The Impact of Direct-to-Consumer Advertising of Cholesterol Reducing by eib63834

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									    The Impact of Direct-to-Consumer Advertising of Cholesterol Reducing
              Drugs on Diagnosis and Treatment of Cholesterol




                                         Hae Kyung Yang*
                                         Cornell University

                                           Alan Mathios
                                         Cornell University

                                         Rosemary Avery
                                         Cornell University


                                              June 2007




*
  Contact author. Department of Policy Analysis and Management, Cornell University, 426 MVR Hall,
Ithaca, NY 14853. Tel: (607) 342 7636, Fax: (607) 255 4071. Email: hy75@cornell.edu. This research was
funded by an unrestricted educational grant from the Merck Company Foundation.
    1. Introduction

         Cardiovascular disease (CVD) is the leading cause of death in the United States,

accounting for about one half of all deaths. About 61 million Americans - almost one-

fourth of the population - have some form of CVD.1 High blood cholesterol is one of the

major risk factors of CVD. For example, clinical studies show that a 1 percent increase in

„bad‟ cholesterol level is associated with a 2 percent increase in the risk of CVD (NIH,

2001). More than 50 percent of U.S. adults have elevated blood cholesterol (NIH, 2001).

         The link between blood cholesterol levels and heart disease was the driving force

in the development of new pharmaceuticals that lower LDL cholesterol levels („bad‟

cholesterol) and raise HDL cholesterol levels (these are associated with lower risk of

heart disease). Clinical trials of cholesterol reducing pharmacological treatment support

the efficacy of drug therapy in preventing CVD in higher risk persons (NIH, 2001). A

class of drugs known as statins dominates the cholesterol reducing market (Saftlas, 2005;

Sprang et al, 2005). Clinical trials have reported that statins reduce the level of LDL

cholesterol by up to 60 percent leading to mortality rate from heart attack or stroke falling

by a third.2 Since its introduction in 1987, statins now have annual sales of over $21

billion globally (92.5 percent of the total cholesterol reducing market) and is still growing

(Sprang et al, 2005). The market for cholesterol reducing drug is expected to grow with
increasing adherence to the updated Adult Treatment Panel III (ATP III) guidelines set by

the NIH. ATP III lowered the LDL threshold (from 130 to 100) for triggering a

recommendation for drug therapy. This reduction brings an additional 8 million adults

into the population for which drug therapy is recommended (NIH, 2001). However,


1
  CDC report is available at http://www.cdc.gov/nccdphp/bb_heartdisease/index.htm
2
  Earlier Clinical Trials such as WHO clofinrate trial or Helsinki Heart Study genfibrozil trial show lipid
lowering therapy reduces major coronary events, but do not address the issue of total mortality. Later statin
trials WOSCOPS, AFCAPS/TexCaps show that statin significantly reduces the relative risks of major
coronary events and WOSCOPS shows a strong trend towards a reduction in total mortality. More recent
major Clinical Trials 4S, CARE, and LIPID indicate that lipid lowering therapy especially statins reduces
the risks of stroke. Table II.2-1 in ATP III shows CVD outcomes in Clinical Trials of LDL-Cholesterol-
Lowering Therapy.


                                                     2
studies show that only about one-fifth of all persons who can benefit from cholesterol

reducing drugs are taking them (Arnold, 2005).

       One reason so many consumers who could benefit from the drug are not taking

the drug is the difficulty of observing elevated levels of high blood cholesterol. Unless

tested for cholesterol, it is years before elevated levels may result in any symptoms. As a

result the dissemination of information about the risks of elevated blood cholesterol plays

a key role in prevention. There are numerous sources of information from which

consumers can learn about the link between cholesterol and CVD and effective

prevention strategies. This study focuses on the role of pharmaceutical drug advertising

on consumer awareness of their blood cholesterol level and on the demand for drug

therapy. Statin drugs are currently only available through prescription, thereby requiring

the patient to visit physician‟s office. The ATP III guidelines recommend that before

prescribing drugs, physicians should prescribe therapeutic lifestyle changes (TLC). TLC

includes improvements in diet and increases in exercise. This suggests that consumers

who learn about statin drugs through advertising and see their physician may receive

advice about diet and exercise. The second focus of this paper is to examine the

empirical relationship between exposure to statin drug advertising and participation in

exercise and attempts to change diet.
       One key unique contribution of this study is the identification strategy used to

identify the impact of advertising on awareness of cholesterol, use of statins, diet and

exercise. We link survey data on individual knowledge of whether they have high

cholesterol, whether they use statin drugs, whether they are attempting to control their

diet, and the extent of their exercise behavior with an archive of magazine advertisements

for statin drugs. The survey also includes information on which magazines each

respondent reads. Thus, we are able to measure the individual exposure to statin drug

advertisements based on their individual magazine-reading habits. Because we observe
the same information about the consumers that the advertisers observe, we can control for


                                             3
the potential endogeneity of advertising due to firms‟ targeting decisions. Our results

demonstrate the important role that controlling for targeting plays in affecting the

estimated impacts of advertising.

       The data employed are extremely rich. The individual-level data comes from a

marketing survey, the Simmons National Consumer Survey (NCS), which contain

magazine-reading and television-viewing habits. Multiple surveys conducted between

the fall of 1995 and the fall of 2004 are pooled to obtain a sample of more than two

hundred thousand respondents. Advertising data are obtained from an archive compiled

by the authors. This is a collection of all print advertisements for statin drug

pharmaceutical products that appeared between 1985 and 2004 in 26 consumer

magazines that represent magazines read by individuals with particular demographic

characteristics. By combining the advertising archive data with information on magazine

reading habits in NCS, we obtain a much richer advertising exposure measure than those

used in previous research. Previous studies use market-level data on advertising, which

requires the implicit assumption that all individuals in a given market are exposed to the

same advertising. Another attractive feature of the research design in this paper is that

we can make a case that the advertising exposure measure will not be correlated with

unobserved determinants of our dependant variables. Our results provide evidence that, if
we do not control for targeting (like other studies that are unable to) DTCA has a

statistically significant and positive impact on health behaviors – diagnosis, statins

purchase, diet control and regular exercise. However, these empirical results are

extremely sensitive to controls for targeting, so much so, that when accounting for which

magazines individuals read (and thereby using within magazine variation in advertising

exposure to identify our impacts), exposure to advertising no longer has an impact of our

behavioral measures.

