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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. Outline. Introduction: Innovation, Health, and Economic Growth

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new drugs health and economic impacts

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

outline

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.)
utility or welfare depends on leisure time as well as goods
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

utility or welfare depends on leisure time as well as goods5
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.

economic importance of longevity increase
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
united nations human development index
United Nations’ Human Development Index

(unweighted) average of three indexes:

  • a life expectancy index
  • an education index
  • an index of per capita GDP
long run economic growth
Long-run economic growth

Two components

  • Increased per capita GDP
  • Increased longevity and quality of life

What are the sources of economic growth?

slide9

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

Technical progress

Economic growth

production function with technical progress
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

exogenous vs endogenous technical progress
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
endogenous technical progress knowledge capital stock model griliches
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

slide13

Endogenous growth models:

Technical progress

Economic growth

R&D

disembodied vs embodied technical progress
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.
slide15

Technical progress

  • disembodied
  • embodied

Economic growth

R&D

equipment embodied technical change
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.
slide17

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

slide18

Technical progress

  • disembodied
  • embodied
  • Economic growth
  • conventional
  • health

R&D

key hypothesis
Key hypothesis

Pharmaceutical R&D investment

Longevity

increase

obstacles to a direct examination of the r d longevity relationship
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
new drug approvals as an intermediate good
New drug approvals as an “intermediate good”

FDA New Drug Approvals

Pharmaceutical R&D investment

Longevity

increase

impact of new drugs on longevity

Impact of new drugs on longevity

  • “Case study”: HIV in U.S.
  • 2. All diseases: U.S.
  • 3. All diseases: 70 countries (preliminary estimates)
hiv mortality
HIV mortality

Source: CDC Compressed Mortality file

hypothesis
Hypothesis:

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

no of hiv drugs approved by fda
No. of HIV drugs approved by FDA

1987-1993: 0.57 drugs/year

1994-1998 2.00 drugs/year

mortality model
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.
mortality model28
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.
regression analysis
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
drugs for treatment of tuberculosis
CAPREOMYCIN

ISONIAZID

CYCLOSERINE

ETHAMBUTAL

ETHIONAMIDE

AMINOSALICYATE SODIUM

ACETYLCYSTEINE (INH)

PYRAZINAMIDE

RIFAMPIN

RIFAMPIN AND ISONIAZID

RIFAPENTINE

Drugs for treatment of TUBERCULOSIS
drugs for treatment of hypercholesterolemia
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

slide36

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.

slide37
Number of drugs available to treat condition in year t, as % of number of drugs available to treat condition in 1979
priority vs standard review drugs
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”
basic model
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
measurement
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
weighted least squares estimation
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
priority review vs standard review drugs
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
parameter estimates
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

contribution of new drugs to longevity increase
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.
estimate is a lower bound
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
costs vs longevity benefits of new drug approvals
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
costs vs longevity benefits of new drug approvals49
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
costs vs longevity benefits of new drug approvals50
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.
social rate of return to pharmaceutical r d investment
“Social rate of return” to pharmaceutical R&D investment
  • DiMasi estimates that, in the last two decades, drug development has taken about 14 years.
  • Suppose that the $182 billion in R&D expenditure is evenly distributed over an initial 14-year period, i.e. $13 billion/year in years 1-14.
  • In year 15 and all future years, the population experiences a gain in life-years whose annual value is $120 billion.
investment costs and returns53
Investment costs and returns
  • The internal rate of return to this series of cash flows is 18%.
  • This rate of return reflects only the value of increased longevity among Americans
  • Foreigners also benefit
  • Additional benefits of new drugs to Americans, including reduced hospital expenditure and reduced limitations on work and other activities
beyond the u s a
Beyond the U.S.A.
  • The U.S. accounts for only one fifth of the population of OECD countries, and 5% of the world population.
  • The purpose of this study is to assess the impact of international diffusion of new drugs on mortality throughout the OECD and in some non-OECD countries.
linkage of several extremely rich databases
Linkage of several extremely rich databases
  • IMS Drug Launches database
  • OECD Health database
  • World Health Organization Mortality database
  • World Bank’s Global Development Network Growth Database
example tenecteplase
Example: tenecteplase

