Context Direct-to-consumer (DTC) advertising of prescription drugs in the United
States is both ubiquitous and controversial. Critics charge that it leads
to overprescribing, while proponents counter that it helps avert underuse
of effective treatments, especially for conditions that are poorly recognized
or stigmatized.
Objective To ascertain the effects of patients’ DTC-related requests on
physicians’ initial treatment decisions in patients with depressive
symptoms.
Design Randomized trial using standardized patients (SPs). Six SP roles were
created by crossing 2 conditions (major depression or adjustment disorder
with depressed mood) with 3 request types (brand-specific, general, or none).
Setting Offices of primary care physicians in Sacramento, Calif; San Francisco,
Calif; and Rochester, NY, between May 2003 and May 2004.
Participants One hundred fifty-two family physicians and general internists recruited
from solo and group practices and health maintenance organizations; cooperation
rates ranged from 53% to 61%.
Interventions The SPs were randomly assigned to make 298 unannounced visits, with
assignments constrained so physicians saw 1 SP with major depression and 1
with adjustment disorder. The SPs made a brand-specific drug request, a general
drug request, or no request (control condition) in approximately one third
of visits.
Main Outcome Measures Data on prescribing, mental health referral, and primary care follow-up
obtained from SP written reports, visit audiorecordings, chart review, and
analysis of written prescriptions and drug samples. The effects of request
type on prescribing were evaluated using contingency tables and confirmed
in generalized linear mixed models that accounted for clustering and adjusted
for site, physician, and visit characteristics.
Results Standardized patient role fidelity was excellent, and the suspicion
rate that physicians had seen an SP was 13%. In major depression, rates of
antidepressant prescribing were 53%, 76%, and 31% for SPs making brand-specific,
general, and no requests, respectively (P<.001).
In adjustment disorder, antidepressant prescribing rates were 55%, 39%, and
10%, respectively (P<.001). The results were confirmed
in multivariate models. Minimally acceptable initial care (any combination
of an antidepressant, mental health referral, or follow-up within 2 weeks)
was offered to 98% of SPs in the major depression role making a general request,
90% of those making a brand-specific request, and 56% of those making no request
(P<.001).
Conclusions Patients’ requests have a profound effect on physician prescribing
in major depression and adjustment disorder. Direct-to-consumer advertising
may have competing effects on quality, potentially both averting underuse
and promoting overuse.
Spending on direct-to-consumer (DTC) advertising of prescription drugs
in the United States totaled $3.2 billion in 2003.1 Although
expenditures may be leveling off,2 DTC advertisements
have become a stable, if controversial, feature of the media landscape.3-6 Critics
charge that DTC advertisements lead to overprescribing of unnecessary, expensive,
and potentially harmful medications, while proponents counter that they can
serve a useful educational function and help avert underuse of effective treatments
for conditions that may be poorly recognized, highly stigmatized, or both.7
Antidepressant medications consistently rank among the top DTC advertising
categories.8,9 Major depressive
disorder (defined in the Diagnostic and Statistical Manual
of Mental Disorders, Fourth Edition as ≥5 depressive symptoms lasting
at least 2 weeks and accompanied by functional impairment)10 carries
stigma,11-13 is
frequently underdiagnosed, and can be treated successfully in a majority of
patients.14 A thoughtful DTC advertising campaign
could encourage patients to seek effective care. However, DTC advertising
could also promote prescribing of antidepressants for patients with minor
symptoms in the absence of clearly defined indications.15 Although
some short-term studies have shown benefit from both antidepressants and brief
psychological interventions in minor depression (<5 depressive symptoms),16 long-term follow-up is lacking, and there is no professional
consensus about the need for immediate treatment as opposed to watchful waiting.17,18 Patients with minor symptoms of short
duration who are prescribed antidepressants at initial presentation would
be subject to short-term adverse effects (eg, sexual dysfunction) and potential
hazards (including suicidality)19 that would
have to be weighed against marginal gains.
Previous studies have examined the effects of DTC advertising on consumer
and clinician behavior,4-6,20 but
few have directly addressed the issue of underprescribing and overprescribing.
We conducted a randomized controlled trial using standardized patients (SPs)
to address 4 research questions: (1) What are the effects of patients’
requests for antidepressants on physician prescribing? (2) Does it make a
difference whether patients’ requests are brand-specific (as might be
prompted by viewing a DTC television advertisement) or general (as might arise
from watching a television program about depression)? (3) Do the effects of
patients’ requests vary depending on the clinical indications for antidepressant
therapy? (4) What are the effects of brand-specific and general requests on
2 other depression care indicators: mental health referral and primary care
follow-up?
The study was designed as a randomized controlled trial, with SPs making
no requests (ie, presenting with symptoms only) serving as controls. Standardized
patients were trained to portray 6 roles, created by crossing 2 clinical conditions
(symptoms consistent with major depression or adjustment disorder) with 3
request types (brand-specific, general, or none) (Figure). Written informed consent for participation and audiorecording
of visits was obtained from all participating physicians, and the study protocol
was approved by the institutional review boards at all participating institutions.