       The remainder of this essay is organized as follows. Section 2 provides an
overview of the development of the market for pharmaceutical treatment of high


                                              4
cholesterol and reviews the literature. Section 3 discusses a simple theoretical framework

for how advertising may influence the demand for statin drugs. Section 4 introduces the

data, followed by section 5 which discusses the econometrical specification and our

identification strategy. Section 6 and 7 presents the results and discusses implications

respectively.



   2. Background



2.1 The Development of the Market for Pharmaceutical Treatment of High Cholesterol



Cholesterol, produced naturally in the body and absorbed from food, is a key component

of cell membranes and hormones (NIH, 2001). Cholesterol is transported around the

body through a complex called lipoprotein, and each type of lipoprotein plays a different

role. Among the key lipoproteins, low density lipoprotein (LDL) is cholesterol rich and is

also known as „bad‟ cholesterol. Over time, it contributes to blockage of the blood flow

through coronary arteries. Lack of blood flow can lead to chest pain and heart attack

(NIH, 2001). Over the years there have been a number of recommendations about

maintaining desirable levels of blood cholesterol with specific recommendations focused
on limiting LDL cholesterol levels. There are a variety of ways to treat high cholesterol

levels including lifestyle changes involving diet (i.e. limitations on intakes of saturated

fats) and increases in physical activity.

       Beginning in the late 1980s, the Food and Drug Administration (FDA) approved

the first of the „statin‟ drugs which in clinical trials resulted in marked reductions in LDL

cholesterol levels for those treated with the drug. Soon after the first introduction in

1987, a number of other drugs within this class were approved for sale, also as

prescription only. The dramatic effectiveness of these drugs soon became a standard way
of treating consumers with high levels of blood cholesterol. Table 1 documents the


                                              5
development of the market for statins. The table reports the rapid increase in the number

of statin products available on the market and that for the entire period FDA required

consumers to obtain a prescription in order to purchase the products. Table 1 also

demonstrates that there are other types of cholesterol reducing drugs, some of which are

sold over-the-counter (OTC).3 However, these products do not exhibit the same

effectiveness and tend to be a small part of the market. For example, in 2004, worldwide

statin sales accounted for over $21 billion of the $22.7 billion of sales for all cholesterol-

reducing drugs (Sprang et al, 2005).

         Tables 1 and 2 show the types of statins in the market and the time they received

FDA approval. Currently there are 5 different types of statins in the U.S.; lovastatin,

simvastatin, pravastatin, fluvastatin, and atrovastatin (cerivastatin was withdrawn from

the market by the manufacturer in August 2001). Only prescription versions of statins

are available in the U.S. However, manufacturers facing patent expiry have applied to

the FDA to switch their statin products into OTC.4 So far, three out of five statins have

lost patent protection. Mevacor (lovastatin)‟s patent expired in June 2001 while

Pravachol (pravastatin) and Zocor (simvastatin) lost their patent protection in April and

June 2006 respectively. Since their patent expiry, firms are facing competition from

generic manufacturers. While generic versions of statins are available in the market, their
market share is still small, though expected to increase significantly over time. For the

time period that we consider in our empirical model generic versions are not therefore

relevant.



2.2 Direct-to-Consumer Advertising as a Source of Information to Consumers




3
 OTC niacin, another cholesterol reducing drug, is available as vitamin B-3 complex.
4
 By switching the drug to OTC status, manufacturers expect to revive declining sales resulting from
competition with generics. Manufacturers get exclusive right to sell their OTC versions for three years.


                                                     6
Advertising of prescription drugs falls under the regulatory authority of the FDA. During

the last two decades there have been significant regulatory decisions – in 1983, 1985, and

1997 – that have affected the advertising of products that are sold by prescription only.

Prior to 1985 there was virtually no Direct-to-Consumer Advertising (DTCA) of

prescription drugs. From 1985 until around 1997, the FDA permitted this advertising but

subject to significant disclosure requirements. In 1997 the FDA substantially reduced the

burden of disclosure for broadcast DTCAs. Many observers believe the 1997 FDA

regulatory change led to a sharp increase in DTC broadcast advertisements including

statin drug advertising. The use and increase of DTCA led researchers to investigate the

impact of this advertising on health outcomes. For this paper we focus on the studies that

have focused on DTCA of statin drugs.

        There have been a number of studies that have focused on the impact of DTCA of

statin drugs on health outcomes. Most studies examine the impact of advertising on

demand for prescriptions.5 For example, Calfee et al (2002) finds that despite the

significant increase in advertising expenditures over time, there is no statistically

significant effect on prescriptions. They do find, however, that television advertising of

prescription statin drugs is associated with an increase in the proportion of cholesterol

patients who had been successfully treated. Wosinska (2005) examines the impact of
advertising on compliance and finds evidence that one of the benefits of DTCA is higher

compliance among patients under statin drug therapy. In another paper, Wosinska (2002)

finds that advertising increases the use of statins if the advertised drug appears on the

insurance formulary list.

        One finding that has appeared in a number of studies is that there is little direct

effect of advertising on specific brand choices but that advertising may affect physician


5
 For DTCA studies other than statins, see Brekke and Kuhn (2005) for theoretical framework, and for
empirical literature see Avery et al, 2007; Donohue and Berndt, 2004; Iizuka and Jin, 2005, 2007; Ling et
al, 2002; Mukherji et al, 2004; Rizzo, 1999; Rosenthal et al, 2003.


                                                    7
visits. For example, Iizuka and Jin (2005) combine data on DTCA from 1994-2000 with

the 1995-2000 National Ambulatory Medical Care Survey (NAMCS) and examine the

effect of DTCA on doctor visits. They find that higher DTCA expenditures are

associated with increased doctor visits, especially after the FDA relaxed its regulations

governing broadcast risk disclosure. Berndt (2005) summarizes the empirical literature in

the area and concludes that empirical tests consistently suggest that DTCA induces

consumers to visit physicians and increase the size of the market, but prescription drug

choice is still primarily determined by the physicians (Berndt, 2005). Berndt adds that

DTCA encourages not only appropriate but also inappropriate treatments.

