The Effects of Pharmaceutical Innovation on Longevity ... · Longevity, Hospitalization and Health...

The Effects of Pharmaceutical Innovation on

Longevity, Hospitalization and Health Expenditures in Turkey,

1999-2010

Prof. Dr. Frank Lichtenberg1

Prof. Dr. Mehtap Tatar2

Assoc. Prof. Zafer Çalışkan3

1 Columbia University, National Bureau of Economic Research, and CESifo

2 Hacettepe University, Faculty of Economics and Administrative Sciences, Department of Health Care Management

3 Hacettepe University Faculty of Economics and Administrative Sciences, Department of Economics

Table of ContentsEXECUTIVE SUMMARY......................................................................................................... 4

1. INTRODUCTION .............................................................................................................. 7

2. METHODOLOGY ........................................................................................................... 12

3. FINDINGS ..................................................................................................................... 15

3. 1 Estimation Results ................................................................................................ 16

3. 1. 1 Mortality Models ......................................................................................... 16

3. 1. 2 Hospitalization Models ................................................................................ 19

3. 2 Incremental cost-effectiveness of pharmaceutical innovation in Turkey, 1999-2008.. 22

4. CONCLUSION ............................................................................................................... 27

REFERENCES .................................................................................................................... 28

TablesTable 1. Data sources ....................................................................................................... 12

Table 2. Disease (ICD8 chapter) classification used in age at death analysis ......................... 14

Table 3. Summary statistics ............................................................................................... 15

Table 4. Estimates of mean age at death model .................................................................. 17

Table 5. Estimates of % of deaths at age > 75 model ......................................................... 19

Table 6. Hospital discharges model estimates ..................................................................... 20

Table 7. Hospital days model estimates .............................................................................. 21

Table 8. Rx expenditure, Turkey, 1999-2010....................................................................... 24

Table 9. Estimation of ICER ................................................................................................ 25

FiguresFigure 1 Comparison of the actual increase in mean age at death to the increase that

would have occurred in the absence of any increase in drug vintage ...................... 18

Figure 2 Hospital days, 2007-2010: Actual vs. in the absence of pharmaceutical innovation . 22

Abbreviations

ATC : Anatomical Therapeutic Chemical

NCE : New Chemical Entities

OECD : Organization for Economic Cooperation and Development

WHO : World Health Organization

EXECUTIVE SUMMARY

The contribution of new technology products to the length and quality of life of people has been discussed from various perspectives for a long time. Technological progress has an invaluable impact on economic growth, and it is recognized that the pharmaceutical and medical devices industries have an important share in this impact with their highly research intensive charac-ter. It is widely believed that health technology has contributed remarkably to the improvements in health indicators. For instance, according to the World Health Organization, improvements in health technology have accounted for approximately 40-50% of the decrease in infant and child deaths and the increase in life expectancy in the 20th century.

A number of health economists argue that the contributions of medical care to increases in lon-gevity and other health status improvements have been modest and that other factors, such as socioeconomic development, lifestyle and environment, have been the major contributing factors. However, recent research investigating the impact of health care related technological innovations on health status has revealed the other side of the story. The research has so far revealed that the contribution of health technology to better outcomes can be twofold. First, new technology can directly improve health outcomes by improving quality of life for a given condition or by increasing the length of life. Second, new technology may have an impact on other aspects of the health care system such as length of stay in hospitals and can decrease health care expenditures.

Turkey has been undergoing a radical reform process since 2003 with special emphasis on improving accessibility, quality and effectiveness of health care services. Pharmaceuticals and the pharmaceutical industry have been the focus of the reforms since their inception mainly because of the relatively higher share of pharmaceutical expenditures in total health care expen-ditures. Several new policies were introduced in marketing approval, pricing and reimbursement of pharmaceuticals. All these measures had a negative impact on the introduction of new drugs in the Turkish market. As a result of these measures the pace of new drug entries slowed down considerably.

The aim of this study is to estimate the effects of pharmaceutical innovation on mortality, hospi-talization and medical expenditure in Turkey during the period 1999-2010. Longitudinal disease level data are used to estimate difference-in-differences models of the impact of pharmaceutical innovation on longevity, medical expenditure and hospitalization. In essence, the study investi-gates whether the diseases that experienced more pharmaceutical innovation had larger increas-es in longevity. The study is the first to explore the effects of pharmaceutical innovation in Turkey.

The results showed that from 1999 to 2008, mean age at death increased by 3.6 years, from 63.0 to 66.6 years. In the absence of any pharmaceutical innovation, mean age at death would have increased by only 0.6 years. Hence, pharmaceutical innovation was estimated to have in-creased mean age at death in Turkey by 3.0 years during the period 1999-2008. Pharmaceutical innovation also had an impact on hospital utilization. The estimates indicated that an increase in the number of molecules used to treat a disease reduces the number of hospital days due to the disease 3-4 years later. In the final analysis it was estimated that pharmaceutical innovation has reduced the number of hospital days by approximately 1% per year.

In the study, the cost per life-year gained from the introduction of new drugs, in other words the incremental cost-effectiveness of pharmaceutical innovation was also calculated. The baseline estimate of the cost per life-year gained from pharmaceutical innovation is $2,776. If the differ-ence in life expectancy is half as large, as our estimates indicate, the cost per life-year gained would be $4,808.

These findings are in line with current literature inquiring the impact of technological innovation on health and health care. The study introduces a new perspective to health policy-makers in Turkey and opens a new debate about the impact of current pharmaceutical policies on the health status of the population and health care services in general.

The Effects of Pharmaceutical Innovation on Longevity, Hospitalization and Health Expenditures in Turkey, 1999-2010 7

1. INTRODUCTION

There is consensus in the economics literature that the development of new technological prod-ucts contributes to the length and quality of life of populations measured from different perspec-tives. It is simply believed that new technological products are the main reasons for being better off today compared to earlier generations. In other words, technological change and progress can explain a significant part of the improvements achieved in human history. Technological change can be induced by intentional research and investment decisions made by for-profit agents (Romer, 1990) and public organizations. It is widely discussed in the literature that newer or later vintage goods and services have to be used to benefit from technological progress. The embodied technological progress suggests that each successive vintage of investment is more productive than the last (Lichtenberg and Duflos, 2008).

Technological progress has an invaluable impact on economic growth, and it is recognized that the pharmaceutical and medical devices industries have an important share in this impact with their highly research intensive character. According to the National Science Foundation, the R&D intensity of drugs and medicines manufacturing is 74% higher than that of machinery and equip-ment manufacturing. Therefore, there is also a high rate of pharmaceutical-embodied technolog-ical progress (Lichtenberg and Duflos, 2008). One way of assessing the major and measurable impact of the contribution of pharmaceutical technological progress is by analyzing the impact in terms of improvements in longevity. As intensively analyzed by Lichtenberg (2005a, 2005b, 2005c), increases in the cumulative number of drugs have contributed positively to the health status of the population and longevity. Another approach in assessing this impact is to investigate whether the health and longevity of people for a given disease is positively related to the mean vintage (FDA approval year) of drugs used to treat that disease. Improvements in longevity are not only important for populations’ own well-being but also for the economic development and welfare of nations because an increase in longevity is regarded as an important driving factor behind economic growth and development. There is a consensus in the literature that healthy individuals are one of the major factors for economic growth. Technological change also positively contributes to this process.

8

Prof. Dr. Frank Lichtenberg, Prof. Dr. Mehtap Tatar, Assoc. Prof. Zafer Çalışkan

Life expectancy has increased globally in the last fifty years. In the OECD countries, life expec-tancy has increased from 67.9 years in 1960 to 80 years in 2010: an 18% increase. There are numerous factors behind this increase, such as improvements in socioeconomic indicators, im-provements in risk factors and improvements in health technology. The debate about the factors influencing the increase in life expectancy has not yet reached a conclusion. A number of health economists argue that the contributions of medical care to increases in longevity and other health status improvements have been modest and that other factors, such as socioeconomic develop-ment, lifestyle and environment, have been the major contributing factors. This argument can be followed in any health economics textbook. However, recent research investigating the impact of health care related technological innovations on health status has revealed the other side of the story. For instance, in a study by Cutler and McClellan (2001), an attempt was made to reveal the impact of technological change on the treatment of heart attacks, low-birth-weight infants, depression, cataracts and breast cancer. They concluded that in most of the cases, technological innovations in medicine have contributed positively, and although technology generally resulted in more spending, the level of improvements in outcomes justified these costs. Cross-country com-parisons have also supported this conclusion. For instance, Lichtenberg (2005d), in his attempt to analyze the impact of new drug launches on longevity using longitudinal disease level data from 52 countries between 1982 and 2001, concluded that launches of New Chemical Entities (NCEs) have a strong positive impact on the probability of survival. The methodology in this study also controlled for other potential determinants of longevity, such as education, income, nutrition, environment, etc. The study concluded that between 1986 and 2000, the average life expectancy of the sample countries increased 1.96 years and that the NCEs accounted for 0.79 years (40%) of the increase in longevity. Lichtenberg also found the incremental cost effectiveness ratio (ex-penditure per person per year on new drugs divided by the increase in life years per person per year attributable to NCE launches) to be $2,250. The results also indicated that launch delays in countries with lower prices or smaller market size reduce longevity.

The World Health Organization (WHO) (1999) has also been involved in the debate about the contribution of technological innovation to improvements in health indicators and stated that new drugs, medical devices and overall improvements in health technology have played an important role in this worldwide positive trend in health status. According to the WHO, improvements in health technology have accounted for approximately 40-50% of the decrease in infant and child deaths and the increase in life expectancy in the 20th century. Similarly, the OECD reports that the 8% death rate within 30 days after admission to a hospital in 2000 has dropped to 4% in a decade (2011). Overall survival rates for different cancers have increased with early diagnosis and new treatment options. The five-year survival rate from breast cancer after diagnosis has increased to 84% in 2004 from 79% in 1997. Various studies have also confirmed that new in-novative drugs for the treatment of widespread diseases with higher treatment costs, such as HIV, have contributed positively to lower mortality rates and lower hospital expenditures (Lichtenberg, 2003, 2006).

These improvements are mainly attributed to improvements in health technology and their high rates of diffusion and utilization. As stated earlier, pharmaceuticals and medical devices comprise



the major part of improvements in medical technology. These developments, coupled with the increasing demand for healthcare services, inevitably result in increasing health expenditures. In addition, in many cases, new technology may also provide an answer to an unmet need. Cutler and McCellan (2001) assess the overall outcome of new technology drugs from two perspectives. The first one is referred to as the ‘substitution effect of treatment’ and connotes the replacement of old therapies by new ones. As the new technology is always more expensive than the old one, the increase in pharmaceutical expenditures is inevitable. However, the increase in pharmaceuti-cal expenditures may be offset by decreases in other components of health expenditure as new drugs may reduce hospital and nursing home utilization. Additionally, the new technology may be so innovative that diseases that could not be treated before may become treatable with the new technology. This is referred to as the ‘treatment expansion effect,’ and this effect can also be the underlying factor in increasing health expenditures. The discussion above indicates that the im-pact of new technology drugs on health expenditures is very complex and needs to be discussed from different perspectives.

