Are the benefits of newer drugs worth their cost? Evidence from the 1996 MEPS

Health Affairs

Volumn 20, Issue 5, 2001, Pages 241-251

  Lichtenberg, Frank R ^a,b  

^a Och Spine at New York Presbyterian Hospitals   (United States)

^b NATIONAL BUREAU OF ECONOMIC RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; COST BENEFIT ANALYSIS; DRUG COST; DRUG THERAPY; ECONOMICS; HEALTH CARE COST; HEALTH CARE QUALITY; HUMAN; MORBIDITY; MORTALITY; STATISTICAL MODEL; UNITED STATES;

COST-BENEFIT ANALYSIS; DRUG COSTS; DRUG THERAPY; HEALTH EXPENDITURES; HUMANS; MODELS, ECONOMETRIC; MORBIDITY; MORTALITY; QUALITY OF HEALTH CARE; UNITED STATES;

EID: 0442277183     PISSN: 02782715     EISSN: None     Source Type: Journal    
DOI: 10.1377/hlthaff.20.5.241     Document Type: Article

References (22)

1. Author's calculations based on Health Care Financing Administration, "National Health Expenditures by Type of Service and Source of Funds, Calendar Years 1960-1999," 〈www.hcfa.gov/stats/nhe-oact/tables/nhe99.csv〉 (2 May 2001); and U.S. Department of Commerce, Bureau of Labor Statistics, Consumer Price Index-All Urban Consumers, Item: Prescription Drugs and Medical Supplies (Washington: BLS, various years).
2. Author's calculations based on Health Care Financing Administration, "National Health Expenditures by Type of Service and Source of Funds, Calendar Years 1960-1999," 〈www.hcfa.gov/stats/nhe-oact/tables/nhe99.csv〉 (2 May 2001); and U.S. Department of Commerce, Bureau of Labor Statistics, Consumer Price Index-All Urban Consumers, Item: Prescription Drugs and Medical Supplies (Washington: BLS, various years).
3. M. Merlis, "Explaining the Growth in Prescription Drug Spending: A Review of Recent Studies," Report prepared for the U.S. Department of Health and Human Services, 8 August 2000, 〈aspe.hhs.gov/health/reports/drug-papers/ merlis/merlis-final.htm〉 (15 June 2001).
4. G. Grossman and E. Helpman, Innovation and Growth in the Global Economy (Cambridge, Mass.: MIT Press, 1991). See also P. Aghion and P. Howitt, "A Model of Growth through Creative Destruction," NBER Working Paper no. 3223 (Cambridge, Mass.: National Bureau of Economic Research, 1990); TJ. Klette and Z. Griliches, "Empirical Patterns of Firm Growth and R&D Investment: A Quality Ladder Model Interpretation," NBER Working Paper no. W5945 (Cambridge, Mass.: NBER, February 1997); and P.T. Segerstrom et al., "A Schumpeterian Model of the Product Life-Cycle," American Economic Review 80, no. 5 (1990): 1077-1092.
5. G. Grossman and E. Helpman, Innovation and Growth in the Global Economy (Cambridge, Mass.: MIT Press, 1991). See also P. Aghion and P. Howitt, "A Model of Growth through Creative Destruction," NBER Working Paper no. 3223 (Cambridge, Mass.: National Bureau of Economic Research, 1990); TJ. Klette and Z. Griliches, "Empirical Patterns of Firm Growth and R&D Investment: A Quality Ladder Model Interpretation," NBER Working Paper no. W5945 (Cambridge, Mass.: NBER, February 1997); and P.T. Segerstrom et al., "A Schumpeterian Model of the Product Life-Cycle," American Economic Review 80, no. 5 (1990): 1077-1092.
6. G. Grossman and E. Helpman, Innovation and Growth in the Global Economy (Cambridge, Mass.: MIT Press, 1991). See also P. Aghion and P. Howitt, "A Model of Growth through Creative Destruction," NBER Working Paper no. 3223 (Cambridge, Mass.: National Bureau of Economic Research, 1990); TJ. Klette and Z. Griliches, "Empirical Patterns of Firm Growth and R&D Investment: A Quality Ladder Model Interpretation," NBER Working Paper no. W5945 (Cambridge, Mass.: NBER, February 1997); and P.T. Segerstrom et al., "A Schumpeterian Model of the Product Life-Cycle," American Economic Review 80, no. 5 (1990): 1077-1092.
7. G. Grossman and E. Helpman, Innovation and Growth in the Global Economy (Cambridge, Mass.: MIT Press, 1991). See also P. Aghion and P. Howitt, "A Model of Growth through Creative Destruction," NBER Working Paper no. 3223 (Cambridge, Mass.: National Bureau of Economic Research, 1990); TJ. Klette and Z. Griliches, "Empirical Patterns of Firm Growth and R&D Investment: A Quality Ladder Model Interpretation," NBER Working Paper no. W5945 (Cambridge, Mass.: NBER, February 1997); and P.T. Segerstrom et al., "A Schumpeterian Model of the Product Life-Cycle," American Economic Review 80, no. 5 (1990): 1077-1092.
8. D. Cutler et al., "Are Medical Prices Declining?" NBER Working Paper no. 5750 (Cambridge, Mass.: NBER, September 1995).
9. Significant geographical variation in treatment patterns was first documented by John Wennberg and colleagues, who studied New England hospital mar-kets. See J. Wennberg and A. Gittelsohn, "Variations in Medical Care among Small Areas," Scientific American 246, no. 4 (1982): 120-134. Other investigators have corroborated this finding in many other settings. See, for example, K. McPherson et al., "Small-Area Variations in the Use of Common Surgical Procedures: An International Comparison of New England, England, and Norway," New England Journal of Medicinc 307, no. 21 (1982): 1310-1313; and S. Folland, A. Goodman, and M. Stano, The Economics of Health and Health Care (Upper Saddle River, N.J.: Prentice Hall, 1991), 216.