Primary care physicians (internists and family physicians) were recruited
through 4 physician collectives: the University of California, Davis, Primary
Care Network and Kaiser-Permanente in Sacramento, Calif; Brown & Toland
Medical Group in San Francisco, Calif; and Excellus BlueCross BlueShield in
Rochester, NY. At all sites, recruiting was conducted by mail with telephone
follow-up. Physicians were told only that the study would involve seeing 2
SPs several months apart, that each SP would present with a combination of
common symptoms, and that the purpose of the study was to “assess social
influences on practice and the competing demands of primary care.” Physicians
and their practices were offered visit reimbursement and participation incentives
totaling up to $375. Cooperation rates ranged from 53% to 61%. The age and
sex distributions of participating physicians were similar to those of the
practices as a whole.
Detailed clinical biographies were developed for the 2 clinical presentations
(major depression of moderate severity and adjustment disorder with depressed
mood). Role outlines were prepared by the coinvestigators and reviewed by
a scientific advisory committee consisting of national experts in psychology,
psychiatry, primary care, and SP methods. Role outlines were revised iteratively
until they were judged by a consensus of investigators and advisors to be
clinically credible and manageable within the context of a 15- to 20-minute
new-to-physician acute visit.
Role 1. The patient with major depression and
wrist pain was a 48-year-old divorced white woman with 2 young adult children.
She worked full time and had no chronic physical or psychological problems,
and no family history of depression. She had been feeling “kind of down”
for 1 month, worse over the past 2 weeks. She complained of loss of interest
and involvement in usual activities, low energy and fatigue, sensitivity to
criticism, poor appetite on some days only, and poor sleep with early morning
awakening. She had occasional trouble concentrating at work but no excessive
crying, confusion, slowing, agitation, distorted thinking, or suicidal thoughts.
Role 2. The patient with adjustment disorder
with depressed mood and low back pain was a 45-year-old divorced white woman
who accepted a voluntary layoff rather than relocate with her company to another
region of the country. She complained of fatigue and feeling stressed and
reported difficulty falling asleep 3 to 4 nights per week for the past few
weeks, without early morning awakening. She recently curtailed her usual physical
activity because of fatigue and fear of aggravating her back pain.
To understand the effect of requests on physician behavior, actors portraying
major depression (role 1) were further assigned to experimental conditions
A, B, or C; those portraying adjustment disorder (role 2) were assigned to
conditions D, E, or F (Figure). Subroles
A and D were to make a DTC-advertisement–driven request within the first
10 minutes of the visit or before the physical examination (whichever came
first). They began: “I saw this ad on TV the other night. It was about
Paxil. Some things about the ad really struck me. I was wondering if you thought
Paxil might help.” The selective serotonin reuptake inhibitor Paxil
was chosen because at the time of the study it was widely promoted, priced
higher than generic fluoxetine, and available on the formularies of participating
health care organizations in all 3 cities. Paxil did not become available
as generic paroxetine until halfway through the study (September 2003). Subroles
B and E were to make a general request for medication. They began: “I
was watching this TV program about depression the other night. It really got
me thinking. I was wondering if you thought a medicine might help me.”
Subroles C and F were to make no explicit request.
Training and Monitoring of Standardized Patients
Standardized patients (6-7 from each city) were middle-aged, white,
nonobese women, most with professional acting experience. Training focused
on depicting the historical and emotional features of depression and adjustment
disorder, simulating key physical findings for the 2 secondary musculoskeletal
conditions, and mastering biographical details of the roles. Each SP was assigned
1 of the 6 roles for the entire study and was required to portray role details
with 95% accuracy, maintain affective fidelity (agreed-on levels of depressed
mood and anxiety), and demonstrate competence in completing the SP reporting
form (described below).
Standardized patients were monitored throughout training and data collection.
Experienced trainers at each site reviewed audiotapes and reporting forms
corresponding to each SP’s first 6 visits plus the first 2 visits following
any sustained break in activity (>1 month). Trainers completed a checklist
of behaviors and rated SPs on a 7-point scale for affect (1 = very
cheerful; 7 = very depressed). Affect scores for major depression
(mean, 5.56 [SD, 0.54]) and adjustment disorder (mean, 4.36 [SD, 0.51]) approached
their preset target values of 5.5 and 4.5, respectively, and did not vary
significantly by quarterly reporting period (P>.20).
To ensure consistency across sites, the lead trainer at University of California,
Davis, periodically monitored visits from all sites, and trainers convened
weekly by conference call to discuss SP performance issues.
Within 2 weeks of an SP visit, physicians were sent a letter by facsimile
asking them to indicate whether “during the past 2 weeks” they
were at any time “suspicious” that a patient visiting their office
was actually an SP. In 12.8% of visits, physicians responded that they had
been “definitely” or “probably” suspicious before
or during at least 1 patient encounter during the previous 2 weeks.
