The Relationship between DTCA, Drug Requests and

Prescriptions:Uncovering Variation across Specialty and Space

Stefan Stremersch

Joint work with Vardit Landsman and Sriram Venkataraman

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)

RxRequestDTCA

DTCA – Request – Rx

• Some studies claim no or a very limited effect of 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).

There is controversy in prior literature over the effect of DTCA:

RxRequestDTCA

DTCA – Request – 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

RxRequestDTCA

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)

RxRequestDTCA

DTCA – Request – Rx

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)

Why should requests have a positive effect?

(1) Increased satisfaction with a physician visit (Kravitz, et al. 2003)

(2) Increased trust in the physician-patient relationship (Berger, et al. 2001)

(3) Improved perception of the patient that the physician met his or her expectations for care (Bell et al. 2002)

(4) Time gained in medical interviews (Schwartz, Soumerai and Avorn 1989

(5) Increased patient compliance to the drug regime (Lloyd 1994; Uhlmann et al. 1988).

Relevant Gap in Literature

• 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?

RxRequestDTCA

Our model

RxRequest

Area DemographicsRace

Income-EducationAge

GenderUrbanization

Specialty

DTCA

Spatial characteristics:

Physician characteristic: Direct to physician

detailing

Competitive Rx’s

Lag Rx’s

Request Equation

RxEquation

• Negative Binomial• Log-link function, specifying log of mean of conditional

distribution as

Model – The Rx equation

k

RXpjt

RX

RXpjt

RXpjt

RX

RX

RX

RXRXRX

pjtpjt

RX

k

kkRX

1),Pr(

RXpjt

tpj4ppjt3ppjt2p

pjt1pj

0pjRXpjt

RXCompRXDet

q

)1ln(1ln1ln

)1ln(Re

)ln(

1,

RxRequest

Heterogeneity in prescriptionHeterogeneity in responsiveness to requests

DTCA

• Hierarchical formulation:

with k = 0, 1

40 _

8,

7,6,5,

4,3,2,

1,,0,

kp

pk

pkpkpk

pkpkpk

pkjk

kpj

PerUrban

PerOverPerMaleFIncEdu

PerAsianPerHispPerBlack

Spec

• Log-link function (requests)

Model – The Request equation

Reqpjt

pjtpj

pjReqpjt

DTCA

)ln(

)ln(

1

0

RxRequestDTCA

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:

p

pp

pp

ppp

p1pjj

pj

PerUrban

PerOverPerMaleFIncEdu

PerAsianPerHispPerBlack

Spec

0

09

080706

050403

020100

0

40 _

Feedback effect

p

pp

pp

ppp

pj

pj

PerUrban

PerOverPerMaleFIncEdu

PerAsianPerHispPerBlack

Spec

1

18

171615

141312

1110

1

40 _

Data

We integrate three datasets for our empirical analysis

1. Physician-level panel data:Monthly brand-specific prescriptions requests5-digit ZIP code Specialty

2. Monthly DTCA expenditures (Designated Market Area - DMA)

3. The 2000 U.S. Census data

We study the statin category in the U.S., June 2002 to July 2003

Data

Our final dataset contains:

• 142,180 prescriptions

• For 2,294 physicians

• Spanning 1,854 ZIP codes

• In 193 DMA’s

• Over 14 months

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

Findings: Model Performance

Full model Homogeneous model

No brand specific effect for α0pj, α1pj,

β0pj and β1pj

No second level in the hierarchy of α0pj, α1pj, β0pj

and β1pj

In-sample fit comparison

DIC 212,876 253,062 228,611 212,804

Out-of-sample fit comparison

Root Mean Squared Error - RX

1.56 1.71 1.70 1.57

Root Mean Squared Error - Requests

0.46 0.78 0.47 0.45

Findings: Parameter Estimates

Population mean

Population Std. Posterior probability*

Drug request Equation Random Coefficients Intercept (Brand A) α01p-7.32 3.68 3%

Intercept (Brand B) α02p-7.34 3.13 1%

Intercept (Brand C) α03p-4.56 3.18 8%

DTCA (Brand A) α11p0.40 0.89 67%

DTCA (Brand B) α12p-1.16 1.00 12%

DTCA (Brand C) α13p-1.35 1.31 15%

Prescription Equation Random Coefficients Intercept Brand A β01p-1.16 1.35 21%

Intercept (Brand B) β02p-2.63 1.59 5%

Intercept (Brand C) β03p-1.91 1.51 10%

Drug request (Brand A) β11p1.12 0.93 89%

Drug request (Brand B) β12p1.53 1.14 91%

Drug request (Brand C) β13p1.06 0.98 86%

Detailing β3p0.70 0.50 94%

Competitive Rx’s β4p0.76 0.58 92%

Lag Rx’s β5p0.46 0.38 90%

Random Coefficients

25 out of the 60 seconds of an Eli Lilly & Co. ad Evista were spent on listing risks (blood clots and dying from

stroke)

Evista

http://

www.evista.com/pat/

index.jsp

Findings: Parameter Estimates

Population mean

Population Std. Posterior probability*

Drug request Equation Random Coefficients Intercept (Brand A) α01p-7.32 3.68 3%

Intercept (Brand B) α02p-7.34 3.13 1%

Intercept (Brand C) α03p-4.56 3.18 8%

DTCA (Brand A) α11p0.40 0.89 67%

DTCA (Brand B) α12p-1.16 1.00 12%

DTCA (Brand C) α13p-1.35 1.31 15%

Prescription Equation Random Coefficients Intercept Brand A β01p-1.16 1.35 21%

Intercept (Brand B) β02p-2.63 1.59 5%

Intercept (Brand C) β03p-1.91 1.51 10%

Drug request (Brand A) β11p1.12 0.93 89%

Drug request (Brand B) β12p1.53 1.14 91%

Drug request (Brand C) β13p1.06 0.98 86%

Detailing β3p0.70 0.50 94%

Competitive Rx’s β4p0.76 0.58 92%

Lag Rx’s β5p0.46 0.38 90%

Random Coefficients

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

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 triggered less 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)

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 triggered

less 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)

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 triggered

less 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)

1. Minorities may have difficulties in expressing themselves to physicians(Helman 1994)

2. Cardiovascular condition of minorities 3. Fewer physicians in DMA’s with a higher proportion of minorities4. Minorities may have more limitations in their insurance policies5. Physicians may unintentionally incorporate racial biases in their medical decision-

making (Cooper-Patrick, et al. 1999)

Findings: second level analysis

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)

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)

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)

1. Temper the hazard of dissatisfaction and distrust due to non-accommodation

2. Allow the physician to change the patient’s mind in the short duration of a visit

Educated patients are seen as capable of understanding medical explanations more clearly (Mathews 1983; Roter and Hall 2006; Waitzkin 1985) and this may:

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!)

ImplicationsWe can also graphically depict the spatial patterns we find (-> targeting of marketing efforts to patients by pharmaceutical firms):

DTCA effect across DMAs

Request accommodation across DMAs

Limitations

1. 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

2. 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)

3. 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

4. Only one category - statins