The impact of pharmaceutical innovation on cancer mortality in Mexico, 2003-2013

Lichtenberg, Frank R. Article The impact of pharmaceutical innovation on cancer mortality in Mexico, 2003-2013 Latin American Economic Review Provided in Cooperation with: Centro de Investigación y Docencia Económica (CIDE), Mexico City Suggested Citation: Lichtenberg, Frank R. (2017) : The impact of pharmaceutical innovation on cancer mortality in Mexico, 2003-2013, Latin American Economic Review, ISSN 2196-436X, Springer, Heidelberg, Vol. 26, Iss. 1, pp. 1-22, https://doi.org/10.1007/s40503-017-0045-6 This Version is available at: https://hdl.handle.net/10419/195244 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ The impact of pharmaceutical innovation on cancer mortality in Mexico, 2003–2013 Frank R. Lichtenberg 1,2 Received: 31 January 2017 / Revised: 26 June 2017 / Accepted: 26 September 2017 / Published online: 17 October 2017 The Author(s) 2017. This article is an open access publication Abstract I assess the impact that pharmaceutical innovation had on cancer mortality in Mexico during the period 2003–2013, by investigating whether there were larger declines in the age-standardized mortality rate of cancer sites (breast, lung, colon, etc.) that were subject to more pharmaceutical innovation, controlling for changes in the age-standardized cancer incidence rate. The estimates indicate that new drugs launched during 1991–2001 reduced the age-standardized cancer mortality rate by 16%, i.e., at an average annual rate of about 1.6%. I estimate that 105,661 life-years before age 70 were gained in 2013 due to cancer drugs launched during 1991–2001, and that the cost per life-year gained was in the neighborhood of $2146. By the standards of the World Health Organization, new cancer drugs have been very cost-effective in Mexico. The contribution of cancer drug innovation to Mexican longevity growth has been valuable, but, perhaps, it could have been even larger. Only half as many new cancer drugs were launched in Mexico during 2010–2014 as were launched in the US. In addition, when new drugs are launched in Mexico, their diffusion tends to be quite slow. Keywords Pharmaceutical Innovation Mortality Longevity Cancer Mexico  Cost-effectiveness JEL classification I1 J11 L65 O33 &Frank R. Lichtenberg [email protected] 1 Columbia University, New York, NY, USA 2 NBER, Cambridge, MA, USA 123 Lat Am Econ Rev (2017) 26:8 https://doi.org/10.1007/s40503-017-0045-6 1 Introduction Cancer mortality has declined in Mexico during the last 2 decades. As shown in Fig. 1, the age-standardized cancer mortality rate 1 of males declined by 13%, and that of females declined by 11%, between 1995 and 2014. 2 The mortality rate, or (unconditional) probability of death from cancer, depends to an important extent on two variables: the probability of getting (being diagnosed with) cancer, and the probability of dying from cancer, conditional on having been diagnosed with cancer: prob(death) &prob(diagnosis) * prob(death|diagnosis). 3 Therefore, the decline in the mortality rate could be due to either a decline in cancer incidence 4 ,a decline in the probability of dying from cancer, conditional on having been diagnosed with cancer (e.g., due to improved treatment), or both. The Mexican cancer incidence rate declined by 13% (from 147.3 to 128.4) between 2002 and 2008, although it increased 2% (from 128.4 to 131.5) between 2008 and 2012. 5 Since the cancer incidence rate declined at least as rapidly as the cancer mortality rate in recent years, the decline in cancer mortality could be entirely due to declining cancer incidence. However, the measurement of cancer incidence is subject to significant potential errors. For example, a decline in cancer surveillance or screening could lead to a decline in measured cancer incidence, even when true incidence is not declining. The previous studies (Lichtenberg 2014a,2015,2016a,b) have shown that pharmaceutical innovation—the introduction and use of new cancer drugs—has significantly reduced cancer mortality in countries at a ‘‘very high’’ level of human development (as defined by the United Nations Development Programme 6 ). In this study, I will assess the impact that pharmaceutical innovation and cancer incidence had on cancer mortality in Mexico, a country at a lower (but still ‘‘high’’) level of 1 An age-standardized rate (ASR) is a summary measure of the rate that a population would have if it had a standard age structure. Standardization is necessary when comparing several populations that differ with respect to age, because age has a powerful influence on the risk of cancer. The ASR is a weighted mean of the age-specific rates; the weights are taken from population distribution of the standard population. The most frequently used standard population is the World Standard Population. The calculated incidence or mortality rate is then called age-standardized incidence or mortality rate (world). It is also expressed per 100,000. See http://globocan.iarc.fr/Pages/glossary.aspx#MORTALITY. 