Direct-to-Consumer Advertising and Alternative Medicine Prescriptions: Evidence from

Advertising Expenditure Data and the Medical Expenditure Panel Survey

February 18, 2012

By Bram Gallagher

Abstract

The United States allows both direct-to-consumer advertising (DTCA) of prescription

medicine and the sale of untested complementary and alternative medicines (CAM) as dietary

supplements. I use a hurdle model to estimate the effect of DTCA for prescription drugs

indicated for five symptoms that both respond to the placebo effect and are most commonly

treated by CAM on prescriptions obtained for alternative remedies on the extensive and

intensive margins. Results from the Medical Expenditure Panel Survey and IMS Health

advertising data suggest that advertising expenditure does not have a significant effect on the

amount spent on CAM, but does have a significant positive effect on the number of CAM

prescriptions obtained in the population of people that obtained at least one CAM

prescription.

I. Introduction and Motivation

Only two industrialized countries, the United States and New Zealand, allow direct-to-

consumer advertisements (DTCA) for prescription pharmaceuticals. However, there is a long-

standing debate in these countries and in others about the potential good or harm DTCA

causes to the consumers of medicine. This is not an insignificant debate, as $4.9 Billion was

spent in 2007 in the US on DTCA of prescription medicines (Frosch et al., 2010). An

examination of the rapidly expanding expenditures on prescription drugs in the United States

found that DTCA by itself may be responsible for one-fifth of the increase in spending

between 1994-2005, not only by increasing the price of prescription drugs but also, and more

1

significantly, by increasing the usage of prescription drugs. The proportion of total

promotional expenditures accounted for by DTCA has risen from 14.2% in 1996 to 18.2% in

2005 while it has also seen a tremendous expansion in terms of absolute dollars, from $150

million in 1993 to the aforementioned $4.9 billion figure in 2007 (Dave and Saffer, 2010).

While the increase in DTCA has happened, the proportion of staff the FDA has employed to

ensure that ads follow regulations has fallen since at least 2002, and the proportion of

broadcast ads screened by the FDA before being approved has fallen dramatically, from 64%

in 1999 to 32% in 2004 (Donohue et al., 2007).

Auton (2006) reviewed the then-current arguments proposed by medical professionals

and economists in support of and against DTCA. Among the benefits of DTCA, he cites better

informed patients, reduction in under-treatment of ailments, and improved patient

compliance. He also lists patients pressuring their doctors for unnecessary drugs, confusion

about side-effects, increased medical risks from new drugs, and, interestingly, turning

“conditions common to human existence and ageing” into medical conditions as harmful

effects. The latter effect may reflect an opinion expressed strongly by Moynihan, Heath, and

Henry (2002), who wrote, “Pharmaceutical companies are actively involved in sponsoring the

definition of diseases and promoting them to both prescribers and consumers” and, on the

specific topic of irritable bowel syndrome, “what for many people is a mild functional

disorder-- requiring little more than reassurance about its benign natural course-- is being

reframed as a serious disease attracting a label and a drug, with all the associated harms and

costs.” Although these benefits and costs were not quantified by Auton (2006), a study by

Mintzes (2005) provides some insight into how these ads are viewed. Advertisers and

pharmaceutical groups viewed DTCA positively, while health professionals, consumer groups,

and public officials viewed them negatively.

2

There is some disagreement over whether DTCA actually encourages patients to seek

appropriate treatment, or merely influences them to switch brands of pharmaceuticals for an

ailment they are already treating. For instance, Rosenthal et al. (2003) find that DTCA

increases the use of the entire class of medicine being advertised rather than specific drugs,

while Kalyanaram (2009) reports empirical evidence of brand-switching as a result of DTCA.

Another potential benefit of DTCA could be that the ads promote a useful placebo

effect, reinforcing the perception of pharmaceuticals' effects. Placebo effects are generally

interpreted as conditioning patients to certain stimuli or establishing expectations for what

effects a drug will have (Almasi et al., 2006). A study of the content of DTCA found that 95%

of the ads contained an emotional appeal, often portraying a patient whose life is out of

control, and associating use of the drug with regaining that control and winning social

approval, a result which is consistent with these theories of placebo (Frosch et al., 2007). The

study concludes that the educational content of the ads is generally low.

If DTCA for prescription medicines increases demand for an entire class of drugs and

reinforces the placebo effect, DTCA ads may also increase the demand for untested,

alternative complementary and alternative medicine (CAM), defined by David Eisenberg as

“treatments that are neither generally taught in medical schools nor available at hospitals

Eisenberg et al., 1993).” CAM saw a marked increase in usage in the United States during the

1990's, with 33.8% in 1990 and 42.1% in 1997 of all Americans having used at least one CAM

therapy. In 2002 and 2007, respectively, 36% and 38.3% of Americans used at least one CAM

therapy in the previous year, excluding prayer (Fontarosa and Lundberg, 1998; Eisenburg et

al., 1998; Barnes et al., 2004; Barnes and Bloom, 2008). The apparent drop in the fraction of

CAM consumers between 1997 and 2002 may be due to differences in how CAM therapies are

defined in these studies rather than an indication of a real decrease in their popularity. The

3

increase in usage of CAM has occurred despite the fact that providers of CAM therapies,

including natural preparations, vitamin supplements, dietary supplements, or homeopathic

remedies, are not required to substantiate rigorously any claims about their effectiveness

(Dietary Supplement Health and Education Act of 1994). Although seemingly a rich subject

for economic analysis, CAM usage has been studied predominately by medical researchers.

