The Dependence of Improvements in Health, Longevity and Productivity on

Incentives for Medical Innovation

Frank R. LichtenbergColumbia University

frank.lichtenberg@columbia.edu

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

Two illustrations

• Orphan drugs• Cancer drugs

DOES MISERY LOVE COMPANY?EVIDENCE FROM PHARMACEUTICAL

MARKETS BEFORE AND AFTERTHE ORPHAN DRUG ACT

Frank R. LichtenbergJoel Waldfogel

15 Mich. Telecomm. Tech. L. Rev. __ (2009), available athttp://www.mttlr.org/volfifteen/lichtenberg&waldfogel.pdf

1983 Orphan Drug ActProvided 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

PolicyDrug

developmentDrug

utilizationHealth

outcomes

6

Rare

diseasesCommon diseases Rare - Common

Before ODA (1980) A B After ODA (1995) C D After - Before C - A D - B (C - A) - (D - B)

Difference-in differences research design

7

Cumulative number of drugs approved, as % of cumulative number of drugs approved in 1979:

orphan vs. other drugs

8

Examine three types of data

• Physician survey (pre & post ODA)• Household survey (post ODA only)• Mortality census (pre & post ODA)

9

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

10

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

11

Physician Survey Summary Statistics

12

Period 1980-1981 1997-1998Number of diseases 2454 1996Total no. of physician visits 627,701,627 981,751,220Mean no. of physician visits 255,787 491,859Minimum no. of physician visits 1,031 1,921Maximum no. of physician visits 44,162,054 54,396,942Rx visits/total visits 0.73 0.74

Hypothesis

13

Rx prob.

Market size

Pre ODA

Post ODA

14

Hypothesis

15

Rx prob.

Initial market size

16

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)

17

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

18

Hypothesis

19

Mean age at death

Market size (no. of deaths)

Pre ODA

Post ODA

Hypothesis

20

Mean age at death

Initial market size (no. of deaths)

Disease Prevalence and Mean Age at Death

21

Prevalence quintile

No. of deaths

Mean age

No. of deaths

Mean age

Mean age t-statistic p-value

Lowest 1,586 49.4 1,839 56.9 7.6 14.6 <.00012 18,537 58.3 18,189 65.2 6.93 56,233 65.9 62,951 67.8 1.94 169,345 68.6 215,638 71 2.3

Highest 1,541,562 72.6 1,836,369 74.4 1.8 106.4 <.0001

Highest - lowest 23.2 17.4 -5.8 11.2 <.0001

1980 1995 1995 - 1980

Disease Prevalence and Mean Age at Death, 1980 & 1995

22

1995

1980

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

23

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

24

Cancer drugs

Basic argument

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

Market size(no. of cancer cases)

27

Cancer incidence and number of core chemotherapy regimens, by site

Site

Number of cases in Canada in 2002

Number of core chemotherapy regimens

Number of cases in the U.S. in 2000

Lung 20,648 11 164,100

Breast 19,540 21 182,800

Prostate 17,900 11 180,400

Colorectal 17,708 3 130,200

Lymphoma - Non-Hodgkin's 5,671 11 54,900

Renal 3,858 1 31,200

Uterine/Sarcoma 3,643 1 36,100

Leukemia 3,636 16 30,800

Melanoma 3,585 4 47,700

Pancreas 3,277 1 28,300

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The relationship between incidence and innovation

0

0.5

1

1.5

2

2.5

3

3.5

6 7 8 9 10 11

log no. of cases in Canada in 2002

log

no. o

f che

mo.

