Medical advertising on demand: A content analysis of YouTube direct-to-consumer pharmaceutical advertisements

Abstract

The medical research community has shown substantial concerns about the quality of healthcare information available on the video-on-demand website of YouTube. In order to look at the role that advertising is having on this knowledge dissemination channel, the current paper presents the results of a content analysis of 100 pharmaceutical advertisements found on YouTube. From this analysis, concerns arose over mismatches in terms of the content and style of the advertisements. The possibility that YouTube pharmaceutical advertisements actually drive consumers away from proper medical care is explored. This paper concludes that, rather than having broadcast advertisements cross-over to YouTube, the medical community may be better served by developing longer, more content base advertisements for the medium of video-on-demand.

Keywords

Medical advertising pharmaceutical advertising direct-to-consumer content analysis YouTube

Part of any effective healthcare delivery system is the dissemination of medical information to healthcare providers and patients. In today’s digital age, the medical community is very cognizant of the role that the internet plays in this information diffusion process. For better or worse, the internet has become a major source that consumers turn to for medical information and that healthcare providers turn to for training. One source that medical researchers have been particularly interested in is the information that comes through the YouTube video-on-demand website. The educational value of YouTube has been lauded by many^1–3 but the medical community has shown great concerns about the accuracy of the information that is being disseminated through YouTube.^4,5

Researchers from almost every major field within medicine have studied the information that is being delivered through YouTube. Studies on the use of YouTube to disseminate medical information can be found in such diverse medical fields as cardiology,^6–8 dentistry,⁹ dermatology,¹⁰ eating disorders,¹¹ epidemiology,¹² gastroenterology,^13,14 gynecology,¹⁵ immunology,¹⁶ mental health,¹⁷ pain management,¹⁸ and pulmonology.^6,19,20

Despite the strong interest in YouTube from the medical community, it has gone mostly unnoticed by advertising researchers who study medical issues. YouTube advertising has clearly not gone unnoticed by pharmaceutical companies. A study by Liang and Mackey found that eight of the 10 highest grossing pharmaceutical corporations had direct-to-consumer (DTC) video advertisements on YouTube in 2009.²¹ This is likely due to the changing nature of the way consumers seek out medical information. Huang found that consumers welcome YouTube advertising by their healthcare providers.²² Through video-on-demand, they can get the information that they seek out in contrast to traditional advertising where they feel forced to view information that is thrust upon them. When consumers welcome the information provided by marketers, it is likely to be more effective in shaping their behavior.

There are three unique aspects of YouTube DTC advertising which should encourage advertising researchers to study its content. First, YouTube advertising is unique from a regulatory perspective. YouTube DTC advertising provides a unique regulatory challenge. DTC advertising of prescription pharmaceuticals is legal only in the United States and New Zealand. Obviously the majority of countries around the world do not see DTC advertising of prescription drugs as something they want their citizens to see. However, as pointed out by Liang and Mackey,²¹ when a DTC advertisement for a drug is legally produced and downloaded to YouTube in the United States or New Zealand, it does not stay in these countries. Those living in countries which prohibit DTC advertising of prescription drugs are just a few clicks away from circumventing their home country’s regulatory restrictions.

Second, since YouTube is a video-on-demand site, its advertising is unique from a consumer response perspective. Typical medical advertising is brought to consumers as they are doing something else (reading a magazine, watching a television program, etc.). However, YouTube is more of an advertising on demand media. Individuals will search for information on specific medical conditions and they will then select the advertisements they want to see. How this is likely to affect the way an advertisement is perceived is something that those who study responses to advertisements should consider.

Third, YouTube advertising is unique from a research methods perspective. Content analysis has a long history in advertising research and even in DTC medical advertising.^23–33 When content analysis of advertising is done, the selection of advertisements is always of concern because, by its very nature, this type of research requires somewhat of a convenience sample of advertisements to study. Typically this is done by selecting advertisements which are taken in a limited geographic area across a limited time frame. Such a sampling technique provides useful information and its contribution to advertising thought should not be overlooked. However, YouTube provides an interesting opportunity to look at the content of advertisements which are from a wider time frame and which are viewed across a much wider geographic area. To be certain, research on YouTube advertisements is no less of a convenience sample than what is done in more traditional content analysis in advertising. But it certainly is different in terms of time and geographic dispersion. Since content analysis of advertising requires convenience samples, it is beneficial to try to expand the nature of those convenience samples whenever possible.