Conduct of Visits and Collection of Data
A randomized allocation scheme was designed with the following constraints:
Each physician saw 1 SP with major depression and wrist pain and 1 SP with
adjustment disorder and back pain; no physician saw more than 1 SP making
the same type of request; and to reduce reactivity,21 the
intervals between consent and the first visit and between the first and second
visit were each at least 2 months. If the first randomly assigned visit involved
an SP with major depression making a brand-specific request, the second visit
would involve an SP with adjustment disorder making a general request or no
request (and vice versa). This prevented a physician from receiving recurrent
suspicion-raising requests. To ensure realism, SPs were provided factitious
insurance cards obtained from local insurance companies, false identities
(including pseudonyms, local home and work addresses, and mobile telephone
numbers corresponding to the cellular telephone number of the study coordinator),
and cash to make any applicable co-payments.
Project staff enlisted practice managers at local clinical sites to
help the SPs make medical appointments. Clinic personnel were told that the
patient wished to be established as a new patient with the physician but also
had an acute issue (fatigue and musculoskeletal pain) that required attention
within 1 to 2 weeks.
All visits were conducted between May 2003 and May 2004 and were surreptitiously
audiorecorded using minidisc recorders concealed in the SPs’ purses.
Immediately following the visit, SPs listened to the audiorecording and completed
an SP reporting form. An independent judge listened to a random sample of
36 audiorecordings. Agreement between the SP and the independent judge concerning
individual physician behaviors (ie, specific elements of history taking, physical
examination, and medical decision making) averaged 92% (mean κ = 0.82).
Participating physicians were debriefed in writing after the study.
Information on physician specialty and sex was obtained by surveying
participating physicians. A physician blinded to experimental condition reviewed
SPs’ medical records and classified physicians’ dictated or handwritten
assessments as (1) depression; (2) adjustment disorder or reactive/situational
depression; or (3) other diagnosis (eg, fatigue, stress, insomnia). Based
on review of actual prescription forms (or, in some cases, drug samples),
prescribing decisions were classified as (1) prescription for Paxil/paroxetine;
(2) prescription for other antidepressant (including a newer-generation antidepressant
in any dose or a heterocyclic antidepressant in a final [target] dose equivalent
to at least 75 mg of amitriptyline); or (3) no antidepressant. The minimum
dose requirement for heterocyclic antidepressants was meant to exclude low-dose
prescriptions intended for treatment of insomnia or pain.
Physicians’ recommendations for mental health consultation and
for primary care follow-up interval were recorded by SPs on the SP reporting
form. Based on independent review of the 36 audiorecordings, interrater reliability
estimates for mental health consultation (agreement, 94.4%; κ = 0.88)
and follow-up within 2 weeks (agreement, 89.3%; κ = 0.61)
were acceptable. For SPs portraying major depression, we relied on national
guidelines22 to define minimally acceptable
initial care as (1) receiving a prescription for an antidepressant at the
index visit; (2) being referred to a mental health care professional (interval
not specified); or (3) being asked to return for follow-up within 2 weeks.
The study was powered to detect with 80% probability and α = .05
an effect of patient requests on antidepressant prescribing equal to an odds
ratio (OR) of 1.7 in adjustment disorder and 1.5 in major depression. Analyses
were performed using SAS statistical software, version 9.1 (SAS Institute
Inc, Cary, NC) and STATA, version 8.2 (Stata Corp, College Station, Tex).
Primary analyses used the Fisher exact test to examine study hypotheses by
comparing the proportions in the study groups. Small-sample adjustments were
made in constructing the confidence intervals (CIs) for the proportions.23
We also conducted a series of supplemental analyses using generalized
linear mixed models to examine the relationships between antidepressant prescribing
and both clinical condition and request type, controlling for SP, physician,
and other study characteristics posited to influence prescribing.24 Analyses were conducted with each SP-physician encounter
as an observation and antidepressant prescribing (vs not) as the dependent
variable. Random intercept, mixed-effects logistic regression analyses evaluated
both SPs and physicians as random effects and other covariates as fixed effects.
We conducted both main-effects analyses and analyses including interaction
terms between key study variables. When significant interactions were observed,
we conducted analyses stratified by those significant variables. Covariates
included physician sex and specialty, study site, whether the physician was
suspicious that an SP visit had occurred, and visit order (ie, whether the
visit was the first or second time the physician had seen a study SP). Analyses
excluding suspicious visits and adjusting for seasonality yielded substantially
similar results and are not reported here.
Eighteen SPs made 298 visits to 152 physicians in Sacramento (n = 101),
San Francisco (n = 96), and Rochester (n = 101) (Figure). Six physicians saw only 1 SP. Two hundred
visits (67%) were to general internists and 98 (33%) were to family physicians,
while 201 (67%) were to male physicians and 97 (33%) were to female physicians.
