stephen parente
TRANSCRIPT
Consumer Response to a National Marketplace for
Individual Insurance
Stephen T Parente
University of Minnesota October 2, 2009, University of Pennsylvania
Contributions by co-authors Roger Feldman, Jean Abraham and Yi (Wendy) Xu as well as coordinator Ruth Taylor and Lisa Tomai of T3
Health LLC were invaluable.
Critical data, software and methods were supported by the Robert Wood Johnson Foundation, AHRQ, DHHS/ASPE and HSI Network LLC
Overview
• Policy Proposal• ARCOLA Simulation Model • National Simulation Steps• Results• Implications
Rational for Analysis• Since 1945, McCarran Ferguson act prohibits
the sale of any insurance across state lines.• Since 1978, ERISA has enabled an opt-out for
employers to self insure across state lines.• Currently 55%+ of non-Medicaid and non-
Medicare insured receive insurance enabled by ERISA.
• For the ‘median voter’ with non-public insurance, McCarran Ferguson applies no more.
•
Policy Proposal
Since 2005, members of Congress (e.g., John Shadegg [R-AZ]) has proposed that individual health insurance be offered nationally instead of in state-specific markets.
The University of Minnesota was awarded a contract to study the likely effect of a national market on take-up of individual health insurance coverage.
The research objective is to simulate the impact of having a national market for individual (non-group) coverage and provide advice to policymakers regarding the strengths and weaknesses ofsuch a proposal.
‘ARCOLA’ Simulation Model• ARCOLA simulates national health plan
take-up from policy proposals in the individual and group markets
• Unique combination of attributes:– Based on conditional logit model of health
plan choice with data from 4 large employers– Includes HRA and HSA plans – Choice model includes measures of chronic
illness burden at contract level• Can simulate effects of policy changes:
– Premium modifications by tax deduction or credit
– Full or select individual mandates– State and national market differences
National Market Simulation
• Background: Our model predicted take-up of HSA plans in the individual market quite accurately (Health Affairs: Feldman, Parente et al., 2005)
• Population: adults in the MEPS who are aged 19-64 and are not students, not covered by public insurance, and not eligible for coverage under someone else’s ESI policy
• Baseline sample uninsured & turned down: 32.3 million people nationally
National Market Simulation Steps
1. Create a synthetic version of the MEPS that assigns people to states based on demographics
2. Identify minimum, moderate and maximum marginal impact of state regulations on individual-market premiums
• Community rating• Guaranteed issue• Any willing provider• Mandated insurance benefits
3. Develop initial set of scenarios for policy• Scenario 1: Competition among 5 largest states• Scenario 2: Competition among all 50 states• Scenario 3: Competition within regions
Health Insurance Regulations
• Mandates require insurers to cover particular services or providers
• Guaranteed issue laws require insurers to sell insurance to all potential customers
• Community rating requires insurers to limit premium differences across individuals
• Any willing provider (AWP) laws restrict insurers’ ability to exclude providers from their networks
Literature Review • We reviewed studies of the individual
insurance market• We could not find any studies that used
ideal ‘dif-in-dif’ research design • Other papers looked at the effects of
regulations on premiums only for people who held insurance – we ruled these out
• Only 4 studies met our criteria: 3 working papers and one peer-reviewed study by Hadley and Reschovsky (Inquiry, 2003)
Effects of Regulations Regulation Minimum
Increase Midpoint Increase
Maximum Increase
Guaranteed Issue
0 57% 114%
Community Rating
0 17.3% 34.6%
AWP 1.5% 6.75% 12% Mandates .4% per
mandate .65% per mandate
.9% per mandate
Simulation Step #4• Select ‘target state’ in which person can
buy insurance• Remove the effect of regulations in home
state from premiums and add the effect of regulations in target state– In general, target state will have fewer
regulations and lower premium – Exceptions: (1) target and home state are the
same; (2) high-cost person with community rating in home state may lose advantage of community rating in target state
• Simulate the net effect of removing regulations on health insurance take-up