2 In addition, between 1998 and 2013, mean age at death from cancer also increased by 1.8 years, from 62.3 to 64.1. Source: author’s calculations based on WHO Mortality Database (World Health Organization (2016b)). 3 This approximation assumes that the probability that someone who has never been diagnosed with cancer dies from cancer is quite small. This is plausible, because the cancer mortality rate (the unconditional probability of dying from cancer) is about half as great as the cancer incidence rate (the probability of being diagnosed with cancer). 4 Incidence is the number of new cases arising in a given period in a specified population. This information is collected routinely by cancer registries. It can be expressed as an absolute number of cases per year or as a rate per 100,000 persons per year. 5 Source: OECD Health Statistics 2016 database. Data on incidence prior to 2002 are not available. The decline in incidence may be due, in part, to a decline in cigarette smoking, a major risk factor for lung cancer. Between 2002 and 2015, the fraction of the population aged 15 ?who are daily smokers declined from 12.4 to 7.6%. 6 http://hdr.undp.org/en/countries. 8Page 2 of 22 Lat Am Econ Rev (2017) 26:8 123 human development. As in the previous studies, a difference-in-difference research design will be used: I will investigate whether the decline in mortality was greater for cancer sites (breast, lung, colon, etc.) subject to more pharmaceutical innovation and greater declines in incidence. As shown in Fig. 2, the rate of decline in the mortality rate varied considerably across cancer sites. The mortality rate declined by at least 34% for 3 cancer sites (cervix, stomach, and lung), but increased for 3 other cancer sites (colon, ovary, and breast). In Sect. 2, I will formulate an econometric model of cancer mortality. The data sources used to estimate these models are described in Sect. 3. Empirical results are presented in Sect. 4. Rough estimates of the number of life-years gained in 2013 from the reduction in cancer mortality attributable to pharmaceutical innovation, and of the average cost-effectiveness (cost per life-year gained) of new cancer drugs, are developed in Sect. 5. Sect. 6provides a summary and conclusions. 2 Econometric model of cancer mortality The basic model which I will use to assess the impact of pharmaceutical innovation and cancer incidence on age-standardized cancer mortality rates in Mexico is: MORTst ¼bkCUM NCEs;tkþcINCIDENCEs;t1þasþdtþest;ð1Þ where MORT st =the age-standardized mortality rate from cancer at site sin year t (t=2003, 2013); CUM_NCE s,t–k =P d IND ds LAUNCHED d,t-k = the number of 77.09 65.46 72.1 61.33 55 60 65 70 75 80 41029002400299914991 Male ASR (W) Female ASR (W) Fig. 1 Age-standardized cancer mortality rates, by sex, Mexico, 1995–2014 (Source: WHO Cancer Mortality database, http://www-dep.iarc.fr/WHOdb/WHOdb.htm) Lat Am Econ Rev (2017) 26:8 Page 3 of 22 8 123 new chemical entities (drugs) to treat cancer at site sthat had been launched in Mexico by the end of year t-k(k=0, 3, 6 ,…,18); INDds ¼1 if drug dis used to treat indicated forðÞcancer at site s¼0 if drug dis not used to treat indicated forðÞcancer at site s;LAUNCHEDd;tk¼1 if drug dhad been launched in Mexico by the end of year tk¼0 if drug dhad not been launched in Mexico by the end of year tk;INCIDENCE s,t-1 =the age-s- tandardized incidence rate of cancer at site sin year t-1; a s =a fixed effect for cancer at site s;d t =a fixed effect for year t. Inclusion of year and cancer-site fixed effects controls for the overall decline in cancer mortality and for stable between-cancer-site differences in mortality. Negative and significant estimates of b k in Eq. (1) would signify that cancer sites for which there was more pharmaceutical innovation had larger declines in mortality, controlling for changes in incidence. Due to data limitations, the number of new chemical entities is the only cancersite-specific, time-varying, measure of medical innovation in Eq. (1). Both a patient-level US study and a longitudinal country-level study have shown that controlling for numerous other potential determinants of mortality does not reduce, and may even increase, the estimated effect of pharmaceutical innovation. The study based on patient-level data (Lichtenberg 2013) found that controlling for race, education, family income, insurance coverage, Census region, BMI, smoking, the mean year the person started taking his or her medications, and over 100 medical conditions had virtually no effect on the estimate of the effect of pharmaceutical innovation (the change in drug vintage) on life expectancy. The study based on longitudinal country-level data (Lichtenberg 2014b) found