          One of the weaknesses of these previous studies of DTCA is that they have not

been able to measure individual level variation in exposure to statin drug advertising and

instead have largely relied on aggregate measures of expenditures either measured at the

year level, or at more aggregate levels. Some studies link these aggregate measures of

DTCA expenditures with aggregate measures of drug use. Improvements and

refinements of these studies focused on obtaining individual level data on the use of

statins but these were still combined with aggregate measures of advertising

expenditures. We believe that our study is the first to be able to combine micro-level

data on both exposure and use.


3. Data



To examine the relationships between advertising, knowledge of cholesterol levels, use of

statin drug therapy and lifestyle changes requires data on each of these components.

Prior to describing our econometric specifications we focus on the various sources of data

we utilize to examine the relationships described in the conceptual framework in previous

section.
The Simmons National Consumer Survey


                                             8
Our individual-level data comes from a marketing survey, the Simmons National

Consumer Survey (NCS). The NCS provides detailed information on consumer

behavior and magazine-reading and television-viewing habits (NCS 2005). In addition

to magazine reading behavior, the data also includes information on whether the

respondent reports that they have high cholesterol, whether they use a statin drug,

which statin drug they use, whether they are attempting to control their diet and

whether they exercise regularly. These variables are the key measures that we need to

examine the empirical relationships described in the conceptual framework. As will be

described later, the magazine reading questions allow us to create a measure of

advertising exposure to statin drugs which can then be linked with the other key

variables.

       The NCS is a repeated cross-sectional survey. The sample for each wave is

independently drawn. The NCS employs a multi-stage stratified probability sample.

The final sample represents a representative probability sample of all adults living in

households in the U.S. (excluding Hawaii and Alaska). In order to minimize

respondent fatigue, the data are collected in several phases. In Phase 1 respondents are

interviewed (face-to-face). In the first part of Phase 1, interviewers collect

demographic data and data on the magazines respondents read. To collect this
information, interviewers present respondents with a deck of cards on each of which

are printed the logo of one of the 182 magazines in the Simmons sample. If a

respondent reports that he has read any portion of a magazine he is asked a set of

questions about readership, including whether he read the whole magazine. During the

second part of Phase 1 respondents report, by filling out a questionnaire, whether they

purchase and use specific products, including specific statin drugs.

       Survey response rates in the NCS are generally high (approximately 70 percent)

and compare well with other widely-used surveys in health economics and health services
research. For example: in the 2002 Behavioral Risk Factor Surveillance Survey the


                                              9
median response rate across states was 58 percent (range: 42 percent to 83 percent); for

the 2002 National Health Interview Survey (NHIS) the total household response rate was

approximately 90 percent and the final response rate for the Adult Sample Person was 74

percent (CDC 2003, 2004). We also examine how the number of statin drug users

compares with other national health data sets available.

         NCS has a comparable amount of statins users compared with other national

health data sets available.6 2 percent of total sample in the 1997 NAMCS7 was using

statins compared to 3 percent in 2000. 4 percent of total sample used statins in 1997

Medical Expenditure Panel Survey (MEPS)8 increasing to 8 percent in 2000. In NCS, 5

percent of total sample use statins in 1997 increasing to 6 percent in 2000 and 9 percent

in 2002.

The Pharmaceutical Advertising Data Archive (PHADS)

         The advertising data are drawn from an archive of all pharmaceutical print

advertisements, hereafter PHADS, appearing in the most widely read magazines. The

magazine set includes: Better Homes & Gardens, Black Enterprise, Business Week,

Cosmopolitan, Ebony, Essence, Family Circle, Glamour, Good Housekeeping, Jet,

McCall's, Modern Maturity, Money, National Geographic, Newsweek, People, Playboy,

Readers Digest, Rolling Stone, Seventeen, Sports Illustrated, Time, TV Guide, U.S. News
&World Report, Vogue, and Women's Day. We selected these magazines to represent

magazines most frequently read by individuals with particular demographic

characteristics.

         We selected these magazines to represent magazines most frequently read by


6
  NCS sample‟s socio-economic characteristics are very similar to that of NHIS. For comprehensive
comparison, see Plassman et al 2005.
7
  NAMCS is national survey for the visits to physician office collecting information the provision and use
of ambulatory medical care services conducted annually. For more details on NAMCS, see
http://www.cdc.gov/nchs/about/major/ahcd/ahcd1.htm
8
  MEPS collects information about cost and use of health care and health insurance coverage
from individual patients, medical providers and employers. For more details on MEPS see
http://www.meps.ahrq.gov/mepsweb/


                                                    10
individuals with particular demographic characteristics. We defined 22 groups by race (3

groups), education (5 groups), income (5 groups), age (5 groups), gender (2 groups), and

smoking status (2 groups). Using data on magazine reading from the 1998 NCS we chose

the ten magazines most frequently read by members of each group. Although we defined

22 groups, members of each group often read the same magazines. Consequently, instead

of 220 magazines, the final set includes the above 26 magazines. We were unable to use

three magazines because we could not locate all issues in our sample period. In those

three cases we substituted the next most widely read magazine for the group in question.

Using magazine circulation data from three independent sources, we estimate that the 26

magazines in PHADS account for between 30.0 and 57.5 percent of magazines

circulating in the US. The higher figure is estimated from readership data for the 172

magazines included in the NCS. The lower figure is based on total circulation across 580

magazines reported in Audit Bureau of Circulation‟s (2003) Magazine Trend Report. The

PHADS magazines represent 5% of the 580 magazines in the Magazine Trend Report

data but account for 30.0 percent of circulation.

The Frequency of Statin Advertisements in the Print Archive

       Table 3 shows statin advertisements by magazine. We do not observe any statin

ads in teenagers‟, young women‟s or adult only magazines such as, Cosmopolitan,
Glamour, Seventeen, Vogue, Playboy, and Rollingstone. The most frequently

advertised magazine for statin is Time, followed by Newsweek, US News and World

Report. The next highest is Reader’s Digest.