Pharmaceutical expenditures comprise approximately 15-20% of the total health expenditures in many countries on average but may be more in some other countries. Especially in countries with a higher share of pharmaceutical expenditures, any increase in the pharmaceutical expenditures may affect total health expenditures to a greater extent. In 2009, pharmaceutical expenditures formed 19% of the total health expenditures in OECD countries, with a total value of 700 billion USD. The analysis of the expenditures made between 2000 and 2012 reveals that the rate of increase for total health expenditures was 16%. The same figure was 26% for treatment and 9.3% for medicines.

Although some people believe that the introduction of new drugs can be regarded as an important reason behind increases in overall health expenditures, they may offset their impact on health budgets by i) shortening treatment duration, ii) increasing effectiveness and iii) decreasing hos-pital costs by decreasing the number of admissions and/or average length of stay in hospitals. These outcomes may both affect the health status of patients positively and offset the increase in pharmaceutical expenditures (Law and Grepin, 2010). For instance, Duggan and Evans (2008), in their analysis of the use of antiviral drugs for the treatment of HIV, have concluded that new medicines have not only contributed to decreasing health expenditures in the short-term but have also helped to achieve lower mortality rates. Similarly, Civan and Köksal (2010), in their study where technological development in the pharmaceutical market is measured by the drug age, concluded that a one year decrease in the drug age decreases the health expenditure per capita by $45. Lichtenberg (2009), in his study analyzing the impact of newer cardiovascular drugs on reduced hospitalization in 20 OECD countries between 1995 and 2003, also concluded that per capita drug expenditure on cardiovascular hospital stays would have been 70% ($89) higher in 2004 had the drug vintage not increased during 1995-2004. At this point, it should be stated that the most frequently discussed topic in the health economics literature is whether the increases in the health expenditures caused by new technologies provide value for money solutions for the whole health care system and economy. This topic obviously requires a comparison of the cost of the technology with the targeted health outcomes. A number of studies have focused on this issue

10


in the last decade. For instance, Lichtenberg (2004), Crémieux, Meilleur and Ouellette (2005), Frech and Miller (2004) and Shaw, Horrace and Vogel (2005) have concluded that pharmaceutical expenditures and new drugs have increased life expectancy over the years.

A number of studies in the literature have attempted to explore the offsetting effect of new drugs on health expenditures and the contribution of innovative drugs on health outcomes. Some stud-ies used individual patient, case or disease level data, and some used aggregate data instead. Studies based on patients or diseases use data at the micro level and compare the old and new drugs for a patient or disease group in terms of length and quality of life, average length of stay in a hospital and similar indicators. Although this type of analysis involves patient-based information such as age, gender and other specific information, the data usually do not include information on education, income level and others. The data cover the impact of the new drugs during the study period but do not cover their impact on longevity after the end of the treatment. In addition, it is often impossible to generalize results at the national level. As can be seen, although these analy-ses provide invaluable information in assessing the contribution of new drugs to improvements in certain indicators, they also have considerable limitations. The most important superiority of using aggregate data is that it reduces the risk of unobserved treatment selection effects; it also permits analysis of the expansion effect of the treatment.

Lichtenberg has undertaken a number of studies in this area based on different data sets and methodologies. In general, he has concluded that new drugs have contributed to not only achiev-ing better outcomes but also to decreasing health expenditures. He has mainly used the health production function model developed by Auster, Levenson and Sarachek (1969), similar to other researchers working in this field. The health production function can be formulated as:

H = f (level of medical technology, income, health expenditure, age, education, drug expenditures, physician visits, hospital days, health insurance, life style …………..)

Level of health or health outcome (indicated by H in this formula) is affected by a number of factors that can have either positive or negative impacts. The inclusion or exclusion of these fac-tors in the health production function formula depends on the aim of the research. In one of the earlier studies of this kind, Lichtenberg tried to analyze the relationship between the number of Rx medicines and the number of hospital days and hospital expenditures (1996). The study covered the period between 1980 and 1991 in the USA. The data for Rx medicines prescribed in outpa-tient visits and data about patients were obtained from the National Ambulatory Medical Care Survey (NAMCS). In cases of more than one diagnosis, drugs prescribed for each diagnosis were weighted by population to determine the weighted number of medicines for this population. The formula also calculated the total number of medicines (i) for a specific disease (j) and for a specific year (t). This allowed the determination of the share of each drug in total drugs and the ratio of a prescribed medicine for a diagnosis in comparison with 1980 and 1991. The study measured the main differences between drugs in 1980 and 1991 in terms of novelty by using a NOVELTY index. In this measurement, if the index was equal to zero, the same drugs were used to treat a disease in 1980 and 1991. If the index was 1, then the drugs used to treat the disease in 1980 and 1991 were completely different. The results of the study indicated that both the increase in the number



of drugs prescribed and the change in their status had a positive impact and decreased hospital days. In addition, the number of surgical operations also decreased in the model. A 10% increase in pharmaceutical expenditures caused a 6.4% decrease in hospital expenditures.

In another study, Lichtenberg (2003) worked on the effect of new drug approvals on HIV mortality in the US between 1987 and 1998. The study was a time-series analysis based on a single dis-ease and health production function, as in other studies. Deaths from HIV in the study period were the dependent variable, whereas the medicine stock used in HIV treatment was the explanatory variable. In other words, mortality was demonstrated to be inversely related to the cumulative drugs approved by the FDA. The study concluded that new drug approvals have decreased deaths from HIV significantly.

The present study is the first to explore the effects of pharmaceutical innovation in Turkey. In the last decade, Turkey has been undergoing a radical reform process that changed the outlook of the organization and financing of the health care system radically. Since the beginning of the reforms, policy-makers have given special attention to pharmaceuticals and the pharmaceutical industry. In the earlier years of the reform, a special emphasis was put on the relatively higher share of pharmaceutical expenditures in total health expenditures. Although some analysts and surveys tried to discuss the underlying reasons for this (Kanavos, et al, 2005; Liu, et al, 2005), the government embarked on a number of measures to strengthen its control in the market. The measures taken affected all components and procedures of the pharmaceutical market, including marketing approval, pricing and reimbursement of new products. Internal and external reference pricing were adopted to determine the market and reimbursement prices of drugs, pharmacoeco-nomic analysis was introduced in applications for reimbursement, mandatory discounts in the reimbursement of drugs by the Social Security Institution were determined, and Good Manufac-turing Practice (GMP) rules that required the inspection of the manufacturing location of the drug by Ministry of Health officials became a statutory obligation before marketing approval. All these measures had a negative impact on the introduction of new drugs in the Turkish market. The pace of new drug entries slowed down considerably in the Turkish market, which is why the results of this study are very timely and are expected to open a new window in the debate between the policy-makers and the pharmaceutical industry.

12


2. METHODOLOGY

The aim of this study is to estimate the effects of pharmaceutical innovation on mortality, hospi-talization and medical expenditure in Turkey during the period 1999-2010. Longitudinal disease level data are used to estimate difference-in-differences models of the impact of pharmaceutical innovation on longevity, medical expenditure and hospitalization. In essence, the study investi-gates whether the diseases that experienced more pharmaceutical innovation had larger increas-es in longevity.

Aggregate data are used instead of patient level data in the analysis. The advantage of using aggregate data instead of patient level data is reducing specification errors as the patient level data may be more subject to selection effects than aggregate data. At the patient level, the sickest patients may get the newest (or oldest) treatment, which may cause a specification error.

For this study, longitudinal, disease-level data were obtained from several rich databases to ex-amine the impact of pharmaceutical innovation on longevity, medical expenditure and hospital utilization (Table 1). By combining the estimates of the effect of pharmaceutical innovation on longevity, medical expenditure and hospital utilization, the incremental cost-effectiveness (cost per life-year gained) of pharmaceutical innovation in Turkey during the period 1999-2010 is investigated.

Table 1. Data sources

Number of deaths by cause of death, age and year

WHO Mortality Database

Number of inpatient hospital discharges and days by ICD10

and yearEurostat hlth_co_disch1 and hlth_co_hosday tables

Quantity (no. of standard units), value (in USD), EphMRA anatomical

classification and active ingredients of all pharmaceutical products; world launch years of

active ingredients

IMS Health MIDAS database

The number of standard units sold is determined by taking the number of counting units sold divided by the standard unit factor, which is the smallest common dose of a product form as defined by IMS HEALTH. For example, for oral solid forms, the standard unit factor is one tablet or capsule, whereas for syrup forms, the standard unit factor is one

teaspoon (5 ml), and for injectable forms, it is one ampoule or vial. Other measures of quantity, such as the number of patients using the drug, prescriptions for the drug, or defined daily doses of the drug, are not

available.

Drug indications (IND)

Thériaque (http://www.theriaque.org/), a database of official, regulatory, and bibliographic information on all drugs available in France, intended

for health professionals. Funding is provided by the Centre National Hospitalier d’Information sur le Médicament.



In the literature, life expectancy at birth is the most commonly used measure of longevity. Howev-er, this measure is based on life tables describing the mortality conditions of a hypothetical cohort for a particular time period. In other words, the figures represent a hypothetical case rather than an actual case. Additionally, life expectancy at birth (or at higher ages) cannot be measured for specific diseases. Therefore, in this analysis, the measure of longevity is based on the age distri-bution of deaths caused by a disease in a given year.1 These measures can easily be calculated from data contained in the WHO Mortality Database, providing data on the number of deaths by cause, age group, country and year. The most informative measure is mean age at death. A sec-ond measure is the fraction of deaths that occur above a given age, e.g., age 75.

The measure of pharmaceutical innovation is based on the (weighted) mean vintage of drugs used to treat a disease. For example, if 20,000 people with a given disease in 2012 were treated with a 1990-vintage drug and 10,000 people with the same disease in 2012 were treated with a 2005-vintage drug, the weighted mean vintage of drugs used to treat the disease in 2012 would be 2000. The measurement of pharmaceutical innovation can take different forms; the Anatomical Therapeutic Chemical (ATC) classification system developed by the WHO was used in classifying pharmaceuticals.