that controlling for ten other potential determinants of longevity change [real per capita income, the -48% -34% -34% -13% -12% -11% -10% -6% -4% 0% 2% 13% 18% -60% -50% -40% -30% -20% -10% 0% 10% 20% 30% cervix uteri stomach trachea, bronchus, lung Hodgkin’s disease liver pancreas prostate bladder leukemia melanoma of skin female breast ovary colon, rectum and anus Fig. 2 % change in age-adjusted mortality rate, by cancer site, 2000–2013 (Source: OECD Health Statistics 2016 database) 8Page 4 of 22 Lat Am Econ Rev (2017) 26:8 123 unemployment rate, mean years of schooling, the urbanization rate, real per capita health expenditure (public and private), the DPT immunization rate among children ages 12–23 months, HIV prevalence, and tuberculosis incidence] increased the coefficient on pharmaceutical innovation by about 32%. Failure to control for non-pharmaceutical medical innovation (e.g., innovation in diagnostic imaging, surgical procedures, and medical devices) is also unlikely to bias estimates of the effect of pharmaceutical innovation on premature mortality, for two reasons. First, more than half of US funding for biomedical research came from pharmaceutical and biotechnology firms (Dorsey et al. 2010). Much of the rest came from the federal government (i.e., the NIH), and new drugs often build on upstream government research (Sampat and Lichtenberg 2011). The National Cancer Institute (2016a,b) says that it ‘‘has played an active role in the development of drugs for cancer treatment for 50 years…[and] that approximately one half of the chemotherapeutic drugs currently used by oncologists for cancer treatment were discovered and/or developed’’ at the National Cancer Institute. Second, the previous research based on US data (Lichtenberg 2014a,c) indicates that non-pharmaceutical medical innovation is not positively correlated across diseases with pharmaceutical innovation. However, while non-pharmaceutical medical innovation may not be correlated with pharmaceutical innovation across diseases in the US, this need not hold for Mexico. The measure of pharmaceutical innovation in Eq. (1)—the number of chemical substances previously registered to treat cancer at site s—is not the theoretically ideal measure. Mortality is presumably more strongly related to the drugs actually used to treat cancer than it is to the drugs that could be used to treat cancer. A preferable measure is the mean vintage of drugs used to treat cancer at site sin year t, defined as VINTAGE st =P d Q dst LAUNCH_YEAR d /P d Q dst , where Q dst =the quantity of drug dused to treat cancer at site sin year t, and LAUNCH_YEAR d =- the world launch year of drug d. 7 Unfortunately, measurement of VINTAGE st is infeasible: even though data on the total quantity of each drug in each year (Q d.t =R s Q dst ) are available, many drugs are used to treat multiple diseases. There is no way to determine the quantity of drug d used to treat cancer at site s in year t. 8 However, Lichtenberg (2014c) showed that in France, there is a highly significant positive correlation across drug classes between changes in the (quantity-weighted) vintage of drugs and changes in the number of chemical substances previously registered within the drug class. 7 According to the Merriam Webster dictionary, one definition of vintage is ‘‘a period of origin or manufacture (e.g., a piano of 1845 vintage)’’. http://www.merriam-webster.com/dictionary/vintage. Solow (1960) introduced the concept of vintage into economic analysis. Solow’s basic idea was that technical progress is ‘‘built into’’ machines and other goods and that this must be taken into account when making empirical measurements of their roles in production. This was one of the contributions to the theory of economic growth that the Royal Swedish Academy of Sciences cited when it awarded Solow the 1987 Alfred Nobel Memorial Prize in Economic Sciences (Nobelprize.org 2016). 8 Outpatient prescription drug claims usually do not show the indication of the drug prescribed. Claims for drugs administered by doctors and nurses (e.g., chemotherapy) often show the indication of the drug, but these data are not available for Mexico. Lat Am Econ Rev (2017) 26:8 Page 5 of 22 8 123 In principle, it could be desirable to control for the length of time between the ‘world launch’ of cancer drugs and their ‘Mexico launch’. 