Could we have covered more people at less cost? Technically, yes; politically, probably not

Health Affairs

Volumn 29, Issue 6, 2010, Pages 1142-1146

  McGlynn, Elizabeth A ^a   Cordova, Amado ^a   Wasserman, Jeffrey ^a   Girosi, Federico ^a  

^a RAND CORPORATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; COST CONTROL; GOVERNMENT; HEALTH CARE COST; HEALTH CARE POLICY; LAW; MEDICAID; POLITICS; UNITED STATES; ECONOMICS; LEGAL ASPECT; NATIONAL HEALTH INSURANCE; ORGANIZATION AND MANAGEMENT; PATIENT;

FEDERAL GOVERNMENT; HEALTH CARE COSTS; HEALTH CARE REFORM; MEDICAID; MEDICALLY UNINSURED; NATIONAL HEALTH INSURANCE, UNITED STATES; POLITICS; UNITED STATES;

EID: 77956911883     PISSN: 02782715     EISSN: 15445208     Source Type: Journal    
DOI: 10.1377/hlthaff.2010.0433     Document Type: Article

References (14)

1. Congressional Budget Office. Updated estimate of budgetary impact of Affordable Health Care for America Act. Letter to Rep. John D. Dingell (D-MI). Washington (DC): CBO; 2009 Nov 6.
2. Congressional Budget Office. Estimate of spending and revenue effects of the Patient Protection and Affordable Care Act. Letter to Sen. Harry Reid (D-NV). Washington (DC): CBO; 2009 Dec 19.
3. Girosi F, Cordova A, Eibner C, Gresenz CR, Keeler E, Ringel J, et al. Overview of the COMPARE microsimulation model [Internet]. RAND Working Paper. Santa Monica (CA): RAND Health; 2009 [cited 2010 May 7]. Available from: http://www.randcompare.org/sites/default/files/docs/pdfs/COMPARE-Model-Overview- 0.pdf
4. The data we used were from the Survey of Income and Program Participation (SIPP), the Medical Expenditure Panel Survey (MEPS), and the Kaiser Family Foundation/Health Research and Educational Trust (HRET) annual survey of employer-sponsored insurance.
5. The Urban Institute also uses a utility based framework. See Garrett AB. The Health Insurance Policy Simulation Model (HIPSM) [slideshow on the Internet]. Prepared for the National Committee on Vital and Health Statistics, Populations Subcommittee Meeting, Modeling Health Insurance Data, including Coverage, Access, Utilization, Quality, and Cost of Care. Washington, DC; 2009 Feb 27. Washington (DC): Urban Institute; [cited 2010 May 7]. Available from: http://www.ncvhs.hhs.gov/090227p4.pdf
6. Congressional Budget Office. CBO's health insurance simulation model: a technical description [Internet]. Washington (DC): CBO; 2007 Oct [cited 2010 May 7]. Available from: http://www.cbo.gov/ftpdocs/87xx/doc8712/10-31- HealthInsurModel.pdf
7. Lewin Group. The Health Benefits Simulation Model (HBSM): methodology and assumptions [Internet]. Falls Church (VA): Lewin Group; 2009 Mar 31 [cited 2010 May 7]. Available from: http://www.lewin.com/content/publications/ HBSMDocumentationMar09.pdf
8. Gruber J, Levitt L. Tax subsidies for health insurance: costs and benefits. Health Aff (Millwood). 2000;19(1):72-85.
9. The factors included in our utility maximization model include current insurance status, age, health status, income, employment status, and firm size (if employed).
10. The Online Appendix is available by clicking on the Online Appendix link in the box to the right of the article online.
11. We took a conservative approach and defined government spending as the sum of the additional Medicaid spending and the cost of subsidies. We did not subtract the revenues that might be collected from the individual and employer mandate penalties or the savings associated with reducing uncompensated care, because they are estimated with considerable uncertainty.
12. We developed a metric to quantify the benefit to the newly insured from reduced financial risk, reduced out-of-pocket spending, and increased health care services received as compared to the premium cost paid. We assigned to each individual a disutility for risk that is proportional to the expected spread of the distribution of out-of-pocket expenditures: the higher the spread, the higher the probability that a person would incur a catastrophic expenditure. The coefficient of proportionality is the so-called coefficient of risk aversion, which we assumed to be constant through the whole population. In calculating the portion of benefit due to increased health care services received (one of the three factors that go into the utility calculation), we used results from the RAND Health Insurance Experiment to quantify how much value, in dollars, people attribute to the additional health care services. This value is about one-third of the expected medical expenditures on that factor.
13. Jonathan Gruber calls this "bang for the buck." Gruber J. Covering the uninsured in the United States. J Econ Lit. 2008;46(3):571-606.
14. Gruber J, Simon K. Crowd-out 10 years later: have recent public insurance expansions crowded out private health insurance? J Health Econ. 2008;27(2):201-17.