However, pharmaceutical innovation is not the only type of medical innovation that is likely to contribute to increases in longevity. Other medical innovations, such as innovations in diagnos-tic imaging, surgical procedures and medical devices, are also likely to affect longevity growth. However, pharmaceuticals are much more research-intensive than other types of medical care. Additionally, Lichtenberg (2013a, 2013b) provides evidence that in the US, rates of pharmaceu-tical and non-pharmaceutical medical innovation are uncorrelated across diseases. Longitudinal disease-level measures of non-pharmaceutical medical innovation are not available for Turkey.

As discussed above, one result of the introduction of new technologies of newer drugs to the health care system is an increase in pharmaceutical expenditures. However, it is also widely dis-cussed in the literature that pharmaceutical innovation may also affect the expenditures made in other parts of the health care sector and may reduce overall health care expenditures. New drugs may decrease the time spent in hospitals or other health care facilities and decrease the utilization of costly health care services. In this study, an attempt is made to estimate the incremental cost effectiveness (cost per life-year gained) of pharmaceutical innovation in Turkey during the period 1999-2010. This enables an interpretation of the results from a health economics perspective and enables us to determine whether the use of innovative drugs during the period provided “value for money.”

The mortality data cover the years between 1999 and 2008. The dependent variables for the mortality model were mean age at death and the fraction of deaths from disease i in year t that occurred after age 75. Our basic hypothesis is that the greater the proportion of the drugs used to treat a disease that are new drugs, the higher the mean age at death from the disease.

1 Lichtenberg (2013) shows that mean age at death is highly correlated across countries with life expectancy at birth.

14


As mentioned above, in this model, pharmaceutical innovation is measured by the change in the mean vintage of drugs consumed. Vintage is defined as the year of invention or first use of a drug. The world launch date of the drug is the vintage of the entity.

The hospitalization data, however, cover the years between 2007 and 2010. The dependent vari-ables in this model were number of inpatient hospital discharges and number of inpatient hospital days.

The measure of pharmaceutical innovation in this model was the percentage change in the num-ber of molecules previously launched. The difference-in-differences model used in other similar research was used. The methodology helped to investigate whether diseases subject to more pharmaceutical innovation had larger increases in mean age at death and smaller increases in hospitalization. The estimated effects of pharmaceutical innovation do not depend on the average rates of the increase of mean age at death and hospitalization.

Table 2. Disease (ICD8 chapter) classification used in age at death analysis

ICD8 Chapter Code ICD ChapterEphMRA/PBIRG ANATOMICAL CLASSIFI-CATION

000-136 Infective and parasitic diseasesJ GENERAL ANTI-INFECTIVES SYSTEMIC;

P PARASITOLOGY

140-239 NeoplasmsL ANTINEOPLASTIC AND IMMUNOMODU-

LATING AGENTS

240-279, 520-577Endocrine, nutritional and metabolic diseases + diseases of the digestive

system

H SYSTEMIC HORMONAL PREPARATIONS, EXCL. SEX HORMONES AND INSULINS; A ALIMENTARY TRACT AND METABOLISM

280-289Diseases of the blood and blood-forming

organsB BLOOD AND BLOOD FORMING ORGANS

290-315, 320-389Mental disorders + diseases of the nervous system and sense organs

N CENTRAL NERVOUS SYSTEM; S SEN-SORY ORGANS

390-458 Diseases of the circulatory system C CARDIOVASCULAR SYSTEM

460-519 Diseases of the respiratory system R RESPIRATORY SYSTEM

580-629 Diseases of the genitourinary systemG GENITOURINARY SYSTEM AND SEX

HORMONES

680-709Diseases of the skin and subcutaneous

tissueD DERMATOLOGICALS

710-738Diseases of the musculoskeletal system

and connective tissueM MUSCULOSKELETAL SYSTEM



3. FINDINGS

In this section, the findings of the study will be presented based on the models developed for the analysis. The summary statistics from the study are presented in Table 3.

Table 3. Summary statistics

1 2 3 4 5 6

Yearnumber of

deathsmean age at

death

fraction of deaths at age greater than

75

mean launch year--no

imputation of missing

launch years

mean launch year--missing launch years set equal to

1900

mean launch year--missing launch years set equal to

1920

1999 140,602 63.0 28% 1963.8 1958.8 1961.2

2000 138,136 63.1 28% 1965.1 1960.4 1962.6

2001 140,160 64.0 30% 1967.3 1962.8 1964.7

2002 143,567 65.1 32% 1967.5 1962.6 1964.7

2004 148,288 65.1 35% 1968.9 1963.6 1965.8

2005 161,823 65.2 36% 1970.5 1965.3 1967.4

2006 170,837 66.1 38% 1971.4 1966.5 1968.5

2007 173,353 66.7 40% 1972.4 1967.3 1969.3

2008 178,174 67.1 42% 1973.5 1968.6 1970.4

change, 1999 to 2008

4.1 14% 9.7 9.8 9.2

Notes: Figures in columns 2-6 are weighted means of disease level data, weighted by number of deaths. The 2003 data are missing as the age classification of deaths used in the WHO Mortality Database in 2003 differed from the age classification used in 1999-2002 and 2004-2008

16


Summary statistics on longevity and pharmaceutical innovation in Turkey are shown in Table 3. The average annual number of deaths during 1999-2008 was approximately 155,000. Mean age at death increased by 4.1 years, from 63.0 to 67.1 years, and the fraction of deaths that occurred at an age greater than 75 increased from 28% to 42%.

3. 1 Estimation Results

3. 1. 1 Mortality Models

The mean age at death model

To investigate the impact of pharmaceutical innovation on longevity in Turkey, we

estimated models of the following form:

AGE_DEATHit = β RX_VINTAGE

it + α

i + δ

t + ε

it (1)

where;

AGE_DEATHit = mean age at death from disease i in year t (t = 1999-2002, 2004-2008); 10

diseases (ICD8 chapters)

RX_VINTAGEit = (∑

p Qp

it WORLD_YEAR

p) / (∑p Q

pit), i the mean vintage of drugs used to treat

disease i in year t

Qpit

= the quantity (number of “standard units”) of product p used to treat disease i in year t

WORLD_YEARp = the mean world launch year of the active ingredients contained in product p

αi = a fixed effect for disease i

δt = a fixed effect for year t

In his model of endogenous technological change, Romer (1990) hypothesized an aggregate production function in which 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. Equa-tion (1) may be considered a health production function in which age at death is an indicator of health output or outcomes, and the cumulative number of drugs approved is analogous to the stock of ideas. Due to the presence of fixed disease effects and year effects, equation (1) is a difference-in-differences model. If the dependent variable is mean age at death, a positive and significant estimate of β would signify that there were above-average increases in mean age at death for diseases with above-average increases in mean vintage of drugs.2

2 There is a potential pitfall in analyzing the relationship between pharmaceutical innovation related to a disease and the age distribution of deaths from the disease. Suppose that the introduction of a new drug for a disease reduces the number of people who die from the disease; people who would have died from the disease absent the new drug may die from other diseases instead. Our estimates will not capture between-disease spillover effects.



The model was estimated using weighted least squares and weighting by N_DEATHSit. The dis-turbances are clustered within diseases. For some active ingredients, there were missing data for the world launch year. However, the ingredients whose world launch years are missing are gener-ally quite old. The fraction of standard units with missing world launch years declined from 32% in 1999 to 20% in 2010. In the study, three alternative measures of RX_VINTAGE, corresponding to three ways of dealing with missing world launch years, were developed. These were:

» RX_VINTAGE1: exclude products with missing world launch years

» RX_VINTAGE2: set world launch year = 1900 for products with missing world launch years

» RX_VINTAGE3: set world launch year = 1920 for products with missing world launch years

Estimates of parameters from longevity (mean age at death) models are presented in Table

Table 4. Estimates of mean age at death model

ModelIndependent

VariableEsti-

mate (β)

Empirical Standard Er-ror Estimates

ZPr > |Z|

ΔY ΔX βΔX(βΔX)/

ΔY

1

RX_VINTAGE1: exclude products

with missing world launch

years

0.2711 0.2754 0.98 0.325 4.07 9.74 2.64 % 65

2

RX_VINTAGE2: set world launch year = 1900 for products with missing world launch years

0.3006 0.1054 2.85 0.0043 4.07 9.84 2.96 % 73

3

RX_VINTAGE3: set world launch year = 1920 for products with missing world launch years

0.4096 0.1582 2.59 0.0096 4.07 9.23 3.78 % 93

The coefficient of RX_VINTAGE1 is not significant in model 1. However, the coefficients of RX_VIN-TAGE2 and RX_VINTAGE3 are positive and highly significant in models 2 and 3. Those estimates suggest that most (73%-93%) of the 4.1-year increase in mean age at death was due to phar-maceutical innovation (increased drug vintage).

We can use our estimates of the first equation to compare the actual increase in mean age at death during the period 1999-2008 to the increase that would have occurred in the absence of

18


any increase in drug vintage. As shown in Figure 1, during this period, mean age at death in-creased by approximately 3 years, from 63.6 to 66.6. The estimates imply that in the absence of any increase in drug vintage, mean age at death would have increased by only 0.6 years.

Figure 1 Comparison of the actual increase in mean age at death to the increase that would have occurred in the absence of any increase in drug vintage

67

66

65

64

63

621998 2000 2002 2004 2006 2008 2010

Actualno pharma innov (constant RX_YEAR2) 66,6

63,663,0

% of deaths at age ≥ 75 model

In the second model, the dependent variable is the fraction of deaths that occurred at an age greater than 75. The model formula was:

%AGE_GE_75it = β RX_VINTAGE

it + α

i + δ

t + ε

it (2)

where

%AGE_GE_75it = the fraction of deaths from disease i in year t in which the decedent’s age was

≥ 75 (t = 1999-2002, 2004-2008); 10 diseases (ICD8 chapters)

AGE_DEATH (mean age at death) is subject to error because mortality data are reported in age groups in Turkey. For this reason, an assumption was made that deaths in age group 65-75 all oc-



cur at age 70, for example. %AGE_GE_75 (% of deaths at age greater than or equal to 75) is not subject to error (in principle). However, estimates of the AGE_DEATH model are easier to interpret than estimates of the %AGE_GE_75 model. Table 5 below presents the estimates of this model.

Table 5. Estimates of % of deaths at age ≥ 75 model

ModelIndependent

VariableEstimate

(b)

Empirical Standard Error

EstimatesZ

Pr > |Z|

ΔY ΔX ßΔX(ßΔX)/

ΔY

4

RX_VINTAGE1: exclude

products with missing world launch years

0.0062 0.0026 2.37 0.0177 0.14 9.74 0.06 % 42

5

RX_VINTAGE2: set world

launch year = 1900 for


0.0038 0.0014 2.71 0.0068 0.14 9.84 0.04 % 26

6

RX_VINTAGE3: set world

launch year = 1920 for


0.0055 0.0024 2.35 0.019 0.14 9.23 0.05 % 36

As seen from the estimates, the vintage coefficients are positive and significant in all three mod-els. These estimates suggest that 26%-42% of the 0.14 increase in the % of deaths at age greater than 75 was due to pharmaceutical innovation.