Launch date Country

6/00USA

3/01Finland

5/01UK

9/01Norway

10/01Canada

10/01South Africa

11/01Ireland

future launches
Future launches
  • As is often the case with duration or survival data, some of the observations are right-censored: the exact time till launch is unknown—it is only known that the time till launch exceeded some value.
  • The analysis methodology must correctly utilize the censored as well as the non-censored observations.
  • I use the SAS LIFETEST procedure to compute nonparametric estimates of the launch-probability distribution, and to perform hypothesis tests.
characterizing country specific launch behavior
Characterizing country-specific launch behavior
  • During the period 1989 to 2001, about 500 NMEs were launched (about 40 per year) in at least one country.
  • From the Drug Launches database, we can determine whether or not NMEi (i = 1,…, 500+) had been launched in country j (j = 1,…, 70) after m months (m = 1,…, 156).
hypotheses
Hypotheses
  • Mortality from a given disease is inversely related to the number of drugs available to treat the disease

Two measures of mortality:

        • Life years lost before age 70
        • Mean age at death
  • International differences in mortality are partly attributable to differences in the probability and timing of new drug launch
  • International differences in the probability and timing of new drug launch are partly attributable to differences in public policy vis-à-vis pharmaceuticals
slide62

Mean age at death from disease i

1987

2002

Number of drugs available to treat disease i

estimates to be provided
Estimates to be provided

How much have new drug launches in OECD and some non-OECD countries since 1988:

  • reduced the number of life-years lost in those countries?
  • increased life expectancy (mean age at death) in those countries?

In which countries is the probability of new drug launch highest (and lowest)?

How much greater would the increase in longevity have been in low-launch countries if they had had high (or average) launch rates?

How does public policy (e.g., government share of pharmaceutical purchases) affect launch rates?

effect of drug launch behavior on longevity
Effect of drug launch behavior on longevity
  • Cross-sectional analysis at the country level
  • Longitudinal analysis at the country level
  • Longitudinal analysis at the disease-by-country level
three ways to analyze the effect of drug launch behavior on longevity
Three ways to analyze the effect of drug launch behavior on longevity
  • cross-sectional analysis at the country level
  • longitudinal analysis at the country level
  • longitudinal analysis at the disease-by-country level. This approach will enable me to control for an extremely large set of unobserved factors, such as country-year fixed effects.
longitudinal analysis at the disease by country level
Longitudinal analysis at the disease-by-country level
  • It is conceivable that unobserved country characteristics are not fixed, i.e. that there are unobserved factors that determine a country’s longevity trend, as well as its growth rate.
  • Fortunately, due to the richness of the available data, we can control for time-varying country effects.
longitudinal analysis at the disease by country level67
Longitudinal analysis at the disease-by-country level

AGE_DEATHijt =  CUM_DRUGijt + ij + jt + it + uijt

AGE_DEATHijt = mean age at death in country i from disease j in year t

CUM_DRUGijt = the (cumulative) number of NMEs launched in country i for disease j up until year t

OVER65ijt =  CUM_DRUGijt + ij + jt + it + uijt

OVER65ijt = fraction of deaths in country i from disease j in year t that occur at age 65 or older

statistical issues
Statistical issues
  • Model controls for a very large number of potential determinants of mortality: it includes 450 country-disease fixed effects (ij), 300 disease-year fixed effects (jt), and 600 country-year fixed effects (it).
  • To maximize the odds of obtaining a consistent estimate of the effect of drug launches on longevity (), we will also include and estimate 1350 “nuisance parameters”.
  • The number of statistical degrees of freedom (= no. of observations – no. of estimated parameters) is quite large  precise estimate of .
measurement issues
Measurement issues

Censoring of drug launch data

In principle, want to measure the number of drugs to treat disease i launched in country j by year t

In practice, can measure the number of post-1986 * drugs to treat disease i launched in country j by year t

*Drugs whose initial world launch occurred after 1986

Assume that latter is a noisy indicator of the former

Linking drugs to diseases

who disease classification
Infectious and and Parasitic Diseases

Neoplasms

Endocrine, Nutritional and Metabolic Diseases

Diseases of the Blood and Blood-forming Organs

Mental Disorders

Diseases of the Nervous System and Sense Organs

Diseases of the Circulatory System

Diseases of the Respiratory System

Diseases of the Digestive System

Diseases of the Genitourinary System

Obstetric Complications

Diseases of the Skin and Subcutaneous Tissue

Diseases of the Musculoskeletal System and Connective Tissue

Congenital Anomalies

Certain Conditions originating in the Perinatal Period

Symptoms, Signs and Ill-defined Conditions

WHO disease classification
ims health drug classification
A Alimentary Tract And Metabolism

