Estimations of generic drug entry in Brazil using count ... - EPGE/FGV

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liquids, (iii) semi-solids (topics and suppositories), (iv) injectables, and (v) sprays. Scott-Morton’s original papers groups orals together, and semi-solids and sprays into "topics".We de…ne then a drug as the triple API-class-form.We have no reliable data on advertisement and promotion (’detailing’). Prescriptiondata have not been explored yet.PMB has an unfortunate feature worldwide: the seller´s name is not tracked alongthe months; when a brand is transfered because of divestiture, licensing, partial or totalmerger or acquisition, the whole sales series are relabeled to the new seller. Dependingon the date of the extraction, the whole series will be completely assigned to the currentseller. We overcame this obstacle with an external data base, from ABCFarma, a monthlymagazine containing all drug price lists. By consolidating all price lists throughout theperiod, we were able to identify each drug’s seller in each month. As the drug labelsof IMS and ABCFarma were not completely comparable, we summarized the transitionpairs of sellers and doublechecked them with merger dates from the Brazilian antitrustagencies and by searching the Web. Parent companies were also checked this way, andadditionally by checking the addresses of the companies registered at CMED/Anvisa. Theheavy workload at this stage of our research process could never be overstated. To the bestof our knowledge, this is the …rst work in the international literature that complementsIMS data with other pieces of information to overcome such a classical data limitationproblem.5.1 Descriptive statisticsWe found 648 single-API drugs that were not under patent at least one year of our sampledperiod; in fact,18 drugs had their patent expired during our sample; only four of them hadgenerics launched. One of them was letrozole, expired in 2007, but with an (own-) genericlaunched in 2006. This was the only case of own-generic launched before expiration in oursample.We then obtained 984 drugs (triples API-class-form). Tables 3 and 4 summarizeentry and generic penetration in the markets. Table 3 bring the shares of drugs containinggeneric versions in each year, both in number and in revenue. It also brings the shares ofmaceutical audits of the Pharmaceutical Industry Syndicate of São Paulo for graciously providing adviceon the classi…cation of production processes.18
drugs where generic entry occurred in the same year, also in number and in revenue. Oneinteresting feature to remark is that the revenue shares of drugs with entry and of drugswith generics moved in opposTable 4 counts drug market yearly observations according to the number of genericdrug entrants recorded in the respective year.INSERT TABLE 3 ABOUT HEREINSERT TABLE 4 ABOUT HEREWe notice that the greatest share of our observations is of drugs without generic drugentry (appr. 85%). The share of each zero-entry class (in number) in the total is similarto the share of the class (in number) in the total. Shares of yearly observations of no entryin the class´s total number of observations range from 77.4% (class J –Antiinfectives) to96.8% (class B –Blood and Blood Forming Organs). Class J is therefore the class withthe greatest rate of generic entry.Accumulating generic drug entry changes a little the picture: the number of classeswith no generic entry whatsoever during the period 2000-2007 goes down to 64% innumber. The cumulative density of entries up to 6 is 90%; see Figure 2.INSERT FIGURE 2 ABOUT HERE5.2 ResultsThe reader should bear in mind that, unlike in the linear regressions, marginal e¤ectsin count data are nonlinear. In particular, the derivative of the expected count w.r.t.a particular regressor x j in the Poisson and Negative Binomial family has the followingformula:@E [yjx]@x j= j exp (x 0 i) (19)The K-vector of derivatives in an ordered multinomial model is calculated for eachvalue of the dependent variable’s support:19
Page 1 and 2: Estimations of generic drug entry i
Page 3 and 4: percent below 1997 level, but twent
Page 5 and 6: doctors had reason to concern that
Page 7 and 8: companies tried to switch patients
Page 9 and 10: Scott-Morton [24] used individual d
Page 11 and 12: of entries, but the longer the excl
Page 13 and 14: model families. We then discuss the
Page 15 and 16: such that i follows a Gamma distri
Page 17: Pr [y i = j] = Pr ( j 1 < yi j )
Page 21 and 22: Ordered multinomial models display
Page 23 and 24: 3. the lagged HHI market concentrat
Page 25 and 26: [12] Fiuza, E.P.S. & Lisboa, M.B. (
Page 27 and 28: TABLE 1Generic Drug Market Share in
Page 29 and 30: FIGURE 2Distribution of Accumulated
Page 31 and 32: TABLE 5 (Continued)POISSON NB2 NB1
Page 33 and 34: TABLE 7Marginal Effects of Ordered
Page 35 and 36: TABLE 8-BPRM: Predicted and actual
Page 37 and 38: FIGURE 3Deviations in Observed vs.
Page 39: FIGURE 5Sources of zeros in the ZIN

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