Because the medical community has shown such a great interest in the information that is being disseminated through YouTube and because of the uniqueness of YouTube to advertising regulation, practice, and research, the need exists to better understand DTC pharmaceutical advertisements which appear on YouTube. One direction that such an inquiry needs to take is to assess the content that exists on DTC YouTube advertisements. The purpose of the research presented in this paper is to address this need by conducting a content analysis of 100 YouTube advertisements.

Methodology

Advertisement selection

The two primary approaches that have been used for identifying YouTube videos for content analysis are search engine identification and snowballing techniques.³⁴ Whereas search engine identification techniques are effective for identifying videos on a narrowly defined topic, snowballing techniques are more effective when diversity in content is desired.³⁴ Search engine identification takes a linear approach by selecting videos based on a specific set of terms. In contrast, snowballing involves using seed videos to start a chain which allows the identification of other videos. As suggested by Sampson et al.,³⁴ for research such as ours where we are targeting a wider range of video content, we used a snowballing technique to identify videos for this study. A search engine was used to identify a seed video and once that seed video was selected, it was used to identify other videos which in turn were used to find subsequent videos. At several times throughout this process, we returned to the search engine to obtain other seed videos utilizing different search terms. Thus, the same search terms were not used over and over to identify the sample videos. This process continued until the desired number of videos was obtained.

One-hundred advertisements were selected in January 2014 for inclusion in our sample and were for the following brands (number in parenthesis indicates multiple advertisement for same brand): Abilify (2), Aciphex, Advair (2), Ambien CR, Amitiza, Androgel, Aricept, Avodart, Beyaz, Boniva (2), Byetta, Caduet, Celebrex (3), Chantix (3), Cialis (2), Crestor (2), Cymbalta (2), Detrol LA, Ditropan, Dulera, Effexor, Enbrel (3), Exelon, Gardasil, Humira (4), Imitrex, Januvia, Latisse, Lipitor (2), Lovaza, Lunesta (2), Lyrica, Lysteda, Mirapex, Mirena (2), Nasonex, Nexium (3), Niaspan (2), Omnaris, Onglyza, Oriencia (2), Paxil (2), Pradaxa, Pristiq, Prolia, Provenge, Requip (2), Restasis (3), Rozarem, Simponi, Singulair (5), Spiriva (7), Stelara, Symbicort (3), Valtrex, Vesicare, Vioxx, Vytorin, Yaz (2), Zelnorm, and Zoloft. Brands from the following companies were represented in our sample: Abbott Laboratories, AbbVie Inc., Allergan Inc., Amgen Inc., Astellas Pharmaceutical, AstraZeneca, Bayer, Boehringer Ingelheim Pharmaceutical, Bristol-Myers Squibb, Dendreon, EISAI Corp, Eli Lilly & Co, Ferring Pharmaceutical, Genetech Inc., GlaxoSmithKline, Immunex, Janssen (Biotech) Pharmaceutical, Janssen Pharmaceutical, Merck, Novartis, Pfizer, Sunovion, and Takeda Pharmaceutical. The dates these advertisements were either uploaded to or initially ran on YouTube were indicated on each video’s launch page. Table 1 presents the years of these videos. As can be seen, these videos span over a decade though 60% of them were more recent than 2012.
Table 1.
Date designation on sample YouTube advertisements

Year Number of advertisements Percent of advertisements

2002 2 2

2005 4 4

2006 5 5

2007 6 6

2008 7 7

2009 11 11

2010 9 9

2011 21 21

2012 10 10

2013 56 56

2014 4 4

Coding the ads

A coding sheet was created to record the information on how these DTC YouTube videos were communicating to the consumers. This approach has been used in previous content analyses of DTC drug print and broadcast advertising.^35–38 The coding topics for the study included brand name, URL of the YouTube video, manufacturer, medical condition, ad format, ad appeals, and message information and characteristics. Additional information such as the type of people present in the “focal” part of the ad and the number of people present, the presence of a healthcare provider, the relationship depicted in the “focal” part of the ad, the age of the “focal” person in the ad, the means of acquiring the product, social benefits of the product, the benefits associated with treating an existing condition and preventing a potential health problem, and potential side effects were coded using the approach used by Bell et al.³⁹ We used categories similar to those used by Frosch et al.⁴⁰ to categorize the appeals of the product. The six appeals used were as follows:
Rational appeals that focused on information about product use, features, or comparisons with similar products;

Positive emotional appeals that evoked favorable affective responses;

Negative emotional appeals that evoked unfavorable affective responses;

Humor appeals that utilized puns, jokes, or satire;

Fear appeals that evoked fearful responses;

Sex appeals that depicted characters in intimate encounters, scantily clad clothing, or provocative situations.