There are some circumstances in which an enhanced placebo effect could actually work

against a patients long-term health, especially if less effective remedies are substituted for

those which are potentially more effective (Gallagher and Snow, 2011). This raises the

question of whether untested treatments are a complement to conventional medicine or if

they are substituting for conventional medicine in response to the increasing average out-of-

pocket expenses for prescription medicines that has occurred in the last decade (Stagnitti,

2007). A survey conducted in 1998 determined that 4.4% of individuals dismiss conventional

medicine in favor of CAM therapies, and found that most people who used CAM did so

because of personal beliefs rather than concerns about medical care costs (Astin, 1998). Druss

and Rosenheck (1999) indicated that, among patients who sought treatment for an ailment

and some preventative treatments, 2.7% used CAM remedies exclusively. One study of HIV

infected individuals found that 3% forwent conventional treatment in favor of CAM therapies,

and a quarter of patients took CAM therapies even though they had potentially adverse

reactions to the conventional treatment they received (Hsiao, 2003). Studies examining the

potential substitution effect usually do not focus on particular treatments or what the

treatments are used for, leaving open the question whether there are specific ailments for

which consumers are particularly likely to employ CAM therapies as a substitute for

conventional medicine.

4

II. Theory

Do advertisements for prescription drugs increase the demand for alternative remedies

traditionally used to treat similar symptoms? Since I would not expect a large fraction of the

population to consume uniform, positive amounts of alternative remedies, both the extensive

and intensive margins of alternative remedy consumption should be investigated. Specifically,

the extent to which DTCA influences the consumer to buy any amount of alternative remedies,

and how much alternative remedies the participating consumers consume in terms of the

number of individual prescriptions and the expenditure on them is the subject of this study.

Some consumers are observed to choose zero alternative remedies and others positive

amounts. This motivates the use of two distinct models for corner solution outcomes. For the

amount spent on alternative remedies I have a roughly continuous variable, expenditure, with

a positive probability of this outlay being zero for any given consumer. I can apply a pooled

Tobit model, specified as

E it=max0, x it uit , t=1,2,. .. ,T

u it | x it ~ N 0, 2(1)

where t is the month when observing new prescriptions and the round when examining total

expenditure, E, on new and existing prescriptions.

A defining feature of the Tobit model is that it forces the processes behind both

acquiring any prescriptions at all and the amount of prescriptions filled, if any are filled, to be

estimated jointly. If the underlying processes determining these two decisions are

fundamentally different, then employing a class of models known as hurdle models is

appropriate (Wooldridge, 2002; Cameron and Trivedi, 1998). I first estimate a model of

whether or not an individual purchases an alternative remedy, using the probit model

5

P y=0 | x =1−x , (2)

where Φ is the standard normal cumulative distribution function and y is binary variable that

is equal to one if there is either positive expenditure on alternative medicine prescriptions or a

positive number of prescriptions acquired and equal zero otherwise. After this step, I can

estimate the amount spent on alternative medicine, conditioned on some positive spending,

using the ordinary least-squares estimator with Huber-White (robust) standard errors.

For the number of acquired prescriptions, I use the Poisson distribution to obtain the

conditional probability

P y | y0, x = P y | x1−exp−

for y0 (3)

where μ=exp(xβ) and

P y | x = exp − y

y! (4)

(Long and Reese, 2006). I use the user-generated Stata module trpois0, created by Joseph

Hilbe (1999). This module is used as the second step of a two-step hurdle model procedure.

Since there is no loss of information between the steps, this two-step procedure is equivalent

to a model that maximizes a single likelihood (McDowell, 2003). This module also includes a

Huber-White (robust) variance estimator.

The negative binomial distribution is a viable alternative distribution for count models

when the data are over-dispersed, so alternative estimates using the negative binomial

distribution for the second step in the hurdle model are presented for comparison. When

using the negative binomial distribution, the conditional probability can be expressed as

P y | y0, x = P y | x

1−1 −1

for y0 (5)

where

6

P y | x = y−1y ! −1 −1

−1 a−1

−1

y

(6)

and α represents a parameter of unobserved heterogeneity (Long and Reese 2006). This

model is estimated using the Stata module trnbin0, also by Hilbe (1999).

The timing of advertisements is also important to the question of how advertising

expenditure affects alternative remedy acquisition and expenditures. While advertising in the

same period could be expected to have the greatest effect on sales, previous period ads may

also affect subsequent sales. The duration of the advertising effect on sales is an empirical

question that has generated some debate; the general consensus seems to suggest that the

effect lasts for months rather than years. Clarke (1976) presents a survey of early attempts to

estimate the duration of the advertising effect, and finds that it generally lasts from between

three to fifteen months, with the majority of the effect usually expressed in the first three

months. Leone (1995) finds empirical support for the conclusion that the advertising effect

duration is between three and nine months when taking into account biases that occur when

advertising data are excessively aggregated. Franses and Vroomen (2006), using two large

data sets, find that 95% of the advertising effect occurs within three months. These empirical

findings suggest that, when advertising data are aggregated by month, expenditure during the

current period and a distributed lag of expenditure over several previous months should

capture most of the advertising effect on sales.

III. Data

To provide some empirical insight into this problem, I use the Medical Expenditure

Panel Survey (MEPS), a survey conducted annually by the Agency for Healthcare Research

and Quality. This survey draws on subjects from the National Health Interview Survey (NHIS)

to provide data on medical expenditures in the United States. I use two components from

7

each of the releases of the survey from 2002 to 2007. The first is the household component,

which includes demographic data on the person being interviewed, such as race, income,

education, and marital status. The observations are then weighted to provide nationally

representative information. The second is the prescription medicine component, which

includes information on medicines and the cost of those medicines prescribed to and

purchased or obtained by the subjects in the household file. I use data publicly available since

the 2002 survey, when the survey began to include total amounts spent on prescriptions for

two alternative remedies: herbal remedies and nutraceutical products, which include dietary

supplements and vitamins.

I combine these two components to produce a data set that includes all individuals who

participated for the entire length of a survey year from the 2002 to 2007 releases. The data

include 116,112 unique individuals interviewed over an eighty-four month period beginning in

January 2001 and ending in December 2007. The MEPS is not a true panel when the survey

years 2002-2007 are pooled. Instead, it consists of seven overlapping panels, the first and last

of which contain information gathered in three rounds of interviews at approximately six

month intervals, with the rest containing five rounds. Therefore, each individual participates

in either three or five rounds, and each round is, on average, five and half months long;

however, the MEPS is structured so that the first round is often only one month long, the

month of the first interview. To include some information on prescription drug consumption

prior to the survey, I extend the first round to six months, resembling the overall average

round length.

