New Drugs: Health and Economic Impacts

1. New Drugs: Health and Economic Impacts Frank R. Lichtenberg, PhD Courtney C. Brown Professor of Business, Columbia University Graduate School of Business Research Associate, National Bureau of Economic Research

2. Outline • Introduction: Innovation, Health, and Economic Growth • Econometric evidence • Longevity • “Case study”: HIV in U.S. • All diseases: U.S. • All diseases: 70 countries (preliminary estimates) • 2. Ability to work (All diseases: U.S.)

3. Conventional (narrow) definition of economic growth: increase in real GDP per capita

4. Utility, or welfare, depends on (leisure) time as well as goods Becker defined an individual’s “full income” as the value of goods consumed plus the value of leisure time “consumed”. Y* = G(Y, L) where Y* = “full income” (or utility) Y = goods consumed L = leisure time

5. Utility, or welfare, depends on (leisure) time as well as goods Simple linear approximation: Y* = Y + pL L pL = the shadow price of leisure time (relative to the price of goods) pL constant  Y* = Y + pLL The change in full income is the change in GDP plus the change in the value of leisure time consumed.

6. Economic importance of longevity increase • Nordhaus: “to a first approximation, the economic value of increases in longevity over the twentieth century is about as large as the value of measured growth in non-health goods and services” • pLL Y • reflects changes in “quantity” (length), but not “quality”, of life

7. United Nations’ Human Development Index (unweighted) average of three indexes: • a life expectancy index • an education index • an index of per capita GDP

8. Long-run economic growth Two components • Increased per capita GDP • Increased longevity and quality of life What are the sources of economic growth?

9. Solow (1956): technical progress is necessary for there to be sustained growth in output per hour worked Technical progress Economic growth

10. Production function with technical progress Yt = At F (Nt, Kt) K t+1 = (1 - ) Kt + It Y = output A = an index of the level of technology (“stock of ideas”) N = labor K = capital I = investment

11. Exogenous vs. endogenous technical progress • Solow assumed exogenous technical progress: A increased at a constant rate • Subsequent research has developed and confirmed the hypothesis of endogenous technical progress

12. Endogenous technical progress:Knowledge-capital-stock model (Griliches) Yt = F (Nt, Kt, Zt) K t+1 = (1 - ) Kt + It Z t+1 = (1 - Z) Zt + RDt

13. Endogenous growth models: Technical progress Economic growth R&D

14. Disembodied vs. Embodied Technical Progress • Suppose that agent i in the economy (e.g. a firm or government agency) engages in research and development. • If technical progress is disembodied, another agent (j) can benefit from agent i’s R&D whether or not he purchases agent i’s products. • If technical progress is embodied, agent j benefits from agent i’s R&D only if he purchases agent i’s products. • Solow conjectured that most technical progress was embodied.

15. Technical progress • disembodied • embodied Economic growth R&D

16. Equipment-embodied technical change • Equipment (e.g. computers) used in manufacturing embodies a lot of R&D • Several authors have tested the equipment-embodied technical change hypothesis by estimating manufacturing production functions, including (mean) vintage of equipment as well as quantities of capital and labor • Finding: technical progress embodied in equipment is a major source of manufacturing productivity growth.

17. Pharmaceutical industry is even more R&D-intensive than the equipment industry: Industrial R&D funds as a percent of net sales in R&D-performing companies, 1997

18. Technical progress • disembodied • embodied • Economic growth • conventional • health R&D

19. Key hypothesis Pharmaceutical R&D investment Longevity increase

20. Obstacles to a direct examination of the R&D-longevity relationship • Very long lags • Divergent estimates of R&D investment (NSF vs. PhRMA)—30% difference in 1997 • Smoothness of aggregate R&D: pharmaceutical R&D investment is very closely approximated by an exponential trend • Lack of disaggregated R&D data • Patent data are subject to similar limitations

21. New drug approvals as an “intermediate good” FDA New Drug Approvals Pharmaceutical R&D investment Longevity increase

22. Impact of new drugs on longevity • “Case study”: HIV in U.S. • 2. All diseases: U.S. • 3. All diseases: 70 countries (preliminary estimates)

23. HIV mortality Source: CDC Compressed Mortality file

24. Hypothesis: The development, FDA approval, and use of new HIV drugs played an important role in this dramatic reduction in HIV mortality.

25. Drugs with HIV indication, by FDA approval date

26. No. of HIV drugs approved by FDA 1987-1993: 0.57 drugs/year 1994-1998 2.00 drugs/year

27. Mortality model • Suppose that the number of HIV deaths in year t is inversely (and linearly) related to the cumulative number of HIV drugs approved up until year t-1 • DEATHSt = a – b CUM_DRUGSt-1 = a – b (FDAt-1 + FDAt-2 + …) • FDAt-1 is the number of drugs approved by the FDA in year t-1, etc.

28. Mortality model • Implies that DEATHSt - DEATHSt-1 = – b (CUM_DRUGSt-1 - CUM_DRUGSt-2) • -  DEATHSt = b FDAt-1 • The reduction in deaths (-  DEATHSt) is proportional to the number of drugs approved in the previous year.

