The Relationship between DTCA, Drug Requests and Prescriptions:

1. The Relationship between DTCA, Drug Requests and Prescriptions: Uncovering Variation across Specialty and Space Stefan Stremersch Joint work with Vardit Landsman and SriramVenkataraman

3. Relevance • The patient is ever more informed and requests drugs by brand name • There is great concern that many of these requests are triggered by firms’ DTCA: • Expenses: • $4.4B in US in 2008 • $1.1B in US in 1997 • Patients turn into consumers (Hollon 1999, Camacho, Landsman and Stremersch 2009) • Patient requests are also on the rise in countries that do not allow DTCA (Weiss, et al. 1996)

4. DTCA – Request – Rx Request DTCA Rx There is controversy in prior literature over the effect of DTCA: • Some studies claim no or a very limited effectof DTCA on brand prescriptions(Calfee, Winston and Stempski 2002; Donohue and Berndt 2004; Manchanda, Xie and Youn 2008; Rosenthal et al. 2003; Zachry et al. 2002) • Other studies raise concerns about the large effect of DTCA on prescriptions for the advertised drug(Bell, Kravitz and Wilkes 1999; ; Iizuka and Jin 2005; Weissman et al. 2004; Wilkes, Bell and Kravitz 2000).

5. DTCA – Request – RX Request Request DTCA Rx DTCA Rx Most studies on DTCA link it directly to prescriptions and forego the study of drug requests as a mediator, although its mediation is often implied

6. DTCA – Request – Rx Request DTCA Rx Patient requests for a drug positively affect prescriptions for that drug • 27% of “actors” requesting Paxil also receive a Rx for it(Kravitz et al. 2005) • Up to 2/3 of all requests are fulfilled (Donohue and Berndt 2004; Slaughter and Schumacher 2001) • Patient demand cited as the prime motivation to over-prescribe given scientific profile (Schwartz, Soumerai and Avorn 1989; Camacho, Landsman and Stremersch 2009) Physician heterogeneity is typically not accounted for (Kravitz, et al. 2003&2005; Mintzes et al. 2002&2003), or accounted for with a random effect (Venkataraman and Stremersch 2007)

7. Why should requests have a positive effect? • Increased satisfaction with a physician visit (Kravitz, et al. 2003) • Increased trust in the physician-patient relationship (Berger, et al. 2001) • Improved perception of the patient that the physician met his or her expectations for care (Bell et al. 2002) • Timegained in medical interviews (Schwartz, Soumerai and Avorn 1989 • Increased patient compliance to the drug regime (Lloyd 1994; Uhlmann et al. 1988).

8. Relevant Gap in Literature DTCA Request Rx • We know: • Patient requests happen frequently • Patient requests positively affect prescription • Unexplained heterogeneity exists in physicians’ reaction to patient requests • Pharmaceutical companies raise DTCA expenditures • But, we do not know: • Whether brand specific requests are triggered by DTCA • The source of request accommodation heterogeneity across physicians • Whether that source is cause for public concern and/or presents useful information to pharmaceutical managers • Therefore, we question: • Is DTCA affecting the number of drug requests? • Are spatial characteristics and physician specialty possible sources for drug request accommodation heterogeneity?

9. Specialty Area Demographics Race Income-Education Age Gender Urbanization Our model Request DTCA Rx Request Equation RxEquation Physician characteristic: Direct to physician detailing Spatial characteristics: Competitive Rx’s Lag Rx’s

10. Model – The Rx equation Request DTCA Rx • Negative Binomial • Log-link function, specifying log of mean of conditional distribution as Heterogeneity in prescription Heterogeneity in responsiveness to requests • Hierarchical formulation: with k = 0, 1

11. Model – The Request equation Request DTCA Rx • Log-link function (requests) Heterogeneity in the number of requests Heterogeneity in the effect of DTCA on requests Brand-specific periodic shocks, IID MVN, own brand correlation between the shocks of both equations • Hierarchical formulation: Feedback effect

12. Data We study the statin category in the U.S., June 2002 to July 2003 • We integrate three datasets for our empirical analysis • Physician-level panel data: • Monthly brand-specific prescriptions requests • 5-digit ZIP code • Specialty • Monthly DTCA expenditures (Designated Market Area - DMA) • The 2000 U.S. Census data

13. Data • Our final dataset contains: • 142,180 prescriptions • For 2,294 physicians • Spanning 1,854 ZIP codes • In 193 DMA’s • Over 14 months

14. Estimation • Hierarchical Bayes Markov Chain Monte Carlo (HB MCMC) • Gibbs sampling with data augmentation(Tanner and Wong 1987) • Allowing for a combination of random and fixed parameters

15. Findings: Model Performance

16. Findings: Parameter Estimates Random Coefficients

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19. Findings: Parameter Estimates Random Coefficients

20. Findings: second level analysis • Specialists: • Receive more drug requests (δ02=1.06, Std. 0.39) • That are not disproportionally triggered by DTCA (δ11=-0.06, Std. 0.17) • But, such drug requests to specialists translate less into prescriptions than drug requests to primary care physicians (ω11=-0.76, Std. 0.16) • More severe medical condition makes specialists' patients more involved and informed (Gould 1988) • But, their greater intellectual mastery (Kravitz, et al. 2003), may enable specialists to convince patients against the requested drug in a shorter time

