outflow at Steady State Eligibility for Coverage Among (2014) 1 Allocation of Outflow at Steady State Total Outflow at Steady State To-Be-Allocated * Ineligible for Coverage Due to Immigration Stuatus 10% Eligible for Medicaid 31% Eligible for Federal Subsidies (via Public Exchanges) 25% Ineligible for Federal Coverage Subsidies (via Gap Public Exchanges) 10% 24% Medicare Public Exchanges 7.5% 1,2 Medicare Public Exchanges 7.5% 1,2 Total w/o ACA (2014):.1M *Outflow population in 2014 is deducted from the total at steady state. Sources: 1 The : A Primer of Key Facts About Health Insurance and The in America. Kaiser Family Foundation. January 2015; 2 Payments of Penalties for Being Under the Affordable Care Act: 2014 Update. CBO. June 2014. 4
4 Apply ramp-up curve Next we apply a ramp-up curve to generate the outflow to each channel from 2015 to 2021. Users of this model have the flexibility to select different ramp-up curves (e.g., logarithm, straight line, sigmoid) and steadystate year. In the example in Figure 3, we assume the steady state will happen in 2018 based on our secondary research. We referred to the Congressional Budget Office prediction, which assumes a steady state will be reached in five years. Figure 3 Apply Ramp-Up Curve Ramp-Up Curve To-Be-Allocated Outflow to Other Channels (2015-2021) Percentage of Reaching Steady State 100% 80% 60% 40% 20% 0% 2015 2016 2017 2018 2019 2020 2021 1.5M Medicare 10.8M Public Exchanges 90.M Number of Patients (Million) 10 8 6 4 2 0 Medicaid Public Exchanges 2015 2016 2017 2018 2019 2020 2021 Model users have the flexibility to select different ramp-up curves (e.g., logarithm, straight line, sigmoid) and steady-state year. Sources: 1 CBO Estimates of the Effects of the Insurance Coverage Provisions of the ACA. CBO. April 2014; http://aspe.hhs.gov/health/reports/2014/targets/ib_targets.pdf. Accessed 01/29/2015. 5 Add on natural channel dynamics In the fifth step, we add on natural channel dynamics to the outputs predicted by the ramp-up curve from 2015 to 2021 (Figure 4). First we calculate the uninsured population from 2014 to 2021 under ACA, based on the population outflow at 2014, population outflow at steady state, and the ramp up curve. Then we calculate the uninsured population from 2014 to 2021 under natural channel dynamics, based on secondary research. Finally, we apply the annual growth/decrease rates from natural channel dynamics to the ACA uninsured population to get the baseline uninsured population from 2014 to 2021. Given the decreasing trends of the uninsured population under both pure ACA impact and natural channel dynamics, the uninsured population is shrinking the fastest in the baseline scenario. 5
Figure 4 Add on Natural Channel Dynamics Pure ACA Impact Year 33.6M 29.3M 21.8M 16.5M.4M.4M.4M.4M Natural Channel Dynamics Year.1M.9M.6M.4M.1M 40.8M 40.6M 40.3M ACA Impact Adjusted by Natural Channel Dynamics Year APPLY THE MODEL 33.6M 29.2M 21.6M 16.2M.1M.0M.0M 11.9M In the final output, we can see the number of enrollees in each channel with and without the ACA (Figure 5). Given the channel dynamics derived from this model, manufacturers can apply their individual disease category epi-data, their market share, rebates, and Unit of Wholesale Acquisition Cost (WAC) in order to predict the financial output for their business through 2021. Figure 5 Apply the Model Number of Enrollees (Millions) 350 300 250 200 150 100 50 0 57 172 59 171 Number of Enrollees by Channel without ACA (2014-2021) 44 61 171 45 62 171 46 64 171 47 66 170 48 67 170 40 50 69 169 Number of Enrollees (Millions) 350 300 250 200 150 100 50 0 34 7 46 57 168 29 11 49 59 167 Number of Enrollees by Channel with ACA (2014-2021) 22 17 54 60 164 16 21 59 62 161 24 62 63 159 24 64 65 159 24 66 67 158 24 67 68 157 Source: Government Risk Assessment Model. Medicare Medicaid Exchanges 6