       It is also interesting to note the increase in the number of print advertisements

over time. For most magazines there has been a rise in the number of print

advertisements for statin drugs especially in the last couple of years of the data. This

increase has occurred despite the relative cost of advertising on television relative to print

decreasing in 1997. Prior to 1997 the FDA required pharmaceutical companies to include
the full fine print disclosure in broadcast advertising if such an advertisement included a


                                             11
discussion of what the pharmaceutical product did. After 1997, the FDA relaxed this

regulation by requiring a shorter disclosure that focused on the main side effects

described in lay terms. Thus, despite television advertising becoming relatively more

attractive, print advertising continues to be an active form of advertising. It is worthy of

note that one way that firms can meet the disclosure requirements in television

advertising is to refer consumers to a print advertisement, which still must include the full

fine print disclosure. Thus, the regulations may contribute to print and television

advertising being complements rather than substitutes.

Measure of adverting exposure

       We base our measure of advertising exposure on the techniques suggested by

Avery et al (2007). First, we merge all of the PHADS advertising data to each person in

the NCS sample. In the NCS, each respondent is shown copies of the covers of over 200

consumer magazines. For each magazine, the respondent was asked whether he has read

or looked at it during the last 6 months, and if so, of the latest 4 issues on average, how

many did the respondent see. The variable Readim is the fraction of issues of magazine m

read by person i. For each magazine in the archive the NCS respondent reported having

read, we multiply the fraction of the four issues he reads time the number of

advertisements that appeared in that magazine over the previous twelve months. Note
that, in doing so, we assume that an individual‟s reading habits over the four issues

reflects his reading habits over the past year. We then sum up all the magazines in the

archive. The result is our estimate of advertising to which a person was potentially

exposed to by reading the magazines in the archive. For example, the exposure to statin

drug advertising of respondent i (AdExpi) is given by:
           26
AdExp i   Adsim * Re ad im                          (11)
           m 1
Where subscript m refers to each of the 26 magazines in the archive. This measure of

advertising exposure is individual specific. Our measurement approach assumes that two
people who read the same number of issues of the same magazines were exposed to the


                                             12
same number of advertisements. This is imperfect because we do not know if both

people actually saw the advertisements, but it represents a vast improvement over

previous research. Previous studies use market-level data on advertising, which requires

the implicit assumption that all individuals in a given market are exposed to the same

advertising.

Measures of Knowledge of Cholesterol, Use of Statin Drugs, Exercise and Diet

       We utilize the NCS to generate our primary dependent variables. The goal of the

study is to examine the impact of advertising exposure on awareness of high cholesterol

and various treatment therapies including pharmaceuticals and lifestyle changes (diet and

exercise). To measure a respondent‟s awareness of whether they have high blood

cholesterol we utilize the following question. Respondents are asked what ailments they

have had in the last 12 months. Respondents can report as many ailments as they choose

and one of the choices is High Cholesterol. All respondents who report having this

ailment are coded as being aware that they have high cholesterol. The data indicate that

9.46 percent of the survey respondents report having high cholesterol. The NCS data also

lists a large number of other ailments and it would be possible to examine the impact of

exposure to statin drug advertising on diagnosis of other ailments. Because a blood test is

required to assess cholesterol, advertising may be effective at increasing the diagnosis of
other ailments that blood tests identify (like diabetes). Moreover, because purchasing

statins require a prescription, exposure to advertising may lead to consumers becoming

aware of other ailments detected through a physician visit. We will examine these

relationships in future work.

       The NCS also asks each respondent in the sample whether they have used a

prescription product in the last 12 months to treat a particular ailment. If a respondent

reports using a prescription pharmaceutical product to treat high cholesterol they are

coded as using a statin drug. Table 4 presents the number of respondents in each wave of
the NCS that uses a statin drug. The data demonstrate the rise in the use of statin drugs


                                             13
over time. In 1995 less than 4 percent of the respondents use the cholesterol reducing

drug whereas in 2004 more than 8 percent of respondents were using the drug. A

significant portion of respondents, 35 percent, who report that they have high cholesterol

are not using a prescription pharmaceutical product to treat this ailment. This could be

due to a number of reasons including non-compliance with drug therapy, condition not

being severe enough to warrant use of a prescription product, the respondent knows that

they have high cholesterol and have not seen a physician (for example, through an

application for life insurance), or that the respondent chooses to treat cholesterol through

other therapies including dietary changes and exercise.

       Our measures of whether a respondent is engaged in exercise or is trying to

control their diet are derived from the answers to the following NCS questions. For

exercise, each respondent is asked if they exercise regularly in last 12 months. With

respect to diet, each respondent is asked whether they are controlling their diet. Table 5

shows approximately 43 percent of the sample exercise regularly and approximately 35

percent of the sample are controlling there diet.

Other Control Variables

       We also include in our models standard socio-economic control variables (age,

sex, race, education, marital status, family size, presence of children in the household,
employment status, family income) as well as controls for overall magazine-reading

habits, television-viewing habits, and radio-listening habits. In table 5, over the previous

12 months, the average person in our sample read 6 issues of one or more of the

magazines consistently included in the NCS. Note that only 4.9 percent of respondents

reported that they read no magazines. The average person in the sample watched 19

hours of television per week and listened to the radio about 2 hours per week. 16 percent

of the sample watched no television and 35 percent did not listen to the radio in an

average week.




                                             14
4. Empirical Specifications and Identification



As defined above, we use various behavioral outcomes (knowledge of high cholesterol,

use of statin drugs, diet and exercise) in the past year as our key dependent variables. We

adopt the usual approach by assuming that the discrete outcomes we observe are related

to underlying latent variables describing the net benefits of engaging in these behaviors.

We assume the underlying latent variables are linear functions of exposure to advertising

and characteristics of the person and his household. We specify the general relationship

as:


Yi *   0   1 AdExp i   2 Z i   i



Where AdExpi refers to exposure to statin drug advertising, Zi is a vector of demographic

control variables and variables that account for readership behavior, and  i is an error

term. The Yi* is the latent index and we estimate separate equations for each of the

behavioral measures: knowledge of whether they have high cholesterol, use of statin drug,

whether they are controlling their diet and whether they exercise regularly. We observe

the given behavior (denoted by Yi), only if the continuous latent variable exceeds a

critical threshold. Under the standard formulation, Yi = 1 if Yi* >0 and Yi = 0 otherwise.
The latent variable Y* describes the net benefits of seeing a physician and thereby finding

out about whether they have high cholesterol, need a statin drug etc. We estimate

equation (11) by maximum likelihood probit and OLS. We report OLS results since

there is little difference in the results and it is easier to interpret the coefficients.