During the complete scope of the data, 559 new prescriptions for alternative remedies

were written and acquired, and 1,549 instances of an individual acquiring a prescribed

alternative remedy were recorded. For individuals who consumed alternative remedies,

8

expenditures on them in a single round ranged from $1.85 to $2824.62, with $135.38 being

the average expenditure in any given round by an individual who consumes at least one

alternative remedy.

The data set includes variables specific to the individuals concerning age, marital

status, gender, race, education, and annual income. Table 1 provides some descriptive

statistics for these variables. Educational attainment is defined as the highest degree obtained

at the time of the first interview. Personal annual income may be negative because it includes

losses by individually owned businesses. The variable Poverty Category measures the

individual's family income as a percentage of the poverty level in 100% increments and is

bottom-coded at 100% and top-coded at 500%. For the purposes of analysis, I have split this

variable into four categories, the lowest category being the excluded dummy variable. The

Hispanic variable includes all individuals who report being Hispanic, regardless of race. The

black and Asian variables indicate individuals who report being black or Asian but not

Hispanic. The excluded race category is all other races besides black and Asian who do not

report being Hispanic. The HS degree variable includes both holders of high school diplomas,

individuals who have passed the GED test, and individuals who reported having some college

education but not having finished college. The excluded education category is not having a

degree, and the apparently low percentage of respondents that have any degree reflects the

oversampling of children. Age is top-coded at 85 for privacy reasons.

9

Because of the original design of the NHIS, specific groups such as Hispanics, children,

and blacks are oversampled. The MEPS is intended to provide nationally representative data

on the non-institutionalized, civilian population, and so includes a sample weight for each

individual based on race, age, poverty status, region, and metropolitan statistical area, using

the Current Population Survey as the control. Unless otherwise indicated, I use these

sampling weights in each estimation procedure.

10

Table 1: Summary Statistics for Entire PopulationVariable Mean Min Max

Income 19102.05 26095.34 -61641 430904Poverty Category 3.4 1.39 1 5Male 47% 50% 0 1Hispanic 26% 44% 0 1Black 16% 37% 0 1Asian 4% 20% 0 1HS degree 34% 47% 0 1Bachelor's 9% 29% 0 1Graduate degree 5% 21% 0 1Other degree 5% 21% 0 1Age 34.4 22.45 0 85Married 0.38 0.48 0 1

N=116112

Std. Dev.

Figure 1

I combine the MEPS with data collected by IMS Health on monthly expenditures on

direct-to-consumer advertising of prescription drugs by firms during the same time period

and several months prior to produce lagged advertising expenditures. The data are aggregated

nationally and divided into four broad categories of distribution: television, print, radio, and

other (which includes outdoor signs and Internet advertising). Individual prescription names

are included, as well as the ailment for which it is prescribed. Of special interest are the ads

for drugs that treat heartburn and stomach ulcers, anxiety, depression, allergy, migraines, and

general pain, since these are the ailments that alternative remedies are most often used for

(Barnes et al., 2002) and are most likely to be sensitive to placebo effects (Evans, 2004). I

limit my analysis to ads that promote these types of drugs. Figure 1 illustrates the total

advertising expenditure over the eighty-four month period beginning in January 2001 and

ending in December 2007.

11

050

000

1000

0015

0000

Ad e

xpen

ditu

re in

thou

sand

s of

dol

lars

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84Month

Ad expenditure over time

There is some apparent annual periodicity leading to large spikes in ad expenditure in

the month of April, which has a mean expenditure that is 154% of the overall mean. The peak

advertising month is April 2004, with an expenditure of $142,097,090. Regressing ad

expenditure on the month before and after this peak shows clear linear trends, robust to

including April as a control variable, as summarized in Table 2.

This is in contrast to the general trend of increasing new prescriptions over the same period,

as illustrated by figure 2.

The ad data are combined with the MEPS data in two ways. New prescriptions

generated during the course of the survey are reported by month, as are ad expenditures. For

the part of the survey population that acquired at least one new prescription of any kind, ad

expenditures are matched to each month that the individual was active in the survey,

including at least six months for round one. This combination is used to examine the effect of

ads for conventional drugs indicated for the five previously identified ailments on new

prescriptions for alternative medicine. I include as explanatory variables for new prescriptions

the advertising expenditure in the current month, as well as a distributed lag of advertising

expenditure for the three previous months.

12

Table 2: OLS Regression of Ad Expenditure Prior to and After April, 2004All Months Prior After Prior, April After, April

Month -515.3** 1089.6** -1731.8** 1039** -1663.5**(118.2) (340.5) (205) (287) (177.3)

April 44989** 32370.4**(11043.1) (8091.6)

n= 84 40 45 40 45Significance levels: +:10% *:5% **:1%

Figure 2

For total expenditure and number of prescriptions, the MEPS aggregates by round.

Each individual has a value for each round that describes the total number of alternative

remedies that each individual consumer reported acquiring during that round and how much

in total was spent on alternative remedies during that round. Since the average round length

is more than five months, only expenditures in the current round are considered. This

aggregation produces some measurement error because remedies could have been bought in

months before ads were released or ads released at the end of one round could convince a

consumer to acquire alternative remedies at the beginning of the next round. Weighting of ads

in the beginning of the round may help to provide better estimates of the causal effect of prior

advertising on current-round consumption.

13

010

2030

New

Pre

scrip

tions

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84Month

New prescriptions over time

IV. Results

Turning first to new prescriptions, since most respondents spend zero dollars on new

alternative remedy prescriptions for any particular month, I employ a Tobit model to estimate

the effect of advertising (expressed in thousands of dollars) on expenditure on new alternative

medicine prescriptions (expressed in dollars). Table 3 summarizes the results for the Tobit

model. The new prescription analysis only includes the survey respondents who had acquired

at least one new prescription during the course of the survey-- 71,019 out the total of 116,112

unique individuals included in all the surveys examined in this study. The number of

observations for these models is, in this case, 1,361,874 since each person is examined over

each month he or she was included in the survey.