Procedure

Two graduate students at a state university in southeastern United States were trained to code the YouTube videos. They were provided with full transcripts of each advertisement as well as the YouTube videos themselves. Thus, the coders could read a transcript of the video while watching the same video. The two coders were instructed not to interact with each other regarding this project throughout the duration of the coding process. The coefficient of reliability, as indicated by k values, ranged from 0.93 to 0.97 for our coding categories. This intercoder reliability was established using the coefficient of reliability.^41,42 Any coding disagreements between the coders were resolved through consensus.

Results

Advertising length, disclaimers, and side effects

Table 2 presents information about advertising length, disclaimer length, and severity of side effects discussed in the advertisements. The average ad length was 62.06 s (SD = 18.52 s, range = 30–150 s). The ads used an average of 178.22 words (SD = 54 words, range = 39–439 words) of which an average of 77.95 words (SD = 41 words, range = 0–249 words) were disclaimers/warnings about the product. The average time of disclaimers/warnings about the product was 24.72 s (SD = 13.42, range = 0–71 s). Thus, 44% of the total words in the ad were disclaimers/warnings about the product and in terms of ad time, 40% of the total ad time was disclaimers/warnings about the product. The number of potential side effects mentioned in the ads was 5.50 (SD = 3.58, range = 0–16). The most severe potential side effects mentioned in individual advertisements were life threatening (35% of the advertisements), nonlife threatening but severe (24% of the advertisements), and nonlife threatening but mild to moderate (41% of the advertisements). Fifteen percent of the advertisements explicitly mentioned death as a possible side effect. In 90% of the advertisements, viewers were instructed to consult with a doctor or medical care provider about obtaining the product. Six percent of the advertisements instructed consumers to a store to obtain the product and 4% of the advertisements neither mentioned a store or a medical consultation.
Table 2.
Advertising length, disclaimer information, and severity of side effects

Time (s) Words

Average advertisement length 62.06 178.22

Average disclaimer length 24.72 77.95

Disclaimer as percent to advertisement 40% 44%

Average number of side effects 5.5

Severity of most potentially severe side effect

Life threatening 35%

Nonlife threatening but severe 24%

Nonlife threatening, mild to moderate 41%

Percent explicitly mention death as a possible side effect 15%

Product benefits and medical consultation

Table 3 presents the benefits depicted for using the advertised product. Social benefits were presented in 41% of the advertisements with 27% being of a general social nature, 7% being cosmetic, and 7% being sexual. Treating existing conditions (91%) by far outweighed preventative benefits (9%). Only 26% of the advertisements mentioned conditions that were life threatening. Another 29% mentioned conditions that were severe but not life threatening and the other 45% mentioned benefits that were moderate to severe. It is interesting to note that 37% mentioned pain reduction as a benefit of the product.
Table 3.
Benefits depicted for using advertised product

Benefit Percent

Social benefits

General social benefits 27

Cosmetic 7

Sexual 7

Total 41

Treating existing condition

Life threatening 23

Severe but not life threatening 27

Mild to moderate 41

Total 91

Preventative

Life threatening 3

Severe but not life threatening 2

Mild to moderate 4

Total 9

Mentioned pain reduction 37

Formats and appeals

Table 4 presents the formats and appeals of the YouTube advertisements. Rational appeals were used by 53% of the advertisements, positive emotional appeals occurred in 51% of the advertisements, and negative emotional appeals were present in 22% of the advertisements. The other appeals used in the ads consisted of fear (8%), relationship (22%), sex (4%), and humor (8%). Note that a given advertisement could use multiple appeals and thus these numbers do not add up to zero. In terms of format, 35% of the ads had no story but simply presented a straight description of product benefits. For the remaining ads, 17% of the ads depicted a slice of life, 34% were testimonials, 7% of the ads showed characters before and after taking the product, 3% depicted a medical crisis, and 4% did not fit into any of these categories.
Table 4.
Advertising formats and appeals