9 According to Solow’s vintage hypothesis, later, vintage goods (e.g., drugs whose world launch years were later) are likely to be of higher quality than earlier vintage goods. Holding constant the Mexican launch year of a drug, the shorter the lag from world launch year to Mexican launch year, the later the world launch year of the drug, and (according to the vintage hypothesis), the higher the drug’s quality. However, controlling for the length of time between the ‘world launch’ of cancer drugs and their ‘Mexico launch’ is problematic. I can compute the mean lag between the world launch year and the Mexican launch year for cancer sites and years in which at least one drug had been launched in Mexico. However, I cannot compute the mean lag for cancer sites and years in which no drugs had been launched in Mexico. As shown in Table 2,8 cancer sites had 0 drug launches by 1995; 4 cancer sites had 0 drugs launches by 2004. Controlling for the length of time between the ‘world launch’ of cancer drugs and their ‘Mexico launch’ would require excluding those observations. In Eq. (1), mortality from cancer at site sin year tdepends on the number of new chemical entities (drugs) to treat cancer at site sthat had been launched in Mexico by the end of year t-k, i.e., there is a lag of kyears. Equation (1) will be estimated for different values of k:k=0, 3, 6,…,18. A separate model is estimated for each value of k, rather than including multiple values (CUM_NCE s,t , CUM_NCE s,t-3 , CUM_NCE s,t-6 ,…) in a single model, because CUM_NCE is highly serially correlated (by construction), which would result in extremely high multicollinearity if multiple values were included. One would expect there to be a substantial lag, because new drugs diffuse gradually—they will not be used widely until years after registration. Data from the IMS Health MIDAS database can be used to provide evidence about the process of diffusion of new medicines. I used data from that source linked to data on Mexican drug launch dates (described below) to estimate the following model: ln N RXdy  ¼qdþpyþedy;ð2Þ where N_RX dy = the number of standard units of cancer drug dsold in Mexico per thousand population yyears after it was launched (y=0–3, 4–7, 8–11, 12–15, 16–19 years); q d =a fixed effect for drug d;p y = a fixed effect for age y. The expression exp pyp1619  is a ‘‘relative utilization index’’: it is the mean ratio of the annual number of standard units of a cancer drug sold per thousand population yyears after it was first launched in Mexico to the annual number of standard units of the same drug sold per thousand population 16–19 years after it was first launched in Mexico. Using annual data on the number of standard units of cancer drugs sold in Mexico during the period 1999–2010, I estimated Eq. (2). Estimates of the ‘‘relative utilization index’’ are shown in Fig. 3. These estimates indicate that utilization of a drug is strongly positively related to how long the drug has been on the market. On average, a drug is used 50 times as often per year 16–19 years post-launch as it is 9 The mean lag between the world launch date and the Mexican launch date is 3.0 years. 8Page 6 of 22 Lat Am Econ Rev (2017) 26:8 123 0–3 year post-launch. Utilization appears to rise especially rapidly after year 11, when drug patents tend to expire and generics enter the market. The effect of a drug’s launch on mortality is likely to depend on both the quality and the quantity of the drug. Indeed, it is likely to depend on the interaction between quality and quantity: a quality improvement will have a greater impact on mortality if drug utilization (quantity) is high. Although newer drugs tend to be of higher quality than older drugs (see Lichtenberg 2014d), the relative quantity of very new drugs is quite low, so the impact on mortality of very new drugs is lower than the impact of older drugs. In principle, mortality in year tshould depend on a distributed lag of incidence, i.e., on INCIDENCE s,t , INCIDENCE s,t-1 , INCIDENCE s,t-2 , INCIDENCE s,t-3 … Unfortunately, data on incidence by cancer site are available for only 2 years (2002 and 2012); this is why INCIDENCE s,t-2 is the only incidence variable included in Eq. (1). The limited availability of incidence data also means that we can only use mortality data for 2 years (2004 and 2014). Writing the model for each of these years: MORTs;2003 ¼bkCUM NCEs;2003kþcINCIDENCEs;2002 þasþd2003 þes;2003; ð3Þ MORTs;2013 ¼bkCUM NCEs;2013kþcINCIDENCEs;2012 þasþd2013 þes;2013: ð4Þ 2% 9% 19% 47% 100% 0% 20% 40% 60% 80% 100% 120% 91-6151-2111-87-43-0 Number of years since launch Fig. 3 Relative utilization of cancer drugs in Mexico, by number of years since launch (index: ratio of utilization yyears since launch to utilization 16–19 years since launch) Lat Am Econ Rev (2017) 26:8 Page 7 of 22 8 123 Subtracting (3) from (4), MORTs;2013 MORTs;2003  ¼bkCUM NCEs;2013kCUM NCEs;2003k  þcINCIDENCEs;2012 INCIDENCEs;2002  þðd2013 d2003Þþðes;2013 es;2003Þ: ð5Þ Equation (5) may be rewritten as follows: DMORTs¼bkDCUM NCE ks þcDINCIDENCE 1sþd0þe0 s; ð6Þ where DMORT s =MORT s,2013 -MORT s,2003 =the 2003–2013 change in the age-standardized mortality rate from cancer at site s;DCUM_NCE_k s = CUM_NCE s,2013k -CUM_NCE s,2003-k =the number of drugs for cancer at site slaunched between year 2003 -kand 2013 -k;DINCIDENCE_1 s = INCIDENCE s,2012 -INCIDENCE s,2002 = the 2002–2012 change in the