Frank R. Lichtenberg Columbia University frank.lichtenberg@columbia.edu

1. The Dependence of Improvements in Health, Longevity and Productivity on Incentives for Medical Innovation  Frank R. Lichtenberg Columbia University frank.lichtenberg@columbia.edu

2. Basic argument Expected private return on R&D investment Amount of R&D investment Number of new drugs, medical devices, and procedures Population health, longevity, and productivity

3. Two illustrations • Orphan drugs • Cancer drugs

4. DOES MISERY LOVE COMPANY?EVIDENCE FROM PHARMACEUTICALMARKETS BEFORE AND AFTERTHE ORPHAN DRUG ACTFrank R. LichtenbergJoel Waldfogel15 Mich. Telecomm. Tech. L. Rev. __ (2009), available athttp://www.mttlr.org/volfifteen/lichtenberg&waldfogel.pdf

5. 1983 Orphan Drug Act Provided incentives to develop drugs for rare conditions (affecting <200,000 Americans) • Higher Returns • 7 years of market exclusivity • Lower Costs • Tax credit for research expense • Research grants

6. Drug development Drug utilization Health outcomes Policy

7. Difference-in differences research design

8. Cumulative number of drugs approved, as % of cumulative number of drugs approved in 1979:orphan vs. other drugs

9. Examine three types of data • Physician survey (pre & post ODA) • Household survey (post ODA only) • Mortality census (pre & post ODA)

10. Physician survey • National Ambulatory Medical Care Survey (NAMCS), 1980-81 and 1997-98 • Representative samples of visits to physicians • Two facts recorded about each visit: • Physician’s diagnosis (or diagnoses) • Whether physician ordered any medication

11. Physician survey • Aggregate data by diagnosis, i.e., compute: • Total number of physician visits in which a diagnosis is recorded • Rx visits as % of total visits in which a diagnosis is recorded • Interpret total number of physician visits in which a diagnosis is recorded as a measure of market size • Physicians are best qualified to determine diagnosis

12. Physician Survey Summary Statistics

13. Hypothesis Rx prob. Post ODA Pre ODA Market size

15. Hypothesis  Rx prob. Initial market size

17. Mortality Census • Vital Statistics—Mortality Detail files, 1980 and 1995 • Complete census of U.S. deaths (approx. 2 million per year) • Two facts recorded about each death: • Cause of death • Age at death • We exclude infant deaths (age < 1)

18. Market size and longevity • Aggregate data to most detailed published disease classification: CDC’s 282 causes of death classification • For each of these 282 causes of death, compute • Number of deaths • Mean age at death • Group these 282 diseases into 5 quintiles, ranked by number of deaths

19. Hypothesis Mean age at death Post ODA Pre ODA Market size (no. of deaths)

20. Hypothesis  Mean age at death Initial market size (no. of deaths)

21. Disease Prevalence and Mean Age at Death

22. Disease Prevalence and Mean Age at Death, 1980 & 1995 1995 1980

23. Disease Prevalence and Increase in Mean Age at Death, 1980-95

24. Discussion of Results • ODA worked, softened “misery loves company” effect • Good policy? Does the rationale extend to other contexts? • Market size results show that incentives matter for drug development • With high FC markets deliver more products (and satisfaction) to larger groups • Markets vs. collective choice

25. Cancer drugs

26. Basic argument Market size (no. of cancer cases) Expected private return on R&D investment • Amount of R&D investment • Number of scientific articles published • Number of new drugs, medical devices, and procedures • Number of distinct chemotherapy regimens Population health, longevity, and productivity

27. Cancer incidence and number of core chemotherapy regimens, by site

28. The relationship between incidence and innovation

29. Incidence in 2002, by region, and number of MEDLINE article citations, for 25 cancer sites as defined in GLOBOCAN

30. Estimates of the relationship between cancer incidence and the number of drug and non-drug MEDLINE citations

31. Both analyses indicate that the amount of pharmaceutical innovation increases with disease incidence. • The elasticity of the number of chemotherapy regimens with respect to the number of cases is 0.53. • The elasticity of MEDLINE drug cites with respect to cancer incidence throughout the world is 0.60. • In the long run, a 10% decline in drug prices would therefore be likely to cause at least a 5-6% decline in pharmaceutical innovation.

32. Comparison with previous studies • Acemoglu and Linn (2003) investigated the response of entry of new drugs and pharmaceutical innovation to changes in potential market size of users, driven by U.S. (or OECD) demographic changes. Their results indicated that a 1 percent increase in the potential market size for a drug category leads to approximately 4-6 percent growth in the entry of new drugs approved by the FDA. However their estimated response reflected the entry of both generics and non-generics, and the effect on generics was larger and somewhat more robust. • Giaccotto, Santerre and Vernon (2005) employed time series econometric techniques to explain R&D growth rates using industry-level data from 1952 to 2001. Their estimate of the elasticity of pharmaceutical industry R&D with respect to the real price of pharmaceuticals was 0.583. • Abbott and Vernon (2005): the elasticity of innovation with respect to price is in the 0.67-1.33 range.

