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Keywords:

  • direct-to-consumer advertising;
  • health services research;
  • breast cancer;
  • outcomes;
  • population sciences

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SOURCES
  8. REFERENCES

BACKGROUND:

Little is known about the impact of direct-to-consumer advertising (DTCA) on appropriate versus inappropriate prescribing. Aromatase inhibitor (AI) therapy for breast cancer provides an ideal paradigm for studying this issue, because AIs have been the focus of substantial DTCA, and because they should only be used in postmenopausal women, age can serve as a simple surrogate marker of appropriateness.

METHODS:

Data regarding national DTCA spending for the AIs were obtained from TNS Multimedia; hormonal therapy prescription data were obtained from IMS Health. Time series analyses were performed to characterize the association between monthly changes in DTCA spending for the AIs and monthly changes in the proportion of all new hormonal therapy prescriptions represented by the AIs from October 2005 to September 2007. Analyses were stratified by age, considering prescriptions for women ≤ 40 (likely premenopausal) to be inappropriate and those for women > 60 (likely postmenopausal) to be appropriate.

RESULTS:

Monthly dollars spent on AI-associated DTCA varied considerably ($118,600 to $22,019,660). Time series analysis revealed that for every million dollars spent on DTCA for the AIs, there was an associated increase 3 months later in the new AI prescription proportion of 0.15% for all ages (P < .0001) and 0.18% for those > 60 years (P < .0001), but no significant change for those ≤ 40 at any time from 0 to 6 months.

CONCLUSIONS:

DTCA for the AIs was associated with increases in appropriate prescriptions with no significant effect on inappropriate prescriptions, suggesting that DTCA may not foster inappropriate medication use for certain drug classes. Cancer 2013. © 2012 American Cancer Society.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SOURCES
  8. REFERENCES

It seems evident that direct-to-consumer advertising (DTCA), which is the marketing of medications or medical services directly to consumers through promotion in the lay media,1 must increase prescriptions, otherwise the marketing departments of pharmaceutical companies would not engage in this expensive practice. Indeed, the literature largely demonstrates that DTCA increases demand.2-7 For example, a Kaiser Family Foundation study found that for every 10% increase in DTCA, there is a 1% increase in prescription drug spending.8 On the other hand, it is unclear whether this increase is ultimately beneficial or harmful to patients. If DTCA causes previously undiagnosed, untreated, or improperly treated patients to receive appropriate care, it is a valuable mode of health communication; if DTCA results in superfluous or harmful new prescriptions, it is not.

Although determining the relative effect of DTCA on appropriate and inappropriate prescribing is the most critical research question relating to DTCA, it remains largely unanswered. This is because it is exceedingly difficult to define appropriate and inappropriate use in an easily measurable way for most medications advertised in this manner. For example, whereas one study found that higher levels of exposure to DTCA for HMG-CoA reductase inhibitors was significantly associated with improvements in the likelihood of attaining cholesterol management goals for some patients (appropriate use), the investigators were unable to define an inappropriate use.2 Indeed, such categorization would generally require detailed data on the recipients of prescriptions, which would be very expensive to generate for a large sample.

In contrast, we identified a paradigm in cancer medicine that permits characterization of DTCA-associated appropriate and inappropriate uses of medication at the population level. The aromatase inhibitors (AIs) are a class of oral breast cancer therapies that reduce the risk of cancer recurrence in postmenopausual women (appropriate use) but not in premenopausal women (inappropriate use), because they do not effectively suppress ovarian estrogen production.9-13 Because age is a strong proxy for menopausal status,14-17 simply by knowing the age of a woman with breast cancer who has received a prescription for an AI, we are able to classify with a reasonable level of certainty whether her treatment was appropriate. It is important to note that unlike prior studies that have assessed the potential impact of DTCA on whether to give any treatment, our paradigm focuses on how DTCA may affect the choice of one treatment over another for patients who are definitely going to be treated. Fortuitously, 2 of the AIs (letrozole and anastrozole) have been marketed aggressively with DTCA. In addition, the only alternative therapy to AIs (tamoxifen) is appropriate for premenopausal women, was the standard for both pre- and postmenopausal women before the AIs, and had not been advertised with DTCA for many years before the AIs became available.