To test the robustness of the estimates to changes in model specification, several

alternative specifications are estimated. The first is a simple bivariate model of total ad

expenditure and prescription expenditure. The second model adds demographic variables.

The third model adds socioeconomic status variables.

The results from Table 3 indicate that current ad expenditure has a weak, negative

effect on expenditures on new alternative medicine prescriptions. The range of total ad

expenditures is $6,804,300 to $142,097,090 per month, leading to a total range of current ad

expenditures' potential effect on alternative remedy expenditures of about $7.50 to $156. The

magnitude of the effect is nearly identical for each of the three model specifications,

suggesting that the result is robust to changes in those model specifications. The three month

lag also has a nearly identical effect in both magnitude and precision.

Males spend about $30 less on alternative medicine acquisition than observationally

equivalent females, while Hispanics about $40 more on average than non-Hispanics. As

people age, they spend $2.50 more per year on alternative medicine, but this effect is

14

imprecisely estimated. The quadratic formulation suggests that this effect attenuates with age.

While education seems not to affect spending on new prescription alternative medicine

prescriptions, being in the middle poverty categories (i.e. being in a household that earns

between 201% and 400% of poverty level income) reduces the acquisition of alternative

medicine by about $50. When taking into account household income, individual income has

no effect on acquisition of alternative remedies.

An important feature of the Tobit model is that it forces the process underlying the

choice to acquire zero or positive amounts of alternative remedies to be the same. An

alternative model for corner-solution outcomes is the hurdle model, which allows the two

processes to be estimated separately. Specifically, a probit model can be used to model the

binomial choice of whether or not to acquire alternative medicine. A zero-truncated count

model can be used to see how many new prescriptions individuals acquire, and a standard

regression model can be used to estimate the amount spent given that they obtain at least one

prescription. Table 4 summarizes the results of estimating a probit model of the effects of

advertising expenditure, this time measured in millions of dollars merely to scale the results,

on the extensive margin of acquiring new prescriptions.

The estimated results match up with the Tobit model of expenditures in sign and

significance. Current and three-month lagged expenditure have a significant, negative effect

on the probability that a respondent will acquire an alternative remedy prescription. Being

male, again, has a negative effect on acquiring prescriptions, while being Hispanic has a

positive effect. Age has a weak positive effect when not including socioeconomic status

variables. The middle poverty categories have a negative effect.

15

16

Table 3: New Prescriptions Tobit on ExpenditureModel 1 Model 2 Model 3

Current ads -0.0011*(0.0006) (0.0006) (0.0006)

One month lag -0.0005 -0.0006 -0.0006(0.0009) (0.0009) (0.0009)

Two month lag 0.0002 0.0002 0.0002(0.0008) (0.0008) (0.0008)

Three month lag -0.0010+(0.0005) (0.0005) (0.0005)

Male -30.48+ -28.07+(16.19) (16.20)

Age 2.5+ 2.48(1.39) (1.70)

-0.027+ -0.03(0.02) (0.02)

Married 1.04 5.2(19.55) (20.21)

Hispanic 43.06* 37.82+(19.15) (20.10)

Black 21.59 15.74(22.21) (22.95)

Asian -19.58 -20.36(46.54) (46.65)

HS degree 9.95(23.29)

Bachelor's 18.63(31.99)

Graduate deg -0.45(41.25)

Other deg -34.81(42.51)

Poverty category 2 -0.11(29.58)

Poverty category 3 -50.78+(28.50)

Poverty category 4 -51.32*(24.91)

Poverty category 5 -35.89(27.25)

Income 0.00(0.00)

N=1361874

-.0011+ -.0011+

-.0010+ -.0010+

Age2

Significance levels: +:10% *:5% **:1%

17

Model 1 Model 2 Model 3Current ads -0.0026+ -0.0026+ -0.0025+

-(0.0013) -(0.0013) -(0.0013)One month lag -0.0013 -0.0014 -0.0014

-(0.0020) -(0.0020) -(0.0020)Two month lag 0.0006 0.0006 0.0006

-(0.0020) -(0.0020) -(0.0020)Three month lag -0.0023+ -0.0023+ -0.0024+

-(0.0013) -(0.0013) -(0.0013)Male -0.0844* -0.0795*

-(0.04) -(0.04)Age 0.01+ 0.01

(0.00) (0.00)0.00 0.00

(0.00) (0.00)Married 0.01 0.02

-(0.05) -(0.05)Hispanic 0.110* 0.0961*

-(0.05) -(0.05)Black 0.06 0.05

-(0.05) -(0.06)Asian -0.07 -0.07

-(0.11) -(0.11)HS degree 0.02

-(0.06)Bachelor's 0.04

-(0.08)Graduate deg 0.00

-(0.10)Other deg -0.08

-(0.10)Poverty category 2 0.00

-(0.07)Poverty category 3 -0.123+

-(0.07)Poverty category 4 -0.127*

-(0.06)Poverty category 5 -0.10

-(0.07)Income 0.00

(0.00)N=1361874

Table 4: New Prescriptions Probit on Decision to Buy

Age2

Significance levels: +:10% *:5% **:1% ***: .1%

18

Table 5: New Prescriptions Hurdle RegressionModel 1 Model 2 Model 3

Current ads -0.0006 -0.0011 -.0013+ (0.0007) (0.0007) (0.0007)

One month lag 0.0003 0.0009 0.0009(0.0011) (0.0010) (0.0011)

Two month lag -0.0005 -0.0008 -0.0006(0.0008) (0.0007) (0.0008)

Three month lag -0.0008 -0.0004 -0.0006(0.0005) (0.0005) (0.0005)

Male 55.12* 67.96** (24.09) (26.05)

Age 5.18* 6.666* (2.57) (2.91)

-.0541* -.066* (0.03) (0.03)

Married -21.86 -37.98(25.50) (31.12)

Hispanic -40.7* -31.7+(19.05) (20.00)