Details & Assumptions• Premium data:
– HSA from ehealthinsurance.com for HSAs– HRA from composite of 3 of our empl0yers – Kaiser/Commonwealth for all other plan designs
• State-specific premium inflators/deflators derived from Musco et al. AHIP report on individual health insurance
• Individual market premiums were experience rated for age and gender (except community rated states)
• Small group market (<250 employees) premiums were adjusted by state-specific regulatory effects
• Employee premiums in large firms were tax-adjusted• HSA premiums include a $1K/$2K investment in
accounts
Scenario 1: Competition among 5 largest States
4,688,254
StatusQuo Mininum Moderate Maximum
IndiviudalHSA 4,655,291 4,493 0% 806,865 17% 1,282,626 28%PPO High 7,515,552 33,396 0% 2,486,440 33% 4,456,992 59%PPO Low 180,379 263 0% (22,243) -12% (30,380) -17%PPO Medium 1,534,799 3,886 0% 20,139 1% 12,174 1%Uninsured 28,848,310 (42,038) 0% (3,291,201) -11% (5,721,413) -20%
Group MarketHMO 5,505,466 (0) 0% (179) 0% (1,487) 0%HRA 6,166,134 (4) 0% (791) 0% (2,711) 0%HSA Offered 307,298 (0) 0% (37) 0% (165) 0%HSA No-offer 11,088 69 1% 27,301 246% 135,973 1226%PPO High 16,535,831 (2) 0% (578) 0% (3,229) 0%PPO Low 665,950 (0) 0% (72) 0% (796) 0%PPO Medium 53,470,814 (62) 0% (25,093) 0% (119,262) 0%Turned Down 3,530,681 (0) 0% (552) 0% (8,323) 0%
Within Sample NationalMininum Insurance Estimate: 42,038 59,873 Moderate Insurance Estimate: 3,291,753 4,688,254 Maximum Insurance Estimate: 5,729,735 8,160,532
Scenario 1Least Regulated Top 5 State - Texas
Scenario 2: Competition among States
8,490,592
StatusQuo Mininum Moderate Maximum
IndiviudalHSA 4,655,291 337,126 7% 1,380,706 30% 1,679,969 36%PPO High 7,515,552 982,018 13% 4,570,144 61% 7,423,340 99%PPO Low 180,379 (10,102) -6% (37,231) -21% (52,021) -29%PPO Medium 1,534,799 39,324 3% 45,805 3% 32,344 2%Uninsured 28,848,310 (1,348,366) -5% (5,959,423) -21% (9,083,632) -31%
Group MarketHMO 5,505,466 (16) 0% (508) 0% (4,985) 0%HRA 6,166,134 (157) 0% (1,711) 0% (5,990) 0%HSA Offered 307,298 (6) 0% (86) 0% (428) 0%HSA No-offer 11,088 3,780 34% 64,982 586% 353,446 3188%PPO High 16,535,831 (79) 0% (1,424) 0% (9,120) 0%PPO Low 665,950 (3) 0% (231) 0% (2,841) 0%PPO Medium 53,470,814 (3,511) 0% (58,965) 0% (297,398) -1%Turned Down 3,530,681 (8) 0% (2,057) 0% (32,684) -1%
Within Sample NationalMininum Insurance Estimate: 1,348,374 1,920,411 Moderate Insurance Estimate: 5,961,480 8,490,592 Maximum Insurance Estimate: 9,116,316 12,983,844
Scenario 2Least Regulated State - Alabama
Scenario 3: Competition among States in 4 Regions
7,772,544
StatusQuo Mininum Moderate Maximum
IndiviudalHSA 4,655,291 264,970 6% 1,220,825 26% 1,546,262 33%PPO High 7,515,552 815,292 11% 4,230,546 56% 6,879,526 92%PPO Low 180,379 (8,763) -5% (35,444) -20% (49,259) -27%PPO Medium 1,534,799 36,709 2% 40,486 3% 26,151 2%Uninsured 28,848,310 (1,108,208) -4% (5,456,413) -19% (8,402,679) -29%
Group MarketHMO 5,505,466 (12) 0% (301) 0% (2,402) 0%HRA 6,166,134 (125) 0% (1,467) 0% (4,667) 0%HSA Offered 307,298 (5) 0% (69) 0% (285) 0%HSA No-offer 11,088 2,894 26% 48,592 438% 224,457 2024%PPO High 16,535,831 (60) 0% (996) 0% (5,184) 0%PPO Low 665,950 (2) 0% (116) 0% (1,264) 0%PPO Medium 53,470,814 (2,685) 0% (44,738) 0% (196,852) 0%Turned Down 3,530,681 (4) 0% (905) 0% (13,803) 0%
Within Sample NationalMininum Insurance Estimate: 1,108,213 1,578,364 Moderate Insurance Estimate: 5,457,318 7,772,544 Maximum Insurance Estimate: 8,416,482 11,987,111
Scenario 3Least Regulated State in 4 Regions - AL,AZ,NE,NH
Implications• Largest insurance take-up is competition
among all 50 states with one winner• Most pragmatic scenario, with good impact,
is one winner in each regional market• No way to assess impact of such a migration
on provider access or quality of care• Significant opportunity to reduce the
number of uninsured in each scenario
Ground Effects % Change in Insured (National Sim/National Sim & SOTU
2008)
• New Jersey [G/C/30] +49% +79%• Pennsylvania [G/25] +7% +15%• Ohio [G/25] +10% +24%• Florida [G/38] +16% +32%• Michigan [G/19] +8% +19%• California [40] +4% +20%• Minnesota [34] +2% +16%• Washington [G/C/29] +18% +29%[Guaranteed issue/Community Rating/Mandates]
Responses to Arguments Against Change in Status
Quo• Insurance is local. Insurers can’t navigate this.– Self-insurance market success proves otherwise.
