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The Effect of Pharmaceutical Innovation on Longevity: Patient Level Evidence from the 1996–2002 Medical Expenditure Panel Survey and Linked Mortality Public-use Files

Frank R. Lichtenberg
Published Online: 2013-01-24 | DOI: https://doi.org/10.1515/fhep-2012-0032


This study uses patient-level data to analyze the effect of technological change embodied in pharmaceuticals on the longevity of elderly Americans. Previous patient-level studies could not control for important patient attributes such as education, income, and race; they did not provide estimates of the effect of using newer drugs on life expectancy, or of the overall cost-effectiveness of new drugs relative to old drugs; and they were not based on nationally representative samples of individuals. Our data, primarily derived from the Medical Expenditure Panel Survey and the Linked Mortality Public-use Files, enable us to overcome those limitations.

We investigate the effect of the vintage (year of U.S. Food and Drug Administration approval) of the prescription drugs used by an individual on his or her survival and medical expenditure, controlling for a number of demographic characteristics and indicators and determinants of health status. When we control only for age, sex, and interview year, we estimate that a 1-year increase in drug vintage increases life expectancy by 0.52%. Controlling for a much more extensive set of other attributes (the mean year the person started taking his or her medications, and dummy variables for activity limitations, race, education, family income as a percent of the poverty line, insurance coverage, Census region, body mass index, smoking, and more than 100 medical conditions) has virtually no effect on the estimate of the effect of drug vintage on life expectancy. Between 1996 and 2003, the mean vintage of prescription drugs increased by 6.6 years. This is estimated to have increased the life expectancy of elderly Americans by 0.41–0.47 years. This suggests that not less than two-thirds of the 0.6-year increase in the life expectancy of elderly Americans during 1996–2003 was due to the increase in drug vintage. The 1996–2003 increase in drug vintage is also estimated to have increased annual drug expenditure per elderly American by $207, and annual total medical expenditure per elderly American by $218. This implies that the incremental cost-effectiveness ratio (cost per life-year gained) of pharmaceutical innovation was about $12,900. This estimate of the cost per life-year gained from the use of newer drugs is a small fraction of leading economists’ estimates of the value of (willingness to pay for) an additional year of life. It is also consistent with estimates from clinical trials.

Keywords: pharmaceuticals; longevity; innovation; mortality; prescription drugs


  • Almond, Douglas and Bhashkar Mazumder (2006) “How Did Schooling Laws Improve Long-Term Health and Lower Mortality?” Federal Reserve Bank of Chicago Working Paper No. WP-06-23. Available at: http://www.chicagofed.org/publications/workingpapers/wp2006_23.pdf.

  • Arias, E., L. R. Curtin, R. Wei and R. N. Anderson (2008) “United States Decennial Life Tables for 1999–2001.” United States Life Tables, National Vital Statistics Reports, Vol. 57, No 1. Hyattsville, MD: National Center for Health Statistics. Available at: http://www.cdc.gov/nchs/data/nvsr/nvsr57/nvsr57_01.pdf.

  • Bresnahan, Timothy F. and Robert J. Gordon (1996) The Economics of New Goods. Chicago: University of Chicago Press.Google Scholar

  • Centers for Disease Control and Prevention (2005) “Annual Smoking-Attributable Mortality, Years of Potential Life Lost, and Productivity Losses – United States, 1997–2001,” Morbidity and Mortality Weekly Report, 54(25):625–628.PubMedGoogle Scholar

  • Davis, Peter and Barry Gribben (1995) “Rational Prescribing and Interpractitioner Variation: A Multilevel Approach,” International Journal of Technology Assessment in Health Care 11:428–442.CrossrefGoogle Scholar

  • de Jong, J. D., P. P. Groenewegen, P. Spreeuwenberg, G. P. Westert and D. H. de Bakker (2009) “Do Decision Support Systems Influence Variation in Prescription?” Health Services Research, 9:20.Web of ScienceGoogle Scholar

  • De Las Cuevas, C., E. J. Sanz and J. A. De La Fuente (2002) “Variations in Antidepressant Prescribing Practice: Clinical Need or Market Influences?” Pharmacoepidemiology and Drug Safety, 11(6):515–522.Google Scholar

  • DeSalvo, Karen, Bruce E. Bowdish, Alys S. Alper, David M. Grossman and William W. Merrill (2000) “Physician Practice Variation in Assignment of Return Interval,” Archives of Internal Medicine, 160:205–208.Google Scholar

  • Duggan, M. G. and W. N. Evans (2008) “Estimating the Impact of Medical Innovation: A Case Study of HIV Antiretroviral Treatments,” Forum for Health Economics and Policy, 11(2).CrossrefGoogle Scholar