        Our econometric approach uses variation in exposure to advertisements due to

magazine-reading habits to identify the causal effect of advertising on the various

behavioral measures we examine. Because the NCS data allow us to include a rich set of
control variables in our models, we believe that the identifying variation in advertising



                                                15
exposure is econometrically exogenous, i.e., AdExpi is uncorrelated with the error term in

equation. In addition to including standard controls for individual socio-economic

characteristics, we take several steps to further strengthen our identification strategy.

First, our models include controls for general magazine-reading habits and exposure to

other media. In this way, we control for the possibilities, say, that heavy magazine-

readers or television-watchers somehow differ in their unobserved propensity to see

physicians, exercise regularly, use statins or control their diet. Second, and probably

more important, our models include a set of dummies reflecting whether an individual

read a particular type or category of magazine. We determine these categories by

conducting a factor analysis of readership characteristics of different magazines. The

factor loadings suggest five particular groups of magazines and we put in variables to

control for these groups. We have labeled these groupings as Home: (Better Homes and

Gardens, Family Circle, Good Housekeeping, McCalls/Rosie, Reader’s Digest, TV

Guide, and Woman’s Day); Young Adults (Cosmopolitan, Glamour, Playboy, Rolling

Stone, Seventeen, Vogue); African-American (Black Enterprise, Ebony, Essence, Jet);

Business (Business Week, Money); Retirees: (Modern Maturity); and General Interest

(National Geographic, Newsweek, People, Sports Illustrated, Time, U.S. News and World

Report). With these controls, our identification is based on within-category variation in
exposure to advertisements in the archived magazines. For example our strategy will

identify the impacts based on respondents being exposed to a different number of

advertisements because they read Time versus Newsweek. Third, we include a dummy

variable for every magazine (whether the respondent reads that magazine) in the NCS.

With this included, our specification now captures the remaining unobserved factors that

are possibly related with firms‟ targeting or individual‟s magazine readership. This

previously could not have been captured through the use only of demographic

characteristics and magazines‟ categories. Here identification is based on respondents
being exposed to different amounts of advertising because they are reading the same


                                              16
magazine at different times. nometric specification approximates the model firms use to

target their advertisements.

         Our identification strategy should also minimize any bias stemming from the fact

that data limitations force us to omit from our empirical models statin drug prices and the

price of a physician visit. Because firms simultaneously choose advertising levels and

product prices, in some empirical contexts it would be important to control for the

product prices consumers face. However, we exploit individual level variation in

advertising exposure that arises because individuals read different nationally distributed

magazines. In this empirical context, the variation in advertising exposure can be

expected to be uncorrelated with state and local variation in product prices, physician

visit prices, the prices of healthy foods and or the price of exercise clubs or exercise

equipment.



5. Results



5.1 Summary Statistics



Table 5 provides descriptive statistics on all the variables used in the analysis. Since
statins are almost exclusively used by older respondents, our sample is limited to those

who are aged over 45. The demographic makeup of the sample as shown in Table 6 is

representative of people aged over 45 in the United States.9 As reported in the cross-

tabulations in our 10 years of NCS data in Table 4, 16.2 percent of respondents report

high cholesterol as an ailment they have had in the last 12 months and were diagnosed,




9
 The demographic characteristics - such as age, family income, education, ethnicity and health insurance -
of NCS respondents are very similar to that of the other national health data sets such as NHIS. For details
see Plassman et al (2005).


                                                     17
11.2 percent purchase statin drugs, 34.7 percent control diet, and 42.9 percent exercise

regularly.

       Around half (50.1 percent) are covered by private insurance, while 17.4 percent

do not have health insurance. The average number of hours of television watching in a

week equals 18.8, with 15.9 percent of the sample reporting that they do not watch

television at all. Only 4.9 percent of the sample reports not reading any magazines.



5.2 Cross Tabulations



To get a first glimpse of the relationship between the key variables of interest and

exposure to advertising, Table 6 presents how these variables are distributed across

different quartiles of the distribution of exposure to advertisements for statin drugs.

Table 6 shows a clear relationship between the number of statin drug advertisements a

respondent sees and reporting that they have high cholesterol. For example, while 13.1

percent of respondents in the first quartile of the exposure distribution reported

cholesterol, 19.5 percent reported having high cholesterol if they were in the highest

quartile of the exposure distribution. Similarly, whereas 8.6 percent of respondents in the

first quartile of the exposure distribution reported using a statin drug, 13.8 percent
reported using a statin drug if they were in the highest quartile of the exposure

distribution. Interestingly, there is a monotonic relationship between levels of exposure

and use of statins. For the second quartile of exposure, 9.8 percent of respondents use a

statin drug and 11.2 percent of those in the third quartile use a statin drug. In general,

these cross-tabulations show a strong correlation between exposure to advertising,

knowledge of high cholesterol and use of statins.

       The results for diet and exercise show similar relationships. 33.9 percent of

respondents in the first quartile of the exposure distribution reported exercising regularly,
whereas 49.8 percent reported exercising if they were in the highest quartile of the


                                             18
exposure distribution. The relationship between exercise and exposure also appears to be

monotonic. 38.8 percent of the second quartile and 45.3 percent of the third quartile is

exercising regularly. Finally, with respect to controlling one‟s diet, 26.9 percent of

respondents in the first quartile of the exposure distribution reported controlling diet,

while 37.0 percent reported controlling diet if they were in the highest quartile of the

exposure distribution. These results appear to be less monotonic in that the results for 3rd

and 4th quartiles are very similar. As with cholesterol knowledge and use of statins, the

cross-tabulation results show strong correlations between advertising exposure and our

therapeutic lifestyle change variables.


         The challenge we face is to understand whether there are causal relationships

between advertising and exposure. It could very well be that advertising exposure is

targeted to those who have high cholesterol, need statin drugs and are interested in

improving their health. This could drive these correlations and say nothing about

whether the advertising itself drives these behavioral outcomes. The specifications we

outline below directly address the „causality‟ issue.