Appeals Percentages

Rational 53

Emotional positive 51

Emotional negative 22

Fear 8

Relationship 22

Humor 8

Sex 4

Formats Percentages

Testimonial 34

Slice of life 17

Medical crisis 3

Before/after story 7

Other 4

No story-straight description of product, benefits 35

Note: Advertisements could have multiple appeals but only one format.

Social dynamics of sample advertisements

Table 5 presents the social dynamics of the advertisements. The most common social dynamic of the YouTube medical advertisements was adult couples interacting (27%) followed by relationships between friends (17%). Only 12% of the advertisements had a healthcare provider interacting with patients. In terms of people in the focal part of the advertisements, females were most often present (71% of the advertisements) and they were also part of the largest groups of people presented in the ads with an average of 3.35 people present in the focal parts of the advertisements they were in. Celebrities were not used much in the advertisements (4%) and, when they were present, they were always alone. When healthcare providers were depicted, it was usually either alone or with one other person. All but one healthcare provider who was depicted appeared to be older than 30 and younger than 60. In contrast, 36% of the nonhealthcare providers depicted in the advertisements appeared to be under 30 years of age and 16% appeared to be 60 years of age or older. Those who are young adults (ages 18–30) had substantially more people present when they were depicted in the advertisements than any other group.
Table 5.
Social dynamics of sample advertisements

Relationships present in the FOCAL of the advertisement Percent of ads

Healthcare provider interacting with patient 12

Adult couple 27

Parent/child or parent/youth relationship 14

Parent/adult child relationship 5

Other relationship among relatives 8

Relationship between friends 17

Relationship but nature is unclear 11

People present in the FOCAL part of the advertisement Percent of ads Number in group when present

Adult male 58 2.56

Adult female 71 3.35

Children 20 2.96

Animation 13 5.00

Celebrity 4 1.00

Healthcare provider 8 2.50

Apparent age of healthcare provider and size of group when present Percent of ads Number in group when present

Young adult (under 30) 0 n/a

Early middle age (approximately 30–45) 7 1.00

Late middle age (approximately 45–60) 8 2.00

Early elderly (approximately 60–75) 1 1.00

Late elderly (approximately 60–75) 0 n/a

Apparent age of nonhealthcare provider and size of group when present Percent of ads Number in group when present

Child/youth (under 18) 21 2.64

Young adult (approximately 18–30) 15 4.25

Early middle age (approximately 30–45) 43 2.96

Late middle age (approximately 45–60) 40 3.20

Early elderly (approximately 60–75) 14 3.19

Late elderly (approximately 75+) 2 1.50

Discussion

A major concern for the medical community is the accuracy of information that consumers obtain from YouTube.¹⁹ Here is where DTC medical advertising can have a positive impact. When people search YouTube for information about a medical condition, most of the information they find is not educationally useful^4,6,43–45 and a substantial portion of it is either inaccurate^7,20,43 or it goes counter to accepted medical practices.⁴⁶ When broadcast drug advertisements crossover and are made available on YouTube, consumers are getting information that meets the stringent FDA requirements for DTC pharmaceutical advertisements. This can be seen in the huge percent of time and words that are devoted to the disclaimer (40% of the time and 44% of the words). Over half of the advertisements used rational appeals and almost all of them encouraged the viewer to consult with their medical care provider. So DTC pharmaceutical ads are likely giving consumers sound medical advice or at least not disseminating false information.