We aimed to determine if increases in dollars spent on DTCA for the AIs are associated with increases in new prescriptions for the AIs. We were particularly interested in comparing appropriate and inappropriate use. We hypothesized that there would be a significant increase in AI use associated with DTCA in all age groups, both for older women for whom this treatment is appropriate, as well as for younger, presumably premenopausal women for whom such treatment is inappropriate. Importantly, our second hypothesis assumes that premenopausal patients who see AI DTCA will sometimes ask for the medications, even if the advertisments specifiy that the medications are not appropriate for them, and also that their providers will sometimes mistakenly prescribe them.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SOURCES
  8. REFERENCES

AI Advertising Content Analysis

To put our main analysis in context, we performed a brief content analysis focusing on magazine advertisements for the AIs that were in circulation during the study period. Our methods have been described previously18; briefly, we reviewed all unique DTCA for the AIs appearing in 3 patient-focused cancer magazines and a sample of selected popular magazines in 2005 and 2006. We determined the Flesch reading ease score (FRES) for the text in each advertisement (a score > or = 65 is readable for the average person). We also assessed the size of text devoted to benefits versus risks/adverse effects, as well as the content of advertising appeals. Finally, we assessed whether the advertisements stated that the AIs were for adjuvant breast cancer treatment and whether they are only for use in postmenopausal women.

Data Sources for the Main Analysis

We obtained data regarding dollars spent for DTCA for the AIs from TNS Media Intelligence Incorporated's “StrADegy”™ database. The database records consumer-directed advertising (television, newspaper, radio, magazines, and Internet) appearing nationally and in the largest 101 United States media markets. This data source has been used extensively in prior research on DTCA.2,3,5,19-21 The marketing data contained information on the medium and venue of the advertising for each product, as well as dollars spent advertising the product at both the national and local level based on DTCA volume for that month. We used total dollars in advertising spending in all national venues by month summed across the 3 brands of AIs (anastrozole, letrozole, and exemestane) from July 2005 to June 2007 as our independent variable. We chose to analyze advertising for all of the AIs together rather than the individual brands separately, because prior data has shown that DTCA for one medication in a class may be associated with increases in prescriptions for all members.7,19,22 We also queried the StrADegy database for DTCA for tamoxifen, finding none during the study period.

Data regarding monthly prescriptions for the AIs and tamoxifen were obtained from IMS Health Incorporated, through their NPA Market Dynamics™, a subset of their National Prescription Audit™ database. We chose the time period October 2005 to September 2007, because it was many months after guidelines for adjuvant AI use had been published (January 2005)13 and close to the dates of US Food and Drug Administration approval of adjuvant use for all 3 agents (anastrozole: September 2005; exemestane: October 2005; letrozole: December 2005). The NPA database, which has been used less frequently in DTCA research23 but more extensively in other medication-related health services research,24-26 measures prescriptions for pharmaceutical products dispensed from retail pharmacies in the United States (27,000 pharmacies at the time of the analysis). During the period of our analysis, it included more than 150 million unique de-identified patients, each of whose information was encrypted and tracked longitudinally to account for patient travel or address change. The database captured approximately 50% of all prescriptions nationally and then used a proprietary method to estimate nationwide volume. It was updated weekly, maintained a 36-month rolling history, and captured more than 1 million providers. Notably, at the time of this analysis, the database did not include prescriptions from mail service pharmacies or long-term care facilities.

For our analysis, we defined “new prescription” as either a prescription that was completely new to a patient or a new prescription for a patient who had received that medication before, because we reasoned that in both cases, a clinician had to make a decision to write or rewrite a prescription that could have been influenced by DTCA. We excluded routine refills, because these did not require clinician decision-making. In addition to type of prescription, the database included information on the age and sex of the patient, and state where the prescription was dispensed (we collapsed these into Northeast, Midwest, South, and West regions). It also provided the specialty of the provider who wrote the prescription, determined through an algorithm that includes data from the American Medical Association, the American Osteopathic Association, and the National Provider Index (updated monthly). Notably, all of our analyses were restricted to women, and we focused only on prescriptions written by hematologists or oncologists in order to exclude the uncommon use of the AIs for benign conditions (eg, infertility).