  • Flegal, K. M., B. I. Graubard, D. F. Williamson and M. H. Gail (2005) “Excess Deaths Associated with Underweight, Overweight, and Obesity,” J Am Med Assoc, 293:1861–1867.Google Scholar

  • Hercowitz, Zvi (1998)“The ‘Embodiment’ Controversy: A Review Essay”, Journal of Monetary Economics, 41:217–224.CrossrefGoogle Scholar

  • Krein, Sarah L., Timothy P. Hofer, Eve A. Kerr and Rodney A. Hayward (2002) “Whom Should We Profile? Examining Diabetes Care Practice Variation among Primary Care Providers, Provider Groups, and Health Care Facilities,” Health Services Research, 37(5):1159–1180.PubMedCrossrefGoogle Scholar

  • Lee, Stephanie J., et al. (2008) “Individual Physician Practice Variation in Hematopoietic Cell Transplantation,” Journal of Clinical Oncology, 26(13):2162–2170.CrossrefWeb of ScienceGoogle Scholar

  • Lleras-Muney, A. (2005) “The Relationship between Education and Adult Mortality in the United States,” Review of Economic Studies, 72(1):189–221.CrossrefGoogle Scholar

  • Lichtenberg, Frank R. (2010)“The Effect of Drug Vintage on Survival: Micro Evidence from Puerto Rico’s Medicaid Program.” In: (M. Grossman, B. Lindgren, R. Kaestner and Kristian Bolin, eds.) Pharmaceutical Markets and Insurance Worldwide (Advances in Health Economics and Health Services Research, Volume 22). Emerald Group Publishing Limited, pp. 273–292.CrossrefGoogle Scholar

  • Lichtenberg, Frank R. (2011) “The Quality of Medical Care, Behavioral Risk Factors, and Longevity Growth,” International Journal of Health Care Finance and Economics, 11(1):1–34.Web of ScienceGoogle Scholar

  • Lichtenberg, Frank R. (2012) “The Contribution of Pharmaceutical Innovation to Longevity Growth in Germany and France, 2001–2007,” PharmacoEconomics, 30(3):197–211.PubMedCrossrefGoogle Scholar

  • Lichtenberg, Frank and Gautier Duflos (2008) “Pharmaceutical Innovation and the Longevity of Australians: A First Look,” Advances in Health Economics and Health Services Research, 19:95–117.CrossrefWeb of ScienceGoogle Scholar

  • Lichtenberg, Frank R., Paul Grootendorst, Marc Van Audenrode, Dominick Latremouille-Viau and Patrick Lefebvre (2009) “The Impact of Drug Vintage on Patient Survival: A Patient-Level Analysis Using Quebec’s Provincial Health Plan Data,” Value in Health, 12(6):847–856.Google Scholar

  • Meara, Ellen R., Seth Richards and David M. Cutler (2008) “The Gap Gets Bigger: Changes In Mortality and Life Expectancy, by Education, 1981–2000,” Health Affairs, 27:350–360.Web of ScienceGoogle Scholar

  • Murphy, Kevin M. and Robert H. Topel (2006) “The Value of Health and Longevity,” Journal of Political Economy, 114(4):871–904.CrossrefGoogle Scholar

  • National Science Foundation, U.S. Corporate R&D: Volume 1: Top 500 Firms in R&D by Industry Category. Available at: http://www.nsf.gov/statistics/nsf00301/expendit.htm.

  • Nordhaus, William D. (2002) “The Health of Nations: The Contribution of Improved Health to Living Standards.” In: (K.M. Murphy and R.H. Topel, eds.) Measuring the Gains from Medical Research: An Economic Approach. Chicago: University of Chicago Press, pp. 9–40.Google Scholar

  • Rochon, P.A., T.A. Stukel, S.E. Bronskill, T. Gomes, K. Sykora, W.P. Wodchis, M. Hillmer, A. Kopp, J.H. Gurwitz and G.M. Anderson (2007) “Variation in Nursing Home Antipsychotic Prescribing Rates,” Archives of Internal Medicine, 167(7):676–683.Web of ScienceGoogle Scholar

  • Romer, Paul (1990) “Endogenous Technological Change,” Journal of Political Economy, 98(52):S71–S102.CrossrefGoogle Scholar

  • Royal Swedish Academy of Sciences (1987) Press release. Available at: http://www.nobelprize.org/nobel_prizes/economics/laureates/1987/press.html. Accessed 24 October 2012.