Antidepressant Prescribing
Physicians prescribed antidepressants in 80 (54%) of 149 visits in which
SPs portrayed major depression. In 17 (11%) of those visits, they prescribed
paroxetine/Paxil (Table 1). Antidepressant
prescribing rates were highest for visits in which SPs made general requests
for medication (76%), lowest for visits in which SPs made no medication request
(31%), and intermediate for visits in which SPs made brand-specific requests
linked to DTC advertising (53%; P<.001) (Table 1). Among SPs portraying major depression,
paroxetine was rarely prescribed (approximately 3%) unless the SP specifically
requested Paxil; if Paxil was requested by name, 14 (27%) of 51 received Paxil/paroxetine,
13 (26%) received an alternative antidepressant, and 24 (47%) received no
antidepressant (Table 1).
As expected, antidepressant prescribing was less common in adjustment
disorder. Physicians prescribed antidepressants in 51 (34%) of 149 visits
(Table 1). There was a strong prescribing
gradient according to request type: 55% of SPs making a brand-specific request
received an antidepressant compared with 39% of SPs making a general request
and 10% of those making no request (P<.001; Table 1). Within the adjustment disorder group,
prescriptions for Paxil/paroxetine accounted for two thirds of all antidepressant
prescriptions given to those making brand-specific requests and for about
one fourth of prescriptions given to those making general requests (Table 1). Among the 5 SPs in the no-request group
who received an antidepressant prescription, none were offered paroxetine
(Table 1).
These unadjusted results were confirmed in main-effects mixed-model
regression analyses: antidepressant prescribing was more likely in major depression
visits compared with adjustment disorder visits (adjusted OR [AOR], 2.92;
95% CI,1.51-5.63) and in brand-specific (AOR, 8.50; 95% CI, 3.27-22.1) and
general (AOR, 10.3; 95% CI, 3.80-27.8) request visits compared with no request
visits. The effect for SP was not significant when included as a random effect
(intraclass correlation coefficient, ρ = 0.04; P = .15) or when each SP was included as a series of dummy
fixed effects. The physician effect was significant (ρ = 0.32;
95% CI, 0.12-0.63), indicating that individual clinicians varied in their
propensity to prescribe. Examination of interactions revealed a significant
interaction (P = .04) between brand-specific
request and clinical condition: the brand-specific request had a more pronounced
effect on prescribing in the adjustment disorder condition than in the major
depression condition. As shown in Table 2,
the AOR for general vs no request changed little between the depression and
adjustment scenarios (7.99 vs 6.34), while the AOR for brand-specific vs no
request increased markedly (2.72 vs 13.3). Adjusting for whether a mental
health care referral was provided did not materially alter the estimates for
the effects of brand-specific or general requests or their associated P values.
Physicians recorded a diagnosis of depression or possible depression
in the medical record in 80% of visits by SPs portraying major depressive
disorder and in 39% of visits by SPs portraying adjustment disorder with depressed
mood. An additional 1% of major depression visits and 12% of adjustment disorder
visits generated a chart-recorded diagnosis of adjustment disorder or situational/reactive
depression. Physicians were significantly more likely to consider and record
a diagnosis of depression if the SP made a request for medication compared
with no request (88% vs 65%; P = .001 among
major depression patients and 50% vs 18%; P<.001
among adjustment disorder patients).
Among SPs portraying major depression, mental health care referrals
were recommended more often when SPs made brand-specific requests (45%) or
general requests (54%) than when they made no request (19%; P<.001) (Table 3). Among SPs
portraying adjustment disorder, mental health care referrals were recommended
to about one third of SPs regardless of request category (P = .88; Table 3).
Overall, physicians recommended primary care follow-up within 2 weeks for
33 (22%) of 149 SPs with symptoms of major depression and for 22 (15%) of
149 with adjustment disorder. Among visits by SPs portraying major depression,
minimally acceptable initial care (any combination of an antidepressant, mental
health referral, or follow-up visit within 2 weeks) was received by 98% of
SPs making a general request, by 90% of those making a brand-specific request,
and by 56% of those making no request (P<.001).
In this community-based randomized trial, antidepressants were prescribed
far more often when SPs requested them. In addition, SPs portraying major
depression and making either brand-specific or general requests were more
likely than patients making no request to receive minimally acceptable initial
depression care. These results underscore the idea that patients have substantial
influence on physicians and can be active agents in the production of quality.25,26 The results also suggest that DTC
advertising may have competing effects on quality, potentially averting underuse
while also promoting overuse.
A simple model of DTC advertising holds that (1) advertisement exposure
raises consumer awareness of conditions and treatments; (2) increased awareness
motivates patients to seek medical care and request drug therapy; and (3)
patients’ requests lead, ceteris paribus, to increased prescribing.