Black -42.11* -31.47(20.26) (20.78)

Asian 97.51 160.72(101.34) (96.16)

HS degree 17.37(26.06)

Bachelor's 31.01(34.92)

Graduate deg 52.43(36.90)

Other deg -40.43(53.69)

Poverty category 2 -36.67(24.85)

Poverty category 3 -7.82(28.05)

Poverty category 4 -5.83(28.96)

Poverty category 5 32.32(37.25)

Income -.0014* (0.00)

N=281

Age2

Significance levels: +:10% *:5% **:1%

The second step of the hurdle model is either an OLS regression for the continuous

variable expenditure, or a zero-truncated count model for the number of acquired new

prescriptions. This can be modeled alternately as having a Poisson or negative-binomial

distribution depending on whether or not the data is over-dispersed. Table 5 summarizes the

results of the regression model with advertising expenditure measured in thousands of

dollars, and table 6 summarizes the results of the zero-truncated count model with advertising

expenditure measured in millions of dollars, again for scaling purposes. Count models

followed by (P) use the Poisson distribution, while models followed by (NB) use the negative

binomial. The number of observations is much smaller than before, since these models are

conditioned on respondents acquiring alternative remedy prescriptions. The regression

model, despite conditioning on acquiring alternative medicine, produces similar, though

generally less precise, estimates of the effects of current and three month lagged advertising

expenditure. However, being male or Hispanic switch signs for the conditional sample,

implying an increase in expenditure of between $55 and $68 and a decrease in expenditure of

between $32 and $41, respectively. The effect of age is more precisely estimated and larger in

magnitude, suggesting an increase in expenditure of $5 to $7 for each year of age, with the

same negative quadratic effect. The coefficient on black becomes significant in Model 2,

reducing expenditure by $42. For the conditional sample, poverty categories no longer affect

expenditure, while a $1,000 increase in annual individual income now predicts a $1.36

reduction in expenditure.

19

Turning to the count model estimates, as summarized in Table 6, I now find that

advertising expenditure has a positive effect on the number of prescriptions for all models.

20

Table 6: New Prescriptions Hurdle Model, Poisson and Negative Binomial DistributionsModel 1 (P) Model 2 (P) Model 3 (P) Model 1 (NB) Model 2 (NB) Model 3 (NB)

Current ads 0.0081* 0.0085* 0.012** 0.0091 0.000010+ 0.000011+(0.0037) (0.0037) (0.0037) (0.0061) (0.0057) (0.0055)

One month lag -0.0067 -0.0075 -0.012* -0.0058 -0.0086 -0.0111(0.0061) (0.0062) (0.0061) (0.0095) (0.0090) (0.0087)

Two month lag 0.0051 0.0073 0.0113 0.0043 0.0092 0.0139(0.0062) (0.0000) (0.0064) (0.0101) (0.0095) (0.0094)

Three month lag -0.0057 -0.0081* -0.011* -0.0067 -0.0099 -0.0137* (0.0040) (0.0041) (0.0042) (0.0068) (0.0064) (0.0064)

Male -0.428** -0.374* -0.551** -0.442* -(0.14) -(0.15) -(0.21) -(0.21)

Age -0.01+ -0.01 -0.01 0.00-(0.01) -(0.01) -(0.02) -(0.02)

0.00 0.00 0.00 0.00(0.00) (0.00) (0.00) (0.00)

Married 0.337* 0.462** 0.431* 0.516* -(0.14) -(0.15) -(0.21) -(0.21)

Hispanic -0.434** -0.574** -0.562* -0.719** -(0.16) -(0.16) -(0.23) -(0.24)

Black 0.20 0.12 0.29 0.16-(0.15) -(0.15) -(0.26) -(0.25)

Asian 0.44 0.44 0.5 0.73-(0.29) -(0.29) -(0.56) -(0.54)

HS degree -0.05 0.02-(0.15) -(0.22)

Bachelor's 0.13 0.23-(0.23) -(0.36)

Graduate deg -0.27 -0.19-(0.32) -(0.47)

Other deg -0.29 -0.43-(0.39) -(0.54)

Poverty category 2 0.08 0.24-(0.20) -(0.32)

Poverty category 3 -0.22 -0.27-(0.18) -(0.29)

Poverty category 4 -0.62 -0.691* -(0.19) -(0.28)

Poverty category 5 -0.562** -0.657* -(0.20) -(0.31)

Income 0.00 0.00(0.00) (0.00)

N=294

Age2

Significance levels: +:10% *:5% **:1%

With the exception of Model 1 for the negative binomial case, this effect is precisely estimated.

Using the range of total ad expenditure, the total difference that advertising makes on

alternative medicine prescriptions for people that acquire at least some alternative medicine is

equivalent to slightly more than one monthly prescription. The three month lagged

expenditure has a smaller, negative effect, and for Model 3 in the Poisson case, so does the

one month lagged expenditure. Being male and an increase in age both now have negative

effects. Thus, while being male and older generally leads to higher expenditure, it reduces the

number of individual prescriptions. Being married increases the average number of

prescriptions by up to one half a prescription on average, and being Hispanic or in the above-

described middle poverty categories reduces the number of prescriptions by as much 0.7

prescriptions.

Next, I estimate the models using the entire sample and the total acquisitions of

alternative remedies, including individuals who did not acquire any new prescriptions during

the duration of the survey and existing prescriptions. Since monthly data are only reported for

new prescriptions, the data for this section are aggregated by round, so expenditure on both

advertising and on all prescriptions is the sum of all monthly expenditures during a particular

round, measured in thousands of dollars for the Tobit and regression models and millions for

the probit and zero-truncated count models. The number of prescriptions includes new

prescriptions and preexisting prescriptions from prior to that round still being taken. The

tables are labeled “round-only” to indicate that the level of measurement is now the individual

respondents' round instead of month.

Table 7 summarizes the results of a estimating Tobit model on the round-only data.