3. 1. 2 Hospitalization Models

Hospital discharges model

Now, we will discuss the following model of the relationship between hospital discharges and pharmaceutical innovation:

ln(HOSP_DISCHARGESit) = β

k ln(CUM_MOL

i,t-k) + α

i + δ

t + ε

it (3)

where

HOSP_DISCHARGESit = number of hospital discharges for disease i in year t (t = 2007,…, 2010);

20


CUM_MOLi,t-k

= ∑m IND

mi APP

m,t-k = the number of molecules (drugs) to treat disease i commer-

cialized by the end of year t-k

INDmi

=1 if molecule m is used to treat (indicated for) disease i

= 0 if molecule m is not used to treat (indicated for) disease i

APPm,t-k

=1 if molecule m was commercialized in Turkey by the end of year t-k

= 0 if molecule m was not commercialized in Turkey by the end of year t-k

αi = a fixed effect for disease i

δt = a fixed effect for year t

Weighted least-squares estimates of βk from equation (3) are presented below in Table 6.

Table 6 Hospital discharges model estimates

Parameter Estimate

Empirical Standard

95% Lower Confidence

95% Upper Confidence

Z Pr > |Z|Error Esti-

matesLimit Limit

lcum_mol0 -0.219 0.381 -0.966 0.528 -0.58 0.5653

lcum_mol1 -0.267 0.347 -0.948 0.413 -0.77 0.4416

lcum_mol2 -0.333 0.234 -0.791 0.125 -1.43 0.1537

lcum_mol3 -0.374 0.187 -0.741 -0.006 -1.99 0.0462

lcum_mol4 -0.325 0.159 -0.637 -0.013 -2.04 0.0415

lcum_mol5 -0.201 0.154 -0.504 0.101 -1.30 0.1926

lcum_mol6 -0.018 0.158 -0.326 0.291 -0.11 0.9113

The estimates indicate that an increase in the number of molecules used to treat a disease re-duces the number of hospital discharges due to the disease 3 and 4 years later. The estimated elasticity when k=4 (when the Z value is largest) is -0.013: a 10% increase in the number of drugs for a disease reduces the number of hospital discharges due to the disease by 0.13% 4 years later. Diseases with larger increases in the cumulative number of molecules had smaller increases in the number of hospital discharges.



Hospital days model

We also estimated similar equations in which the dependent variable was ln(HOSP_DAYSit), where HOSP_DAYSit = the number of hospital days for disease i in year t; in this equation, the weight used was the total number of hospital days for disease i during the entire period (HOSP_DAYS

i).

ln(HOSP_DAYSit) = β

k ln(CUM_MOL

i,t-k) + α

i + δ

t + ε

it (4)

Model estimates are presented in Table 7, and interpretation of the model is presented in Figure 2

Table 7. Hospital days model estimates

Parameter Estimate

Empirical Standard

95% Lower Confidence

95% Upper Confidence

Z Pr > |Z|Error Esti-

matesLimit Limit

lcum_mol0 -0.166 0.312 -0.777 0.445 -0.53 0.5946

lcum_mol1 -0.481 0.283 -1.037 0.074 -1.70 0.0893

lcum_mol2 -0.270 0.205 -0.672 0.131 -1.32 0.1872

lcum_mol3 -0.147 0.222 -0.582 0.288 -0.66 0.5069

lcum_mol4 -0.409 0.185 -0.771 -0.047 -2.21 0.0268

lcum_mol5 -0.398 0.137 -0.666 -0.129 -2.91 0.0037

lcum_mol6 -0.156 0.130 -0.410 0.098 -1.20 0.2291

Diseases with larger increases in the cumulative number of molecules had smaller increases in the number of hospital days.

Figure 2 Hospital days, 2007-2010: Actual vs. in the absence of pharmaceutical innovation Index: 2007 = 1.001,30

1,25

1,20

1,25

1,10

1,05

1,00

0,95

0,90

2006 2007 2008 2009 2006 2010 2011

Actualno pharma innovation (constant lcum_nce5)

1,251,22

1,00

22


The results of this calculation are shown in Figure 2. According to the model estimates, the number of hospital days increased 22% during the period 2007-2010. The estimates of equation (4) indicate that in the absence of pharmaceutical innovation, the number of hospital days would have increased by 25% during this period. Hence, 3 years of pharmaceutical innovation reduced the number of hospital days in 2010 by approximately 3%. Pharmaceutical innovation reduced the number of hospital days by approximately 1% per year.

The results above all show that in line with other research in the literature, pharmaceutical inno-vation has increased longevity and reduced hospitalization in Turkey. As stated earlier, the findings and the study are unique as this is the first attempt of its kind. Given that there is a slowdown of the entry of new drugs to the market, policy-makers should be motivated to start looking at the outcomes of their earlier decisions to curb pharmaceutical expenditures. Although, statistically, the pharmaceutical expenditures are under control, this may have been accomplished at the expense of better health outcomes for the population. In addition, the additional cost savings from the decrease in hospital days should also be taken into account in the analysis of the re-sults of these policies. One way of looking at the value of money spent on any intervention is by undertaking a cost effectiveness analysis. The section below represents the cost effectiveness of pharmaceutical innovation in Turkey during the period 1999-2008.

3. 2 Incremental cost-effectiveness of pharmaceutical innovation in Tur-key, 1999-2008

We have presented estimates of the effect of pharmaceutical innovation on age at death (Table 4), % of deaths at age > 75 (Table 5) and hospital utilization (Tables 6 and 7). Now, we will use these estimates to calculate the incremental cost-effectiveness of pharmaceutical innovation, i.e., the cost per life-year gained from pharmaceutical innovation. We define the incremental cost-effec-tiveness ratio (ICER) as follows:

(İMEO) = MED_SPEND_LIFEactual-MED_SPEND_LIFEno_innov

LIFE_EXPECTactual-LIFE_EXPECTno_innov

where

MED_SPEND_LIFEactual = actual lifetime medical expenditure (projected based on 2008 data)

MED_SPEND_LIFEno_innov = estimated lifetime medical expenditure in the absence of the 9 pre-vious years of pharmaceutical innovation

LIFE_EXPECTactual = actual life expectancy (mean age at death) in 2008

LIFE_EXPECTno_innov = estimated life expectancy (mean age at death) in the absence of the 9 previous years of pharmaceutical innovation



Table 9 shows a baseline calculation of the ICER. Column 1 shows the actual value of life expec-tancy (mean age at death) in 2009 (67.1 years) and the estimated value (64.1 years,

derived from model 2 in Table 3) if no pharmaceutical innovation had occurred from 1999 to 2008. We estimate that life expectancy would have been 3 years lower in 2008 in the absence of pharmaceutical innovation.

We also estimate how much pharmaceutical innovation increased medical expenditures during the same period.

MED_SPEND_LIFEactual = MED_SPEND_YEARactual * LIFE_EXPECTactual

MED_SPEND_LIFEno_innov = MED_SPEND_YEARno_innov * LIFE_EXPECTno_innov

where

MED_SPEND_YEARactual = actual (annual) per capita medical expenditure in 2008

MED_SPEND_YEARno_innov = estimated per capita annual medical expenditure in 2008 in the ab-sence of the 9 previous years of pharmaceutical innovation = MED_SPEND_YEARactual-ΔMED_SPEND_YEAR

ΔMED_SPEND_YEAR = annual per capita medical expenditure in 2008 attributable to the 9 previous years of pharmaceutical innovation.

The 2008 actual values (expressed in USD PPP) were obtained from http://stats.oecd.org/. Ac-cording to the OECD data, the actual (annual) per capita health expenditure in 2008 in Turkey (MED_SPEND_YEAR

actual) was $906. As shown in Table 8, between 1999 and 2008, the real per

capita drug expenditure increased by $104. In this analysis, we assume that all of that increase was due to pharmaceutical innovation during 1999-2008. This assumption is very conservative as some of the $104 increase in real per capita drug expenditure was due to other factors. A spe-cial note about the increasing accessibility to health care services should be made here. As stated earlier, since 2003, major changes were made in the organization and financing of health care services, and utilization of health care services has increased to unprecedented levels. According to the MoH statistics, the annual average number of physician visits per person has increased to 8.2 in 2011 from 3.2 in 2002 (Ministry of Health, 2012). This and other possible factors, such as an aging population, indicate that there are factors other than pharmaceutical innovation contrib-uting to the increase in per capita drug expenditure over the years. However, we will maintain the conservative approach and assume that this increase was the result of pharmaceutical innovation during 1999-2008.

24


In our study, the hospitalization results indicate that pharmaceutical innovation during the study period reduced hospital expenditures in 2008 by approximately 9%. Unfortunately, the share of hospital expenditure in total health expenditures data is only available for the year 2000. Accord-ing to the available figures, hospital expenditures accounted for approximately 20% of total health expenditures in 2000 (OECD health data). This indicates that pharmaceutical innovation during 1999-2008 may have reduced per capita hospital expenditures in 2008 by approximately $16 (= 9% * 20% * $906); at least 16% of the increase in drug expenditure was offset by a reduction in hospital expenditure. We estimated that in the absence of the 9 previous years of pharmaceutical innovation, per capita medical expenditure in 2008 would have been no less than $818 (= 906 - 104 + 16).

Table 8. Rx expenditure, Turkey, 1999-2010

Year Rx expend (USD 000s)

Population (000s)

Per capita Rx expend (USD)

US CPI (2008=1.00)

Real per capita Rx expend (2008 USD)

1999 2. 083. 859 $ 63. 364 33 $ 0,77 43 $

2000 2. 430. 955 $ 64. 252 38 $ 0,80 48 $

2001 2. 119. 627 $ 65. 133 33 $ 0,82 40 $

2002 2. 665. 392 $ 66. 008 40 $ 0,84 48 $

2003 3. 707. 246 $ 66. 873 55 $ 0,85 64 $

2004 4. 500. 758 $ 67. 723 66 $ 0,88 75 $

2005 6. 939. 366 $ 68. 566 101 $ 0,91 111 $

2006 7. 289. 817 $ 69. 395 105 $ 0,94 112 $

2007 9. 412. 930 $ 70. 215 134 $ 0,96 139 $

2008 10. 553. 097 $ 71. 625 147 $ 1,00 147 $

2009 10. 172. 217 $ 72. 484 140 $ 1,00 140 $

2010 10. 520. 367 $ 73. 328 143 $ 1,01 141 $

Source: IMS MIDAS database; BLS

Based on the analysis so far, the ICER for pharmaceutical innovation is presented in Table 9 below.