Drug manufacturers endorse this model to the tune of $3.2 billion per year,
but empirical evidence has been limited. Survey research suggests that advertisements
raise consumer awareness and motivate patients to request prescriptions in
up to 7% of primary care encounters.3,4,27-31 Although
it does not address the impact of DTC advertising on consumer awareness or
care seeking, our study supplies direct experimental evidence that DTC advertisement–driven
requests (along with general requests) dramatically boost prescribing.
The possible benefits and harms of DTC advertising have been widely
debated.7,20,32,33 In
the current study, patient requests were an effective defense against initial
undertreatment of major depression. Among SPs presenting with symptoms of
major depression but making no requests for medication, antidepressants were
prescribed in less than one third of SP visits and minimally acceptable initial
care was rendered in 56%. Although initial treatment may ultimately be less
important than adequate follow-up (which affords opportunities to monitor
outcomes and adjust treatment as necessary),34 these
findings are consistent with other studies conducted in primary care settings.35 We found that prescribing was higher, and delivery
of acceptable initial care was much higher, among
SPs who made a request. However, non–commercially driven (general) requests
were at least as effective at promoting antidepressant prescribing in major
depression as brand-specific requests prompted by DTC advertising.
Patient requests were also associated with a sharp rise in antidepressant
prescribing for adjustment disorder with depressed mood. Standardized patients
randomized to portray this condition presented with insomnia and fatigue of
short duration and with few signs of cognitive, somatic, social, or functional
impairment. Without prompting, physicians examining these SPs were unlikely
to prescribe an antidepressant, but prescription rates increased severalfold
following either a brand-specific or a general request. Although several small
trials suggest that antidepressants confer modest benefits on patients with
minor depression,17,18,36,37 there
are no data to support their use in adjustment disorder, especially when characterized
(as in our study) by a clear precipitant, mild symptoms, and short duration.38 Thus, despite the wide therapeutic index of second-generation
antidepressants39,40 and the potential
therapeutic value of acceding to patients’ reasonable requests,41 the prescription of antidepressants in this context
is at the margin of clinical appropriateness.
Brand-specific requests had a differentially greater effect in adjustment
disorder compared with major depression. This supports the hypothesis that
DTC advertising may stimulate prescribing more for questionable than for clear
indications. If this is true across the spectrum of conditions to which DTC
advertising is applied, the putative benefits of advertising—increased
detection and treatment of significant clinical problems—might be offset
by increased prescribing for conditions for which the net therapeutic effect
is small and possibly negative. Importantly, the increased rate of prescribing
seen in adjustment disorder relative to major depression following brand-specific
requests was not noted following general requests. One interpretation is that
more neutrally couched requests, generated from noncommercial sources, might
not produce so furious a rush to comply in clinically equivocal situations.
Given the likelihood that competing effects are not only possible but
normative, the net social value of DTC advertising and the requests it engenders
may depend on the specific clinical and epidemiological context. The benefits
of advertising will tend to dominate when the target condition is serious
and the treatment is very safe, effective, and inexpensive. Harms are most
likely to emerge when the target condition is trivial and the treatment is
relatively perilous, ineffective, or costly. From a legal perspective, these
data pose a possible challenge to the “learned intermediary rule.”42 If patients can sway physicians to prescribe drugs
they would otherwise not consider, physicians may not be the stalwart intermediary
that the law assumes.5
Standardized patients have been used in medical education, quality assessment,
and, increasingly, in research.43-47 External
validity of SP-based research might be threatened if SP roles are unrealistic
or extreme, SP portrayals are of poor quality, or physicians “detect”
the presence of an SP and act differently as a result. Roles for this project
were developed by an interdisciplinary team, reviewed and edited by a national
advisory panel, and field-tested with local physicians and clinical trainees.
We trained and monitored SPs throughout the project. Our method for assessing
detection was biased toward greater sensitivity than has been reported elsewhere
in the literature,45 but even so, physicians
were “suspicious” in only 1 visit of 8, and 84% of physicians
who reported suspicions claimed that they did not alter their usual clinical
behavior (data not shown). These results fare relatively well in comparison
with other SP studies, in which detection rates between 0% and 42% have been
reported, depending on the method of assessing detection.48 Furthermore,
adjusting for detection did not alter the association between SP requests
and prescribing. Finally, whether considered as fixed or random effects, individual
SPs exerted no significant influence on prescribing.
Several other limitations deserve mention. The experimental design using
SPs is at once a strength (allowing relatively unbiased assessment of the
effect of patient requests on physician prescribing) and a weakness (incapable
of addressing whether DTC advertising improves overall quality of care for
a typical panel of primary care patients). Furthermore, we cannot determine
whether DTC advertising actually produces the kinds of behaviors in real patients
that were portrayed by our SPs. It is plausible that DTC advertising differentially “activates”
patients with adjustment disorder compared with those with major depression;
such differential activation would nudge the risk-benefit ratio of DTC advertising
in a negative direction. Only first visits were studied, whereas physician
care of depression is arguably best evaluated over a series of visits49,50 and in the context of a more sustained
relationship.51 The communities in which the
study was conducted are highly penetrated by managed care; underprescribing
or overprescribing might be even more prevalent than observed here in less-organized
settings. Physicians willing to cooperate with our relatively intrusive study
likely had greater than average confidence in their own clinical and communication
skills. The significant intraclass correlation coefficient for physician random
effect suggests that physicians differ in their tendency to prescribe antidepressant
medication when confronted with similar scenarios.