21. Findings: second level analysis • Minorities: • Requests occur more frequently in DMA’s with a higher percentage of Blacks (δ03=3.01, Std. 1.3) and Hispanics (δ04=2.85, Std. 1.03) • The requests in DMA’s with a higher proportion of Blacks are triggeredless by DTCA (δ12=-1.44, Std. 0.58) • Drug requests in DMA’s with a higher proportion of minorities translate less to prescriptions – for Blacks (ω12=-1.1, Std. 0.67), Hispanics (ω13=-1.14, Std. 0.53) and Asians (ω14=-4.47, Std. 2.27)

22. Findings: second level analysis • Minorities: • Requests occur more frequently in DMA’s with a higher percentage of Blacks (δ03=3.01, Std. 1.3) and Hispanics (δ04=2.85, Std. 1.03) • The requests in DMA’s with a higher proportion of Blacks are triggeredless by DTCA (δ12=-1.44, Std. 0.58) • Drug requests in DMA’s with a higher proportion of minorities translate less to prescriptions – for Blacks (ω12=-1.1, Std. 0.67), Hispanics (ω13=-1.14, Std. 0.53) and Asians (ω14=-4.47, Std. 2.27) Higher reliance on alternative forms of information such as word of mouth among Blacks ( Matthews et al. 2002)

23. Findings: second level analysis • Minorities: • Requests occur more frequently in DMA’s with a higher percentage of Blacks (δ03=3.01, Std. 1.3) and Hispanics (δ04=2.85, Std. 1.03) • The requests in DMA’s with a higher proportion of Blacks are triggeredless by DTCA (δ12=-1.44, Std. 0.58) • Drug requests in DMA’s with a higher proportion of minorities translate less to prescriptions – for Blacks (ω12=-1.1, Std. 0.67), Hispanics (ω13=-1.14, Std. 0.53) and Asians (ω14=-4.47, Std. 2.27) • Minorities may have difficulties in expressing themselves to physicians(Helman 1994) • Cardiovascular condition of minorities • Fewer physicians in DMA’s with a higher proportion of minorities • Minorities may have more limitations in their insurance policies • Physicians may unintentionally incorporate racial biases in their medical decision-making (Cooper-Patrick, et al. 1999)

24. Findings: second level analysis • Education and Income: • Does not affect the baseline level of drug requests • Nor the effect of DTCA on drug requests • There is a higher baseline level of prescriptions in DMA’s with higher education and income population (ω05=0.10, Std. 0.04) • But in such DMA’s requests translate less into prescriptions (ω15=0.18, Std. 0.08)

25. Findings: second level analysis • Education and Income: • Does not affect the baseline level of drug requests • Nor the effect of DTCA on drug requests • There is a higher baseline level of prescriptions in DMA’s with higher education and income population (ω05=0.10, Std. 0.04) • But in such DMA’s requests translate less to prescriptions (ω15=0.18, Std. 0.08) Higher income/education patients manage their health better – statin intake – than lower income/education patients(Cowie and Eberhardt 1995; Langlie 1977)

26. Findings: second level analysis • Education and Income: • Does not affect the baseline level of drug requests • Nor the effect of DTCA on drug requests • There is a higher baseline level of prescriptions in DMA’s with higher education and income population (ω05=0.10, Std. 0.04) • But in such DMA’s requests translate less to prescriptions (ω15=0.18, Std. 0.08) Educated patients are seen as capable of understanding medical explanations more clearly (Mathews 1983; Roter and Hall 2006; Waitzkin 1985) and this may: • Temper the hazard of dissatisfaction and distrust due to non-accommodation • Allow the physician to change the patient’s mind in the short duration of a visit

27. Implications • DTCA does not deliver upon the high expectations and should be re-evaluated by manufacturers • Objectives: category size/treatment compliance/firm prices • Content: avoidance of mentioning side effects (FDA) • Drug requests have a large influence: Deserve managerial attention beyond DTCA (E.g. WoM communication between patients or within patient communities) • Our findings should reassure policy makers that DTCA efforts cannot adversely inflate the brand’s edge over competing brands • Our findings may concern policy makers: • The large effect of patient requests (potentially threatening the gate-keeping function of the physician) • Its relation to the demographic make-up of the geographic area (e.g. race!)

28. Implications We can also graphically depict the spatial patterns we find (-> targeting of marketing efforts to patients by pharmaceutical firms): Request accommodation across DMAs DTCA effect across DMAs

29. Limitations • We cannot offer any normative claims on whether it is good/bad for a patient when a physician responds favorably to a patient drug request • The aggregation at the DMA level may lead to loss of information, as we do not observe which patients visit which physicians within a DMA (although as DMAs are fairly large, it is unlikely for patients to visit a physician outside their own DMA) • We do not account for patients’ payment mode (health insurance coverage such as Medicaid, Medicare vs. private insurance) – however 97% of our data is on insured patients, and we control for education, income and age • Only one category - statins