B Blood and Blood Forming Organs

C Cardiovascular System

D Dermatologicals

G Genitourinary System and Sex Hormones

H Systemic Hormonal Preparations (Excluding Sex Hormones)

J General Anti-Infectives, Systemic

K Hospital Solutions

L Cytostatics

M Musculoskeletal System

N Central Nervous System (CNS)

P Parasitology

R Respiratory System

S Sensory Organs

T Diagnostic Agents

V Various

IMS Health Drug Classification
mean age
Mean age

Mean age at death increased 4.5 years from 1987 to 2000. Holding number of drugs constant,

mean age at death increased 3.5 years. Increase in stock of drugs increased mean age by 1.0 years.

over65
OVER65

Prob. of dying after 65 increased 930 basis points from 1987 to 2000. Holding number of drugs constant, prob. of dying after 65 increased 590 basis points. Increase in stock of drugs increased prob. of dying after 65 by 440 basis points .

is globalization beneficial
Is globalization beneficial?
  • Estimates suggest that international diffusion (a.k.a. globalization) of new drugs during 1987-2000 increased mean age at death in the sample countries by about 1 year
  • There are about 12 million deaths per year in these countries
  • Hence, globalization of new drugs added 12 million life-years per year in these countries
  • More rapid diffusion would have resulted in a larger increase in life-years
effect of public policy on the probability of drug launch
Effect of public policy on the probability of drug launch
  • Hypothesize that variation in drug launch behavior is due, to some extent, to variation in market size (population), per capita income, and average health expenditure.
  • Drug launch behavior may also be influenced by the country’s ratio of public expenditure on pharmaceuticals to total expenditure on pharmaceuticals:

Government formularies may be more likely to exclude new drugs than private formularies

Even when new drugs are not excluded from public formularies, governments are likely to pay lower prices than private payers

indirect effect of public policy on longevity via probability of drug launch
Indirect effect of public policy on longevity, via probability of drug launch

Public policy vis-à-vis pharmaceuticals (e.g., RX_PUBLIC%)

Availability of new drugs (e.g., LAUNCH6)

Longevity

effect of public policy on the probability of drug launch77
Effect of public policy on the probability of drug launch
  • Examine the effect of the public share of pharmaceutical expenditure on the probability of new drug launch by estimating equations, using data on a cross-section of countries.
controlling for country size
Controlling for country size

LAUNCH10i = 0.11 -.14 PUB%i + .04 log(POPi)

t-stats: (0.9) (1.9) (3.7)

(GDP not significant, controlling for population)

slide82

Suppose death occurs if H < Hmin

Probability of death can fall for two

reasons.

Health status (H)

Hmin

slide83

Hmin declines 

  • Mean H of survivors falls

Health status (H)

Hmin

slide84

2. H distribution shifts to the right 

Mean H of survivors rises

Health status (H)

Hmin

ability to work labor supply
Ability to work & labor supply
  • UNABLE_WORKit =  CUM_DRUGit + i + t+ it
  • UNABLE_WORKit = fraction of people with condition i in year t who are unable to work, mainly due to condition i
  • MISSED_WORKit =  CUM_DRUGit + i + t+ it
  • MISSED_WORKit = mean number of work days missed by currently employed people with condition i in year t
  • i = 1, 2, …, 110 (approximately) “2-digit” diseases
  • t = 1983, 1984, …, 1996
  • “Year effects” (i‘s) control for changes in aggregate determinants of ability to work
1983 1996 increase in number of drugs reduced all of the following by about 12 in 1996
1983-1996 increase in number of drugs reduced all of the following by about 12% in 1996
  • Number of people unable to work
  • Work-loss days of currently employed persons
  • Restricted-activity days of all persons
  • Bed days of all persons
increase in number of drugs available decrease in inability to work
Increase in number of drugs available decrease in inability to work

20 most prevalent conditions

estimated effects of 1983 96 new drug approvals
Estimated effects of 1983-96 new drug approvals
  • reduction in number of people unable to work: 1.44 million
  • value of reduction in number of people unable to work (@ $30K/year): $43.3 billion/year
  • reduction in work loss days per year of currently employed persons: 98.8 million/year
  • value of reduction in work loss days (@ $100/day): $9.9 billion/year
  • reduction in restricted activity days of all persons: 423 million/year
  • reduction in bed days of all persons: 178 million/year
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