On the other hand, the information given in Table 2 could be a cause for concern in the internet environment of search engines. The high percent of words and times devoted to the disclaimer and the severity of consequences included in these disclaimers should be a focus of future research. As mentioned earlier, research shows that a lot of the messages consumers are likely to get from YouTube are not from medical providers or pharmaceutical companies and are highly inaccurate. Many of these messages are presenting unproven medical treatments without disclaimers. Does the heavy presence of the severe disclaimers in legitimate pharmaceutical advertisements on YouTube encourage people to try the unproven treatments that come up in the same net search—treatments which are likely to come with grand promises and few if any disclaimers? Perhaps the law of unintended consequences comes in to play when strict requirements regarding disclaimers are present in an environment where so many of these advertisements crossover to YouTube and are placed alongside advice from nonmedical professionals who do not provide similar disclaimers. This could possibly result in consumers taking the unscientific advice of lay people over the research grounded advice of medical professionals and pharmaceutical companies. The current research does not specifically address this question but, as can be clearly seen in Table 2, it certainly does show that much of what consumers see when they view YouTube DTC pharmaceutical advertisements are disclaimers listing the potential of very negative consequences for utilizing scientifically proven medical treatments. This appears to be a huge downside risk of YouTube’s broadcasting of regulated DTC medical advertising.

A way to address this issue from a pharmaceutical company’s perspective could be to produce significantly longer advertisements specifically targeted for YouTube, thereby making the relative proportion of time devoted to disclaimers substantially smaller. Then, they could monitor YouTube to ensure that unauthorized uploaded short advertisements are taken down and engage in search engine optimization to ensure the longer ones are being accessed. Unlike traditional advertising media where you are paying for the space or time, on YouTube the main expense of running advertisements is getting click-throughs. These longer advertisements are likely to be well received in the YouTube environment where people often are viewing an advertisement specifically because they found it while searching for information about a specific medical issue.

This longer and more straightforward approach to YouTube advertising would likely change the structure and format of YouTube DTC medical advertising in general. Only about one-third of the advertisements simply presented product information. Over 40% of the advertisements used testimonials or before/after stories and 20% of them used slice of life or medical crises stories. In a broadcast media environment, there is the need to gain the viewer’s attention but the video-on-demand nature of YouTube means that most viewers are likely already interested in the content of the advertisement. If this is so, then more emphasis on information and less emphasis on the other formats may be effective. Though one-half of the YouTube advertisements already focus on rational appeals, a much higher percent would likely emerge if the advertisements were substantially longer.

One question for future research revealed in the current study relates to what types of advertisements would work best in a YouTube environment and what types would not. The current study found that over one-fourth of the YouTube advertisements were addressing life threatening conditions and another 29% were addressing problems that were severe but not life threatening. Less than one-half were addressing problems that were mild to moderate. This raises the questions about what types of medical information are people looking for when they happen upon YouTube advertisements. Do consumers actually trust the internet rather than a doctor for severe or life threatening conditions? Or is the severity of the problem not the issue but the type of problem or the characteristic of the viewer. Fourteen percent of the YouTube advertisements were for sexual or cosmetic medical problems. Are these the types of problems people feel more comfortable viewing on YouTube? Or perhaps the age of the viewer is important. Prybutok⁴⁷ found that teens and young adults feel more comfortable looking to the internet for medical information than discussing it with their parents or peers.

One possible mismatch with YouTube advertisements was the small portion of healthcare providers who were depicted in the advertisements (less than 20% of the total advertisements). If people are using YouTube as a surrogate for seeking medical advice then it seems to follow that they would be more receptive if that advice comes from medical professionals. The extent to which the depiction of medical professionals would be more effective in video-on-demand advertisements is worthy of further exploration.

One interesting match-up found by the current research was the ages of those depicted in the advertisements. Though healthcare is a product disproportionately consumed by the elderly,^48–50 only 16% of those depicted in the focal part of the advertisements appeared to be 60 years of age or older. This could be due to the age of people who would be most likely to be on the internet looking for information. As the Center for Disease control has stated, YouTube is a media well suited for younger consumers⁴⁷ and so it would make sense that they would be more likely to be depicted in this medium, even if they are not the ones most likely to consume healthcare products.

Conclusion

The current paper presents an initial attempt by advertisers to understand something that is of great concern to medical professionals. What type of medical information is being disseminated through the on-demand video website YouTube? As is common with content analysis studies, we may have raised more questions than we have answered. We identified what medical advertisements are present on the largest video-on-demand website in the world. However, we have not really delved into whether this is what should be there. Some of our findings appear to indicate that what is out there may not be what best serves the medical industry, pharmaceutical companies, or most importantly consumers of medical information. With this glimpse into what actually is on YouTube, those interested in consumer health issues now need to consider whether this is the type of information that should be on YouTube. We have raised issues concerning how content, disclaimers, and appeals are used on YouTube. Now research is needed to see if these communications are helping or hurting the cause of health and wellness of those who view them.