age-stan- dardized incidence rate of cancer at site s;d0=d 2013 -d 2003 . Equation (6) indicates that the 2003–2013 change in the age-standardized mortality rate depends on two variables: the number of drugs launched between year 2003 -k and 2013 -k, and the 2002–2012 change in the age-standardized incidence rate. For estimates of b k from Eqs. (1) and (6) to be consistent estimates of the effect of drug launches on mortality, the ‘‘parallel trends’’ assumption needs to be satisfied. A simple way to test the validity of this assumption is to estimate a version of Eq. (6) that includes a control for the trend in mortality in the ‘pre-period’, e.g., the period 1993–2003. Therefore, I will estimate the following model: DMORTs¼bkDCUM NCE ksþcDINCIDENCE 1s þpDMORT PREsþd0þe0 s; ð7Þ where DMORT_PRE s =MORT s,2003 -MORT s,1993 =the 1993–2003 change in the age-standardized mortality rate from cancer at site s. 3 Data sources Age-standardized cancer mortality rate data were obtained from the WHO Cancer Mortality database (World Health Organization (2016a)). Age-standardized cancer incidence rate data were obtained from GLOBOCAN International Agency for Research on Cancer (2016). Mortality and incidence data are reported separately by sex. For cancers affecting both sexes, we computed the simple mean of the sex-specific rates. For cancers affecting only one sex (breast, cervical, ovarian, and prostate), we computed 50% of the single-sex rate. Data on drugs approved for different types of cancer were obtained from the US National Cancer Institute. Data on Mexican launch dates of drugs were obtained from the IMS Health New Product Focus database. This database contains data on drug launches (in Mexico and 8Page 8 of 22 Lat Am Econ Rev (2017) 26:8 123 Table 4 Estimates of b k parameters of Eq. (7), DMORT s =b k DCUM_NCE_k s ?cDINCIDENCE_1 s ? pDMORT_PRE s ?d0?e s 0excluding and including DMORT_PRE s Row Lag Parameter Estimate Std. err. Tpvalue Without control for DMORT_PREV 22 0 b 0 20.26361 0.09837 22.67978 0.03155 23 3 b 3 20.26209 0.10704 22.44851 0.0442 24 6 b 6 -0.16993 0.10048 -1.69117 0.13465 25 9 b 9 -0.20238 0.09249 -2.18828 0.06484 26 12 b 12 20.26289 0.07605 23.45699 0.01059 27 15 b 15 20.27366 0.08422 23.24925 0.01407 28 18 b 18 -0.27399 0.12188 -2.24806 0.05937 With control for DMORT_PREV 29 0 b 0 -0.23336 0.09856 -2.36765 0.0557 30 3 b 3 -0.22552 0.11339 -1.98885 0.09387 31 6 b 6 -0.12894 0.11267 -1.14446 0.29603 32 9 b 9 -0.16884 0.10131 -1.66657 0.14665 33 12 b 12 20.23641 0.08384 22.81985 0.03036 34 15 b 15 20.26438 0.06602 24.00487 0.00708 35 18 b 18 20.3807 0.03866 29.84795 0.00006 Estimates in bold are statistically significant (p-value \.05) C33-34 Lung (incl. trachea and bronchus) C61 Prostate C50 Breast C91-95 Leukaemia C53 Cervix uteri C15 Oesophagus C16 Stomach C43 Melanoma of skin C00-14 Lip, oral cavity and pharynx C18-21 Colon, rectum and anus y = -0.3807x + 1E-06 R² = 0.9417 -1.5 -1 -0.5 0 0.5 1 1.5 32101-2-3- Change in mortality rate, 2003-2013 (residual) Number of new drugs launched, 1985-1995 (residual) Fig. 5 Relationship across cancer sites between the number of new drugs launched during 1985–1995 and the 2003–2013 change in the mortality rate, controlling for the 1993–2003 change in the mortality rate and the 2002–2012 change in the incidence rate Lat Am Econ Rev (2017) 26:8 Page 15 of 22 8 123 the largest 2003–2013 reductions in the mortality rate, controlling for the 2002–2012 change in the incidence rate and the trend in mortality in the preperiod. Excluding these three cancer sites from the sample does not have much effect on the estimate of b 18 ; when they are excluded, the estimate of b 18 is -0.357 (t=8.15; p=0.0039). 5 Discussion The estimates indicate that the launch of new drugs subsequently reduced cancer mortality. New drugs launched during 1991–2001 are estimated to have reduced the age-standardized cancer mortality rate by 16%, i.e., at an average annual rate of about 1.6%. Now, I will develop a rough estimate of the number of life-years gained in 2013 from the reduction in cancer mortality attributable to pharmaceutical innovation, and of the average cost-effectiveness (cost per life-year gained) of new cancer drugs. To do this, I will estimate the decline in the premature mortality rate attributable to pharmaceutical innovation. The premature mortality rate is the number of potential years of life lost (PYLL) per 100,000 population (OECD 2017a,b). PYLL is a summary measure of premature mortality that provides an explicit way of weighting deaths occurring at younger ages. The calculation of PYLL involves summing up deaths occurring at each age and multiplying this with the number of remaining years to live up to a selected age limit. 