Finally, to create a measure of physician-directed marketing over the study time period, we reviewed all the issues of The Journal of Clinical Oncology, a high-impact journal with advertising that is read widely by oncology clinicians, from October 2005 to July 2007. We counted the size, position, and number of advertisements for the 3 AIs each month and multiplied them by the journal's commercial advertising rates to generate an aggregate monthly amount of dollars spent. Although this amount likely represents only a fraction of what was actually spent on physician-directed marketing for the AIs each month, we reasoned that month-to-month changes would likely reflect changes in overall spending on physician-directed marketing for the AIs.

Statistical Methods

We performed a time series analysis,27 comparing monthly dollars spent on DTCA for the AIs with monthly changes in the “new AI prescription proportion,” defined as:

  • equation image

The data from IMS Health provided aggregate categories of prescriptions over time. We chose the above proportion as our primary outcome variable because it allowed us to assess the association of DTCA with changes in AI prescriptions while taking into account any temporal changes in overall use of hormonal therapy for breast cancer (represented by the denominator). It is important to note, however, that the denominator of our measure does not capture the population of all patients who may be candidates for a prescription for hormonal breast cancer therapy, but rather all prescriptions received by patients who did in fact receive a prescription for one of the types of hormonal therapy.

We fit time series models to test whether a statistically significant change occurred in new AI prescription proportion associated with changes in monthly DTCA spending for AIs. We made no assumptions about the likely lag between the exposure and the outcome; instead, we used the time series analysis to determine the interval at which the maximum effect potentially occurred. We modeled the relationship between the cost of DTCA and the proportion of new AI prescriptions over the 2-year study period in 3 groups: 1) women of all ages; 2) women aged 40 years or less (a conservative age estimate of premenopausal status); and 3) women over 60 (a conservative age estimate of postmenopausal status). We did not separately analyze the group of 41- to 60-year-olds, because the appropriateness of AI use in this group is less certain.

The first step was to ensure that the dollars spent on DTCA for the AIs and proportion of new AI prescriptions were stationary over time using the Box-Jenkins approach.28 Autocorrelation plots of the monthly changes in new AI prescription proportion were examined to determine appropriate autoregressive moving average models in the 3 age strata. Autocorrelation and partial autocorrelation plots identified a first-order autoregressive, or AR(1), dependence structure in the dollars spent monthly on DTCA for the AIs. In the second step, cross-correlation functions (CCF) were used to identify how many months a potential change in proportion of new AI prescriptions may have occurred after DTCA, using standard selection criteria.29,30 A final transfer function model was fit to estimate the effect of DTCA on the proportion of new AI prescriptions, incorporating the preliminary results of the autoregressive moving average structures and lag values.31 Model diagnostics checking autocorrelation and cross correlation of residuals were used to assess the fit of all 3 models. Finally, an independent series of linear regression models including AI prescription proportion at different time lags after DTCA was fit to confirm the lag values suggested by the time series analysis.

To assess the possibility that physician-directed marketing for AIs could be influencing the new AI prescription proportion, an analogous time series approach was used to model the relationship between that proportion and monthly cost of AI advertising in the Journal of Clinical Oncology over the 2-year period. We then fit stratified linear regression models for the different age groups including both dollars spent on DTCA as well as dollars spent on Journal of Clinical Oncology advertising to determine if the effects on the new AI prescription proportion of one could be accounted for by the other. All analyses were carried out using SAS, version 9.1 (SAS Institute, Cary, NC). Our analysis was submitted to the Dana-Farber Office for Human Research Studies, which determined it was exempt from review.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SOURCES
  8. REFERENCES

We found 4 unique print advertisements for our content analysis, 2 for anastrozole, and 2 for letrozole (there were none for exemestane). Both products had appeared in cancer-related patient magazines such as Cure as well as popular magazines such as Good Housekeeping. On average, slightly more text was devoted to benefits versus risk/adverse effects (mean ratio = 0.9), and all text was difficult to read (overall mean FRES below 40). Appeals using clinical trial data were made in all 4, and all specifically stated that patients must be postmenopausal to take an AI. Finally, only 3 mentioned that the product was for use in the “adjuvant” setting, but the remaining advertisement did state that it is used to prevent cancer recurrence.