Model 4 includes disaggregated current-round ad expenditure in the four broad media

classes. The estimated coefficients on current expenditure are again negative and precisely

21

estimated. Their magnitudes are predictably smaller, since I am not conditioning on

respondents having acquired at least one prescription. Being male lowers expenditure about

$65, but the effect of age is not statistically significant. While being Hispanic now only barely

decreases expenditure, being Asian is associated with about $120 to $130 lower expenditure.

Of the socioeconomic status variables, having an “other degree” is associated with about $80

lower expenditure, and the middle poverty categories are both associated with about $85

lower expenditure and are precisely estimated. When disaggregated current ad expenditure is

introduced, only radio advertisements have a significant effect and represents more than one

and a half cent lower expenditure on alternative medicine per $1,000 of radio advertisements.

22

23

Table 7: Round Only Tobit on ExpenditureModel 1 Model 2 Model 3 Model 4

Current ads(0.0001) (0.0001) (0.0001)

Male -66.7** -63.72** -63.29**(17.59) (17.87) (17.85)

Age 1.35 2.38 2.46(1.56) (1.78) (1.78)0.02 0.01 0.01

(0.02) (0.02) (0.02)Married 8.84 4.53 4.95

(19.69) (20.13) (20.00)Hispanic -0.80 -1.13+ -2.25

(23.03) (25.34) (25.37)Black -7.85 -9.3 -9.20

(24.76) (25.47) (25.35)Asian -122.03* -127.15* -128.2*

(51.04) (51.39) (51.33)HS degree -16.33 -17.24

(23.92) (23.87)Bachelor's 39.72 38.94

(31.61) (31.55)Graduate deg -30.92 -31.02

(43.31) (43.21)Other deg -78.91+ -80.81+

(43.35) (43.29)Poverty category 2 -54.92 -54.17

(39.04) (38.82)Poverty category 3 -86.27** -85.16**

(32.56) (32.38)Poverty category 4 -85.45 -84.26**

(26.33) (26.15)Poverty category 5 -17.01 -14.92

(29.55) (29.36)Income 0.00 0.00

(0.00) (0.00)TV ads in round 0.00

(0.00)Print ads in round 0.00

(0.00)Radio ads in round

0.004Other ads in round 0.00

(0.00)N=466298

-.0003** -.0003** -.0003**

Age2

-.016***

24

Model 1 Model 2 Model 3 Model 4Current ads -0.0005** -0.0005** -0.0005**

-(0.000094) -(0.000096) -(0.000096)Male -0.124** -0.12** -0.12**

-(0.03) (0.03) (0.03)Age 0.0022 0.0033 0.0035

-0.0026 0.0029 0.00290.00 0.00 0.00

(0.00) (0.00) (0.00)Married 0.02 0.02 0.016458

-(0.03) (0.03) (0.033932)Hispanic 0.01 0.01+ 0.01

-(0.04) (0.04) (0.04)Black -0.01 -0.01 -0.01

-(0.04) (0.04) (0.04)Asian -0.223**

-(0.08) (0.08) (0.08)HS degree -0.02 -0.021980

(0.04) (0.040534)Bachelor's 0.08 0.08

(0.05) (0.05)Graduate deg -0.06 -0.06

(0.07) (0.07)Other deg

(0.07) (0.07)Poverty category 2 -0.1 -0.1

(0.07) (0.07)Poverty category 3

(0.05) (0.053550)Poverty category 4

(0.05) (0.05)Poverty category 5 -0.05 -0.05

(0.05) (0.05)Income 0.00 0.00

(0.00) (0.00)TV ads in round 0.000335

(0.000235)Print ads in round 0.00

(0.00)Radio ads in round

(0.01)Other ads in round 0.00

(0.00)N=466298

Table 8: Round Only Probit on Decision to Buy

Age2

-.231** -.234**

-.123+ -.127+

-.148** -.147**

-.166** -.165**

-.0247**

Significance levels: +:10% *:5% **:1% ***: .1%

25

Table 9: Round Only Hurdle RegressionModel 1 Model 2 Model 3 Model 4

Current ads -0.0002** 0.00005 0.00005 0.00005

Male 75.28** 82.76** 79.69** (28.96) (27.92) (27.29)

Age 1.52 4.92 4.23(3.63) (4.06) (4.26)-0.03 -0.06 -0.05(0.03) (0.04) (0.04)

Married -36.38 -60.68* -43.50(24.20) (25.39) 26.59

Hispanic -108.36** -97.95+ -93.66** (31.52) (32.27) (31.26)

Black -38.94 -9.50 7.2(34.50) (33.68) (32.29)

Asian 121 154.35* 152.53** (71.74) (62.16) (54.79)

HS degree -22.15 -21.81(27.46) 28.19

Bachelor's -34.11 -27.64(36.78) (36.80)

Graduate deg 94.44 103.76(57.41) (57.26)

Other deg -82.82+ -64.26(45.85) (47.98)

Poverty category 2 34.17 50.35(33.37) (35.18)

Poverty category 3 15.67 17.54(26.90) 26.75

Poverty category 4 134.51** 139.6** (48.52) (47.51)

Poverty category 5 136.92** 134.11**(33.45) (34.19)

Income0.0005 (0.0005)

TV ads in round -0.0001(0.0003)

Print ads in round 0.0009(0.0005)

Radio ads in round(0.0063)

Other ads in round -0.0016(0.0015)

N=646

-.0002** -.0002**

Age2

-.0017** -.0018**

-.013*

Significance levels: +:10% *:5% **:1%

Table 8 summarizes the results from estimating a probit model on the extensive margin

of consuming alternative medicine using the round only data. The signs and significance

levels from the estimated coefficients are similar to those of the previously estimated in the

Tobit model.

Table 9 presents a summary of results from an OLS regression on alternative medicine

expenditures, using the group that spent at least some money on alternative medicine during

the course of the survey. For this group, advertising expenditure has a negative and precisely

estimated effect, though only for radio advertising in the disaggregate model. Estimated

effects from the new prescriptions group were also negative, but only model three produced a

(marginally) significant estimate. The effect of being male switches from negative in the Tobit

and probit results to positive in the OLS estimates, leading to $75 to $83 higher expenditure.

