The impact of medical innovation on the longevity of Australians, 1998 ‐ 2007 Frank R. Lichtenberg Columbia University and Victoria University frank.lichtenberg@columbia.edu
I. The impact of new drugs on the longevity II. The impact of therapeutic procedure and hospitalization of Australians, 1998 ‐ innovation on hospital patient longevity: 2006: evidence from longitudinal, disease ‐ evidence from Western Australia, 2000 ‐ 2007 level data • • “Treatment”: “Treatment”: inpatient pharmaceutical innovation procedure innovation • • Treatment measure: Treatment measure: number of products for vintage of products used to treating a condition treat a condition • • Outcomes: Outcome: – – longevity longevity – • hospitalization Research design: • Research design: – cross ‐ sectional, patient ‐ level longitudinal, disease ‐ level data – data longitudinal, DRG ‐ level data 2
In the U.S., prescribed medicine use is 9 times as likely as hospital use Source: Medical Expenditure Panel Survey 3
New products and economic growth • Longevity increase is an important part of economic growth and development. – Nordhaus estimated that, “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”. • In the long run, the rate of economic growth is determined by the rate of technological progress, which is generated by private and public R&D investment. • Most technological progress is embodied in new goods. – Hercowitz: “'embodiment' is the main transmission mechanism of technological progress to economic growth.” • Economists believe that the development of new products is the main reason why people are better off today than they were several generations ago. • Grossman and Helpman ( Innovation and Growth in the Global Economy , Cambridge: MIT Press, 1991) argued that “innovative goods are better than older products simply because they provide more ‘product services’ in relation to their cost of production.” • Bresnahan and Gordon ( The Economics of New Goods , 1996) stated simply that “new goods are at the heart of economic progress.” • Jones ( Introduction to Economic Growth , 1998) argues that “technological progress [is] the ultimate driving force behind sustained economic growth” (p.2), and that “technological progress is driven by research and development (R&D) in the advanced world” (p. 89). • Bils ( Measuring the Growth from Better and Better Goods , 2004) makes the case that “much of economic growth occurs through growth in quality as new models of consumer goods replace older, sometimes inferior, models.” 4
The impact of new drugs on the longevity and hospitalization of Australians, 1998 ‐ 2006: evidence from longitudinal, disease ‐ level data
• Investigate the impact of introduction and use of new drugs on the longevity and hospitalization of Australians during the period 1998 ‐ 2006 using longitudinal, disease ‐ level data • Have the diseases that have experienced more pharmaceutical innovation had larger increases in longevity and smaller increases in hospitalization? 6
Difference ‐ in ‐ differences approach • As Cheng Hsiao ( Analysis of panel data , Cambridge University Press, 2003) and others have shown, panel data enable us to reduce or eliminate biases that arise from use of cross ‐ sectional or time ‐ series data. 7
General approach = + i + t + it OUTCOME it DRUG_STOCK i,t ‐ k = + i + t + it ∑ d IND di APP d,t ‐ k (i = 1,…, I; t = 1998,…,2006) OUTCOME it = an outcome of (mortality or hospitalization due to) disease i in year t DRUG_STOCK i,t ‐ k = ∑ d IND di APP d,t ‐ k = the cumulative number of drugs approved by the beginning of year t ‐ k that are used to treat disease i IND di = 1 if drug d is used to treat (indicated for) disease i = 0 if drug d is not used to treat (indicated for) disease i APP d,t ‐ k = 1 if drug d has been approved by the beginning of year t ‐ k = 0 if drug d has not been approved by the beginning of year t ‐ k i = a fixed effect for disease i t = a fixed effect for year t it = a disturbance 8