29. HIV drug approvals and HIV mortality reduction

30. Regression analysis • - DEATHSt = -6328 + 6093 FDAt-1 • t-stats: (3.40) (4.74) • Probability value associated with the FDAt-1 coefficient is .0015 • R2 = .7378 • The annual number of HIV deaths is reduced by 6093, on average, by one additional HIV drug approval

31. All diseases: U.S.

32. Increase in longevity (mean age at death)

33. Longevity gains vary across diseases

34. CAPREOMYCIN ISONIAZID CYCLOSERINE ETHAMBUTAL ETHIONAMIDE AMINOSALICYATE SODIUM ACETYLCYSTEINE (INH) PYRAZINAMIDE RIFAMPIN RIFAMPIN AND ISONIAZID RIFAPENTINE Drugs for treatment of TUBERCULOSIS

35. LOVASTATIN PRAVASTATIN SIMVASTATIN CHOLESTYRAMINE COLESTIPOL PROBUCOL FLUVASTATIN ATORVASTATIN NIACIN(SA-LIPOTROPIC) CERIVASTATIN GARLIC PSYLLIUM,BRAN* NEOMYCIN* CONJ. ESTROGEN,M-PROGESTERONE* Drugs for treatment of HYPERCHOLESTEROLEMIA * Unlabeled indication

36. There is considerable variation across diseases—even diseases in the same broad disease groups—in the extent and timing of increases in the stock of available drugs.

37. Number of drugs available to treat condition in year t, as % of number of drugs available to treat condition in 1979

38. Priority- vs. Standard-review drugs • “Priority Review” drug: one that represents a “significant improvement compared to marketed products, in the treatment, diagnosis, or prevention of a disease” • “Standard Review” drug: one that “appears to have therapeutic qualities similar to those of one or more already marketed drugs”

39. Number of new drugs approved, 1979-1998

40. Basic model • ADit =  CUM_DRUGit + i + t+ it • ADit = mean age at which deaths caused by disease i in year t occur • CUM_DRUGit = number of drugs approved to treat disease i up until year t • i = 1, 2, …, 110 (approximately) “2-digit” diseases • t = 1979, 1980, …, 1998 (~2200 obs.) • “Year effects” (i‘s) control for changes in aggregate determinants of mortality

41. Measurement • ADit: 1979-1998 Compressed Mortality File • CUM_DRUGit: • Linkage of drugs to diseases: National Drug Data File drug-disease indications table • Drug approval dates: FDA • Errors in matching drugs to diseases & determining approval dates   biased towards zero

42. Weighted least-squares estimation • ADit =  CUM_DRUGit + i + t+ it • Estimate via weighted least-squares, using weight N_DEATHit • N_DEATHit = number of deaths caused by disease i in year t

43. Priority-review vs. standard-review drugs • ADit = P CUM_PRIit + S CUM_STDit + i + t+ it • CUM_PRIit = number of priority-review drugs approved to treat disease i up until year t • CUM_STDit = number of standard-review drugs approved to treat disease i up until year t • CUM_DRUGit = CUM_PRIit + CUM_STDit

44. Parameter estimates • Model 1: = .013, t = 2.27, p-value = .0232 • Model 2: P = .075, t = 4.01, p-value < .0001 S = -.009, t = 1.05, p-value = .295 • Only priority-review drug approvals increase mean age at death Exclude CUM_STDit: P = .065, t = 4.03, p-value < .0001

45. Contribution of new drugs to longevity increase • Mean age at death increased by 3.8 years from 1979 to 1998 • The increase in the stock of priority-review drugs is estimated to have increased mean age at death by 0.39 years (4.7 months) during this period. • About 10% of the total increase in mean age at death is due to the increase in the stock of priority-review drugs.

46. Contribution of new drug approvals to longevity increase, 1979-1998

47. Estimate is a lower bound? • This estimate is based on an analysis of changes in AD within diseases • About 19% of the increase in AD was due to a shift in the distribution of diseases; the remainder was due to increase in AD from given diseases • New drug approvals affect the disease-distribution of deaths as well as age at death from given diseases

48. Costs vs. Longevity Benefits of New Drug Approvals Costs • During the period 1979-1998, 508 NMEs (about 25/year) were approved by the FDA • OTA study: the average cost of an NME approval was $359 million • Total cost = 508 NMES * $359 m./NME = $182 billion

49. Costs vs. Longevity Benefits of New Drug Approvals Longevity Benefits • The increase in the stock of priority-review drugs is estimated to have increased mean age at death by 0.39 years during this period. • There are about 2 million deaths per year • Total number of life-years gained per year is 0.39 * 2 million = 800,000 life-years/year

50. Costs vs. Longevity Benefits of New Drug Approvals Longevity Benefits • A number of authors have estimated that the value of a life-year is approximately $150,000. • The value of the annual gain in life-years is 800,000 * $150,000 = $120 billion. • This $120 billion may be viewed as an annuity.