Objective: To investigate whether there were racial and gender disparities in the effect of new drug approvals on U.S. cancer mortality since 1990. Study design: We estimate two-way (by cancer site and year (1990–2019)) fixed-effects models of the age-adjusted mortality rate for four race/sex groups: white males, white females, black males, and black females. The main explanatory variables of the models are distributed lags of the number of drugs approved for a cancer site. We control for the current and lagged age-adjusted incidence rate. Principal findings: For all four demographic groups, the age-adjusted mortality rate is significantly inversely related to either the number of drugs approved 0–5 years earlier, the number of drugs approved 6–10 years earlier, or both. The approval of one additional drug for a cancer site reduced the mortality rate of white males and black males by about 2% and 1%, respectively, controlling for lagged incidence. The approval of one additional drug for a cancer site 6–10 years earlier reduced the black female mortality rate by about 1.3–58% as much as it reduced the white female mortality rate (2.2%). Conclusions: Some demographic groups may have had greater access to new cancer drugs than other groups. Some cancer drug innovations for certain sites have been shown to be more effective for some groups than for others. Also, there may be racial differences in rates of usage of some drugs for some cancers due to racial differences in levels of trust of biomedical/drug institutions.

We analyze the role that the launch of new drugs has played in reducing the number of years of life lost (YLL) before 3 different ages (85, 70, and 55) due to 66 diseases in 27 countries. We estimate 2-way fixed-effects models of the rate of decline of the disease- and country-specific age-standardized YLL rate. The models control for the average decline in the YLL rate in each country and from each disease.One additional drug launch 0-11 years before year t is estimated to have reduced the pre-age-85 YLL rate (YLL85) in year t by 3.0%, and one additional drug launch 12 or more years before year t is estimated to have reduced YLL85 by 5.5%. (A drug’s utilization peaks 8-10 years after it was launched.) Controlling for the number of drugs previously launched, YLL rates are unrelated to the number of drug classes previously launched. The estimates imply that, if no new drugs had been launched after 1981, YLL85 in 2013 would have been 2.16 times as high as it actually was. We estimate that pharmaceutical expenditure per life-year saved before age 85 in 2013 by post-1981 drugs was $2837. This amount is about 8% of per capita GDP, indicating that post-1981 drugs launched were very cost–effective, overall. But the fact that an intervention is cost-effective does not necessarily mean that it is “affordable.”

In Portugal, during the period 2002–2010, longevity (mean age at death) increased by 2.5 years. The aim of this study was to examine the effects of pharmaceutical innovation on the longevity from all diseases in Portugal (2002–2010). Longitudinal disease-level data was analyzed to determine whether diseases for which there was greater pharmaceutical innovation - a larger increase in the number of new chemical entities (NCEs) previously launched (1994-2002) - had higher increase in mean age at death, controlling for the effects of macroeconomic trends and overall changes in the healthcare system. The diseases for which more drugs were registered during the period 1994-2002 had larger increases in mean age at death during 2002-2010. The increase in mean age at death for “high-innovation diseases” (mean number of NCE 1994-2002 = 12.9) was 3.1 years, while 1.7 years for “low-innovation diseases” (mean number of NCE 1994-2002 = 3.9). Furthermore, our estimates indicate that about 1/3 of the total increase in longevity (i.e. 0.8 years) was due to NCE 1994-2002. Thus, pharmaceutical innovation increased mean age at death by 1.2 months per year. There were 106,242 deaths in Portugal in 2010. Hence the number of life-years gained in 2010 due to drugs registered during the period 1994-2002 was 84,994 (= 0.80 years * 106,242 deaths). These findings demonstrate that pharmaceutical innovation brought significant health gains in Portugal in the past decade. Access to innovation is therefore crucial if society desires to mantain the positive momentum of longevity increase.

ABSTRACT Germany is the prototypical economy where universal banks, which offer a wide range of financial services, allegedly exert substantial influence over firms. Despite frequent assertions about the influence of German banks, empirical support for the German Bank Influence Model (GBIM) is largely lacking, an omission,that,is particularly,crucial,given,recent,challenges,to the conventional,wisdom.,This,paper,outlines,a systematic,approach,for,examining the impact of German bank influence, using 3 measures of influence, and a unique data,set,of 91 German,firms,for,the,period,1965-1990. One,robust,finding,is that,bank,influence,seems,to be,strongest,among,firms,with,highly,dispersed ownership structures. Another is that contrary to findings in previous studies, bank,influence,does,not,appear,to be,consistently,associated,with,either,higher or lower profitability of the firm. In addition, there is evidence to suggest that,bank,influenced,firms,use,less,debt,and,more,equity,financing,than independent,firms.,An,important,implication,of this,work,for,future,research,is that,both,ownership,concentration,and,bank,debt,appear,to be,crucial,variables to control,for,in testing,the,impacts,of bank,influence,on firm,behavior. I,INTRODUCTION The German,economy,has,generated,interest,among,academics,across,disciplines,for

