Taking your medicine? Attitudes toward direct-to-consumer advertising (DTCA)

Abstract

Highlights

► Important divides exist when predicting individual level patterns of support for DTCA. ► The shared decision making model can help explain consumer support for increased DTCA. ► Searching for health information on the Internet, education, and ethnicity are strong predictors of support for DTCA. ► DTCA support is increased by select characteristics generally associated with weaker health outcomes. ► Patterns of support for increased DTCA are not necessarily uniform across important segments of the population.

Abstract

The U.S. Food and Drug Administration (1997) reinterpretation of existing regulatory guidelines facilitated a substantial increase in direct-to-consumer advertising (DTCA) of prescription medicines. Consumer attitudes toward this regulatory phenomenon and their political implications have been understudied by social scientists. This study develops and tests several individual-level explanations, and shows that there are no simple explanations for understanding support for DTCA, including a tempting appeal to what a person thinks about the doctor–patient relationship. We use an ordered probit analysis to find that searching for health information on the internet, education, and ethnicity are strong and consistent predictors of support for DTCA. As patterns of support for DTCA vary across important segments of the population, we find that DTCA support is increased by select characteristics generally associated with weaker outcomes from the American health system.

Keywords:

• DTCA
• Public opinion
• Food and Drug Administration
• Prescription medicines
• Doctor–patient relationship

Notes

1 The authors thank Ben Lyon and Kim Stevenson for their research assistance.

2 Tel.: [phone].

3 This figure does not include expenditures on promotion to physicians ($6.8 billion in 2011) and advertising in professional medical journals ($322 million in 2011).

4 O’Hara (2010, p. 55) defines medicalization as the practice of “labeling more and more human experience and behavior as a medical problem or illness for which the medical profession has treatment.” See O’Hara (2010) for a discussion of the implications of medicalization.

5 The survey's cross-section raw response rate was 51.2%, with an over-sample raw response rate of 30.5%. See Lo (2004) for more details about the survey.

6 We also employed an alternate coding scheme for this variable by creating a series of dummy variables for the “doctor decides” and “I decide” responses. Replacing the variable's original trichotomous coding with these alternately coded responses produces no change in the direction or significance of the doctor–patient relationship effect.

7 The 219 respondents who answered “not sure” or declined to answer the question were excluded from our analysis. Listwise deletion is used for other missing observations in the data.

8 Marginal change calculations reflect the standardized coefficients for X_k and can be interpreted as the change in the dependent variable (as measured in standard deviation units) for a one-unit change in the explanatory variable of interest as all other variables are held constant (Long and Freese, 2006).

9 Predicted probability calculations for all explanatory variables are available from the authors on request.

10 Although there is some correlation between independent variables, multicollinearity does not reach a problematic threshold.

11 Separate estimations of the model replaced the non-white variable with dummy variables for Hispanic, black or African American, and Asian-Pacific Islander respondents, respectively. The coefficients on the dummy variables for each group were positive and statistically significant, indicating no change in results from the original model.