11 The limit of 70 years was chosen for the calculations in OECD Health Statistics. To assure cross-country and trend comparison, the PYLL are standardized, for each country and each year. The total OECD population in 2010 is taken as the reference population for age standardization. As shown in Table 1, between 2003 and 2013, the age-standardized mortality rate for all cancers combined declined by 15%, from 73.0 to 62.3. The estimates in Table 4 imply that virtually, this entire decline was due to the previous launches of new cancer drugs. During the same period, according to the OECD, the premature cancer mortality rate (the number of PYLL before age 70/100,000 population below age 70) declined by 11.4%, from 782.4 to 693.4. It seems reasonable to assume that this entire decline was also due to the previous launches of new cancer drugs. Therefore, in the absence of the previous new drug launches, premature cancer mortality would have been 12.8% (= (1/ (1 -0.114)) -1) higher in 2013 than it actually was. Actual PYLL before age 70 due to cancer in 2013 was 823,209 (= 693.4 * (118,720,632/100,000)). I estimate that in the absence of previous new drug launches, premature cancer mortality would have been 928,870 (= 112.8% * 823,209). This calculation implies that 105,661 life-years before age 70 were gained in 2013 due to new cancer drugs. This is a rough estimate of the longevity benefit in 2013 to people under 70 of cancer drugs launched during the period 1991–2001. To calculate the average cost-effectiveness of these drugs, I would like to measure 11 This measure incorporates both the reduction in the number of deaths and the increase in mean age at death. 8Page 16 of 22 Lat Am Econ Rev (2017) 26:8 123 expenditure in 2013 by (or on behalf of) people under 70 on cancer drugs launched during the period 1991–2001. Unfortunately, these data are not available. However, I do have unpublished data from the IMS Health MIDAS database on expenditure (by or on behalf of all patients) by drug in 2010. Expenditure in 2010 on cancer drugs launched during 1991–2001 was $315 million. 12 72% of people diagnosed with cancer in 2012 were below age 70 (source: GLOBOCAN). Expenditure in 2010 by or on behalf of people below age 70 on cancer drugs launched during 1994–2004 may, therefore, have been $227 million (= 72% * $315 million). These calculations imply that the cost per life-year gained by people below age 70 from new cancer drugs was in the neighborhood of $2146 (= $227 million/ 105,661 life-years). This figure may be somewhat underestimated, since it is based on 2010 expenditure data. 13 On the other hand, Lichtenberg (2014a) showed that in the US, about 25% of the cost of new drugs (for all diseases) tends to be offset by reduced expenditure on old drugs, so the cost per life-year gained may have been below $2000. The World Health Organization considers interventions whose cost per qualityadjusted life-year (QALY) gained is less than per capita GDP to be ‘‘very costeffective’’ (Bertram et al. 2016); Mexico’s per capita GDP in 2011 was $10,307. 14 The estimated cost per life-year gained from the previous pharmaceutical innovation is also well below the vast majority of estimates from the value-of-life literature of the value of a life-year (see Hirth et al. 2000). 6 Summary and conclusions I assessed the impact that pharmaceutical innovation had on cancer mortality in Mexico during the period 2003–2013, by investigating whether there were larger declines in mortality for cancer sites (breast, lung, colon, etc.) that were subject to more pharmaceutical innovation, controlling for changes in cancer incidence. New drugs launched during 1991–2001 are estimated to have reduced the agestandardized cancer mortality rate by 16%, i.e., at an average annual rate of about 1.6%. I estimated that 105,661 life-years before age 70 were gained in 2013 due to cancer drugs launched during 1997–2007, and that the cost per life-year gained was in the neighborhood of $2146. By the standards of the World Health Organization, new cancer drugs have been very cost-effective in Mexico. The contribution of cancer drug innovation to Mexican longevity growth has been valuable, but, perhaps, it could have been even larger. According to the IMS Institute for Healthcare Informatics (2016), during the period 2010–2014, 49 new cancer medicines were launched worldwide. As shown in Fig. 6, about twice as 12 Expenditure in 2010 on all post-1981 cancer drugs was $393 million. This represents 2.4% of total 2010 pharmaceutical expenditure ($16.6 billion of US dollars at exchange rate) reported in MIDAS. The OECD estimate of total pharmaceutical sales in 2010 is 24% higher: $20.6 billion. 13 According to the OECD, between 2010 and 2013, total pharmaceutical sales (in US$ at exchange rate) increased 3.6% (from $20.6 billion to $21.3 billion). 14 Lichtenberg (2009) demonstrated that the number of QALYs gained from pharmaceutical innovation could be either greater than or less than the number of life-years gained. Lat Am Econ Rev (2017) 26:8 Page 17 of 22 8 123 many of these have been launched in the US as have been launched in Mexico. 