events during the first year of treatment. This study was conducted to estimate the early clinical and economic consequences of initiating statin therapy with atorvastatin vs. simvastatin from a Canadian societal perspective. METHODS: A costconsequence model was developed to estimate CV events and costs over the first 2 years of treatment associated with initiating atorvastatin or simvastatin in a hypothetical cohort of 100,000 patients. Four groups of new users were considered, including patients with: 1) diabetes; 2) multiple CV risk factors; 3) coronary heart disease; and 4) acute coronary syndrome. RCT data were used to estimate the CV event rate for each statin. CV events included myocardial infarction, stroke, and revascularization procedures. Corresponding direct costs (i.e., health care utilisation, drug) were obtained from the Ontario Drug Benefit and Ontario Case Costing Initiative. Estimates of indirect costs (loss of productivity) were obtained from Statistics Canada. All costs were expressed in 2007 Canadian dollars. Multivariate (Monte Carlo simulation) and univariate sensitivity analyses were conducted on model assumptions. RESULTS: Within two years of treatment initiation, the use of atorvastatin is predicted to prevent 1648 CV events (95% CI: 1343–1956) per 100,000 new patients compared with simvastatin. Similarly, the cost of CV events was reduced by $50.8 million (95% CI: $41.9–$59.8). The incremental cost associated with atorvastatin treatment was $31.3 million. This resulted in a net saving of $19.5 million (95% CI: $10.7–$28.7). Savings were also observed across all four groups considered. Results were sensitive to assumptions regarding simvastatin efficacy and levels of persistence. CONCLUSION: Based on this model, atorvastatin use is predicted to result in cost savings to the Canadian society over simvastatin use within 2 years of therapy initiation.