Table 9. Estimation of ICER

Column (1) (2) (3) = (1) * (2) ICER ($)

Life expectancy (mean age at

death)

Annual per capita health expend

(USD)

Lifetime per cap-ita health expend

(USD)

2. 776Actual value in 2008 67.1 $906 $60,798

Estimated value in 2008 in the absence of the 9 previous years of phar-maceutical innovation

64.1a $818b $52,471

Difference 3.0 $88 $8,327

a: estimated from model 2

b: assuming that the entire 1999-2008 increase in real per capita pharmaceutical expenditure is due to the use of newer drugs

The results of the analysis show that the cost per life-year gained is $2,776. If the difference in life expectancy is half as large as that estimated from model 2—1.5 years instead of 3 years—the cost per life-year gained will be $4,808. The results indicate that innovative drugs are highly cost effective options for the Turkish health care system. Apart from their invaluable contribution to longevity, this contribution is also value for money. Given that the entrance of new molecules into the Turkish market has slowed down mainly because of the delays in the marketing approval and reimbursement decisions, the Turkish policy-makers should seriously consider taking measures to encourage new market entries.

26




4. CONCLUSION

This study aimed to explore the impact of pharmaceutical innovation on longevity, hospitaliza-tion and medical expenditures in Turkey. Longitudinal, disease-level data were used to analyze the impact of pharmaceutical innovation on longevity and medical expenditure during the period 1998-2010. The measures of longevity were based on the age distribution of deaths caused by a disease in a given year. Our estimates do not capture between-disease spillover effects, but data from other countries (e.g., France and Sweden) indicate that these effects are quite modest in practice: almost all of the increase in mean age at death was due to within-disease increases rather than a shift in the distribution of causes of death. The measure of pharmaceutical innova-tion we used was based on the mean vintage of drugs consumed.

From 1999 to 2008, mean age at death increased by 3.6 years, from 63.0 to 66.6 years. We estimated that in the absence of any pharmaceutical innovation, mean age at death would have increased by only 0.6 years. Hence, pharmaceutical innovation was estimated to have increased mean age at death in Turkey by 3.0 years during the period 1999-2008.

We also examined the effect of pharmaceutical innovation on hospital utilization. The estimates indicated that an increase in the number of molecules used to treat a disease reduces the number of hospital days due to the disease 3-4 years later. We estimated that pharmaceutical innovation has reduced the number of hospital days by approximately 1% per year.

We used our estimates of the effect of pharmaceutical innovation on age at death, hospital utiliza-tion and pharmaceutical expenditure to assess the incremental cost-effectiveness of pharmaceu-tical innovation, i.e., the cost per life-year gained from the introduction of new drugs. The baseline estimate of the cost per life-year gained from pharmaceutical innovation is $2,776. If the differ-ence in life expectancy is half as large, as our estimates indicate, the cost per life-year gained would be $4,808. Even the latter figure is a very small fraction of the leading economists’ esti-mates of the value of (or consumers’ willingness to pay for) a one-year increase in life expectancy.

28


REFERENCES

Auster, R. ; Leveson I. ; Sarachek , D. (1969 ) ”The Production of Health, an Exploratory Study”, The Journal of Human Resources, 4(4) (winter), 411-36.

Civan, A. ; Köksal, B. (2010) “The Effect of Newer Drugs on Health Spending: Do They Really Increase the Costs?”, Health Economics, 19, 581-595.

Crémieux, P. Y; Meilleur, M. C. ; Ouellette P. et al. (2005) “Public and private pharmaceutical spending as determinants of health outcomes in Canada”, Health Econ. 14(2), 107-116.

Cutler, D. M. ; McClellan, M. (2001), “Is technological change in medicine worth it’, Health Affairs, 20(5), 11-29.

Duggan, M. G. ; Evans, W. N. (2008) “Estimating the impact of medical innovation: a case study of HIV antiretroviral treatments”, Forum for Health Economics & Policy, 11 (Economics of the HIV Epidemic), Article 1. Available at: http://www. bepress. com/fhep/11/2/1.

Frech, H. E. ; Miller R. D. (2004) “The effects of pharmaceutical consumption and obesity on the quality of life in the OECD countries”, Pharmacoeconomics 22(2, Suppl. 2), 25-36.

Kanavos, P. (2005) Pharmaceutical reimbursement policy in Turkey, SUVAK, Ankara.

Law, M. ; Grépin, K. E. (2010) ”Is newer always better? Re-evaluating the benefits of newer phar-maceuticals”, Journal of Health Economics 29, 743-750.



Lichtenberg F. (1996) “Do (more and better) drugs keep people out of hospitals?”, The American Economic Review, 86(2), s. 384-388.

Lichtenberg, F. (2003) “The Effect of New Drugs on HIV Mortality in the U. S. , 1987-1998,” Eco-nomics and Human Biology, 1, 259-266.

Lichtenberg, F. (2004) “Sources of U. S. Longevity Increase, 1960-2001,” Quarterly Review of Economics and Finance, 44(3), 369-389.

Lichtenberg, F. (2005a), ‘Pharmaceutical knowledge capital accumulation and longevity’ in Corne-do, C. ; Haltiwenger, J; Sichel, D. (eds), Measuring Capital in the New Economy, University of Chicago Press, pp. 237-269.

Lichtenberg, F. (2005b) “The impact of new drug launches on longevity: evidence from longitu-dinal disease-level data from 52 countries, 1982-2001,” International Journal of Health Care Finance and Economics, 5, 47-73.

Lichtenberg, F. (2005c), ‘Availability of new drugs and Americans’ ability to work’, Journal of Occupational and Environmental Medicine, 47(4), 373-380.

Lichtenberg, F. (2005d), ‘The Impact of New Drug Launches on Longevity: Evidence from Longi-tudinal, Disease-Level Datafrom 52 Countries, 1982-2001’, International Journal of Health Care Finance and Economics, 5, 47-73.

Lichtenberg, F. (2006) “The effect of using newer drugs on admissions of elderly Americans to hospitals and nursing homes: state-level evidence from 1997-2003,” Pharmacoeconomics, 24 Suppl 3, 5-25.

Lichtenberg, F. Duflos, G (2008) ‘Pharmaceutical innovation and the longevity of Australians: A first look’, Beyond Health Insurance: Public Policy to Improve Health Advances in Health Econom-ics and Health Services Research, Volume 19, 95-117

Lichtenberg, F. (2009) “Have newer cardiovascular drugs reduced hospitalization? Evidence from longitudinal country-level data on 20 OECD countries, 1995-2003,” Health Economics 18 (5), 519-534.

Lichtenberg, F. (2012) “The contribution of pharmaceutical innovation to longevity growth in Ger-many and France, 2001-2007,” PharmacoEconomics, 30 (3), 197-211.

30


Lichtenberg, F. (2013a), ‘The Effect of Pharmaceutical Innovation on Longevity: Patient Level Ev-idence from the 1996-2002 Medical Expenditure Panel Survey and Linked Mortality Public-use Files’, Forum for Health Economics and Policy, 16(1): 1-33.

Lichtenberg, F. (2013b), ‘The impact of new (orphan) drug approvals on premature mortality from rare diseases in the United States and France, 1999-2007’, The European Journal of Health Economics, 14:41-56.

Liu, Y. , et. al. (2005) Health care and pharmaceutical spending in Turkey, SUVAK, Ankara.

OECD (2008), Pharmaceutical Pricing Policies in a Global Market, OECD Publishing, Paris

OECD (2011), Health at a Glance 2011: OECD Indicators, OECD Publishing, Geneva.

Romer, P. (1990), ‘Endogenous technological change’, Journal of Political Economy, 98, (5, Part 2) S71-S103.

Sağlık Bakanlığı, (2012), Sağlık İstatistikleri Yıllığı 2011, Sağlık Bakanlığı, Ankara

Shaw, J. W; Horrace, W. C. ; Vogel, R. J. “The determinants of life expectancy: an Analysis of the OECD health data”. South. Econ. J. 71(4), 768-783 (2005).

World Health Organization (1999), The World Health Report 1999: Making a difference, Geneva, WHO.



REPORT ON ACCESS TO NEW DRUGS

Table of ContentsREPORT ON ACCESS TO NEW DRUGS ............................................................................... 33

Purpose ...................................................................................................................... 33

Parameters Studied ..................................................................................................... 34

Scope ......................................................................................................................... 34

Method ....................................................................................................................... 34

Data Collection ............................................................................................................ 34

EVALUATION .................................................................................................................... 39

REFERENCES .................................................................................................................... 39

TablesTable 1. Taking USA-FDA as baseline, new molecular entities registered since 2012-2013 (Q2) by EU-EMA and TR-TITCK .......................................................................................... 40

Table 2. Drugs containing the new molecular entities registered as of 2012-2013 Q2 by EU-EMA and TR-TITCK .................................................................................................. 35

Table 3. Classification of the Drugs Newly Approved by EU-EMA and TR-TITCK on the Basis of USA-FDA as of 2013-2013-Q2 by ATC Groups ...................................................... 36

Table 4. Classification of the Drugs Newly Approved by USA-FDA and TR-TITCK on the Basis of EU-EMA as of 2013-2013-Q2 by ATC Groups ........................................................ 36

Table 5. Comparison of the New Molecular Entities (NMEs) Placed upon the Market by USA-FDA and EU-EMA between 2012 and 2013 (Q2) ......................................................... 37

Table 6. New drug approvals by years as of June 2013 ....................................................... 38

Table 7. Comparison of the New Molecular Entities (NMEs) Placed upon the Market by USA-FDA and EU-EMA in the Period from 2005 to 2013 (Q2) .............................................. 38


REPORT ON ACCESS TO NEW DRUGS

That faster access to new drugs enhances health and quality of life is supported in the literature by many studies. The present study was prepared for the purpose of putting forward the differ-ence in the practice between Turkey and the world in access to new drugs.

The comparison of the innovative molecules approved in Turkey (TR) between 2005 and 2001 with the innovative drug molecules approved by USA-FDA and EU-EMA was performed for the first time within the scope of the “Access of Patients to New Drugs” study (Kanzık, Hıncal). In the study, within the framework of updating the related study, Turkey, USA and EU member states were compared with respect to new molecular entities approved in 2012 and 2013 (Q2) and the data obtained from the study performed by Kanzık and Hıncal between 2005 and 2011 were updated as of June 30, 2013 and the access to drugs index for the period 2005-2013 (Q2) was determined.