15 In addition, as shown in Fig. 3, when new drugs are launched in Mexico, their diffusion is quite slow. Due to unavailability of data, this study is subject to several limitations. The measure of pharmaceutical innovation—the number of chemical substances previously launched to treat cancer—is not the theoretically ideal measure. The number of chemical substances previously launched was the only cancer-site- specific, time-varying, measure of medical innovation. The previous research based on US data indicates that non-pharmaceutical medical innovation is not positively correlated across diseases with pharmaceutical innovation, but this may not apply to Mexico. Future research may be able to overcome these and other limitations. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Appendix See Table 5. 49 41 38 37 31 28 28 24 23 22 20 20 19 13 13 9 7 6 6 6 5 1 0605040302010 Global US Germany UK Italy France Canada Japan Spain Poland South Korea Mexico Russia Brazil Phillipines Turkey India China Indonesia Kazakhstan S. Africa Vietnam Source: IMS Instute for Healthcare Informacs, Global Oncology Trend Report: A Review of 2015 and Outlook to 2020, p. 15. Fig. 6 Number of 2010–2014 cancer medicines that have been launched in various regions 15 The IMS Institute for Healthcare Informatics (2016, p 22) also notes that ‘‘Mexico and South Africa are the only pharmerging countries in which oncology costs have fallen in proportion to total medicines costs in the last 5 years.’’. 8Page 18 of 22 Lat Am Econ Rev (2017) 26:8 123 Table 5 Mexican launch dates of drugs used to treat different types of cancer Cancer site Drug Launch year C00–C14 head and neck cancer Docetaxel 1995 C00–C14 head and neck cancer Cetuximab 2004 C15 esophageal cancer Docetaxel 1995 C15 esophageal cancer Trastuzumab 2000 C16 stomach (gastric) cancer Docetaxel 1995 C16 stomach (gastric) cancer Trastuzumab 2000 C18, C20 colon and rectal cancer Irinotecan 1998 C18, C20 colon and rectal cancer Capecitabine 2000 C18, C20 colon and rectal cancer Oxaliplatin 2002 C18, C20 colon and rectal cancer Cetuximab 2004 C18, C20 colon and rectal cancer Bevacizumab 2005 C18, C20 colon and rectal cancer Panitumumab 2011 C18, C20 colon and rectal cancer Aflibercept 2014 C25 pancreatic cancer Paclitaxel 1995 C25 pancreatic cancer Gemcitabine 1997 C25 pancreatic cancer Irinotecan 1998 C25 pancreatic cancer Erlotinib 2006 C25 pancreatic cancer Everolimus 2006 C25 pancreatic cancer Sunitinib 2006 C34 lung cancer Carboplatin 1992 C34 lung cancer Docetaxel 1995 C34 lung cancer Paclitaxel 1995 C34 lung cancer Gemcitabine 1997 C34 lung cancer Vinorelbine 1998 C34 lung cancer Gefitinib 2004 C34 lung cancer Bevacizumab 2005 C34 lung cancer Pemetrexed 2005 C34 lung cancer Erlotinib 2006 C34 lung cancer Topotecan 2008 C34 lung cancer Crizotinib 2012 C40–C41 bone cancer Denosumab 2012 C43 melanoma Interferon alfa-2b 1987 C43 melanoma Aldesleukin 1996 C43 melanoma Peginterferon alfa-2b 2001 C43 melanoma Ipilimumab 2012 C43 melanoma Vemurafenib 2012 C44 basal cell carcinoma Imiquimod 1999 C45 malignant mesothelioma Pemetrexed 2005 C46 kaposi sarcoma Interferon alfa-2b 1987 C46 kaposi sarcoma Paclitaxel 1995 C49 soft tissue sarcoma Imatinib 2001 C49 soft tissue sarcoma Trabectedin 2010 Lat Am Econ Rev (2017) 26:8 Page 19 of 22 8 123 Table 5 continued Cancer site Drug Launch year C49 soft tissue sarcoma Pazopanib 2012 C50 breast cancer Epirubicin 1987 C50 breast cancer Goserelin 1991 C50 breast cancer Docetaxel 1995 C50 breast cancer Paclitaxel 1995 C50 breast cancer Gemcitabine 1997 C50 breast cancer Anastrozole 1998 C50 breast cancer Raloxifene 1998 C50 breast cancer Toremifene 1999 C50 breast cancer Capecitabine 2000 C50 breast cancer Letrozole 2000 C50 breast cancer Trastuzumab 2000 C50 breast cancer Exemestane 2004 C50 breast cancer Everolimus 2006 C50 breast cancer Fulvestrant 2009 C50 breast cancer Ixabepilone 2009 C50 breast cancer Lapatinib 2009 C50 breast cancer Trastuzumab emtansine 2014 C53 cervical cancer Bevacizumab 2005 C53 cervical cancer Topotecan 2008 C56 ovarian, fallopian tube, or primary peritoneal cancer Carboplatin 1992 C56 ovarian, fallopian tube, or primary peritoneal cancer Paclitaxel 1995 C56 ovarian, fallopian tube, or primary peritoneal cancer Gemcitabine 1997 C56 ovarian, fallopian tube, or primary peritoneal cancer Bevacizumab 2005 C56 ovarian, fallopian tube, or primary peritoneal cancer Topotecan 2008 C61 prostate cancer Flutamide 1987 C61 prostate cancer Mitoxantrone 1987 C61 prostate cancer Leuprorelin 1989 C61 prostate cancer Goserelin 1991 C61 prostate cancer Docetaxel 1995 C61 prostate cancer Bicalutamide 1997 C61 prostate cancer Degarelix 2010 C61 prostate cancer Cabazitaxel 2012 C64–C65 kidney (renal cell) cancer Aldesleukin 1996 C64–C65 kidney (renal cell) cancer