Table 1 summarizes the characteristics of the prescription data used in our main analysis: new prescriptions for the AIs plus tamoxifen, new prescriptions for the AIs alone, and the new AI prescription proportion for the entire study period (October 2005 to September 2007). The total national market for new hormonal therapy prescriptions (new AIs plus new tamoxifen) written by hematologists and oncologists remained stable (approximately 180,000 per quarter). In contrast, the proportion of that market made up by new prescriptions for the AIs increased from approximately 61% to 68%.

Table 1. New Aromatase Inhibitor and Tamoxifen Prescriptions Written by Hematologists and Oncologists by Age, Region, and Time Period, October 2005 to September 2007
 New AI + New Tam Prescriptions (Units of 10,000)%New AI Prescriptions (Units of 10,000)%New AI/ (New AI + New Tam)
  • Abbreviations: AI, aromatase inhibitor; Tam, tamoxifen.

  • a

    P < .0001 for differences in new AI/(new AI + new Tam) for the 3 age groups.

  • b

    P < .0001 for differences in new AI/(new AI + new Tam) for the 4 regions.

  • c

    P < .0001 for differences in new AI/(new AI + new Tam) for the 8 time periods.

All145.75100%96.49100%0.662
Agea     
 <404.883.35%1.721.78%0.352
 ≥40 and ≤6063.4843.55%36.2437.56%0.571
 >6077.3953.10%58.5460.67%0.756
      
Regionb     
 Northeast31.4521.58%21.1921.96%0.674
 Midwest33.2022.78%21.2422.01%0.640
 South55.2437.90%37.1638.51%0.673
 West25.8617.74%16.9017.51%0.654
      
Time Periodc     
 Oct-Dec, 200518.1112.43%11.1111.51%0.613
 Jan-Mar, 200618.6012.76%12.1912.63%0.655
 Apr-Jun, 200618.8312.92%12.2812.73%0.652
 Jul-Sep, 200618.0012.35%11.9612.40%0.664
 Oct-Dec, 200617.9912.34%12.0412.48%0.669
 Jan-Mar, 200718.2012.49%12.3912.84%0.681
 Apr-Jun, 200718.8312.92%12.2812.73%0.652
 Jul-Sep, 200717.7312.16%12.0212.46%0.678

The most common venues of DTCA for AIs were national magazines and magazines included in Sunday papers. For example, in the month with the highest spending on DTCA, 79.0% was spent on national magazines and 20.8% on Sunday magazines. There was also considerable variation in monthly DTCA expenditures for the AIs over time, with a high of $22,019,660 in October of 2005 and a low of $118,600 in January of 2007 (Fig. 1).

thumbnail image

Figure 1. Monthly variation in aggregate spending on direct-to-consumer advertising (DTCA) is shown for the aromatase inhibitors (anastrozole, letrozole, and exemestane) in the United States between July 2005 to June 2007.

Download figure to PowerPoint

Time series analysis demonstrated that for every million dollars spent on DTCA for the AIs during the study time period, there was an associated increase in the overall new AI prescription proportion of 0.15% after 3 months, a time period determined by the analysis itself. In analyses stratified by age, there was an associated increase of 0.18% for those over age 60, also after 3 months (representing approximately 118 new AI prescriptions per million dollars spent), but no significant change associated with DTCA spending for AIs for those aged 40 years or less at any time from 0 to 6 months (Table 2). Importantly, power analysis demonstrated that, with an alpha of .05, there was 91% power to detect a similar difference of 0.18% in the monthly new AI proportion in this younger age group, had it existed.

Table 2. Time Series Analysis Relating Millions of Monthly Dollars Spent on Direct to Consumer Advertising for the Aromatase Inhibitors (AIs) to Monthly Change in New AI Prescription Proportion
 Best Lag for Effect (mo)Estimate of Change in AI Prescription Proportion (P Value)
  • a

    Indicates a significant effect.

All ages30.0015 (<.0001)a
Age 40 y or lessnone, reported for 00.0006 (.71)
Age 41 to 60 y20.0013 (.014)a
Age above 60 y30.0018 (<.0001)a

In linear regression models taking into account possible lags from 0 to 4 months, the only time periods that were significant were a positive association at 3 months for all ages (0.14% increase in new AI prescription proportion; P = .0015) and at 3 months for age greater than 60 years (0.17% increase in new AI prescription proportion; P = .0015). As in the time series analysis, DTCA for AIs was not associated with new AI prescription proportion for those aged 40 years or less for any time period. Notably, we found similar results when limiting the analysis to the period after January 2006 (excluding the large spike in DTCA seen in October of 2005).