10. Significant geographical variation in treatment patterns was first documented by John Wennberg and colleagues, who studied New England hospital mar-kets. See J. Wennberg and A. Gittelsohn, "Variations in Medical Care among Small Areas," Scientific American 246, no. 4 (1982): 120-134. Other investigators have corroborated this finding in many other settings. See, for example, K. McPherson et al., "Small-Area Variations in the Use of Common Surgical Procedures: An International Comparison of New England, England, and Norway," New England Journal of Medicinc 307, no. 21 (1982): 1310-1313; and S. Folland, A. Goodman, and M. Stano, The Economics of Health and Health Care (Upper Saddle River, N.J.: Prentice Hall, 1991), 216.
11. Significant geographical variation in treatment patterns was first documented by John Wennberg and colleagues, who studied New England hospital mar-kets. See J. Wennberg and A. Gittelsohn, "Variations in Medical Care among Small Areas," Scientific American 246, no. 4 (1982): 120-134. Other investigators have corroborated this finding in many other settings. See, for example, K. McPherson et al., "Small-Area Variations in the Use of Common Surgical Procedures: An International Comparison of New England, England, and Norway," New England Journal of Medicinc 307, no. 21 (1982): 1310-1313; and S. Folland, A. Goodman, and M. Stano, The Economics of Health and Health Care (Upper Saddle River, N.J.: Prentice Hall, 1991), 216.
12. See 〈www.meps.ahrq.gov/data_pub/hc_toc.htm〉 (2 May 2001).
13. Because persons can be seen for more than one condition per event, frequencies of events in the conditions file will not match the person- or event-level utilization counts. For example, if a person had one hospital stay and was treated for a fractured hip, a fractured shoulder, and a concussion, each of these conditions has a unique record, and a hospital stay is recorded in each. MEPS HC-006R, C-10, 〈wwv.meps.ahrq.gov/Data_Pub/HC_FYData96.htm# hc006〉 (7 August 2001).
14. Of the remainder, 5.3 percent are linked to more than one condition, and 4.4 percent are not linked to any condition.
15. The MEPS HC010I file (Appendix to MEPS 1996 Event Files) contains the variables needed to link records in the MEPS 1996 event files to records in the MEPS 1996 condition file.
16. The first set of models includes the following regressors: the log of the age of the drug; 513 condition (ICD-9 code) effects; year that condition began (71 categories); number of conditions the patient has (10 categories); patient age (87 categories); patient income (28 categories); education (17 categories); race (5 categories); sex; whether the person is covered by Medicare, Medicaid, or private insurance; and percentage of prescription costs paid by self, private insurance, and Medicaid. Weighting factors included with the MEPS data were applied through a weighted least squares approach in estimating the model parameters. Alternative logit or probit specifications for the mortality and morbidity models, while theoretically preferable, are computationally infeasible, because of the large number of estimated parameters. This may lead to incorrect standard errors, but not to any bias in the estimates.
17. Inclusion of individual effects also helps to address the problem of correlation between the errors of an individual's multiple prescriptions for a given condition. The second set of models includes the following regressors: the log of the age of the drug; 513 condition (ICD-9 code) effects; 12,385 individual effects; the year the condition began (71 categories); and the percentage of prescription costs paid by self, private insurance, and Medicaid.
18. The individual effects capture all attributes of the individual that do not vary across prescriptions and conditions, including sex, age, education, race, income, insurance status, and number of medical conditions reported.
19. The conditions file reports whether or not any bed, work-loss, or school-loss days were associated with a condition, but not the number of days.
20. F. Lichtenberg, "Do (More and Better) Drugs Keep People Out of Hospitals?" American Economic Review (May 1996): 384-388.
21. Unlike the morbidity and expenditure variables, the mortality variable does not exhibit any within-individual variation - that is, the cause of death is not indicated - so we are unable to estimate the mortality equation with individual effects.
22. Essential hypertension, diabetes mellitus, asthma, ill-defined heart disease, otitis media, depressive disorder, allergic rhinitis, chronic sinusitis, arthropathies, acute nasopharyngitis, lipoid metabolism, menopausal disorders, and bronchitis.