Declaration of interest: All authors have received research grants and honoraria for lectures and advisory from the pharmaceutical industry at different occasions, including funding from Roche for the study A Global comparison regarding patient access to cancer drugs. Professor Coleman wrongly states that the key question addressed in our report is ‘whether national cancer survival is associated with national cancer drug licensing’. That explains his comments, but also reveals that he has no interest in, or contribution to, the main issues addressed: how does patients' access to cancer drugs vary between countries, what are the explanations for this variation, and what are the possible policy responses at national and international levels. In particular we are interested in the impact of health technology assessment, and the role of reimbursement and mechanisms for funding. Everyone who is interested in these issues has to read the report. When the Karolinska report was published in 2005, Professor Coleman made sweeping criticisms of the chapter on the relation between new drug introduction and survival (5 pages out of 95) in different media. This chapter was a summary of two studies by Professor Frank Lichtenberg, Columbia University, which were of interest in the context of the report, but not its core. However, his interest and critique triggered us to address some shortcomings of those studies: mainly that they were based on availability and not actual utilization of drugs introduced at different times, which we call ‘vintage’. In the extended and updated report we therefore include three new studies with new data sets. Coleman chooses to ignore two of them, and focus on the third. We will take Coleman's critical points on this study seriously and try to address them, even if it becomes a little technical. The method used in one of the three analyses is to examine the relationship between drug vintage and survival rates for 18 different types of cancer in France, Spain, Germany, Italy and the UK. Vintage is measured as the share of the sample treated with drugs introduced after 1985. Survival rates are estimated by dividing 1-year and 5-year prevalence with incidence. In the statistical analysis the country dummies (αi's) in Equation 2 control for overall (not site-specific) differences across countries in expected survival (background mortality). The cancer-site dummies (δj's) in Equation 2 control for average (across countries) differences across cancer sites in expected survival [1.Jönsson B. Wilking N. A global comparison regarding patient access to cancer drugs.Ann Oncol. 2007; 18 (72)PubMed Google Scholar]. Since the survival rate is the dependent variable in Equation 2, random errors of measurement will not bias the drug vintage coefficient. Moreover, if survival estimates for a given country are systematically over- or under-estimated (e.g. estimates for France are overestimated by 25% for every cancer site), this will have no effect on our estimates of the effect of vintage on survival (due to inclusion of country dummies (αi's) in Equation 2). The fact that the periods studied do not completely overlap for drug usage and survival estimates is also a shortcoming, but nothing that necessary leads to systematic bias. But it may explain why the difference in post-1985 drugs accounts for only 14–19% of the observed survival difference between UK and the other countries. We are looking forward to re-estimating the model when the data from EUROCARE-4 are published later this year, and see if the results change. The idea to use individual patient data from IMS Oncology, and include other treatments as explanatory variables may work, but it has to be investigated further. Actually, in the US survival study, the first of our studies which is ignored by Professor Coleman, we are including measures of innovation in surgery and radiology, but they do not show any systematic effect. It may be that the measures are too crude or that the effect is small. To address the problem with survival measures we also did a third study with mortality as outcome. That also has its pros and cons, but is certainly of interest since reduction in cancer mortality is a defined goal for many cancer programs. Being aware of the data problems involved we approach the question about the relation between drug ‘vintage’ and survival in three different ways. We got consistent results so far. The studies on the relation between innovation and survival have never been intended for use by regulatory authorities as basis for claims of effectiveness of specific drugs. They belong in a very different research tradition where the purpose is to understand the forces behind and consequences of medical research and innovation[2.Murphy K.M. Topel R.H. Measuring the gains from medical research. An economic approach. The University of Chicago Press, 2003Crossref Google Scholar, 3.Luce B.R. Mauskopf J. Sloan F.A. Osterman J. Paramore L.C. The Return on Investment in Health Care: From 1980–2000.Value in Health. 2006; 9: 146-156Abstract Full Text PDF PubMed Scopus (53) Google Scholar, 4.Buxton M. Substantial Returns to Health care Spending: But Do We Spend to Little or Too Much?.Value in Health. 2006; 9: 144-145Abstract Full Text PDF PubMed Scopus (2) Google Scholar, 5.Lichtenberg F. The impact of new drug launches on longevity: evidence from longitudinal disease level data from 52 countries, 1982–2001.Int J Health care Finance Econ. 2005; 5: 47-73Crossref PubMed Scopus (102) Google Scholar]. That this field uses other data sets and research methods, developed in economics and econometrics gives no reason to label them as unscientific. They are complementary to clinical and epidemiological studies, but of course compete with such studies for policy relevance. It is understandable that Professor Coleman is irritated that economists dare to come into his field, but to call such studies ‘a subversion of science by pharmaceutical industry’ does not contribute to a serious discussion. Professor Coleman's comments indicate that he doesn't really understand the econometric methods, and is eager to use any imperfections in the data as an excuse to dismiss the results, even when those imperfections in the data are irrelevant or even render our tests of the key hypothesis ‘strong tests’. Let us make this clear. Despite insinuations about the opposite, neither Roche nor any other pharmaceutical firm influenced the content of the report. The potential conflict of interest related to the funding from Roche has been openly declared. We have had no knowledge about anything related to Cancer United, and we have not approved their use of our report. We have not made any attempts to market the results to influence the UK health policy debate. Our study includes 29 countries, of which the UK is only one. As for other countries we make some observations. For UK we stressed in the first report particularly that HTA studies to be a useful instrument in policy have to be timely, and can also note that NICE has taken account of this. We also observed that it was nearly impossible to find any reliable estimates about cancer spending in the UK, and that this may explain the lack of correspondence between assessment and resource allocation. Our advice was to make sure that this information should be produced and made public. We can have an informed debate about priorities only if we know what we spend. ‘Prof Coleman, Harpal Kumar, the new chief executive of Cancer Research UK, and the government's cancer tsar, Mike Richards, all dismissed a report last week’ (The Guardian, 16 May 2007 after the publication in Annals). Of course the UK public can be reassured that everything is fine, guaranteed by science, the country's leading cancer funding body and the government, but does it make Professor Coleman's critique of our report credible? How independent are you in this discussion when you head ‘Cancer Research UK Survival group’? As Professor Coleman recognizes in his conclusions, the issues we are researching are interesting and important. We have not misused, and have no intention of misusing, cancer statistics for any purposes. The problem is that current cancer statistics is inadequate to address the important issues we are studying, and the need for improvements is urgent. If Professor Coleman agrees, we are happy to collaborate on both data and methodology in any future studies.