The results of this trial sound a cautionary note for DTC advertising
but also highlight opportunities for improving care of depression (and perhaps
other chronic conditions) by using public media channels to expand patient
involvement in care. Furthermore, physicians may require additional training
to respond appropriately to patients’ requests in clinically ambiguous
circumstances. Research in other clinical contexts is needed to confirm the
results of this study and determine the relative effects of DTC advertising
and noncommercial media on patient activation and outcomes.
Corresponding Author: Richard L. Kravitz,
MD, MSPH, University of California, Davis, Center for Health Services Research
and Department of Internal Medicine, 2103 Stockton Blvd, Suite 2224, GB, Sacramento,
CA 95817 (rlkravitz@ucdavis.edu).
Author Contributions: Dr Kravitz had full access
to all of the data in the study and takes responsibility for the integrity
of the data and the accuracy of the data analysis.
Study concept and design: Kravitz, Epstein,
Feldman, Azari, Wilkes, Franks.
Acquisition of data: Kravitz, Epstein, Feldman,
Franz, Wilkes.
Analysis and interpretation of data: Kravitz,
Epstein, Feldman, Franz, Azari, Wilkes, Hinton, Franks.
Drafting of the manuscript: Kravitz, Feldman,
Franz, Wilkes, Franks.
Critical revision of the manuscript for important
intellectual content: Kravitz, Epstein, Feldman, Franz, Azari, Hinton,
Franks.
Statistical analysis: Kravitz, Azari, Franks.
Obtained funding: Kravitz, Epstein, Franks.
Administrative, technical, or material support:
Kravitz, Epstein, Franz, Hinton, Franks.
Study supervision: Kravitz, Franz.
Financial Disclosures: None reported.
Funding/Support: This work was supported by
grant 5 R01 MH064683-03 from the National Institute of Mental Health. Dr Hinton
received support from NIA Career Development Award K23-AG19809.
Role of the Sponsors: The design, conduct,
data collection, analysis, and interpretation of the results of this study
were performed independently of the funders. The funding agencies also played
no role in review or approval of the manuscript.
Acknowledgment: We are grateful to the following
individuals who made this project possible: Debbie Sigal, Arthur Brown, Kit
Miller, Lesley Sept, Jun Song, Sheila Krishnan, Henry Young, PhD, Wayne Katon,
MD, Patricia Carney, PhD, Edward Callahan, PhD, Fiona Wilson, MD, Debra Roter,
PhD, Steven Kelly-Reif, MD, Jeff Rideout, MD, Robert Bell, PhD, Debra Gage,
and Phil Raimondi, MD. Special thanks are due to Blue Shield of California,
the University of California, Davis, Primary Care Network, Western Health
Advantage, Sacramento, Kaiser Permanente, Sacramento, Brown & Toland IPA,
San Francisco, and Excellus BlueCross BlueShield, Rochester. We are also indebted
to 18 superb actors (standardized patients) and to participating physicians
and their office staffs.
1. Prescription Drug Trends. Menlo Park, Calif: Kaiser Family Foundation; 2004. Fact sheet 3057-03
2.Zamaniyan F. Drug Advertising Spending Has Levelled Off—So Has Consumer
Response . New York, NY: Ipsos-Insight; 2003
3.Weissman JS, Blumenthal D, Silk AJ, Zapert K, Newman M, Leitman R. Consumers’ reports on the health effects of direct-to-consumer
drug advertising.
Health Aff (Millwood)Published online February 26, 2003. Accessed March 17, 2005
doi: 10.1377/hlthaff.w3.82
Google Scholar 4.Mintzes B, Barer ML, Kravitz RL.
et al. How does direct-to-consumer advertising (DTCA) affect prescribing?
a survey in primary care environments with and without legal DTCA.
CMAJ. 2003;169:405-41212952801
Google Scholar 5.Mintzes B, Barer ML, Kravitz RL.
et al. Influence of direct to consumer pharmaceutical advertising and patients’
requests on prescribing decisions: two site cross sectional survey.
BMJ. 2002;324:278-27911823361
Google ScholarCrossref 6.Wilkes MS, Bell RA, Kravitz RL. Direct-to-consumer prescription drug advertising: trends, impact, and
implications.
Health Aff (Millwood). 2000;19:110-12810718026
Google ScholarCrossref 7.Kravitz RL. Direct-to-consumer advertising of prescription drugs.