Purpose:

The main purpose of the study is:

» To compare USA-FDA, EU-EMA and TR-TITCK (Turkish Drug and Medical Device Institution) with respect to new molecular entities approved in 2012 and 2013 (Q2) and to determine the access to drugs indices.

» To update the study performed by Kanzık and Hıncal between 2005 and 2011 as of June 30, 2013.

» To determine the access to drugs index for the period covering the years 2005 to 2013 (Q2).

» To compare USA-FDA, EU-EMA and TR-TITCK with respect to registration of drugs contain-ing new molecular entities and to conduct investigations and assessments on the processes of supplying drugs to patients in Turkey.

34


Parameters Studied:

In the period covering the years of 2012 and 2013 (Q2), the approvals for registration of new molecular entity (NME) in the USA, EU and Turkey were determined and the study previously con-ducted by IDE covering the years of 2005 to 2013 was updated and the access index for the 2nd quarter of 2013 was determined.

Scope:

» For each country, the new molecular entities with the first marketing authorizations covering the period from 2012 to Q2 2013 were listed.

» Subsequently, approvals for the new molecular entities for the operating period from 2005 to 2011 were updated as of June 30, 2013. Finally, the total access to drugs index for the period covering the years 2005 to 2013 (Q2) was assessed.

» For each molecule, the first marketing authorization was taken into consideration.

» Combination products were not included in the assessment.

» Diagnostics and vaccines were excluded.

Method:

The present report was prepared on the basis of the new drugs approved by the USA-FDA in 2012-2013 (Q2), in comparison with the approvals by EU-EMA and TR-TITCK granted in the same period.

Data Collection:

The data of the USA were obtained from FDA’s page called Approved Drug Products. http://www.accessdata.fda.gov/scripts/cder/drugsatfda/index.cfm

The data of the EU were collected from the websites of the EMA and the Commission. The present information for the period between 2012 and 2013 (Q2) was obtained from http://www.ema.europa.eu/ema/index.jsp?curl=pages/medicines/landing/epar_search.jsp&mid =WC0b01ac-058001d124&jsenabled=true ile http://mri.medagencies.org/Human/.

Database searching was conducted on the websites related with the USA-FDA and EU-EMA for molecular entities. Subsequently, database searching was performed on the ATC website of World Health Organization (http://www.whocc.no/atc_ddd_index/) and on the official websites of the drugs. For Turkey, the new drug registration list and the drug prices list published on the website of Turkish Drug and Medical Device Institution (www.titck.gov.tr) were scanned. The data in the study were obtained from the data published until July 1, 2013.



In the first phase, the products registered in the same period by EU-EMA and TR-TITCK were first compared with respect to molecular entities on the basis of the drugs containing new molecular entities registered in 2012-2013 (Q2) and the results of the comparison are presented in Table 1 (Annex 1).

During the investigations conducted, 9 products were detected that were approved by EU-EMA, but not approved by the FDA. Thereupon, this time, EMA was predicated upon and

it was compared with the drugs containing the new molecular entities registered as of 2012-2013 Q2 by TR-TITCK (Table 2).

Table 2. Drugs containing the new molecular entities registered as of 2012-2013 Q2 by EU-EMA and TR-TITCK

MOLECULAR ENTITY ATC CLASSEMA TURKEY

Date of Registration Date of Registration

Autologous cultured chondrocytes

Musculoskeletal System 27/06/2013 -

Nalmefen HCl dihydrateNervous System-Toxicology

Drugs-Antidotes25/02/2013 -

Lixisenatide Not defined yet-Diabetes 01/02/2013 -

Insulin degludec Not defined yet 21/01/2013 -

Concentrate of proteolytic enzymes enriched in

bromelainNot defined yet (Orphan drug) 18/12/2012 -

Dapagliflozin propanediol monohydrate

Digestive System and Metabolism - Blood Glucose

Lowering Drugs12/11/2012 -

Glycopyrronium bromideRespiratory system - Anticho-

linergics28/09/2012 -

CatridecacogBlood and Blood Forming

Organs - Blood Coagulation Factors

03/09/2012 -

Pixantrone maleateAntineoplastic and Immuno-

modulating Agents10/05/2012 -

As can be seen in the table above, Turkey has not approved any of the innovative drugs approved only by EMA between 2012 and 2013 (Q2).

The new molecular entities registered in the period from 2012 to 2013 (Q2) were also classified by ATC therapeutic groups (Table 3).

36


Table 3. Classification of the Drugs Newly Approved by EU-EMA and TR-TITCK on the Basis of USA-FDA as of 2013-2013-Q2 by ATC Groups

Therapeutic Class USA-FDA EU-EMA TR-TITCK

A-Digestive System and Metabolism 7 2 0

B-Blood and Blood Forming Organs 1 1 1

C-Cardiovascular System 2 0 0

D-Dermatologic Agents 0 0 0

G-Genitourinary System and Sex Hormones 3 2 0

H-Systemic Hormonal Preparations other than Sex Hormones and Insulin

1 1 0

J-Anti-infectives for Systemic Use 1 0 0

L-Antineoplastic and Immunomodulating Agents 14 3 0

M-Musculoskeletal System 0 0 0

N-Nervous System 2 1 0

O-Antiparasitic Products, Insecticides, Repellants 0 0 0

R-Respiratory system: 3 2 0

S-Sensory Organs 1 0 1

V-Other 1 1 0

Total 36 13 2

Table 4. Classification of the Drugs Newly Approved by USA-FDA and TR-TITCK on the Basis of EU-EMA as of 2013-2013-Q2 by ATC Groups

Therapeutic Class EU-EMA USA-FDA TR-TITCK

A- Digestive System and Metabolism 3 2 0

B-Blood and Blood Forming Organs 2 1 1

C-Cardiovascular System 0 0 0

D-Dermatologic Agents 0 0 0

G-Genitourinary System and Sex Hormones 2 2 0

H-Systemic Hormonal Preparations other than Sex Hormones and Insulin

1 1 0

J- Anti-infectives for Systemic Use 0 0 0

L-Antineoplastic and Immunomodulating Agents 4 3 0

M-Musculoskeletal System 1 0 0

N-Nervous System 2 1 0

O-Antiparasitic Products, Insecticides, Repellants 0 0 0

R-Respiratory system: 3 2 0



Therapeutic Class EU-EMA USA-FDA TR-TITCK

S-Sensory Organs 0 0 0

V-Other 1 1 0

Not known yet 3 - 0

Total 22 13 1

When new molecular entity (NME) launches and access to drugs in Turkey were compared with the total number of compounds approved in the USA and EU, it was determined that

» while the rate of the new drugs approved in our country was 29% in the period from 2005 to 2011,

» it fell to 4% in the period 2012 to -2013 Q2 and that many patients in our country could barely benefit from many drugs and treatments in the same periods as USA and EU (Table 5).

Table 5. Comparison of the New Molecular Entities (NMEs) Placed upon the Market by USA-FDA and EU-EMA between 2012 and 2013 (Q2)

Countries

NMEs Placed upon the market in

2012-2013 Q2

NMEs Available both in Turkey and the Com-pared Country

NMEs Available in Turkey but

not Available in the Compared

Country

NMEs Available in the Compared

Country but not Available in

Turkey

Access to Drugs Index

ABD-FDA + EU-EMA

45 2 0 43 1

TR-TITCK 2 0.04

USA-FDA 36 2 0 34 0.8

EU-EMA 22 1 0 37 0.48

*Access to drugs index was calculated by dividing the number of NMEs placed upon the market in each country by 45, which is the total number of NMEs (36 in the USA and 9 in EU) placed upon the market in the USA and EU

Despite the fact that 36 new molecules were approved by FDA and 22 by EMA in the period from 2012 to 2013 Q2, only two of the products were approved in Turkey during the same period.

In addition, the 19 new molecular entities approved with the 2 aforementioned molecular entities in TR in the relative period were registered and provided to patients by EMA and FDA before 2012.

The findings above indicate that access to drugs in our country falls behind the USA and EU countries, that our patients are not able to benefit from many new drugs and treatments simul-taneously with developed countries and that they have to wait for a long time to benefit from the new treatments.

38


Table 6. New drug approvals by years as of June 2013

YEAR USA-FDA EU-EMA TR-TITCK

2005 16 5 2

2006 20 12 3

2007 17 27 5

2008 20 14 12

2009 22 26 8

2010 17 16 4

2011 30 21 19

2012 28 21 10

Q2 2013 8 10 11

Toplam 178 152+6* 74+1*

*The drugs registered before 2005

FDA made a great leap in the approval processes of new molecular entities and biological drugs through the latest practices it has implemented (such as Breakthrough, Fast track designation, accelerated approval and priority review). Consequently, it seems that Turkey falls far behind the USA and EU with respect to the number of yearly approvals. When the number of approvals of new drugs by years is examined, it is seen that FDA approves 21 new drugs and EU approves 18 drugs, whereas TR-TITCK approves 9 new drugs per year on average.

Table 7. Comparison of the New Molecular Entities (NMEs) Placed upon the Market by USA-FDA and EU-EMA in the Period from 2005 to 2013 (Q2)

Countries

NMEs Placed upon the market in

2005-2013 Q2

NMEs Present both in Turley and the Com-pared Country

NMEs Available in Turkey but

not Available in the Compared

Country

NMEs Available in the Compared

Country but not Available in

Turkey

Access to Drugs Index

ABD-FDA + EU-EMA

210 75 0 135 1

TR-TITCK 75 0.36

USA-FDA 178 67 8 111 0.85

EU-EMA 158 75 0 83 0.75



Evaluation

The positive contribution of new drugs to survival and quality of life has been put forward in detail in many studies. Many new drugs launched in the USA and Europe wait in our country for quite a long time in registration process. This delays the access of patients to new drugs and treatments. The process of bringing innovative drugs to the use of patients takes a longer time in our country compared with developed countries. The difference between the number of the innovative drugs registered and put into the service of patients by FDA and EMA and the number of new drugs registered in Turkey in the same period is gradually increasing. When the launches of innovative drugs and access to drugs is compared with the number of innovative drugs approved by FDA and EMA between 2005 and 2013 (Q2), it is observed that whereas the rate of innovative drugs registered in our country is around 36%, it has fallen to 4% as of 2012-2013 Q2. The above findings mean that the patients in our country are unable to benefit from many innovative drugs and treatment opportunities simultaneously with developed countries and that they have to wait for years to use the innovative treatments in question.

While FDA registered 28 innovative drugs in 2012, EMA registered 21 and Turkey-TITCK regis-tered only 10 innovative products in the same year. 4 of the innovative drugs approved by FDA in the said period were orphan drugs, and 9 were approved within the framework of the “priority review” program applied by FDA to drugs promising a significant improvement in the treatment.