  • Solomon, D.H., S. Schneeweiss, R.J. Glynn, R. Levin and J. Avorn (2003) “Determinants of Selective Cyclooxygenase-2 Inhibitor Prescribing: Are Patient or Physician Characteristics More Important?” American Journal of Medicine, 115(9):715–720.Google Scholar

  • Solow, R. (1960) “Investment and Technical Progress.” In: (K. Arrow, S. Karlin and P. Suppes, eds.) Mathematical Methods in the Social Sciences, 1959. Chicago: University of Chicago Press.Google Scholar

  • Wennberg, John (2004) “Practice Variations and Health Care Reform: Connecting the Dots,” Health Affairs, October 7. Available at: http://www.pnhp.org/news/2004/october/practice_variations_php.

  • Wennberg, John and David Wennberg, “Practice Variations and the Use of Prescription Drugs.” In: Dartmouth Atlas of Health Care in Michigan. Available at: http://www.bcbsm.com/atlas/profile.shtml.

  • Zink, A., J. Listing, S. Ziemer and H. Zeidler (2001) “Practice Variation in the Treatment of Rheumatoid Arthritis among German Rheumatologists,” Journal of Rheumatology, 28(10):2201–2208.Google Scholar

About the article

Corresponding author: Frank R. Lichtenberg, Columbia University, 504 Uris Hall, 3022 Broadway, New York, 10027 NY, USA, e-mail:

Published Online: 2013-01-24

Published in Print: 2013-01-01

See, e.g., Nordhaus (2002) and Murphy and Topel (2006).

According to the Merriam-Webster dictionary, one definition of vintage is “a period of origin or manufacture (e.g., a piano of 1845 vintage)” (http://www.merriam-webster.com/dictionary/vintage). Solow (1960) introduced the concept of vintage into economic analysis; this was one of the contributions to the theory of economic growth that the Royal Swedish Academy of Sciences (1987) cited when it awarded Solow the 1987 Alfred Nobel Memorial Prize in Economic Sciences. Solow’s basic idea was that technical progress is “built into” machines and other goods and that this must be taken into account when making empirical measurements of their roles in production.

According to the National Science Foundation, the pharmaceutical and medical devices industries are the most research-intensive industries in the economy.

Lichtenberg (2010) examined whether Puerto Rico Medicaid beneficiaries using newer drugs during January–June 2000 were less likely to die by the end of 2002, conditional on the covariates.

Nursing home residents, which account for about 4% of the elderly population (http://www.cdc.gov/nchs/data/hus/hus09.pdf#105), are not included in our sample.

NHIS/MEPS Public-Use Person Record Linkage files contain crosswalks that allow data users to merge MEPS full-year public-use data files to NHIS person-level public-use data files that contain data collected for MEPS respondents in the year prior to their initial year of MEPS participation; see http://www.meps.ahrq.gov/mepsweb/data_stats/more_info_download_data_files.jsp#hc-nhis.


The shape parameter is what gives the Weibull distribution its flexibility. By changing the value of the shape parameter, the Weibull distribution can model a wide variety of data. If k=1, the Weibull distribution is identical to the exponential distribution; if k=2, the Weibull distribution is identical to the Rayleigh distribution; if k is between 3 and 4, the Weibull distribution approximates the normal distribution. The Weibull distribution approximates the lognormal distribution for several values of k.

See http://en.wikipedia.org/wiki/Weibull_distribution and http://www.engineeredsoftware.com/nasa/weibull.htm.

CDC (2005) provides estimates of smoking-attributable mortality; Flegal et al. (2005) provide estimates of the effects of obesity on U.S. mortality.

MEPS does not provide information about provider-administered drugs, e.g., chemotherapy. Provider-administered drugs may account for about 15% of total U.S. drug expenditure.

SEER Cancer Statistics Review, 1975–2008, http://seer.cancer.gov/csr/1975_2008/results_merged/topic_survival.pdf.

The dummy variables were constructed using data in the MEPS Medical Conditions files.

In some previous studies based on claims data, a person would be considered to have a medical condition only if the diagnosis code for that condition appeared in a medical claim.

Lleras-Muney (2005) provided perhaps the strongest evidence that education has a causal effect on health. Using state compulsory school laws as instruments, Lleras-Muney found large effects of education on mortality. Almond and Mazumder (2006) revisited these results, noting they were not robust to state time trends, even when the sample was vastly expanded and a coding error rectified. They employed a data set containing a broad array of health outcomes and found that when using the same instruments, the pattern of effects for specific health conditions appeared to depart markedly from prominent theories of how education should affect health. They also found suggestive evidence that vaccination against smallpox for school-age children may account for some of the improvement in health and its association with education. This raised concerns about using compulsory schooling laws to identify the causal effects of education on health.

Using clinical and administrative data obtained from all facilities in a Department of Veterans Affairs integrated service network, Krein et al. (2002) showed that there was variation in diabetes practice patterns at the primary care provider, provider group, and facility levels, and that the greatest amount of variance tended to be attributable to the facility level.