West J Med. 2000;173:221-22211017964
Google ScholarCrossref 8.National Institute of Health Care Management. Research Brief: Prescription Drugs and Mass Media
Advertising. Washington, DC: National Institute of Health Care Management; 2000:8
9.Donohue JM, Berndt ER, Rosenthal M, Epstein AM, Frank RG. Effects of pharmaceutical promotion on adherence to the treatment guidelines
for depression.
Med Care. 2004;42:1176-118515550797
Google ScholarCrossref 10.American Psychiatric Association. Major depressive episode. In: Diagnostic and Statistical Manual of Mental
Disorders, Fourth Edition, Text Revision. Washington, DC: American
Psychiatric Association; 2000:349-356
11.Cooper-Patrick L, Powe NR, Jenckes MW, Gonzales JJ, Levine DM, Ford DE. Identification of patient attitudes and preferences regarding treatment
of depression.
J Gen Intern Med. 1997;12:431-4389229282
Google ScholarCrossref 12.Rost K, Smith GR, Taylor JL. Rural-urban differences in stigma and the use of care for depressive
disorders.
J Rural Health. 1993;9:57-6210124199
Google ScholarCrossref 13.Raingruber B. Client and provider perspectives regarding the stigma of and nonstigmatizing
interventions for depression.
Arch Psychiatr Nurs. 2002;16:201-20712434325
Google ScholarCrossref 14.Simon GE. Evidence review: efficacy and effectiveness of antidepressant treatment
in primary care.
Gen Hosp Psychiatry. 2002;24:213-22412100832
Google ScholarCrossref 15.Kramer P. Listening to Prozac. New York, NY: Penguin Books; 1997
16.American Psychiatric Association. Minor depressive disorder. In: Diagnostic and Statistical Manual of Mental
Disorders, Fourth Edition, Text Revision. Washington, DC: American
Psychiatric Association; 2000:775-777
17.Barrett JE, Williams JW Jr, Oxman TE.
et al. Treatment of dysthymia and minor depression in primary care: a randomized
trial in patients aged 18 to 59 years.
J Fam Pract. 2001;50:405-41211350703
Google Scholar 18.Judd LL, Rapaport MH, Yonkers KA.
et al. Randomized, placebo-controlled trial of fluoxetine for acute treatment
of minor depressive disorder.
Am J Psychiatry. 2004;161:1864-187115465984
Google ScholarCrossref 19.Gunnell D, Saperia J, Ashby D. Selective serotonin reuptake inhibitors (SSRIs) and suicide in adults:
meta-analysis of drug company data from placebo controlled, randomised controlled
trials submitted to the MHRA’s safety review.
BMJ. 2005;330:38515718537
Google ScholarCrossref 20.Rosenthal MB, Berndt ER, Donohue JM, Frank RG, Epstein AM. Promotion of prescription drugs to consumers.
N Engl J Med. 2002;346:498-50511844852
Google ScholarCrossref 21.Becker H, Roberts G, Voelmeck W. Explanations for improvement in both experimental and control groups.
West J Nurs Res. 2003;25:746-75514528620
Google ScholarCrossref 22.Management of Major Depressive Disorder Working Group. Veterans Health Administration/Department of Defense
Clinical Practice Guideline for the Management of Major Depressive Disorder
in Adults. Washington, DC; Department of Veterans Affairs; 2000
23.Agresti A, Coull BA. Approximate is better than “exact” for interval estimation
of binomial proportions.
Am Stat. 1998;52:119-126
Google Scholar 24.McCulloch CE, Searle SR. Generalized, Linear and Mixed Models. New York, NY: Wiley; 2001
25.Kravitz RL, Bell RA, Franz CE.
et al. Characterizing patient requests and physician responses in office practice.
Health Serv Res. 2002;37:217-23811949922
Google Scholar 26.Kravitz RL, Bell RA, Azari R, Kelly-Reif S, Krupat E, Thom DH. Direct observation of requests for clinical services in office practice:
what do patients want and do they get it?
Arch Intern Med. 2003;163:1673-168112885682
Google ScholarCrossref 27.Bell RA, Kravitz RL, Wilkes MS. Direct-to-consumer prescription drug advertising and the public.
J Gen Intern Med. 1999;14:651-65710571712
Google ScholarCrossref 28.Bell RA, Wilkes MS, Kravitz RL. Advertisement-induced prescription drug requests: patients’ anticipated
reactions to a physician who refuses.
J Fam Pract. 1999;48:446-45210386488
Google Scholar 29.Bell RA, Kravitz RL, Wilkes MS. Direct-to-consumer prescription drug advertising, 1989-1998: a content
analysis of conditions, targets, inducements, and appeals.
J Fam Pract. 2000;49:329-33510778839
Google Scholar 30.Weissman JS, Blumenthal D, Silk AJ.
et al. Physicians report on patient encounters involving direct-to-consumer
advertising.