• Physician panels and credentialing are local.– Self-insurance and FEHBP prove otherwise.
• Claims information systems are incompatible.– Medicare & 1990s vintage IS for pharmaceutical
benefit management firms prove otherwise.
• States’ reflect local cultural/ethical standards.– Human anatomy, medical science and risk do not.
EPILOGUE
Estimating the Impact Policies to Expand Private Coverage for New York’s Non-
Poor Uninsured
Sponsored by the New York State Health FoundationAlbany, New York
September 22, 2009
Scenarios Modeled
• Removing restrictions on underwriting– community rating – guaranteed issue
• Allowing Health Savings Accounts into the market– Currently, these high-deductible savings plans may
not be sold in the New York State individual market.
• Allowing the purchase of policies issued by insurers based in and regulated by neighboring states.
• Allow the sale of “mandate lite” plans
ARCOLA’s strengths & weaknesses for task
Strengths• Peer-reviewed in Health Affairs• Can be used for federal &
state estimates• Is based on a microeconomic
model of health insurance demand published in three journals
• Is supported by consumer driven health plan choice, cost & use
Weaknesses• Needs survey data from a
state to make estimates – Zogby provided data for this analysis
• Has not been bench-tested with Urban or Columbia University models with state data
• Works only through price effects, but that is the dominant factor affecting insurance choice
Plan Choices in the Simulation• Direct Pay Low PPO
– restrictive network– high co-pay– 15 percent coinsurance
• Direct Pay Medium PPO– Lower co-pay and coinsurance than the Low
PPO
• Direct Pay High PPO– lowest co-pay– no coinsurance
• HSA– High deductible , low account contribution
What is the Impact of Eliminating Community Rating (CR) and Guaranteed
Issue (GI) and Introducing Health Savings Accounts?
New York Health Insurance Reform Options2009 Estimates
Baseline Rx New York % Rx New York % Rx New York %
Individual Market Population No GI Change No CR & GI Change No CR & GI Change
& HSAs
Direct Pay - HSA 0 0 N/A 0 N/A 35,383 N/ADirect Pay - PPO High 16,939 365,817 2060% 766,953 4428% 741,572 4278%Direct Pay - PPO Low 9,658 8,903 -8% 5,914 -39% 5,648 -42%Direct Pay - PPO Medium 7,649 31,172 308% 35,786 368% 34,259 348%Uninsured 2,107,530 1,735,884 -18% 1,333,122 -37% 1,324,915 -37%
Total Direct Pay 34,246 405,891 808,653 816,861 Total Population 2,141,776 2,141,776 2,141,776 2,141,776
The combined effect of No CR & GI is a 37% reduction in the Number of uninsured in NYS.
What is the Impact of Interstate Market Competition?
If everyone took advantage of lower premiums, therewould be a 26% reduction. A 17% reduction if ¼ buy CT,PA
New York Health Insurance Reform Options2009 Estimates
Status Quo PA & CT % PA & CT %
Individual Market Population Entry - 100% Change Entry - 25% Change
participation participation
Direct Pay - HSA 0 49,662 N/A 65,036 N/ADirect Pay - PPO High 16,939 464,498 2642% 208,108 1129%Direct Pay - PPO Low 9,658 9,108 -6% 15,828 64%Direct Pay - PPO Medium 7,649 54,511 613% 106,874 1297%Uninsured 2,107,530 1,563,997 -26% 1,745,930 -17%
Total Direct Pay 34,246 577,778 395,846 Total Population 2,141,776 2,141,776 2,141,776
What is the Impact of Reducing the Number of Mandates in New York?
If 20 mandates were removed, the impact would be a 3% reduction in the uninsured, 9% reduction if 40 mandates removed.
New York Health Insurance Reform Options2009 Estimates
Status Quo Mandate- % Mandate- %
Individual Market Population Lite Plan Change Lite Plan Change
20 Mandates 40 Mandates
Direct Pay - HSA 0 15,515 N/A 28,141 N/ADirect Pay - PPO High 16,939 53,343 215% 152,665 801%Direct Pay - PPO Low 9,658 12,041 25% 13,799 43%Direct Pay - PPO Medium 7,649 15,885 108% 29,887 291%Uninsured 2,107,530 2,044,992 -3% 1,917,284 -9%
Total Direct Pay 34,246 96,783 224,492 Total Population 2,141,776 2,141,776 2,141,776
Summary of Simulation Results
• Removing Community Rating & Guaranteed Issue has the greatest impact on reducing the number of uninsured.
• Introducing HSAs into the market reduces the uninsured, but does not have nearly the impact of removing CR & GI.
• Letting New Yorkers purchase insurance across state lines can lead to up a 26% reduction in the uninsured.
• Reducing the number of mandates will have an impact, but not as great as interstate competition or the removal of CR & GI.