The second time series analysis examining the association between calculated Journal of Clinical Oncology advertising dollars and new AI prescription proportion revealed a significant decrease in new AI prescription proportion at a time lag of 0 months for those aged 40 years or less (2.1%, P = .039), with no significant effect among those over 60 or for all ages combined. Finally, the results of linear regression models that included both DTCA for the AIs and Journal of Clinical Oncology advertising for the AIs are shown in Table 3. In this combined model, we found no significant effect of either type of advertising on the new AI prescription proportion for those aged 40 years or less (although there was a trend toward a decrease associated with Journal of Clinical Oncology advertising, P = .07). In contrast, for those over 60 years of age, DTCA for AIs had a significant positive effect (increase of new AI prescription proportion of 0.12% for each million dollars; P < .0001) but Journal of Clinical Oncology advertising of AIs had no significant effect (P = .45).

Table 3. Linear Regression Models of the Association of Spending on DTCA for the AIs and on AI Advertising in the Journal of Clinical Oncology With New AI Prescription Proportion
AgeEstimateP
  • Abbreviations: AI, aromatase inhibitor; DTCA, direct-to-consumer advertising.

  • a

    Indicates a significant effect.

40 y or less  
 Intercept0.0175.
 DTCA0.000902.69
 Journal of Clinical Oncology  Advertising–0.197.070
41 to 60 y  
 Intercept–0.00813 
 DTCA0.00130a.0040
 Journal of Clinical Oncology  Advertising0.0461.238
Greater than 60 y  
 Intercept0.00132 
 DTCA0.00121a<.0001
 Journal of Clinical Oncology  Advertising–0.0188.45

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SOURCES
  8. REFERENCES

We expected to find that DTCA for the AIs increased both appropriate and inappropriate prescriptions. Instead, we found that increases in DTCA for the AIs prompted prescriptions only in the patient population for whom they were appropriate. Two mechanisms might account for this finding. It is possible that the advertisements, which our content analysis revealed include statements about the need to be postmenopausal to receive AI treatment, were perfectly successful targeted marketing, defined as “marketing products/services for a specific segment rather than the whole market.”32 More likely, the required participation of physicians in the pathway between a patient seeing an advertisement and actually receiving a prescription functioned to selectively block the effect of advertising-induced interest in these agents among patients for whom they would be medically inappropriate.

Much of our analysis hinges on our definitions of appropriate and inappropriate. Indeed, it may be more reasonable to consider AIs for those over 60 years of age as “not inappropriate” rather than “appropriate.” This is because despite the recommendation, just before our study period, of the 2005 American Society of Clinical Oncology guidelines that “optimal adjuvant hormonal therapy for a postmenopausal woman with receptor-positive breast cancer includes an aromatase inhibitor,”13 some clinicians consider tamoxifen alone to be a reasonable alternative in that age group as well.13,33 In contrast, although there are other potentially inappropriate uses (eg, prescribing AIs to women whose cancer is not hormone receptor–positive), our “inappropriate” definition of AIs for those aged 40 years or less is robust, because prescribing AIs to women who are likely still premenopausal may not only fail to prevent breast cancer recurrence but may actually increase ovarian production of cancer-driving hormones such as estradiol.34

Most prior attempts at characterizing inappropriate use driven by DTCA have not focused on the possibility of a high-morbidity outcome such as cancer recurrence, but on less devastating effects such as obtaining unnecessary screening tests35 or prescribing an expensive medication when there is a cheaper alternative.36 One exception is a landmark randomized trial that assessed the effects of DTCA on clinicians' approach to severe depression, manipulating the reported symptoms and DTCA-related medication requests of patient-actors. The study found that DTCA promoted appropriate use of antidepressants when they were clinically necessary, but also promoted inappropriate use of antidepressants when they were not.4 In contrast, we found that DTCA promoted appropriate use of the AIs but did not have an effect on inappropriate use. The difference in our findings may be due to our methods (population-based analysis versus randomized trial) or due to the type of medication we studied, because DTCA effects may be governed by separate principles given the nature of oncologic illness.37,38 Indeed, it is important to emphasize that our results may not be generalizable to all DTCA, because the AIs are specialized medications most often prescribed by providers with high levels of expertise in their use.