5.3 The Effect of DTCA Exposure on Behavioral Outcomes


We begin our multivariate analysis of the impact of advertising exposure on diagnosis,

drug use and life style changes. Table 7 presents estimates of the effect of DTCA

advertising exposure on each of the behavioral outcomes.10 All specifications include the

demographic variables discussed above as well as our controls for television watching

and magazine readership behavior. In particular, we include the number of PHADS

magazines that each individual reads in order to control for unobserved heterogeneity


10
  We also tried probit estimation for robustness check purposes. As expected, the qualitative results from
probit estimations were identical to OLS estimations.


                                                    19
between statins use and readership behavior. This is important because our measure of

advertising exposure is based, in part, on the readership behavior of the respondents. We

use alternative specifications to test the sensitivity of our estimated impact of advertising

exposure. Model 1 is the baseline specification which does not control for magazine

types or magazine fixed effects, model 2 includes magazine categories, while model 3

replaces magazine categories with a set of dummy variables on whether an individual has

read the magazines found in NCS.

        We first focus on the models where the dependent variable is whether the

respondent reports having high blood cholesterol. The results in model 1 and model 2

show that the coefficients on advertising exposure reveal a positive and statistically

significant effect on exposure on being diagnosed high blood cholesterol. The coefficient

on advertising exposure in model 1, which controls for magazine reading habit, is

positive and statistically significant. The magnitude of the effect of advertising exposure

is reduced significantly when controlling for magazine categories suggesting that some of

the estimated impact is driven by what category of magazines individuals read rather than

the advertising in these magazines. Moreover, when we replace the magazine categories

with the NCS magazine dummy variables, the coefficient of the advertising exposure is

not statistically significant. Throughout the specifications, when we include NCS
magazine fixed effects, the effect of DTCA exposure on behavior is eliminated.

        We next focus on the impact of advertising exposure on the use of statin drugs.

The results for models 1 and 2 in Table 7 show that the coefficients on advertising

exposure reveal a positive and statistically significant impact of exposure on the purchase

of statin drugs. As we have seen previously in the results for diagnosis, the coefficient of

DTCA exposure in model 2 is reduced from model 1. The coefficient of DTCA exposure

in model 3 is positive, but not statistically significant.

        Our next focus is on the impact of advertising exposure on diet control and
exercise. The results for these models are presented in table 7. Overall, the results suggest


                                               20
that DTCA exposure to statin drugs has statistically significant effect on exercise only.

Even after being diagnosed, the effect of DTCA exposure is still positive and statistically

significant on exercise throughout the different specifications. Except for model 1, we do

not find any statistically significant effect of DTCA exposure on diet control for people

aged over 45. Conditional on being diagnosed, DTCA exposure does not significantly

affect diet control.

        We also describe the coefficients on a subset of the other variables included in the

model. The coefficients on other variables in the model are generally what we would

expect. The coefficients on the age variables show a positive and significant relationship

between age and diagnosis of high cholesterol, purchase of statins, and dietary control.

Not surprisingly, the relationship between age and regular exercise is negative and

statistically significant. Females are less likely to be diagnosed with high cholesterol and

are less likely to purchase statins. Interestingly, even after conditioning the sample on

those with high cholesterol, females are less likely to be prescribed statin drugs. Females

are also more likely to engage in dietary control and in regular exercise. Compared with

Whites, African Americans are less likely to be diagnosed with high cholesterol and less

likely to purchase statins. As with females, even after conditioning the sample on those

who have high cholesterol, they are less likely to be prescribed statin drugs. African
Americans are less likely to engage in diet control but are more likely to engage in

regular exercise. The coefficients on Hispanic are very similar to the coefficients

described for African Americans.

        There appears to be some education gradient with respect to diet and exercise.

Compared with high school graduates, dropouts are less likely to control diet and exercise

than college graduates. One possible explanation for this result is that the relatively more

educated respondents are more likely to engage in regular exercise and dietary control.

This is true even when conditioned on having high cholesterol. It is possible that the




                                             21
more educated prefer to invest in therapeutic lifestyle changes compared with drug

therapy.

       The coefficients on magazine reading and television watching habits are positive

and significant in the diagnosis and purchase models. One possible explanation for this is

that watch more hours of television are exposed to more statins advertisements through

this medium. However, for television watching, because we do not measure the number

of advertisements they are exposed to, this is speculative.

       The uninsured are less likely to be diagnosed, purchase statins, control their diet

or engage in regular exercise. This is also true conditional on diagnosis. That is, even for

those diagnosed with high cholesterol the uninsured are less likely to purchase statins.

Whereas Medicare recipients are more likely to be diagnosed, purchase statins and

engage in dietary control. These results are as expected.

       Finally, the coefficients on the survey wave variables are consistent with what we

would expect. Diagnosis is increasing over time as is statins use. This is as expected

because the ATP III has lowered the threshold level of high cholesterol recently and size

of the statins market has been increased.



7. Discussion


       Using a unique set of advertising data that allows us to measure individual level

advertising exposure together with a rich set of individual level control variables, this

paper studies the effect of DTCA exposure on patient‟s getting diagnosis, purchasing

statins, controlling diet and exercising regularly. Our results show that there is a positive

and significant effect of DTCA exposure on getting diagnosed, purchasing statins and

exercising regularly, but the effect of DTCA exposure largely goes away with exception

on exercise when we control more intensely on firms‟ targeting. Some of our results are
opposite to previous findings that DTCA increases the likelihood of visiting physician‟s


                                             22
office or decrease exercising. But in most studies of the impact of DTCA it is difficult,

if not impossible, with the data that is utilized, to adequately address the targeting issue.

Adequately accounting for targeting is one of the most significant challenges to research

on the impact of advertising. Careful examination of how other identification strategies

used in other research account for targeting is warranted. For example, some studies use

instrumental variables to identify the causal impact of advertising. In these studies

advertising of alternative products are used to instrument the product under examination.

However, it is likely that all DTC Rx advertising is likely to be targeted toward those

with health insurance coverage thus creating a need to deal with controls for individual

characteristics that account for this targeting. Additionally, if there are common factors

that determine the targeting of prescription drug advertising then instrumental variables

using alternative products is not likely to solve this problem.