When the number of innovative drugs approved by FDA and EMA between 2005 and 2011 is compared with the number of innovative drugs approved in our country, the rate of innovative drugs approved in our country is estimated to be 1/3, which fell to 1/25 as of 2012-2013 Q2.

Today, a majority of the innovative drugs particularly intended for cancer, organ transplants, renal and cardiovascular diseases approved by the USA-FDA and EU-EMA are not registered in Turkey. Therefore, it is essential that detailed studies be performed on access to innovative drugs in our country and the effect of contributions that may be made by those drugs, such as the increase in survival and quality of life and that this issue be brought to the agenda of policy makers.

Source

Kanzık, İ.; Hıncal, A. (no date), Access of Patients to Innovative Drugs. Comparison of the Regis-tration Processes of Drugs Containing New Molecular Entities in Turkey with those in the USA and EU Countries, IDE Scientific and Sectoral Reports Series 1.

40


Na

me

of th

e m

olec

ular

ent

ityAT

C Cl

ass-

Indi

catio

nUS

A-FD

AEU

-EM

ATR

-MoH

TIT

CK

Trad

e Na

me

Firm

Date

of

Regi

stra

tion

Trad

e Na

me

Firm

Date

of

Regi

stra

tion

Trad

e Na

me

Firm

Date

of

Regi

stra

tion

1Da

braf

enib

M

esyla

teAn

tineo

plas

tics

and

Imm

unom

odul

atin

g Dr

ugs

- M

elan

oma

TAFI

NLAR

GLAX

OSM

ITH-

KLIN

E29

/05/

2013

-

- -

-

-

-

2Tr

amet

inib

Dim

eth-

yl Su

lfoxid

eAn

tineo

plas

tics

and

Imm

unom

odul

atin

g Dr

ugs

- M

elan

oma

MEK

INIS

TGL

AXOS

MIT

H-KL

INE

LLC

29/0

5/20

13

- -

- -

-

-

3Ca

nagl

ifloz

inDi

gest

ive

Syst

em a

nd M

etab

olis

m -

Typ

e 2

Diab

etes

INVO

KANA

JANS

SEN

PHAR

MS

29/0

3/20

13 -

- -

-

-

-

4Di

met

hyl F

umar

ate

Nerv

ous

Syst

em -

Mul

tiple

Scl

eros

isTE

CFID

ERA

BIOG

EN ID

EC IN

C27

/03/

2013

- -

- -

-

-

5Os

pem

ifene

Geni

to-U

rinar

y Sy

stem

and

Sex

Hor

mon

es -

Se-

lect

ive O

estro

gen

Rece

ptor

Mod

ulat

ors

- In

dica

ted

for d

yspa

reun

iaOS

PHEN

ASH

IONO

GI IN

C26

/02/

2013

- -

- -

-

-

6Po

mal

idom

ide

Antin

eopl

astic

s an

d Im

mun

omod

ulat

ing

Drug

s -

Mul

tiple

Mye

lom

aPO

MAL

YST

CELG

ENE

08/0

2/20

13 -

- -

-

-

-

7M

ipom

erse

n So

dium

Card

iova

scul

ar S

yste

m D

rugs

- C

hole

ster

ol

Low

erin

g Ag

ents

KYNA

MRO

GENZ

YME

CORP

29/0

1/20

13 -

- -

-

-

-

8Al

oglip

tin B

enzo

ate

Dige

stiv

e Sy

stem

and

Met

abol

ism

- T

ype

2 Di

abet

esNE

SINA

TAKE

DA

PHAR

MS

USA

25/0

1/20

13 -

- -

-

-

-

9Cr

ofel

emer

Dige

stiv

e Sy

stem

and

Met

abol

ism

- O

ther

Ant

idi-

arrh

oeal

Dru

gs -

Indi

cate

d fo

r Dia

rrhoe

a As

soci

ated

w

ith A

nti-H

IV D

rugs

FULY

ZAQ

SALI

X PH

ARM

S31

/12/

2012

- -

- -

-

-

10Ap

ixaba

nBl

ood

and

Bloo

d Fo

rmin

g Or

gans

- A

ntith

rom

bot-

ic -

Fac

tor X

a Di

rect

Inhi

bito

r - S

troke

and

Sys

tem

ic

Embo

lism

ELIQ

UIS

BRIS

TOL

MYE

RS

SQUI

BB28

/12/

2012

ELIQ

UIS

BRIS

TOL

MYE

RS

SQUI

BB18

/ 05/

201

1EL

IQUI

SBR

ISTO

L M

YERS

SQ

UIBB

25/1

0/20

12

11Be

daqu

iline

Fum

arat

e

Syst

emic

ally

Use

d An

ti-In

fect

ive

- Ot

her T

uber

cu-

losi

s Dr

ugs

- In

dica

ted

for r

esis

tanc

e de

velo

ped

in

mul

tiple

dru

g us

e in

tube

rcul

osis

SIRT

URO

JANS

SEN

THER

AP28

/12/

2012

- -

- -

-

-

12Te

dugl

utid

e Re

com

bina

ntDi

gest

ive

Syst

em a

nd M

etab

olis

m -

Typ

e 2

Diab

etes

GATT

EX K

ITNP

S PH

ARM

S IN

C21

/12/

2012

REVE

STIV

ENY

COM

ED

DENM

ARK

APS

30/0

8/20

12 -

-

-

13Lo

mita

pide

M

esyla

teCa

rdio

vasc

ular

Sys

tem

- O

ther

Lip

id R

egul

atin

g -

Fam

ilial H

yper

chol

este

role

mia

JUXT

APID

AEGE

RION

21/1

2/20

12 -

- -

-

-

-

14Po

natin

ib H

ydro

-ch

lorid

eAn

tineo

plas

tics

and

Imm

unom

odul

atin

g Dr

ugs

- Ch

roni

c M

yelo

id L

euka

emia

ICLU

SIG

ARIA

D14

/12/

2012

- -

- -

-

-

15Pa

sire

otid

e Di

aspa

rtate

Syst

emic

Hor

mon

al P

repa

ratio

ns -

Som

atos

tatin

e An

alog

ue -

Cus

hing

’s S

yndr

ome

SIGN

IFOR

NOVA

RTIS

14/1

2/20

12SI

GNIF

ORNO

VART

IS24

/04/

2012

-

-

-

16Ca

boza

ntin

ib

S-M

alat

e

Antin

eopl

astic

s an

d Im

mun

omod

ulat

ing

Drug

s -

Prot

ein

Kina

se In

hibi

tor -

non

resp

onsi

ve T

hyro

id

Canc

erCO

MET

RIQ

EXEL

IXIS

29/1

1/20

12 -

- -

-

-

-

17To

faci

tinib

Citr

ate

Antin

eopl

astic

s an

d Im

mun

omod

ulat

ing

Drug

s -In

flam

mat

ory

Bow

el D

isea

se a

nd O

ther

Imm

uno-

logi

c Di

seas

esXE

LJAN

ZPF

IZER

06/1

1/20

12 -

- -

-

-

-

18Om

acet

axin

e M

epes

ucci

nate

Antin

eopl

astic

s an

d Im

mun

omod

ulat

ing

Drug

s -

Chro

nic

Mye

loid

Leu

kaem

iaSY

NRIB

OIV

AX IN

TL26

/10/

2012

- -

- -

-

-

19Pe

ram

pane

lNe

rvou

s Sy

stem

- A

ntie

pile

ptic

- P

artia

l Ons

et

Epile

psy

Seizu

reFY

COM

PAEI

SAI I

NC22

/10/

2012

FYCO

MPA

EISA

I EUR

OPE

LTD.

23/0

7/20

12 -

-

-

Addi

tiona

l 1Ta

ble

1. Ta

king

USA

-FDA

as

base

line,

new

mol

ecul

ar e

ntiti

es re

gist

ered

sin

ce 2

012-

2013

(Q2)