Bevacizumab 2005 C64–C65 kidney (renal cell) cancer Everolimus 2006 C64–C65 kidney (renal cell) cancer Sunitinib 2006 C64–C65 kidney (renal cell) cancer Temsirolimus 2011 C64–C65 kidney (renal cell) cancer Pazopanib 2012 C71 brain tumors Temozolomide 1999 C71 brain tumors Bevacizumab 2005 8Page 20 of 22 Lat Am Econ Rev (2017) 26:8 123 References Bertram MY, Lauer JA, De Joncheere K, Edejer T, Hutubessy R, Kieny MP, Hill SR (2016) Costeffectiveness thresholds: pros and cons. Bull World Health Organ 94(12):925–930 Dorsey ER et al (2010) Financial anatomy of biomedical research, 2003–2008. J Am Med Assoc 303(2):137–143 Hirth RA, Chernew ME, Miller E, Fendrick AM, Weissert WG (2000) Willingness to pay for a qualityadjusted life year: in search of a standard. Med Decis Mak 20(3):332–342 IMS Institute for Healthcare Informatics (2016) Global oncology trend report: a review of 2015 and outlook to 2020, June. http://www.imshealth.com/en_US/thought-leadership/quintilesims-institute/ reports/global-oncology-trend-report-a-review-of-2015-and-outlook-to-2020. Accessed 14 Oct 2017 International Agency for Research on Cancer (2016) GLOBOCAN database. http://globocan.iarc.fr/ Default.aspx. Accessed 14 Oct 2017 Lichtenberg FR (2013) The effect of pharmaceutical innovation on longevity: patient level evidence from the 1996–2002 medical expenditure panel survey and linked mortality public-use files. Forum Health Econ Policy 16(1):1–33 Lichtenberg FR (2014a) Has medical innovation reduced cancer mortality? CESifo Econ Stud 60(1):135–177 Lichtenberg FR (2014b) Pharmaceutical innovation and longevity growth in 30 developing and highincome countries, 2000–2009. Health Policy Technol 3(1):36–58 Lichtenberg FR (2014c) The impact of pharmaceutical innovation on longevity and medical expenditure in France, 2000–2009. Econ Human Biol 13:107–127 Table 5 continued Cancer site Drug Launch year C71 brain tumors Everolimus 2006 C73 thyroid cancer Vandetanib 2014 C81 hodgkin lymphoma Brentuximab vedotin 2014 C82–C85 non-hodgkin lymphoma Interferon alfa-2b 1987 C82–C85 non-hodgkin lymphoma Rituximab 1999 C82–C85 non-hodgkin lymphoma Bortezomib 2006 C82–C85 non-hodgkin lymphoma Lenalidomide 2008 C82–C85 non-hodgkin lymphoma Plerixafor 2009 C82–C85 non-hodgkin lymphoma Brentuximab vedotin 2014 C90 multiple myeloma and other plasma cell neoplasms Zoledronic acid 2001 C90 multiple myeloma and other plasma cell neoplasms Bortezomib 2006 C90 multiple myeloma and other plasma cell neoplasms Lenalidomide 2008 C90 multiple myeloma and other plasma cell neoplasms Plerixafor 2009 C91, C92 leukemia Interferon alfa-2b 1987 C91, C92 leukemia Mitoxantrone 1987 C91, C92 leukemia Idarubicin 1992 C91, C92 leukemia Rituximab 1999 C91, C92 leukemia Imatinib 2001 C91, C92 leukemia Dasatinib 2007 C91, C92 leukemia Nilotinib 2007 C91, C92 leukemia Clofarabine 2009 C91, C92 leukemia Obinutuzumab 2014 Lat Am Econ Rev (2017) 26:8 Page 21 of 22 8 123 Lichtenberg FR (2014d) The impact of pharmaceutical innovation on disability days and the use of medical services in the United States, 1997–2010. J Hum Cap 8(4):432–480 Lichtenberg FR (2015) The impact of pharmaceutical innovation on premature cancer mortality in Canada, 2000–2011. Int J Health Econ Manag 15(3):339–359 Lichtenberg FR (2016a) The impact of pharmaceutical innovation on premature cancer mortality in Switzerland, 1995–2012. Eur J Health Econ 17(7):833–854 Lichtenberg F (2016b) The impact of pharmaceutical innovation on cancer mortality in Belgium, 2004–2012. Forum Health Econ Policy. doi:10.1515/fhep-2015-0042 National Cancer Institute (2016a) Enhancing drug discovery and development. https://www.cancer.gov/ research/areas/treatment/enhancing-drug-discovery. Accessed 14 Oct 2017 National Cancer Institute (2016b) Drugs approved for different types of cancer. https://www.cancer.gov/ about-cancer/treatment/drugs/cancer-type. Accessed 14 Oct 2017 Nobelprize.org (2016) The Prize in Economics 1987—Press Release. http://www.nobelprize.org/nobel_ prizes/economic-sciences/laureates/1987/press.html. Accessed 13 Oct 2017 OECD (2017a) OECD Health Statistics 2016 database OECD (2017b) OECD Health Statistics 2016, Definitions, Sources and Methods, Potential years of life years lost by ICD categories Sampat BN, Lichtenberg FR (2011) What are the respective roles of the public and private sectors in pharmaceutical innovation? Health Aff 30(2):332–339 Solow RM (1960) Investment and technological progress. In: Arrow K, Karlin S, Suppes P (eds) Mathematical methods in social sciences 1959. Stanford University Press, Palo Alto, pp 89–104 World Health Organization (2016a) WHO Cancer Mortality database. http://www-dep.iarc.fr/WHOdb/ WHOdb.htm. Accessed 13 Oct 2017 World Health Organization (2016b) WHO Mortality database. http://www.who.int/healthinfo/statistics/ mortality_rawdata/en/. Accessed 13 Oct 2017 8Page 22 of 22 Lat Am Econ Rev (2017) 26:8 123