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.

http://www.cdc.gov/nchs/data_access/data_linkage/mortality/nhis_linkage_public_use.htm.

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=(β₂-β₁)²/[var(β₂)-var(β₁)], where β_i 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.