The coefficient on being Asian also switches, leading to about $33 higher consumption, and

being Hispanic has an effect of much greater magnitude and precision, implying from $94 to

$108 lower expenditure.

For the socioeconomic status variables, having some “other degree” has an effect

similar to the Tobit model, though is not statistically significant in Model 4. Unlike in the

Tobit results, being in the top poverty categories (i.e. those households that make 400% of

poverty level income or more) implies an increase in expenditure from $135 to $140, and an

additional dollar of personal income decreases expenditure by about 0.17 cents. The person

with the highest personal income in this sample made $171,213, so while being in the highest

poverty category will have the effect of lowering expenditure by $135, the personal income

itself will have the effect lowering expenditure by about $280, leading to a net decrease in

expenditure. The combination of these effects and the estimates from the Tobit suggest a dip

in expenditure among middle income that is lessened at higher incomes.

26

Finally, Table 10 summarizes the results from estimating the zero-truncated Poisson

and negative binomial count models for the number of alternative prescriptions acquired

during a round. The Poisson model produces more precise estimates, notably for advertising

during a round. The estimated effect is smaller than when I use the round-aggregate data.

When attention is focused on new prescriptions, the three month lag of expenditure has a

negative effect while the current month has a positive effect. Since rounds are generally at

least three months long, the smaller coefficient may reflect the combination of monthly

effects. The disaggregate model shows that, for the Poisson results and among different types

of advertising, only radio advertising has a statistically significant, positive effect. Being black

has a positive effect on expenditure, leading to a third to two-fifths additional prescriptions

more per round. Being Hispanic has a somewhat smaller negative effect, implying a fifth to a

quarter of one prescription fewer on average. Income has a negative effect; using the

wealthiest person in the sample as an illustrative example, a respondent that earned $171,213

would take two-thirds of one prescription fewer on average than a respondent who earned

zero dollars. Being in various poverty categories, on the other hand, seems to have no effect on

the number of prescriptions.

27

V. Conclusions and Suggestions for Further Research

The effect of DTCA on CAM usage appears nuanced and not entirely straightforward.

Figures 1 and 2 illustrate that expenditures on DTCA for the five ailments associated with

placebo effects have been declining, while prescriptions for CAM reported in the MEPS have

been increasing. Since only a small fraction of people in the MEPS acquire prescriptions for

CAM, these broad trends may apply differently to different parts of the population.

28

Table 10: Round Only Hurdle Model With Poisson and Negative Binomial DistributionsModel 1 (P) Model 2 (P) Model 3 (P) Model 4 (P) Model 1 (NB) Model 2 (NB) Model 3 (NB) Model 4 (NB)

Round ads 0.0004** 0.0004* 0.0004* 0.0006+ 0.0004 0.0004-(0.0002) (0.0002) (0.0002) (0.0003) (0.0003) (0.0003)

Male 0.00 0.05 0.0431 0.03 0.09 0.09(0.07) (0.07) (0.0675) (0.11) (0.11) (0.11)

Age 0.008 .0159* 0.014+ 0.012 0.021+ 0.0190.007 (0.0076) (0.01) (0.0105) (0.0119) (0.0120)

0.000006 0.00 0.00 0.00 0.00 0.00(0.0001) (0.00) (0.00) (0.00) (0.00) (0.00)

Married 0.152* 0.149* 0.174* 0.17 0.18 0.2(0.07) (0.07) (0.07) (0.11) (0.12) (0.12)

Hispanic -0.163+ -0.230* -0.214* -0.17 -0.267+ -0.251+ (0.09) (0.09) (0.09) (0.13) (0.14) (0.14)

Black 0.307** 0.295** 0.310** 0.421** 0.394** 0.406** (0.08) (0.08) (0.08) (0.15) (0.15) (0.15)

Asian 0.11 0.15 0.15 0.14 0.22 0.20(0.19) (0.19) (0.19) (0.33) (0.33) (0.33)

HS degree -0.01 -0.01 -0.01 -0.01(0.08) (0.08) (0.13) (0.13)

Bachelor's 0.10 0.10 0.13 0.14(0.11) (0.11) (0.19) (0.19)

Graduate deg 0.02 0.04 0.12 0.15(0.15) (0.15) (0.25) (0.25)

Other deg -0.25 -0.21 -0.29 -0.24(0.18) (0.18) (0.28) (0.28)

Poverty category 2 -0.04 -0.02 -0.05 -0.04(0.13) (0.13) (0.21) (0.21)

Poverty category 3 -0.171 -0.164 -0.102 -0.093(0.1061) (0.1065) (0.1696) (0.1688)

Poverty category 4 -0.13 -0.13 -0.12 -0.11(0.09) (0.10) (0.15) (0.15)

Poverty category 5 -0.11 -0.12 -0.16 -0.16(0.11) (0.11) (0.18) (0.18)

Income -0.00000385* -0.00000378* -0.000004 -0.000004(0.000002) (0.000002) (0.000003) (0.000003)

TV ads in round 0.00 0.00(0.0006) (0.0009)

Print ads in round 0.00240** 0.00(0.0008) (0.0014)

Radio ads in round 0.00 0.00(0.0133) (0.0216)

Other ads in round 0.00 0.002350(0.0042) (0.0067)

N=646Significance levels: +:10% *:5% **:1%

Age2

Generally, advertising expenditure reduce expenditure on CAM using both monthly

and round-only data , even when conditioning on purchasing at least some CAM. However,

the number of total CAM prescriptions acquired is generally positively related to advertising

expenditure when I condition on purchasing some CAM. Older people have higher

expenditures, and income leads to higher expenditures at the top and bottom extremes. Racial

effects vary widely in sign, magnitude, and precision depending on the population examined.

Education appears to have little or no effect, controlling for income and other factors.

The analysis is limited by the scope of the data. In particular, respondents' use of CAM

is defined by the number of prescriptions acquired during the period covered by the MEPS.