33. Physicians and other health care providers are also responsive to financial incentives • Empirical evidence indicates that the supply behavior of physicians and other health care providers, not just drug companies, is affected by exogenous changes in financial incentives (including changes in reimbursement). • Some of the best evidence about the physician supply response to variation in reimbursement comes from the Medicaid program.

34. Doctors Objecting to Planned Cut in Medicare Fees NY Times, November 20, 2005 Dr. Duane M. Cady, chairman of the American Medical Association, said: "Physicians cannot absorb the pending draconian cuts. A recent A.M.A. survey indicates that if the cuts begin on Jan. 1, more than one-third of physicians would decrease the number of new Medicare patients they accept."

35. The effect of new cancer drug approvals on thelife expectancy of American cancer patients, 1978-2004 Frank R. LichtenbergColumbia University andNational Bureau of Economic Research frank.lichtenberg@columbia.edu

36. Age-adjusted mortality rates,1950-2006 Source: Health, United States, 2009, Table 26

37. Bailar and Gornik (1997): “The effect of new treatments forcancer on mortality has been largely disappointing.” Bailar JC 3rd, Gornik HL (1997). “Cancer undefeated,” N Engl J Med. 336 (22), 1569-74, May 29, http://content.nejm.org/cgi/content/full/336/22/1569 • Black and Welch (1993): “The increasing use of sophisticated diagnostic imaging promotes a cycle of increasing intervention that often confers little or no benefit.” Black, William C., and H. Gilbert Welch (1993), “Advances in Diagnostic Imaging and Overestimations of Disease Prevalence and the Benefits of Therapy,” N Engl J Med. 328 (17), 1237-1243, April 29. • Welch, H. Gilbert, Lisa M. Schwartz, and Steven Woloshin (2000), “Are Increasing 5-Year Survival Rates Evidence of Success Against Cancer?,” JAMA 283(22): 2975-2978

38. Objective • Attempt to determine the extent to which new cancer drugs introduced during the last 40 years have prolonged the lives of Americans diagnosed with cancer. Methodology • A reliable estimate of the overall effect of new cancer drugs on the longevity of cancer patients can’t be obtained by simply surveying previous clinical studies of specific drugs and cancer sites.

39. FDA approval years of chemotherapy agents with approved uses for 3 cancer sites Sources: NCI Thesaurus; Drugs@FDA database

40. Cumulative number of chemotherapy agents approved by the FDA with accepted uses for six types of cancer, 1975-2005

41. Estimates of utilization of cancer drugs, relative to their utilization in the year they were launched (approved by the FDA)

42. Methodology • I analyze the correlation across cancer sites (breast, prostate, lung, etc.) between changes in the mortality rate of people previously diagnosed with that cancer and changes in the number of drugs that have been introduced to treat that cancer. • I control for variables likely to reflect changes in diagnostic techniques • cancer stage distribution • age at diagnosis • number of people diagnosed (incidence) • use of surgery and radiation

43. Data sources • Data on cancer-site-specific drug introductions were constructed using • the NCI Thesaurus • the Drugs@FDA database • Data on all other variables were obtained from the NCI’s SEER 9 Registries Database, an authoritative source of information on cancer incidence and survival in the United States

44. Results • Cancer sites with larger increases in the lagged stock of approved drugs had larger reductions in the mortality rate, ceteris paribus. • The impact of the stock of FDA approvals on the mortality rate tends to increase steadily for a number of years, peak about 8-12 years after launch, and then decline. • This finding is consistent with evidence about the product life-cycle of cancer drugs: utilization tends to increase steadily after FDA approval, peak about 6-10 years after launch, and then decline.

45. Results • New cancer drugs introduced during the period 1968-1994 were estimated to have increased the life expectancy of cancer patients by almost one year (0.94 years). • Although the health of cancer patients is less than perfect, the increase in quality-adjusted life-years is not necessarily less than the increase in life expectancy. • Since the lifetime risk of being diagnosed with cancer is about 40%, the1978-2004 increase in the lagged stock of cancer drugs increased the life expectancy of the entire U.S. population by 0.38 years. This represents about 8.8% of the overall increase in U.S. life expectancy at birth. • The cost per life-year gained does not exceed $6908, which is far below recent estimates of the value of a statistical life-year.

46. Extensions • Different country: Chemotherapy innovation accounted for at least one-sixth of the decline in French cancer mortality rates during 2002-2006, and may have accounted for as much as half of the decline. • Different technology: Diagnostic imaging innovation (CT scans and MRIs) has also prolonged the lives of American cancer patients

47. Summary Expected private return on R&D investment Amount of R&D investment Number of new drugs, medical devices, and procedures Population health, longevity, and productivity