Less than half of MEPS respondents were eligible for mortality follow-up. See http://www.cdc.gov/nchs/data/datalinkage/nhis_frequency_of_selected_variables_public_2010.pdf.

In 2000, 88% of elderly MEPS respondents had at least one prescription drug during the year.

The Social Security Administration publishes both period and cohort U.S. life tables (http://www.ssa.gov/oact/NOTES/as120/LifeTables_Body.html). The estimate of life expectancy of 70-year-olds in 2000 from the (1930 birth) cohort life table is higher than the estimate of life expectancy of 70-year-olds in 2000 from the period life table, but only about 2% higher.

These statistics describe the vintage of prescription drugs used by all elderly MEPS respondents, including those not eligible for mortality follow-up.

In 1996, the mean age (number of years since FDA approval) of drugs consumed was 20.1 years; in 2008, it was 24.1 years.

The absolute increase in life expectancy (LE) due to a 1-year increase in drug vintage depends on mean life expectancy: because β=d ln LE/d rx_year=(1/LE)×(d LE/d rx_year), d LE/d rx_year, d LE/d rx_year=β×LE. As discussed above, we have two estimates of sample mean life expectancy: mean life expectancy based on the 1999–2001 CDC life table (11.9 years), and mean life expectancy (survival time) computed from the right-censored surv_time observations: (13.7 years). Below we will calculate the absolute increase in life expectancy and incremental cost effectiveness using each of these estimates of mean life expectancy.

Coefficients on marital status and medical condition dummies are not shown to conserve space.

We performed a Hausman test of the difference between the estimates of β in models 1 and 2. The Hausman test statistic is H=(β21)2/[var(β2)-var(β1)], where βi is the estimate of β in model i (i=1, 2) (see, e.g., http://en.wikipedia.org/wiki/Hausman_test). H follows a χ2 distribution with one degree of freedom. H=(0.0058-0.0056)2/[(0.0018)2-(0.0016)2]=0.059. The 0.95 critical value of the χ2 distribution with one degree of freedom is 3.841.

The value of the Hausman H statistic is H=(0.0082-0.0095)2/((0.0040)2-(0.0032)2)=0.29.

Of respondents, 11.1% were current smokers. The BMI distribution is underweight (BMI < 19) 4.1%; healthy weight (19 < BMI < 25) 34.9%; overweight (25 < BMI < 30) 36.4%; obese (30 < BMI) 24.7%.

We also estimated models of the probability of surviving at least 6 years using the sample of people 65 years and older who were interviewed during 1996–2000. In a specification including the same covariates as model 6, the estimate of the rx_year coefficient (β) was 0.0059 (S.E.=0.0019, p=0.0017). In a specification including the same covariates as model 7, the estimate of β was 0.0074 (S.E.=0.0021, p=0.0005). In a specification including the same covariates as model 8, the estimate of β was 0.0067 (S.E.=0.0021, p=0.0018). The differences between these estimates of β were not statistically significant.

This method of calculating the ICER of new drugs is similar to (albeit simpler than) the method used by Duggan and Evans (2008) to simulate the impact of a specific drug (Epivir/PI) on long-term health care spending in the Medicaid program. They recognized that there are two factors that diverge when calculating these costs. First, their results indicated that average annual spending declined when these treatments were introduced. In contrast, the large reduction in mortality generated by Epivir/PI use increased life expectancy, and hence the amount of time that individuals were eligible for Medicaid. They built an illustrative model that allowed them to capture these two opposing factors in a simple calculation.

If we simulate the effect of a 1-year increase in drug vintage, rather than a 6.6-year increase in drug vintage, the calculated ICER is slightly lower: $12,679.

The growth in per capita medical expenditure was uncorrelated across states with the growth in drug vintage. Therefore, Eq. (3) implies that the ICER of pharmaceutical innovation is equal to per capita medical expenditure, which was about $3645 in the population studied by Lichtenberg (2011).

Lichtenberg and Duflos (2008) and Lichtenberg (2012) did not actually examine the correlation between drug vintage and medical expenditure. Instead, they simply assumed that observed growth in per capita drug expenditure was entirely due to pharmaceutical innovation and that pharmaceutical innovation had no impact on nondrug medical expenditure.

Life expectancy at birth is based on the survival rates of all age groups.

The CEA Registry (https://research.tufts-nemc.org/cear4/) is produced by the Center for the Evaluation of Value and Risk in Health, part of the Institute for Clinical Research and Health Policy Studies at Tufts Medical Center in Boston, MA.

Citation Information: Forum for Health Economics and Policy, Volume 16, Issue 1, Pages 1–33, ISSN (Online) 1558-9544, ISSN (Print) 2194-6191, DOI: https://doi.org/10.1515/fhep-2012-0032.

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