Figure 6 provides a summary of hypothetical assumptions we can apply to the model for a Product X. In Figure 7 we can then see, by channel and year to year, how the ACA will impact Product X in terms of patient number, gross revenue, rebate, and net revenue. This provides a valuable view into not only trends in the population movement across channels but also the financial impact of population movement on Product X in different channels. Figure 6 Hypothetical Model Assumptions Steady-State Switching Matrix (Assume Reaching Steady State in 2018) To / From Medicare Medicaid Public Exchanges 92.2% 7.5% Medicare 100% Medicaid 1.4% 100% 30.3% Public Exchanges 6.3% 100% 32.7% 29.5% Product X Market Share (Assume No Share in Channel) 2014-2021 23.0% 22.0% 21.0% 20.0% 19.0% 19.0% 18.0% 18.0% Medicare 19.0% 18.2% 17.3% 16.5% 15.7% 15.7% 14.9% 14.9% Medicaid 17.0% 16.3% 15.5% 14.8% 14.0% 14.0% 13.3% 13.3% Public Exchanges 23.0% 22.0% 21.0% 20.0% 19.0% 19.0% 18.0% 18.0% 2014-2021 Product X Rebate Rate 15.0% 15.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% Medicare 20.0% 20.0% 15.0% 15.0% 15.0% 15.0% 15.0% 15.0% Medicaid 25.0% 25.0% 25.0% 25.0% 25.0% 25.0% 25.0% 25.0% Public Exchanges 15.0% 15.0% 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% Product X Unit WAC 2014-2021 All Channels $1,600 $1,700 $1,800 $1,900 $2,000 $2,200 $2,200 $2,300 Other Assumptions All Channels Prevalence: 2.0% Diagnosis Rate: 100% Treatment Rate: 50.0% Patient-to-Unit Ratio: 5 7
Figure 7 ACA Impact on Product X 700 Product X Patient Number by Channel with ACA (2014-2021) Thousands 600 500 400 300 200 100 17 97 99 387 23 100 98 367 35 106 97 344 108 95 323 46 110 92 302 46 1 94 301 44 109 92 284 44 1 94 283 0 $7, 000 Product X Gross Revenue by Channel with ACA (2014-2021) Millions $6,000 $5,000 $4,000 $3,000 $135 $774 $795 $199 $848 $834 $318 $950 $870 $402 $1,030 $902 $465 $1,096 $923 $487 $1,179 $991 $482 $1,199 $1,009 $503 $1,285 $1,079 $2,000 $1,000 $3,099 $3,0 $3,095 $3,065 $3,024 $3,163 $3,5 $3,253 $0 Millions $1,000 $900 $800 $700 $600 $500 $400 $300 $200 $100 $0 Product X Rebate by Channel with ACA (2014-2021) $20 $30 $50 $49 $48 $194 $2 $32 $40 $46 $295 $321 $300 $159 $167 $237 $258 $274 $131 $149 $162 $135 $151 $138 $465 $468 $309 $306 $302 $316 $313 $325 $7, 000 Product X Net Revenue by Channel with ACA (2014-2021) $6,000 Millions $5,000 $4,000 $3,000 $2,000 $1,000 $115 $581 $636 $2,634 $286 $169 $7 $636 $667 $740 $362 $773 $767 $8 $822 $784 $2,652 $2,785 $2,758 $2,722 $438 $434 $453 $885 $900 $963 $843 $857 $918 $2,847 $2,813 $2,928 $0 Note: Tricare and VA are not included. Dual-eligible enrollees and CHIP are categorized as Medicaid Source: Government Risk Assessment Model. Medicare Medicaid Exchanges 8
CONCLUSION In the near future, the Public Exchange channel will increase to 24 million, and the Medicaid channel will increase by 21 million, so it is critical for manufacturers to prioritize their investment in different channels, particularly if a significant amount of their business resides in the Medicaid and Public Exchange channels. By leveraging the methodology described in this article, manufacturers will see how these shifts will impact the performance of their product based on a combination of internal assumptions and the high level market dynamics reshaping the healthcare landscape. Armed with this insight, manufacturers will be better positioned to make critical resourcing decisions across all of these channels. CONTACT Stephen Lu Engagement Manager Campbell Alliance stephen.lu@inventivhealth.com Mengran Wang Senior Consultant Campbell Alliance mengran.wang@inventivhealth.com Matthew Shindel Consultant Campbell Alliance matt.shindel@inventivhealth.com Campbell Alliance is a leading management consulting firm focused on helping biopharma companies address key strategic business questions, and achieve tangible results. Campbell Alliance is the strategic consulting business line of inventiv Health, a best in class solutions provider of clinical and commercial support and expertise to elevate our clients performance. inventiv Health combines a broad range of deep therapeutic expertise with an unparalleled breadth of commercialization capabilities. Campbell Alliance: Strategy. Results. www.campbellalliance.com Headquarters Address 370 Lexington Avenue, Suite 1100 New York, NY 10017 Telephone: 919.844.7100 Fax: 646.805.0351 Toll Free: 888.297.2001 Scan here to download an electronic version of this white paper. 9