The report in question purports to support the concept that new drugs save lives. However, the analysis fails to control for differences among states in infant mortality rates, demographics, or causes of death. Inadequate surrogate measures of health status are used, and reverse causation could explain several key findings.
In economic analyses, a statistical regression stands or falls in its entirety. A regression that passes inspection for internal validity should be tested for its ability to withstand manipulation; for example, removing a variable, splitting a time period, or running the regression in two halves should not qualitatively change the results of a robust regression. Lack of internal consistency indicates that there is something wrong.
In the Lichtenberg analysis, the negative relationship between income and life expectancy, the finding that health insurance coverage lowers costs while increasing productivity, and the lack of a relationship between education and productivity are all anomalous results inconsistent with a large body of previous research. The most likely explanation is that the regressions in this analysis were improperly performed.
Previous studies by Lichtenberg have also been criticized.4–6
Two previous studies, using Medical Expenditure Panel Survey (MEPS) data, concluded that replacing older drugs with newer drugs would save money. The first study30
was criticized for a cross-sectional design inappropriate for determining cause and effect.4
Additionally, the use of prescriptions as a unit of analysis rather than people meant that the death of a person using ten drugs would have been counted as ten deaths.5
The second study (NBER) failed to control for prior health status and other variables.4
An independent reanalysis, using the same data and methodologies in conjunction with more accurate drug approval dates and the consideration of plausible alternative assumptions, could not confirm Lichtenberg’s conclusions.4
An analysis of cardiovascular drugs that used the same MEPS data set found no association between the use of newer drugs and non-drug expenditures after controlling for the number of drugs or the mix of drugs of different ages.6
Lichtenberg’s claims that adopting new drugs saves money, increases life expectancy, and increases productivity are unreliable and should not be considered in health policy decisions. Moreover, while some new drugs are therapeutic advances, others are not. For example, the federally-funded ALLHAT study found that chlorthiazide, an older, inexpensive diuretic, was superior to new drugs for treating hypertension.30
The CATIE study, also government-funded, found that older antipsychotics are as effective as newer ones for treating schizophrenia.33,34
Also, newer drugs may amplify risks rather than benefits. Novel drugs carry more risks than older drugs because problems associated with long-term use or in special populations (for example, the elderly) are revealed only after a drug is in widespread use.35
Half of drug withdrawals or black box warnings occur within two years of approval.36
The Lichtenberg study argues that there have been very large returns from the development of new drugs in the form of longer life expectancy and in the form of reduced non-drug health care costs and increased productivity. The implication is that the current model of drug development and financing is extremely successful and that altering it, for example through the bulk purchases of drugs in the Medicare prescription drug benefit, could seriously undermine the further development of new drugs.
Our critique shows that Lichtenberg’s methods are seriously flawed and his conclusions are therefore unsupportable. In the absence of more compelling research on the impact of newer drugs, policymakers should actively seek ways to contain spending on novel pharmaceuticals. Prescription drugs are the most rapidly growing component of health care costs and will impose a major burden on government budgets and the economy as a whole if they are not reined in.