Since CAM, by definition, does not require a prescription to acquire, much of the actual CAM

use is probably obscured. More comprehensive data on CAM usage would provide clearer,

more widely applicable, results. Also, the advertising data are aggregated to provide national

totals, without regard to location of residence within the US, so including location to control

for that dimension of unobserved heterogeneity is not possible.

References

Almasi, Elizabeth A., Randall S. Stafford, Richard L. Kravitz, Peter R. Mansfield. 2006.

What Are the Public Health Effects of Direct-to-Consumer Drug Advertising? PLoS Medicine.

3(3): e145.

Astin, John A. 1998. Why Patients Use Alternative Medicine: Results From a National

Survey. JAMA. 279: 1548-1553.

Auton, Frank. 2006. Direct to Consumer Advertising (DTCA) of Pharmaceuticals: an

Updated Review of the Literature and Debate Since 2003. Economic Affairs. 26(3): 24-32.

Barnes, PM, E Powell-Griner, K McFann, RL Nahin. 2004. Complementary and

alternative medicine use among adults: United States, 2002. Advance Data from Vital and

29

Health Statistics. 343: 1-19.

Barnes, P.M. and Barbara Bloom. 2008. Complementary and Alternative Medicine Use

Among Adults and Children: United States, 2007. National Health Statistics Reports. 12.

Cameron, A. C. and P. K. Trivedi. 1998. Regression Analysis of Count Data. New York:

Cambridge University Press.

Clarke, Darral G. 1976. Econometric measurement of the duration of advertising effect

on sales. Journal of Marketing Research. 13(4): 345-357.

Dave, Dhaval, and Hanry Saffer. 2010. The Impact of Direct-to-Consumer Advertising

on Pharmaceutical Prices and Demand. NBER Working Paper No. w15969.

Dietary Supplement Health and Education Act of 1994. Pub L No. 103-417, Stat 4325

(codified at USC §301 [1994]).

Division of Health Interview Statistics, CDC. 2008. NHIS Survey Description.

Donohue, Julie M., Marisa Cevasco, and Meridith B. Rosenthal. 2007. A Decade of

Direct-to-Consumer Advertising of Prescription Drugs. New England Journal of Medicine.

357: 673-681.

Druss, B. G. and R. A. Rosenheck. 1999. Association between use of unconventional

therapies and conventional medical services. JAMA. 282(7): 651-6.

Eisenberg, David M., R.C. Kessler, C. Foster, F.E. Norlock, D.R. Calkins, and T.L.

Delbanco. 1993. Unconventional medicine in the United States. Prevalence, costs, and

patterns of use. New England Journal of Medicine. 328(4):246-52.

Eisenberg, David M., R.B. Davis, S.L. Ettner, S. Appel, S. Wilkey, M. Van Rompay, and

R.C. Kessler. 1998. Trends in Alternative Medicine Use in the United States, 1990-1997.

JAMA. 280: 1569-1575.

Evans, Dylan. 2004. Suppression of the acute-phase response as a biological

30

mechanism for the placebo effect. Medical Hypotheses. 64(1): 1-7.

Fontanarosa, Phil B. and George Lundberg. 1998. Alternative Medicine Meets Science.

JAMA. 280: 1618-1619.

Franses, Philip Hans and Bjørn Vroomen. 2006. Estimating confidence bounds for

advertising effect duration intervals. Journal of Advertising. 35(2): 33-37.

Frosch, Dominick L., Patrick M. Krueger, Robert C. Hornik, Peter F. Cronholm, and

Frances K. Barg. 2007. Creating Demand for Prescription Drugs: A Content Analysis of

Television Direct-to-Consumer Advertising. Annals of Family Medicine. 5: 6-13.

Frosch, Dominick L., David Grande, Derjung M. Tarn, and Richard L. Kravitz. 2010. A

Decade of Controversy: Balancing Policy With Evidence in the Regulation of Prescription

Drug Advertising. American Journal of Public Health. 100(1): 24-32.

Hsiao, An-Fu. 2003. Complementary and Alternative Medicine Use and Substitution

for Conventional Therapy by HIV-Infected Patients. Journal of Acquired Immune Deficiency

Syndromes. 33(2): 157-65.

Hilbe, J. 1999. sg102: Zero-truncated Poisson and negative binomial regression. Stata

Technical Bulletin 47: 37–40.

Kalyanaram, G. K. 2009. The endogenous modeling of the effect of direct-to-consumer

advertising in prescription drugs. International Journal of Pharmaceutical and Healthcare

Marketing. 3(2): 137-148.

Leone, Robert P. 1995. Generalizing what is known about temporal aggregation and

advertising carryover. Marketing Science. 14(3): G141-G150.

Long, J. Scott and Jeremy Freese. 2006. Regression Models for Categorical Dependent

Variables Using Stata. 2d ed. College Station: Stata Press.

McDowell, Allen. 2003. From the help desk: hurdle models. The Stata Journal. 3(2):

31

178-184.

Mintzes, B, M. Barer, J. Lexchin, and K.L. Basset. 2005. Introduction of direct-to-

consumer advertising of prescription drugs in Canada: an opinion survey on regulatory policy.

Research in Social and Administrative Pharmacy. 1(2):310-30.

Moynihan, Ray, Iona Heath, and David Henry. 2002. Selling Sickness: The

Pharmaceutical Industry and Disease Mongering. British Medical Journal. 324(7342): 886-

890.

Rosenthal, Meridith B., Ernst R. Berndt, Julie M. Donohue, Arnold M. Epstein, and

Richard G. Frank. 2003. Demand Effects of Recent Changes in Prescription Drug Promotion.

Forum for Health Economics and Policy. 6: (Frontiers in Health Policy Research), Article 2.

Stagnitti, Marie. 2007. Trends in Outpatient Prescription Drug Utilization and

Expenditures, 1997 and 2004. Medical Expenditure Panel Survey Statistical Brief #168.

Wooldridge, Jeffrey. 2002. Econometric Analysis of Cross Section and Panel Data.

Cambridge: MIT Press.

32