• In his model of endogenous technological change, Paul Romer (“Endogenous technical change," Journal of Political Economy 98, S71 ‐ S102, 1990) hypothesized an aggregate production function such that an economy’s output depends on the “stock of ideas” that have previously been developed, as well as on the economy’s endowments of labor and capital. • The previous equation may be considered a health production function, in OUTCOME is an indicator of health output or outcomes, and the cumulative number of drugs approved (DRUG_STOCK) is analogous to the stock of ideas. 9
Model of mean age at death To investigate the impact of pharmaceutical innovation on longevity in Australia, I will estimate models of the following form: = + i + t + it AGE_DEATH it DRUG_STOCK i,t ‐ k = + i + t + it ∑ d IND di APP d,t ‐ k (i = 1,…, I; t = 2000,…,2007) • Estimate by weighted least ‐ squares, weighting by the number of deaths from disease I in year t (N_DEATH it ) • Allow for clustering of disturbances within diseases 10
= + i + t + it AGE_DEATH it DRUG_STOCK i,t ‐ k = + i + t + it ∑ d IND di APP d,t ‐ k (i = 1,…, I; t = 1998,…,2006) AGE_DEATH it = a measure of the age distribution of deaths caused by disease i in year t DRUG_STOCK i,t ‐ k = ∑ d IND di APP d,t ‐ k = the cumulative number of drugs approved by the beginning of year t ‐ k that are used to treat disease i IND di = 1 if drug d is used to treat (indicated for) disease i = 0 if drug d is not used to treat (indicated for) disease i APP d,t ‐ k = 1 if drug d has been approved by the beginning of year t ‐ k = 0 if drug d has not been approved by the beginning of year t ‐ k i = a fixed effect for disease i t = a fixed effect for year t it = a disturbance 11
• Since the model includes disease and year fixed effects, it is a difference ‐ in ‐ differences model. Positive and significant estimates of • would indicate that, ceteris paribus , diseases with above ‐ average increases in the lagged cumulative number of drugs approved had above ‐ average increases in mean age at death. • All models will be estimated via weighted least ‐ squares, weighting by N_DEATHS it : the number of deaths caused by disease i in year t. • Clustered (within disease) standard errors will be reported. 12
Data sources and descriptive statistics Estimation of eq. (1) requires data on four variables: • AGE_DEATH it (a measure of the age distribution of deaths caused by disease i in year t) • N_DEATHS it (the number of deaths caused by disease i in year t) • APP d,t ‐ k (which indicates whether drug d had been launched in Australia by year t ‐ k) • IND di (which indicates whether drug d is used to treat (indicated for) disease i) 13
Mortality data • Data on AGE_DEATH it and N_DEATHS it were obtained from the WHO Mortality Database http://www.who.int/whosis/mort/download/ en/ • It’s easier to get Australian data from Geneva than it is from Canberra! 14
Longevity trends Crude death rate Age ‐ standardized Mean Potential years of PYLL per 1000 Total (per 100,000 rate (per 100,000 Age at life lost before population age < Life expectancy at Year Deaths population) population) Death age 75 (PYLL) 75 birth 1998 127,202 679.8 722.6 72.4 941,793 53.1 78.63 1999 128,102 676.9 705.5 72.6 938,078 52.4 78.93 2000 128,291 669.8 684.0 73.0 908,058 50.2 79.23 2001 128,544 662.1 662.1 73.3 881,733 48.2 79.63 2002 133,707 680.4 669.6 73.8 876,770 47.4 79.94 2003 132,292 664.9 646.7 73.9 866,298 46.3 80.24 2004 132,508 658.3 632.5 74.2 844,728 44.7 80.49 2005 130,714 640.9 604.8 74.2 845,315 44.2 80.84 2006 133,739 646.1 599.9 74.6 834,468 43.0 81.04 2007 137,854 654.2 597.7 74.7 849,013 43.0 81.29 change, 1998 to 2007 2.3 2.7 Source: Australian Institute of Health and Welfare (AIHW) 2010. GRIM (General Record of Incidence of Mortality) Books. AIHW: Canberra. 15
Data on PBS drug launch dates • Data on APP d,t ‐ k (which indicates when drug d began to be used in Australia by year) were obtained from HEALTHWIZ (based on data from the Pharmaceutical Benefits Scheme) 16
No. of PBS Rx’s of 8 new drugs 17
Number of drugs prescribed Number of drugs increased 36% (3.6% per year) from 1995 to 2005 18
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