five-year relative survival rate from all malignant cancers increased from 50.0% in 1975-1979 to 62.7% in 1995. This increase is not due to a favorable shift in the distribution of cancers. A variety of factors, including technological advances in diagnostic procedures that led to earlier detection and diagnosis, have contributed to this increase. This paper?s main objective is to assess the contribution of pharmaceutical innovation to the increase in cancer survival rates. Only about one third of the approximately 80 drugs currently used to treat cancer had been approved when the war on cancer was declared in 1971. percentage increase in the survival rate varied considerably across cancer sites. We hypothesize that these differential rates of progress were partly attributable to different rates of pharmaceutical innovation for different types of cancer, and test this hypothesis within a ?differences in differences? framework, by estimating models of cancer mortality rates using longitudinal, annual, cancer-site-level data based on records of 2.1 million people diagnosed with cancer during the period 1975-1995. We control for fixed cancer site effects, fixed year effects, incidence, stage distribution of diagnosed patients, mean age at diagnosis, percent of patients having surgery, and percent of patients having radiation. Overall, the estimates indicate that cancers for which the stock of drugs increased more rapidly tended to have greater increases in survival rates. estimates imply that, ceteris paribus, the 1975-1995 increase in the stock of drugs increased the 1-year crude cancer survival rate from 69.4% to 76.1%, the 5-year rate from 45.5% to 51.3%, and the 10-year rate from 34.2% to 38.1%. increase in the stock of drugs accounted for about 50-60% of the increase in age-adjusted survival rates in the first 6 years after diagnosis. We also estimate that the 1975-1995 increase in the lagged stock of drugs made the life expectancy of people diagnosed with cancer in 1995 just over a year greater than the life expectancy of people diagnosed with cancer in 1975. This figure increased from about 9.6 to 10.6 years. This is very similar to the estimate of the contribution of pharmaceutical innovation to longevity increase I obtained in an earlier study, although that study was based on a very different sample and methodology. Since the lifetime risk of being diagnosed with cancer is about 40%, the estimates imply that the 1975-1995 increase in the lagged stock of cancer drugs increased the life expectancy of the entire U.S. population by 0.4 years, and that new cancer drugs accounted for 10.7% of the overall increase in U.S. life expectancy at birth. estimated cost to achieve the additional year of life per person diagnosed with cancer?-below $3000-?is well below recent estimates of the value of a statistical life-year. We are unable to measure quality-adjusted life-years (QALYS), but if new cancer drugs increased the quality of life as well as delayed death, the increase in QALYS is not necessarily less than the increase in life expectancy. This paper is closely related to another recent paper of mine, The impact of new drug launches on longevity: evidence from longitudinal disease-level data from 52 countries, 1982-2001 (http://www.nber.org/papers/w9754) I could present that paper instead, or a combination of the two papers.

Only about one third of the approximately 80 drugs currently used to treat cancer had been approved when the war on cancer was declared in 1971. We assess the contribution of pharmaceutical innovation to the increase in cancer survival rates in a differences in differences' framework, by estimating models of cancer mortality rates using longitudinal, annual, cancer-site-level data based on records of 2.1 million people diagnosed with cancer during the period 1975-1995. We control for fixed cancer site effects, fixed year effects, incidence, stage distribution of diagnosed patients, mean age at diagnosis, and surgery and radiation treatment rates. Cancers for which the stock of drugs increased more rapidly tended to have greater increases in survival rates. The increase in the stock of drugs accounted for about 50-60% of the increase in age-adjusted survival rates in the first 6 years after diagnosis. New cancer drugs increased the life expectancy of people diagnosed with cancer by about one year from 1975 to 1995. The estimated cost to achieve the additional year of life per person diagnosed with cancer below $3000 is well below recent estimates of the value of a statistical life-year. Since the lifetime risk of being diagnosed with cancer is about 40%, the estimates imply that new cancer drugs accounted for 10.7% of the overall increase in U.S. life expectancy at birth.

We hypothesize that pharmaceutical-embodied technical progress increases per capita output via its effect on labor supply (the employment rate and hours worked per employed person). We examine the effect of changes in both the average quantity and average vintage (FDA approval year) of drugs consumed on labor supply, using longitudinal, condition-level data. The estimates indicate that conditions for which there were above-average increases in utilization of prescriptions during 1996-1998 tended to have above-average reductions in the probability of missed work days. The estimated value to employers of the reduction in missed work days appears to exceed the employer's increase in drug cost. The estimates are also consistent with the hypothesis that an increase in a condition's mean drug vintage reduces the probability that people with that condition will experience activity and work limitations, and reduces their average number of restricted-activity days. The estimates imply that activity limitations decline at the rate of about one percent per year of drug vintage, and that the rate of pharmaceutical-embodied technical progress with respect to activity limitations is about 18% per year. Estimates of the cost of the increase in drug vintage necessary to achieve reductions in activity limitations indicate that increases in drug vintage tend to be very 'cost-effective.'

This paper examines the output contributions of capital and labor deployed in information systems (IS) at the firm level during the period 1988-91 throughout the business sector, using two different sources of data on these inputs. Our production function estimates suggest that there are substantial excess returns to both IS capital and IS labor, although the size and significance of the excess returns to IS capital is larger. Computer capital and labor jointly contribute, or account for, about 21 percent of output, although only about 10% of both capital and labor income accrue to IS factors. Although IS employees accounted for a very small share of total employment by 1986, IS employment growth is estimated to have made a larger contribution to 1976-86 output growth than non-IS employment, due to the very rapid growth (16% per annum) of IS employment. The estimated marginal rate of substitution (MRS) between IS and non-IS employees, evaluated at the sample mean, is 6: one IS employee can be substituted for six non-IS employees without affecting output.