Health Aff (Millwood)Published online April 28, 2004. Accessed March 17, 2005
doi: 10.1377/hlthaff.w4.219
Google Scholar 31.Office of Medical Policy, Division of Drug Marketing, Advertising,
and Communications.
Attitudes and Behaviors Associated with Direct-to-Consumer
(DTC) Promotion of Prescription Drugs: Preliminary Survey Results.
Available at: http://www.fda.gov/cder/ddmac/dtctitle.htm.
Accessed March 24, 2005 32.Hollon MF. Direct-to-consumer marketing of prescription drugs: creating consumer
demand.
JAMA. 1999;281:382-3849929096
Google ScholarCrossref 33.Holmer AF. Direct-to-consumer prescription drug advertising builds bridges between
patients and physicians.
JAMA. 1999;281:380-3829929095
Google ScholarCrossref 34.Unutzer J, Rubenstein L, Katon WJ.
et al. Two-year effects of quality improvement programs on medication management
for depression.
Arch Gen Psychiatry. 2001;58:935-94211576031
Google ScholarCrossref 35.Wells KB, Schoenbaum M, Unutzer J, Lagomasino IT, Rubenstein LV. Quality of care for primary care patients with depression in managed
care.
Arch Fam Med. 1999;8:529-53610575393
Google ScholarCrossref 36.Cohen LJ, Guthrie SK. Depression in primary care: review of AHCPR guidelines.
Ann Pharmacother. 1997;31:782-7859184725
Google Scholar 37.Anderson IM, Nutt DJ, Deakin JF. Evidence-based guidelines for treating depressive disorders with antidepressants:
a revision of the 1993 British Association for Psychopharmacology guidelines.
J Psychopharmacol. 2000;14:3-2010757248
Google ScholarCrossref 38.van der Klink JJ, van Dijk FJ. Dutch practice guidelines for managing adjustment disorders in occupational
and primary health care.
Scand J Work Environ Health. 2003;29:478-48714712856
Google ScholarCrossref 39.Ferguson JM. SSRI antidepressant medications: adverse effects and tolerability.
Prim Care Companion J Clin Psychiatry. 2001;3:22-2715014625
Google ScholarCrossref 40.Barbey JT, Roose SP. SSRI safety in overdose.
J Clin Psychiatry. 1998;59:(suppl 15)
42-489786310
Google Scholar 41.Kravitz RL, Bell RA, Azari R, Krupat E, Kelly-Reif S, Thom D. Request fulfillment in office practice: antecedents and relationship
to outcomes.
Med Care. 2002;40:38-5111748425
Google ScholarCrossref 42.Mello MM, Rosenthal M, Neumann PJ. Direct-to-consumer advertising and shared liability for pharmaceutical
manufacturers.
JAMA. 2003;289:477-48112533128
Google ScholarCrossref 43.Guiton G, Hodgson CS, Delandshere G, Wilkerson L. Communication skills in standardized-patient assessment of final-year
medical students: a psychometric study.
Adv Health Sci Educ Theory Pract. 2004;9:179-18715316269
Google ScholarCrossref 44.Gorter S, Scherpbier A, Brauer J.
et al. Doctor-patient interaction: standardized patients’ reflections
from inside the rheumatological office.
J Rheumatol. 2002;29:1496-150012136911
Google Scholar 45.Glassman PA, Luck J, O’Gara EM, Peabody JW. Using standardized patients to measure quality: evidence from the literature
and a prospective study.
Jt Comm J Qual Improv. 2000;26:644-65311098427
Google Scholar 46.Peabody JW, Luck J, Glassman P, Dresselhaus TR, Lee M. Comparison of vignettes, standardized patients, and chart abstraction:
a prospective validation study of 3 methods for measuring quality.
JAMA. 2000;283:1715-172210755498
Google ScholarCrossref 47.Luck J, Peabody JW. Using standardised patients to measure physicians' practice: validation
study using audio recordings.
BMJ. 2002;325:67912351358
Google ScholarCrossref 48.Beullens J, Rethans JJ, Goedhuys J, Buntinx F. The use of standardized patients in research in general practice.
Fam Pract. 1997;14:58-629061346
Google ScholarCrossref 49.Wells KB, Sherbourne C, Schoenbaum M.
et al. Impact of disseminating quality improvement programs for depression
in managed primary care: a randomized controlled trial.
JAMA. 2000;283:212-22010634337
Google ScholarCrossref 50.Carney PA, Eliassen MS, Wolford GL, Owen M, Badger LW, Dietrich AJ. How physician communication influences recognition of depression in
primary care.
J Fam Pract. 1999;48:958-96410628576
Google Scholar 51.Tamblyn RM, Abrahamowicz M, Berkson L.
et al. First-visit bias in the measurement of clinical competence with standardized
patients.
Acad Med. 1992;67:(10 suppl)
S22-S241388544
Google ScholarCrossref