                                              23
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Calfee, J., Winston, C., Stempksi, R. 2002. “Direct-to-Consumer Advertising and the
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Donohue, J.M., Berndt, E.R. 2004. “Effects of Direct-to-Consumer Advertising on
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Mukherji, P., Dutta, S., Rajiv, S. 2004. “Estimating the Effect of Direct-to-Consumer
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Norton, E.C., Wang, H., Al, C. 2004. “Computing interaction effects and standard errors
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Plassmann, V., Kenkel, D., Lillard, D.R., Mathios, A. 2005. “Smoking Cessation: An
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Rosenthal, M.B., Berndt, E., Donohue, J., Epstein, A., Frank, R. 2003. “Demand Effects
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.




                                          26
Table 1: The Development of statins market


                                                                Number of products per year
 Year                  FDA approval dates                        Brand    Generic      Total
 1987        Mevacor (Lovastatin) - 8/1987                            1           0         1
 1988                                                                 1           0         1
 1989                                                                 1           0         1
 1990                                                                 1           0         1
             Pravachol (Pravastatin) - 10/1991
 1991                                                                  3              0         3
             Zocor (Simvastatin) - 12/1991
 1992                                                                  3              0         3
 1993        Lescol (Fluvastatin) - 12/1993                            4              0         4
 1994                                                                  4              0         4
 1995                                                                  4              0         4
 1996        Lipitor (Atrovastatin) - 12/1996                          5              0         5
 1997        Baycol (Cerivastatin) - 6/1997                            6              0         6
 1998                                                                  6              0         6
 1999        Lovastatin - Teva 9/1999                                  6              1         7
 2000        Lescol XL (Fluvastatin) - 12/2000                         7              1         8
             withdrawal of Baycol - 8/2001
             Advicor (Lovastatin) - 12/2001
 2001                                                                  7              5        12
             Lovastatin - Purepac Pharm, Gen Pharm,
             Mylan, Sandoz, 12/2001
             Altoprev4) (Lovastatin) - 6/2002
 2002                                                                  8              6        14
             Lovastatin - Carlsbad 6/2002
             Pravigard pac (Aspirin/Pravastatin) -
 2003        6/2003                                                   10              6        16
             Crestor (Rosuvastatin) - 8/2003
 2004        Vytorin (Extimibe/Simvastatin) - 7/2004                  11              6        17
Source: Drugs@FDA Drug information Pathfinder http://www.fda.gov/cder/Offices/DDI/pathfinder.htm,
Medline Plus (National Library of Medicine)
Note:
1) Based on FDA's approval date.
2) Product is counted if approved by September of given year.
3) Other cholesterol reducers, Zetia, Niaspan (niacin) and Welchol are not included as they are not statins.
4) Altoprev was previously Altocor but changed the name in 2004 to avoid possible confusion with
Advicor




                                                     27
Table 2: Brand Profile


                                                                Max
    Brand             Substance              Manufacturer       dose   Total cholesterol   LDL   HDL
 Lipitor         atrovastatin           Pfizer                  80mg               -45%     -60%  5%
 Zocor           simvastatin            Merck                   80mg               -31%     -36% 16%
 Pravachol       pravastatin            BMS                     80mg               -27%     -37%  3%
 Zetia/Vytorin   extimibe/simvastatin   Schering-Plough/Merck   80mg               -43%     -60%  6%
 Crestor         rosuvastatin           AstraZeneca             40mg               -46%     -63% 10%
 Mevacor         lovastatin             Merck                   80mg               -12%     -17% 12%
 Advicor         lovastatin             Kos Pharmaceutical      40mg                 NA     -45% 41%
 Lescol          fluvastatin            Norvatis                80mg               -27%     -36%  6%

Source: Company data; Facts and Comparisons; SG Cowen




                                                                28
Table 3: Number of statins Advertisements by Magazine


                                               1995 - 1999                                                  2000 - 2004
Magazines                  Lipitor   Mevacor   Pravachol Zocor   Subtotal   Crestor   Lipitor   Pravachol    Vytorin    Zocor   Subtotal   Total
Better homes & gardens        2        0           3       3         8        2         15          5           0          8       30       38
Black Enterprise              0        5           1       0         6        0          0          0           0         12       12       18
Business Week                 0        0          13       0        13        8          0          10          3          0       21       34
Ebony                         0        0           0       1         1        0          4          0           0         17       21       22
Essence                       0        0           0       0         0        0          0          0           0          8       8         8
Family Circle                 0        0           5       3         8        0          4          0           0          7       11       19
Good Housekeeping             0        4           5       5        14        5          8          17          0          3       33       47
McCall's                      1        0           1       0         2        0          0          0           0         17       17       19
Money                         0        3           5       0         8        4         10          0           0          4       18       26
Modern Maturity               2        0           0       1         3        0          8          6           0          1       15       18
National Geographic           0        0           4       0         4        2         14          0           0          5       21       25
Newsweek                      1        3          18       17       39        13        12          14          3         29       71      110
People                        2        0           6       9        17        7          0          2           5          0       14       31
Reader's Digest               4        5           8       15       32        2         19          15          0         24       60       92
Sports Illustrated            0        0           0       1         1        12         0          0           4         11       27       28
Time                          2        6          18       15       41        11        16          20          4         23       74      115
TV Guide                      1        0           3       1         5        4         12          6           3         19       44       49
U.S. News & World Report      8        7          16       13       44        12        10          12          6         28       68      112
Women's Day                   0        5           3       9        17        0          4          10          0         13       27       44
Total                        23        38         109      93      263        82       136         117         28        229      592      855




                                                                    29
Table 4: Number of Prescription Cholesterol Reducing Drug Users in NCS by Year


                    All age                                     Age over 45
 Year     Total       Users      Percentage       Year    Total      Users    Percentage
 1995      15,038          594            3.95    1995      6,980       500           7.16
 1996       8,516          346            4.06    1996      3,883       289           7.44
 1997      26,758        1,187            4.44    1997     13,162     1,056           8.02
 1998      29,826        1,483            4.97    1998     14,830     1,328           8.95
 1999      33,054        1,915            5.79    1999     16,692     1,749          10.48
 2000      31,575        1,870            5.92    2000     15,980     1,722          10.78
 2001      21,452        1,593            7.43    2001     11,970     1,455          12.16
 2002      20,024        1,766            8.82    2002     11,447     1,641          14.34
 2003      18,173        1,692            9.31    2003     10,359     1,544          14.90
 2004      22,410        1,890            8.43    2004     11,066     1,696          15.33
 Total    226,826      13,092             5.77    Total   116,369    12,980          11.15