Year	Number of advertisements	Percent of advertisements
2002	2	2
2005	4	4
2006	5	5
2007	6	6
2008	7	7
2009	11	11
2010	9	9
2011	21	21
2012	10	10
2013	56	56
2014	4	4

	Time (s)	Words
Average advertisement length	62.06	178.22
Average disclaimer length	24.72	77.95
Disclaimer as percent to advertisement	40%	44%
Average number of side effects	5.5
Severity of most potentially severe side effect
Life threatening	35%
Nonlife threatening but severe	24%
Nonlife threatening, mild to moderate	41%
Percent explicitly mention death as a possible side effect	15%

Benefit	Percent
Social benefits
General social benefits	27
Cosmetic	7
Sexual	7
Total	41
Treating existing condition
Life threatening	23
Severe but not life threatening	27
Mild to moderate	41
Total	91
Preventative
Life threatening	3
Severe but not life threatening	2
Mild to moderate	4
Total	9
Mentioned pain reduction	37

Appeals	Percentages
Rational	53
Emotional positive	51
Emotional negative	22
Fear	8
Relationship	22
Humor	8
Sex	4

Formats	Percentages
Testimonial	34
Slice of life	17
Medical crisis	3
Before/after story	7
Other	4
No story-straight description of product, benefits	35

Relationships present in the FOCAL of the advertisement	Percent of ads
Healthcare provider interacting with patient	12
Adult couple	27
Parent/child or parent/youth relationship	14
Parent/adult child relationship	5
Other relationship among relatives	8
Relationship between friends	17
Relationship but nature is unclear	11

People present in the FOCAL part of the advertisement	Percent of ads	Number in group when present
Adult male	58	2.56
Adult female	71	3.35
Children	20	2.96
Animation	13	5.00
Celebrity	4	1.00
Healthcare provider	8	2.50

Apparent age of healthcare provider and size of group when present	Percent of ads	Number in group when present
Young adult (under 30)	0	n/a
Early middle age (approximately 30–45)	7	1.00
Late middle age (approximately 45–60)	8	2.00
Early elderly (approximately 60–75)	1	1.00
Late elderly (approximately 60–75)	0	n/a

Apparent age of nonhealthcare provider and size of group when present	Percent of ads	Number in group when present
Child/youth (under 18)	21	2.64
Young adult (approximately 18–30)	15	4.25
Early middle age (approximately 30–45)	43	2.96
Late middle age (approximately 45–60)	40	3.20
Early elderly (approximately 60–75)	14	3.19
Late elderly (approximately 75+)	2	1.50

Footnotes

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Conflict of interest

None declared.

Author biographies

James A Muncy (PhD, Texas Tech University) is a Professor of Marketing at the Harley Langdale, Jr. College of Business, Valdosta State University. His publications have appeared in Journal of Advertising, Journal of Advertising Research, Journal of Current Issues and Research in Advertising, Journal of Marketing, Journal of Business Research, Marketing Education Review, Journal of Marketing Education, Journal of Business Ethics, Journal of Public Policy and Marketing and other scholarly journals and proceedings. He has also served as the Executive Director for the Association for Consumer Research. His research interests include advertising, ethics, and marketing education among others.

Rajesh Iyer (PhD, Southern Illinois University at Carbondale) is an Associate Professor of Marketing and the Director of International Business at Foster College of Business, Bradley University. His publications have appeared in Journal of Advertising Research, Journal of Business Research, Journal of Business Ethics, Journal of Consumer Marketing, Journal of Marketing Education, Journal of Marketing Theory and Practice, Journal of Retailing and Consumer Services and other scholarly journals and proceedings. His research interests include Services Marketing, Health Care Marketing and Scales Research.

Jacqueline K Eastman (PhD, Florida State University) is a Professor of Marketing at Georgia Southern University. She has published in journals including Journal of Business Research, Journal of Business Ethics, Journal of Consumer Marketing, Journal of the Academy of Marketing Science, Journal of Marketing Theory and Practice and Journal of Marketing Education among others. Her research interests include Internet Marketing, status consumption, online Marketing education, and ethics.

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