THE EFFECTS OF MEDIA EXPOSURE IN DIRECT-TO CONSUMER ADVERTISING OF

PRESCRIPTION DRUGS, PATIENT DEMAND, AND PATIENT SATISFACTION

140th Annual American Public Health Association (APHA) Conference

San Francisco, California

October 31,2012

Marvin Rock, Mian B. Hossain1, Andrea Kidd-Taylor1, H. Eduardo Velasco2

1 School of Community of Health and Policy, Morgan State University2 College of Osteopathic Medicine, Touro University

Presentation Outline 2

Rationale of the study Direct-to Consumer Advertising Definition Research Question & Hypotheses Conceptual Model & Adapted Model Data Source Univariate, Bivariate, Multivariate Models Analytical Model Conclusions

Rationale for the Study

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Why Prescription Drugs? Cost (fastest rising health care expense) Leading to an increase in new medicines being

approved and marketed Consumer Demand is growing for new medications Intensification of prescription drug marketing has lead

to DTCA

Congressional Budget Office, 2006

Direct to Consumer Advertising (DTCA)-- Defined

DTCA : the promotion of prescription drugs directly to the consumer

through the following methods:

Sources of DTCA:

Newspaper

Magazine Print

Television or radio Broadcast

Internet marketing (Social Marketing i.e. Facebook, Twitter, and Blogs )

Online

Family & Friends Other

Healthcare providers

Consumer for Media and Democracy, 2007

Research Question and Research Hypotheses5

RQ1: What are the health determinants of patient demand?

Relationship between differing levels of media exposure of DTCA prescription drugs

H1: Do differing levels of media exposure (hi, med, lo) affect patient demand

H2: Does health insurance status moderate the effects of media exposure and patient demand

H3: Are patients with higher levels of media exposure more likely to have patient satisfaction adjusting for all other variables

Bero’s Conceptual Model: (2001)

1. DTC Advertising• Exposure (Ad

Expenditures)• Content• Accuracy

2. Consumer Attitudes:• Consumer Assess. of DTC

Ads• Profit Motivated Ads vs.

Public Service Announcements

• Promotion vs. Education• Medicalization of Everyday

Problems

3. Patient Demand

5. Patient-Physician Relationship

• Satisfaction• Trust• Visit Efficiency

4. Physician Attitudes

6. Prescribing(Physician Actions)• Quality of Prescribing• Quantity of Generic Drug Prescribe• Quantity of Brand Name Drug

Prescribed(Physician Group/HMO Actions)• Formulary Compliance• Economic Viability of the Group /HMO

7. Adherence with Drug Therapy

9. Health Care Costs• Drug Costs to Consumers &

Health Plans• Physician Visits• Diagnostic Tests• Hospitalization

8.Patients’ Health Outcomes

• Perceived Health Status

• Objective Measures of Health Status

10. Pharmaceutical Industry• Market Share• Profit Level

Bero, 2001

Adapted Conceptual Model: (Rock)

DTC Advertising• Media Exposure

Patient Demand

Patients’ Health Outcomes• Adverse Drug Events• Patient Satisfaction• Health Related QoL

Rock, 2011

“Public Health Impact of Direct to Consumer Advertising of Prescription Drugs”(ICPSR 3687)

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Cross-sectional study design Secondary data taken from 2001/2002 Public Health Impact

of Direct-to-Consumer Advertising of Prescription Drugs (n=3000)

Twenty minute telephone interview, with a nationally representative sample (telephone households in Continental United States) Sample size (3000), 18 years of age and older Stratified sampling process Households selected computerized by Random Digit Dialing (RDD)

Genesys Sampling System Based on # households in the exchange Proper representations of households in different regions Central city, suburban, rural

Weissman et al., 2003

Study Covariates9Variables Type

Health status Categorical

Gender Dichotomous

Age Categorical

Education Categorical

Health insurance status Dichotomous

Marital status Categorical

Income Categorical

Employment status Dichotomous

Geographic region Categorical

Health Related Quality of Life Dichotomous

Race/ethnicity Categorical

Adverse Drug Events Dichotomous

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Variable Proportion Sample size

(n)

Socio-demographic variables

Media exposure (n=2,957)

Low media exposure 0.35 992

Medium media exposure 0.34 989

High media exposure 0.47 976

Overall Exposure to health information (n=2,879)

Television or radio 0.15 412

Internet 0.13 359

Mass media & print media 0.14 412

Print media 0.17 522

Family & Friends 0.16 480

Health provider 0.24 694

Weighted socio-demographic and Health characteristics of the study population

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Variable Proportion Sample size (n)

Patient Demand

Advertisement prompted to talk to Dr. (n=2,592)

Yes 0.31 801

No 0.69 1,791

Patient Satisfaction

Overall do you feel better after taking prescribed

drug (n=650)

Better 0.81 520

Worse 0.19 130

Weighted distributions of patient demand and patient satisfaction, two outcome variables for the study

Bivariate Association Between Media Exposure and Other Covariates with Patient Demand

Independent Variables Patient Demand

Media Exposure S

Geographic Region NS

Income NS

Health insurance NS

Gender S

Race/Ethnicity NS

Marital Status NS

Employment Status NS

Age NS

Education NS

Health Status S

Geographic Location S

S: Statistically significant with at least 95% confidence.