by

EU-E

MA

and

TR-T

ITCK



Na

me

of th

e m

olec

ular

ent

ityAT

C Cl

ass-

Indi

catio

nUS

A-FD

AEU

-EM

ATR

-MoH

TIT

CK

Trad

e Na

me

Firm

Date

of

Regi

stra

tion

Trad

e Na

me

Firm

Date

of

Regi

stra

tion

Trad

e Na

me

Firm

Date

of

Regi

stra

tion

1Da

braf

enib

M

esyla

teAn

tineo

plas

tics

and

Imm

unom

odul

atin

g Dr

ugs

- M

elan

oma

TAFI

NLAR

GLAX

OSM

ITH-

KLIN

E29

/05/

2013

-

- -

-

-

-

2Tr

amet

inib

Dim

eth-

yl Su

lfoxid

eAn

tineo

plas

tics

and

Imm

unom

odul

atin

g Dr

ugs

- M

elan

oma

MEK

INIS

TGL

AXOS

MIT

H-KL

INE

LLC

29/0

5/20

13

- -

- -

-

-

3Ca

nagl

ifloz

inDi

gest

ive

Syst

em a

nd M

etab

olis

m -

Typ

e 2

Diab

etes

INVO

KANA

JANS

SEN

PHAR

MS

29/0

3/20

13 -

- -

-

-

-

4Di

met

hyl F

umar

ate

Nerv

ous

Syst

em -

Mul

tiple

Scl

eros

isTE

CFID

ERA

BIOG

EN ID

EC IN

C27

/03/

2013

- -

- -

-

-

5Os

pem

ifene

Geni

to-U

rinar

y Sy

stem

and

Sex

Hor

mon

es -

Se-

lect

ive O

estro

gen

Rece

ptor

Mod

ulat

ors

- In

dica

ted

for d

yspa

reun

iaOS

PHEN

ASH

IONO

GI IN

C26

/02/

2013

- -

- -

-

-

6Po

mal

idom

ide

Antin

eopl

astic

s an

d Im

mun

omod

ulat

ing

Drug

s -

Mul

tiple

Mye

lom

aPO

MAL

YST

CELG

ENE

08/0

2/20

13 -

- -

-

-

-

7M

ipom

erse

n So

dium

Card

iova

scul

ar S

yste

m D

rugs

- C

hole

ster

ol

Low

erin

g Ag

ents

KYNA

MRO

GENZ

YME

CORP

29/0

1/20

13 -

- -

-

-

-

8Al

oglip

tin B

enzo

ate

Dige

stiv

e Sy

stem

and

Met

abol

ism

- T

ype

2 Di

abet

esNE

SINA

TAKE

DA

PHAR

MS

USA

25/0

1/20

13 -

- -

-

-

-

9Cr

ofel

emer

Dige

stiv

e Sy

stem

and

Met

abol

ism

- O

ther

Ant

idi-

arrh

oeal

Dru

gs -

Indi

cate

d fo

r Dia

rrhoe

a As

soci

ated

w

ith A

nti-H

IV D

rugs

FULY

ZAQ

SALI

X PH

ARM

S31

/12/

2012

- -

- -

-

-

10Ap

ixaba

nBl

ood

and

Bloo

d Fo

rmin

g Or

gans

- A

ntith

rom

bot-

ic -

Fac

tor X

a Di

rect

Inhi

bito

r - S

troke

and

Sys

tem

ic

Embo

lism

ELIQ

UIS

BRIS

TOL

MYE

RS

SQUI

BB28

/12/

2012

ELIQ

UIS

BRIS

TOL

MYE

RS

SQUI

BB18

/ 05/

201

1EL

IQUI

SBR

ISTO

L M

YERS

SQ

UIBB

25/1

0/20

12

11Be

daqu

iline

Fum

arat

e

Syst

emic

ally

Use

d An

ti-In

fect

ive

- Ot

her T

uber

cu-

losi

s Dr

ugs

- In

dica

ted

for r

esis

tanc

e de

velo

ped

in

mul

tiple

dru

g us

e in

tube

rcul

osis

SIRT

URO

JANS

SEN

THER

AP28

/12/

2012

- -

- -

-

-

12Te

dugl

utid

e Re

com

bina

ntDi

gest

ive

Syst

em a

nd M

etab

olis

m -

Typ

e 2

Diab

etes

GATT

EX K

ITNP

S PH

ARM

S IN

C21

/12/

2012

REVE

STIV

ENY

COM

ED

DENM

ARK

APS

30/0

8/20

12 -

-

-

13Lo

mita

pide

M

esyla

teCa

rdio

vasc

ular

Sys

tem

- O

ther

Lip

id R

egul

atin

g -

Fam

ilial H

yper

chol

este

role

mia

JUXT

APID

AEGE

RION

21/1

2/20

12 -

- -

-

-

-

14Po

natin

ib H

ydro

-ch

lorid

eAn

tineo

plas

tics

and

Imm

unom

odul

atin

g Dr

ugs

- Ch

roni

c M

yelo

id L

euka

emia

ICLU

SIG

ARIA

D14

/12/

2012

- -

- -

-

-

15Pa

sire

otid

e Di

aspa

rtate

Syst

emic

Hor

mon

al P

repa

ratio

ns -

Som

atos

tatin

e An

alog

ue -

Cus

hing

’s S

yndr

ome

SIGN

IFOR

NOVA

RTIS

14/1

2/20

12SI

GNIF

ORNO

VART

IS24

/04/

2012

-

-

-

16Ca

boza

ntin

ib

S-M

alat

e

Antin

eopl

astic

s an

d Im

mun

omod

ulat

ing

Drug

s -

Prot

ein

Kina

se In

hibi

tor -

non

resp

onsi

ve T

hyro

id

Canc

erCO

MET

RIQ

EXEL

IXIS

29/1

1/20

12 -

- -

-

-

-

17To

faci

tinib

Citr

ate

Antin

eopl

astic

s an

d Im

mun

omod

ulat

ing

Drug

s -In

flam

mat

ory

Bow

el D

isea

se a

nd O

ther

Imm

uno-

logi

c Di

seas

esXE

LJAN

ZPF

IZER

06/1

1/20

12 -

- -

-

-

-

18Om

acet

axin

e M

epes

ucci

nate

Antin

eopl

astic

s an

d Im

mun

omod

ulat

ing

Drug

s -

Chro

nic

Mye

loid

Leu

kaem

iaSY

NRIB

OIV

AX IN

TL26

/10/

2012

- -

- -

-

-

19Pe

ram

pane

lNe

rvou

s Sy

stem

- A

ntie

pile

ptic

- P

artia

l Ons

et

Epile

psy

Seizu

reFY

COM

PAEI

SAI I

NC22

/10/

2012

FYCO

MPA

EISA

I EUR

OPE

LTD.

23/0

7/20

12 -

-

-

Na

me

of th

e m

olec

ular

ent

ityAT

C Cl

ass-

Indi

catio

nUS

A-FD

AEU

-EM

ATR

-MoH

TIT

CK

Trad

e Na

me

Firm

Date

of

Regi

stra

tion

Trad

e Na

me

Firm

Date

of

Regi

stra

tion

Trad

e Na

me

Firm

Date

of

Regi

stra

tion

20Re

gora

feni

bAn

tineo

plas

tics

and

Imm

unom

odul

atin

g Dr

ugs

- Pr

otei

n Ki

nase

Inhi

bito

rST

IVAR

GABA

YER

HLTH

-CA

RE27

/09/

2012

- -

- -

-

-

21Te

riflun

omid

eAn

tineo

plas

tics

and

Imm

unom

odul

atin

g Dr

ugs

- M

ultip

le S

cler

osis

AUBA

GIO

SANO

FI A

VENT

IS

US12

/09/

2012

- -

- -

-

-

22Bo

sutin

ib M

ono-

hydr

ate

Antin

eopl

astic

s an

d Im

mun

omod

ulat

ing

Drug

s -

Prot

ein

Kina

se In

hibi

tors

- C

hron

ic M

yelo

id

Leuk

aem

ia

BOSU

LIF

WYE

TH P

HARM

S IN

C04

/09/

2012

BOSU

LIF

PFIZ

ER L

TD.

27/0

3/20

13 -

-

-

23En

zalu

tam

ide

Antin

eopl

astic

s an

d Im

mun

omod

ulat

ing

Drug

s -

Andr

ogen

Rec

epto

r Ant

agon

ist -

Pro

stat

e Ca

ncer

XTAN

DIAS

TELL

AS31

/08/

2012

- -

- -

-

-

24Li

nacl

otid

eDi

gest

ive

Syst

em a

nd M

etab

olis

m -

Indi

cate

d fo

r co

nstip

atio

nLI

NZES

SFO

REST

LAB

S IN

C30

/08/

2012

CONS

TELL

AAL

MIR

ALL,

S.A

.26

/11/

2012

-

-

-

25Ac

lidin

ium

Bro

mid

eRe

spira

tory

Sys

tem

- In

dica

ted

for A

ntic

holin

ergi

c Ch

roni

c Br

onch

itis

and

Bron

chos

pasm

TUDO

RZA

PRES

SAIR

FORE

ST L

ABS

INC

23/0

7/20

12EK

LİRA

GEN

UAİR

VE

BRE

TARİ

S GE

NUAİ

RAL

MİR

ALL,

S.A

. 20

/07/

2013

-

-

-

26Ca

rfilzo

mib

Antin

eopl

astic

s an

d Im

mun

omod

ulat

ing

Drug

s -

Sele

ctive

Pro

teas

ome

Inhi

bito

r, In

dica

ted

for t

he

Trea

tmen

t of M

ultip

le M

yelo

ma

KYPR

OLIS

ONYX

PHA

RMS

20/0

7/20

12 -

- -

-

-

-

27M

irabe

gron

Geni

to-U

rinar

y Sy

stem

and

Sex

Hor

mon

es -

Uro

l-og

y -

Over

activ

e Bl

adde

r and

Urin

e In

cont

inen

ceM

YRBE

TRIQ

APGD

I28

/06/

2012

BETM

IGA

ASTE

LLAS

PH

ARM

A EU

ROPE

B.V

.20

/12/

2012

-

-

-

28Lo

rcas

erin

Hyd

ro-

chlo

ride

Dige

stiv

e Sy

stem

and

Met

abol

ism

- A

nti-o

be-

sity

Pro

duct

s -

Indi

cate

d fo

r Chr

onic

Ove

rwei

ght

Man

agem

ent

BELV

IQEI

SAI I

NC27

/06/

2012

- -

- -

-

-

29Ta

liglu

cera

se A

lfaDi

gest

ive

Syst

em a

nd M

etab

olis

m -

Gau

cher

’s

Dise

ase

ELEL

YSO

PFIZ

ER01

/05/

2012

- -

- -

-

-

30Av

anafi

lGe

nito

-Urin

ary

Syst

em a

nd S

ex H

orm

ones

-

Urol

ogy

- Er

ectil

e Dy

sfun

ctio

nST

ENDR

AVI

VUS

27/0

4/20

12SP

EDRA

VIVU

S21

/06/

2013

-

-

-

31Lu

cina

ctan

tRe

spira

tory

Sys

tem

- P

ulm

onar

y Su

rfact

ants

-

Resp

irato

ry D

istre

ss S

yndr

ome

SURF

AXIN

DISC

OVER

Y LA

BS06

/03/

2012

- -

- -

-

-

32Ta

flupr

ost

Sens

ory

Orga

ns -

Oph

thal

mol

ogic

Pro

stag

land

in

Anal

ogue

s -

Open

-Ang

le G

lauc

oma

and

Eye

Hype

rtens

ion

ZIOP

TAN

MER

CK S

HARP

DO

HME

10/0

2/20

12 -

- -

SAFL

UTAN

MER

CK S

HARP

DO

HME

05/1

0/20

12

33Iva

cafto

rRe

spira

tory

Sys

tem

Age

nts

- Re

spira

tory

Sys

tem

Di

sord

ers

- Cy

stic

Fib

rosi

sKA

LYDE

COVE

RTEX

PHA

RMS

31/0

1/20

12KA

LYDE

COVE

RTEX

PHA

R-M

ACEU

TİCA

LS

(U.K

.) LT

D.23

/07/

2012

-

-

-

34Vi

smod

egib

Antin

eopl

astic

s an

d Im

mun

omod

ulat

ing

Drug

s -

Basa

l Cel

l Car

cino

ma

ERIV

EDGE

GENE

NTEC

H30

/01/

2012

- -

- -

-

-

35Ax

itini

bAn

tineo

plas

tics

and

Imm

unom

odul

atin

g Dr

ugs

- Pr

otei

n Ki

nase

Inhi

bito

rIN

LYTA

GENE

NTEC

H27

/01/

2012

INLY

TAPF

İZER

LTD

03/0

9/20

12 -

-

-

36In

geno

l Meb

utat

eOt

her C

hem

othe

rape

utic

s -

Actin

ic K

erat

osis

PICA

TOLE

O PH

ARM

A AS

23/0

1/20

12Pİ

CATO

LEO

PHAR

MA

A/S

15/1

1/20

12 -

-

-

Date post:	24-Aug-2020
Category:	Documents
Upload:	others
View:	0 times
Download:	0 times

The Effects of Pharmaceutical Innovation on Longevity ... · Longevity, Hospitalization and Health...

Documents