Although we found no association of DTCA with inappropriate prescribing, there may be other negative effects of cancer-related DTCA that were not captured in our study. For example, some of the cost to fund DTCA may be passed on to patients who receive drugs marketed in this manner, and DTCA may also have a negative impact on the physician-patient relationship, such as reducing patients' confidence in their clinician.39,40 For the AIs specifically, DTCA may also negatively impact the time management of clinicians who have to explain to premenopausal patients that they are not candidates for AI therapy. On the other hand, DTCA may also have positive effects in addition to the increased demand for appropriate medication use that we found, such as increases in high-priority diagnoses,41 fostering medication compliance,20 and encouraging patients to communicate with their physicians.42 Such ancillary risks and benefits of DTCA are not well-characterized and merit further study.

Although we could have found an effect of decreased inappropriate prescribing due to increased patient awareness of AI prescribing indications, we did not. Clearly, our content analysis demonstrated that DTCA for the AIs did an adequate job of presenting indications for treatment. It is interesting that neither DTCA nor physician-targeted advertising decreased the proportion of inappropriate prescriptions, even though the level of patient and physician knowledge about appropriate prescribing should have increased. This may be due to lack of patient awareness of AI DTCA; however, our prior survey of breast cancer patients as to their DTCA awareness suggests that this is not the case (86.2% reported being aware).39

Our analysis has limitations. First, our outcome measure, namely, new AI prescription proportion, does not truly characterize prescription use, because all patients represented by its denominator necessarily received prescriptions. Thus, we are not able to shed any light on the potential influence of DTCA on clinical decisions regarding whether to give an AI prescription or in which context (eg, with or without chemotherapy, or for chemoprevention), just on which type of hormonal therapy prescription was given to those patients who received one. Second, our prescription data excluded mail service pharmacies and long-term care facilities, and contained no information on breast cancer status or outcomes such as recurrence of disease, survival, or the quality of physician-patient discussions. Indeed, if a large number of patients who were prescribed these agents by hematologists and oncologists did not have breast cancer, our conclusions about the effects of AI patient-directed advertising (which was for adjuvant breast cancer use) would be different. Third, although we attempted to account for other types of AI marketing with our inclusion of advertisements in the Journal of Clinical Oncology, this is admittedly only one source of physician-directed journal advertising, and it is also still possible that DTCA may be a marker for other types of marketing such as physician detailing.

Finally, the US Food and Drug Administration approval of all 3 AIs for adjuvant use close to the start of our study period was associated with a spike in DTCA, and this approval, rather than the spike in DTCA, may have been partially responsible for some of the pattern of increased AI use observed. On the other hand, consensus guidelines for the use of the AIs in adjuvant breast cancer were in place well before they were specifically approved for such use, and there is evidence that, at least at academic centers, patients were receiving them as initial adjuvant therapy several years before our study period.43 More importantly, our results did not change appreciably with the spike removed.

In conclusion, spending on DTCA for the AIs was associated with an increase in appropriate prescriptions, with no significant effect on inappropriate prescriptions. Our data thus suggest that this controversial form of medical communication may not be harmful for certain classes of drugs such as cancer medicines. Indeed, in some situations, such as when an appropriate use is the only option, DTCA may actually be beneficial. It is interesting to note, however, that in cases where there is only one appropriate treatment for a condition, DTCA is unlikely to be widespread. The converse is certainly true. For example, a recent study found that the medications most highly advertised directly to patients were for insomnia, gastrointestinal reflux disease, and hypercholesterolemia.22 For all of these conditions, there are many appropriate treatment options, so even if no inappropriate prescribing resulted from their associated DTCA, the ultimate benefit of such marketing for patients and society remains unclear.

FUNDING SOURCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SOURCES
  8. REFERENCES

This work was partially funded by an American Society for Clinical Oncology Conquer Cancer Foundation Career Development Award.

CONFLICT OF INTEREST DISCLOSURES

The authors made no disclosure.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. FUNDING SOURCES
  8. REFERENCES
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