                                             30
Table 5: Descriptive Statistics of NCS 1995 – 2004 Age Over 45 (N=116,369)
 Variable                             Mean        St.d      Min        Max       Variable                      Mean    St.d   Min   Max
 % Purchase                             11.15                                    % Marital status
 % Diagnosed                            16.21                                     Never married                 5.21
 % Exercise                             42.90                                     Divorced/separated/widowed   24.06
 % Diet control                         34.65                                    % Health insurance
 Exposure to statins ad                 23.97      29.61      0.00      292.25    Private insurance            50.06
 Age                                    59.47       9.97     47.00       77.00    Medicaid                      2.74
 Family size                             2.34       1.33      1.00       14.00    Medicare                     20.53
 Magazine readership                     6.06       6.00      0.00      131.00    Uninsured                    17.42
 % Read no magazines                     4.86                                    % Magazine type
 Avg. weekly hours of TV watching       18.79      18.47      0.00      166.80    Home                         68.26
 % Watch no TV in avg. week             15.87                                     Young Adults                 21.51
 Avg. hours of radio listening           2.10       3.94      0.00       72.00    African American              6.87
 % Listen to no radio in avg. week      34.63                                     General Interest             65.75
 % Female                               54.99                                     Business                     14.63
 % Children in the household            47.86                                     Retiree                      15.55
 % Employed                             76.85                                    % Year
 Family income                       63589.21   60275.62   2500.00   341984.60    1995                          6.00
 % Race                                                                           1996                          3.34
  White                                 88.19                                     1997                         11.31
  Black                                  5.87                                     1998                         12.74
  Hispanic                               6.94                                     1999                         14.34
  Other race                             2.07                                     2000                         13.73
 % Education                                                                      2001                         10.29
  Less than high school                 12.53                                     2002                          9.84
  Some college                          25.93                                     2003                          8.90
  High school graduate                  31.03                                     2004                          9.51
  College graduate                      30.51




                                                                       31
Table 6: Percentage of Diagnosed, Purchase, Exercise and Diet Control by Quartile of Distribution of statins Advertisements (Age
over 45)

             Quartile            Not diagnosed    Diagnosed        Total    % Diagnosed
Q! (0 to 0.5 advertisements)             21,404         3,208      24,612           13.03
Q2 (0.5 to 9 advertisements)             21,745         3,642      25,387           14.35
Q3 (9 to 28 advertisements)              24,882         4,849      29,731           16.31
Q4 (28 or more advertisements)           29,479         7,160      36,639           19.54
Total                                    97,510        18,859     116,369
             Quartile            Not purchased    Purchased        Total    % Purchased
Q! (0 to 0.5 advertisements)             22,505           2,107    24,612            8.56
Q2 (0.5 to 9 advertisements)             22,895           2,492    25,387            9.82
Q3 (9 to 28 advertisements)              26,393           3,338    29,731           11.23
Q4 (28 or more advertisements)           31,596           5,043    36,639           13.76
Total                                   103,389         12,980    116,369
                                 Not controled    Controlled                % Controlled
             Quartile                 diet           diet          Total       Diet
Q! (0 to 0.5 advertisements)             16,494           6,073    22,567           26.91
Q2 (0.5 to 9 advertisements)             14,786           8,642    23,428           36.89
Q3 (9 to 28 advertisements)              17,786         10,050     27,836           36.10
Q4 (28 or more advertisements)           21,784         12,803     34,587           37.02
Total                                    70,850         37,568    108,418
             Quartile            Not exercised    Exercised        Total    % Exercised
Q! (0 to 0.5 advertisements)            14,924          7,646      22,570           33.88
Q2 (0.5 to 9 advertisements)            14,089          8,916      23,005           38.76
Q3 (9 to 28 advertisements)             14,988         12,402      27,390           45.28
Q4 (28 or more advertisements)          17,022         16,873      33,895           49.78
Total                                   61,023         45,837     106,860




                                                                     32
Table 7: OLS Estimates of Behavioral Outcomes for NCS 1995 – 2004 Adults Age Over 45

                                                Diagnosed                                              Purchased
 Variable                    Model 1            Model 2            Model 3          Model 1           Model 2        Model 3
 Ad exposure/100               0.0266    ***      0.0157 ***        -0.0038           0.0255   ***      0.0162 ***     0.0041
                             (0.0048)           (0.0051)           (0.0065)         (0.0041)          (0.0044)       (0.0056)
 Wave fixed effects            Yes                Yes                Yes              Yes               Yes            Yes
 Magazine category
 fixed effects                  No                Yes                 NA              No                 Yes           NA
 NCS magazine fixed
 effects                       No                  NA                 Yes             No                 NA            Yes
 Observations                 116369              116369             116369          116369             116369        116369
 R-squared                      0.04                0.04                0.05           0.04               0.04          0.04
                                             Controlled diet                                           Exercised
 Variable                    Model 1          Model 2              Model 3          Model 1           Model 2        Model 3
 Ad exposure/100               0.0237    ***    0.0008               0.0088           0.0382   ***      0.0278 ***     0.0148   *
                             (0.0063)         (0.0067)             (0.0085)         (0.0067)          (0.0071)       (0.0090)
 Wave fixed effects            Yes              Yes                  Yes              Yes               Yes            Yes
 Magazine category
 fixed effects                 No                 Yes                NA               No                 Yes           NA
 NCS magazine fixed
 effects                       No                 NA                 Yes              No                 NA            Yes
 Observations                 108418             108418             108418           106860             106860        106860
 R-squared                       0.09                0.09               0.10            0.07                0.07        0.09
Note: Robust standard error in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%




                                                                               33
Note: Robust standard error in parentheses; * significant at 10%; ** significant at 5%; *** significant at
1%




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