regi

men

s

29

Incidence in 2002, by region, and number of MEDLINE article citations,

for 25 cancer sites as defined in GLOBOCAN

Cancer site ICD10 codes

total number of MEDLINE articles pertaining to cancer site

number of MEDLINE articles pertaining to drug therapy for cancer site

incidence of cancer at site in the less developed region

incidence of cancer at site in the more developed region

Leukaemia C91-C95 138,971 30,529 175,898 124,202

Lung C33-C34 98,796 14,341 672,221 676,681

Non-Hodgkin lymphoma C82-C85,C96 52,485 9,064 149,191 151,096

Colon and rectum C18-C21 80,738 8,744 355,701 665,731

Ovary etc. C56,C57.0-4 38,142 7,636 107,541 96,769

Brain, nervous system C70-C72 106,896 7,435 114,630 74,549

Prostate C61 44,355 7,015 165,347 513,464

Liver C22 77,313 6,464 513,060 110,404

Melanoma of skin C43 46,321 5,039 29,352 130,815

Hodgkin lymphoma C81 22,973 4,628 34,264 28,033

30

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

Model 1 2 3 4

dep. Var. ln DRUG_CITESi ln NONDRUG_CITESi ln DRUG_CITESi ln NONDRUG_CITESi

         

ln INC_WORLDi 0.597 0.598    

std. err. 0.210 0.138    

t-stat 2.850 4.330    

p-value 0.009 0.000    

         

ln INC_MOREi     0.670 0.433

std. err.     0.209 0.145

t-stat     3.200 3.000

p-value     0.004 0.007

         

ln INC_LESSi     -0.065 0.167

std. err.     0.222 0.154

t-stat     -0.290 1.090

p-value     0.774 0.289

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• 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.

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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.

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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.

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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."

The effect of new cancer drug approvals on thelife expectancy of American cancer patients, 1978-2004

Frank R. Lichtenberg

Columbia Universityand

National Bureau of Economic Research

frank.lichtenberg@columbia.edu

36

Age-adjusted mortality rates,1950-2006

1950 1960 1970 1980 1990 20000

100

200

300

400

500

600

700Diseases of heart

Cerebrovascular diseases

Malignant neoplasms

Source: Health, United States, 2009, Table 26

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• Bailar and Gornik (1997): “The effect of new treatments for cancer 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

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.

38

FDA approval years of chemotherapy agents with approved uses for 3 cancer sites

151 Malignant neoplasm of stomach

LEUCOVORIN CALCIUM 1952

METHOTREXATE SODIUM 1953

FLUOROURACIL 1962

DOXORUBICIN HYDROCHLORIDE

1974

CISPLATIN 1978

MITOMYCIN 1981

ETOPOSIDE 1983

DOCETAXEL 1996

EPIRUBICIN HYDROCHLORIDE

1999

174 Malignant neoplasm of female breast

METHOTREXATE SODIUM 1953

CYCLOPHOSPHAMIDE 1959

FLUOROURACIL 1962

DOXORUBICIN HYDROCHLORIDE

1974

CARBOPLATIN 1989

PACLITAXEL 1992

VINORELBINE TARTRATE 1994

DOCETAXEL 1996

GEMCITABINE HYDROCHLORIDE

1996

CAPECITABINE 1998

TRASTUZUMAB 1998

EPIRUBICIN HYDROCHLORIDE

1999

188 Malignant neoplasm of bladder

METHOTREXATE SODIUM 1953

CYCLOPHOSPHAMIDE 1959

VINBLASTINE SULFATE 1965

DOXORUBICIN HYDROCHLORIDE

1974

CISPLATIN 1978

GEMCITABINE HYDROCHLORIDE

1996

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Sources: NCI Thesaurus; Drugs@FDA database

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

3

5

7

9

11

13151 Malignant neoplasm of stomach

162 Malignant neoplasm of trachea, bronchus, and lung

170 Malignant neoplasm of bone and articular cartilage

174 Malignant neoplasm of female breast

188 Malignant neoplasm of bladder

189 Malignant neoplasm of kidney and other andunspecified urinary organs

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

1.0

4.3

6.88.5

10.612.5

17.4

20.121.7

19.7 20.1

11.0

8.87.3

6.25.2

0

5

10

15

20

25

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+

Years after FDA approval

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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

42

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

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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.

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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%, the 1978-2004 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.

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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

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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