NS: Not statistically significant.

Independent Variables Patient Satisfaction

Media Exposure NS

Geographic Region NS

Income S

Health Insurance NS

Gender NS

Race/Ethnicity S

Marital Status NS

Employment Status NS

Age NS

Education NS

Health Status NS

Geographic location NS

HRQL S

ADE S

Bivariate Association Between Media Exposure and Other Covariates with Patient Satisfaction

S: Statistically significant with at least 95% confidence.

NS: Not statistically significant.

Analytical Models

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Logistic regression- Patient Demand (yes/no) and Patient Satisfaction (better/worse)

Model 1-3: Media exposure (TV, radio, print, and Internet) Patient demand Socio-demographic variables Health status

Model 4: Model 1+interaction between health insurance status Model 5-7: Media exposure & patient satisfaction controlling for SD,

HS, Adverse Drug Events, and Health Relate Quality of Life Model 8: Model 5+interaction between health insurance status Odds ratios

Less likely to demand & less satisfied OR<1 PD(no)/ PS (worse) More likely to demand & more satisfied OR>1 PD(yes)/ PS (better)

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CovariatesModel 1 Model 2 Model 3

OR 95% CI OR 95% CI OR 95% CI

Media exposure

Low media exposure (RC) 1.00 - 1.00 - 1.00 -

Medium media exposure 1.53*** 1.11, 3.48 1.54*** 20,1.97 1.51** 1.16, 1.97

High media exposure 2.32*** 95,2.78 2.33*** 1.83, 2.96 2.30*** 1.77, 2.98

Health Insurance

Insured (RC) - - 1.00 - 1.00 -

Uninsured - - 0.86 0.63, 1.18 0.79 0.56, 1.13

Logistic regression models estimates for the relationship between patient’s levels of media exposure and patient demand

Significance: * p<0.05; ** p<0.01; *** p<0.001

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Covariates Model 4

OR 95% CI

Media exposure

Low media exposure (RC) 1.00 -

Medium Media exposure 2.14 0.93. 4.90

High media exposure 2.71** 1.22, 5.99

Health Insurance

Insured (RC) 1.00 -

Uninsured 0.64 0.33, 1.26

Media exposure x Health Insurance status

int_media_exp_low_new_healthins2 - -

int_media_exp_low_new_healthins1(RC) 1.00 -

Logistic regression models estimates for the relationship between patient’s levels of media exposure, patient demand ,

and the interaction term

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Covariates Model 5 Model 6 Model 7

OR 95% CI OR 95% CI OR 95% CI

Media exposure

Low media exposure (RC) 1.00 - 1.00 - 1.00 -

Medium media exposure 1.96* 1.11, 3.48 1.98** 1.12, 3.50 2.14* 1.08, 4.22

High media exposure 1.63 0.95, 2.78 1.63 0.95,2.79 1.83 0.94,3.60

Experiencing any side effects from taking rx

drug

Yes (RC) - - - - 1.00 -

No - - - - 3.25*** 1.90, 5.56

Prescribed drug had effect on your ability

Decreased ability (RC) - - - - 1.00 -

Increased ability - - - - 5.80*** 2.69,12.50

Intercept 1.03 0.967 0.077

Sample Size 643 643 583

Logistic regression estimates for the relationship between media exposure and patient’s satisfaction controlling for all other variables

Significance: * p<0.05; ** p<0.01; *** p<0.001

Conclusions

Patients with higher levels of exposure to media (medium and high exposure) are significantly more likely to have patient demand

The interaction between patient demand and health insurance status was not a significant factor or moderator in the media exposure patient demand relationship

Patients with medium level of media exposure are significantly more likely to have patient satisfaction, adjusting for adverse drug events and health related quality of life, socio-demographic variables, and health status

Future Implications 19

Further need to conduct research on DTCA of prescription drugs & patient health outcomes

Readdressing current policies on this form of health communication

Risk/benefit drug information available to patients Post approval risk/benefit analysis Quicker evaluation of drug ads that violate quality of life

and economic claims Reevaluation of the current regulations on DTCA

Acknowledgements

Dr. Mian Hossain Dr. Andrea Kidd-Taylor Dr. Neil Alperstein Dr. Lisa Bero Dr. Mary Carter Dr. Barbara Mintzes Dr. Joel Weissman Dr. Lester Spence

Dr. Eduardo Velasco Dr. Timothy Akers Dr. Jonathan VanGeest Dr. Robert Jagers University of Michigan

Inter-university Consortium for Political and Social Research

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Thank you Thank You

Marvin A. Rock, Dr.P.H., M.P.H.

Health Outcomes Scientist/ Epidemiologist

marvinarock@gmail.com

Contact Information 21

Questions?22