Risky business: Risk perception and the use of medical services among customers of DTC personal genetic testing

Transcription

1 Risky business: Risk perception and the use of medical services among customers of DTC personal genetic testing September 27, 2011 Dave Kaufman, Ph.D. Genetics and Public Policy Center Johns Hopkins University, Washington, DC Work funded by NHGRI Grant 1R1HG

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3 Several outstanding questions about DTC Testing Who is purchasing these tests? How do customers interpret the data? How do they act upon these interpretations? Empirical data being collected

4 DTC consumers perspectives Survey of customers of 3 companies January-May 2010 Randomly sampled US customers 18+ Purchased or received as gift from friend or family Viewed results 2-8 months after receiving results Online survey, 20 minutes, $10 3,167 invited 1,163 (37%) responded 1,048 (90%) qualified

5 Design Aspects Cross-sectional survey No data on individuals specific genes or risks Data from all three companies aggregated Interpreting 2 sample risk results

6 Demographics Age DTC Customers (%) US population 18+ (%) Women Household Income $0-59, $60,000-84, $85, , $125, Education Attained 0-12 Years years College Postgraduate 54 9 Race/Ethnic Group White non-hispanic Asian/PI (non-hispanic) 5 4 Black non-hispanic 1 12 Hispanic 3 15 AI/AN (non-hispanic) Other non-hispanic 3.5 2

7 Satisfaction How satisfied were you with the [23andMe/deCODEme/Navigenics] service? Very Dissatisfied (0.5) Very Satisfied Satisfied Neither Dissatisfied (1.5)

8 INTERPRETING RESULTS

9 Clarity Reports were easy to understand Agree 88% Disagree 12%

10 Utility Did you learn anything that you could use to improve your health that you did not already know? Yes 58% No 42%

11 Interpreting the data Participants were shown two hypothetical test results, using the exact format their company utilizes in its reports, and asked to interpret them Navigenics Diabetes, type 2 Mary: 25% Avg: 30% Mary s estimated risk Mary s relative risk: below average Mary s estimated lifetime risk: 25% Mary has 17 of the 36 risk markers We took the average risk for women and used Mary s genetic markers to estimate her lifetime risk for type 2 diabetes: 25%, or 250 out of 1,000. Here's another way to look at Mary s risk. In a sample population: 49% have a higher genetic risk than Mary 4% have the same genetic risk as Mary 47% have a lower genetic risk than Mary

12 Interpreting the data Participants were shown two hypothetical test results, using the exact format their company utilizes in its reports, and asked to interpret them 23 and Me

13 Interpreting the data Participants were shown two hypothetical test results, using the exact format their company utilizes in its reports, and asked to interpret them decodeme

14 Second sample result: 11% risk of colorectal cancer (CRC) Navigenics Colon cancer Mike 11% Avg: 5% Mike s relative risk: above average Mike s estimated lifetime risk: 11% Mike has 6 of the 10 risk markers Mike s estimated risk We took the average risk for men and used Mike s genetic markers to estimate his lifetime risk for colon cancer: 11%, or 110 out of 1,000. Here's another way to look at Mike s risk. In a sample population: 3% have the same genetic risk as Mike 97% have a lower genetic risk than Mike

15 Second sample result: 11% risk of colorectal cancer (CRC) 23andMe

16 Second sample result: 11% risk of colorectal cancer (CRC) decodeme

17 Based on the information above, which answer best describes how Mary s/mike s risk of [disease] compares to the average person s risk? Mary (25% risk of DM2 vs. 30% in pop) Mike (11% risk of CRC vs. 5% in pop) More likely to get disease 5 94 Less likely to get disease 90 3 Has the same risk 4 2 I don t know 2 1

18 Frequency of incorrect responses

19 Personal interpretation of elevated colorectal cancer risk "If you were Mike, knowing this information would you consider yourself to be at: high risk, moderate risk, or low risk"? High risk 45% Moderate risk 42% Low risk 13%

20 ACTIONS BASED ON RESULTS

21 What do people do based on the data? 28% discussed results with a healthcare prof. 20% with primary health care providers 1% contacted a genetic counselor 19% with other health care professionals 9% followed up with additional laboratory tests, (including 26% who shared w/ h.c professional)

22 Discussing results with a healthcare professional increased perceived utility People who discussed results with provider People who did not discuss results % who felt they learned something new to improve their health 73% 52% odds ratio =1.7, adjusted p<0.0001

23 16% changed medication or supplement regimens 10% changed dietary supplements 3% changed OTC medicines 4% changed Rx, in consult with doctor 0.4% changed Rx, without consulting doctor Among those changing one or more regimens, 54% shared results w/ h.c. provider

24 Behavioral changes as a result -at one point in time! 34% more careful about diet 0.1% less careful 65% the same 14% exercise more 31% more determined to exercise nobody exercises less or is less determined

25 Personal interpretation of colon cancer scenario "If you were Mike, knowing this information would you consider yourself to be at: high risk, moderate risk, or low risk"? High risk 45% Moderate risk 42% Low risk 13%

26 Behaviors are related to subjective interpretation of sample CRC risk result Those who said that knowing Mike s 11% colorectal cancer risk, they would consider themselves to be at: Low Risk Moderate Risk High Risk p=0.03 p=0.05 p= p= Sought information Talked to healthcare prof. More careful diet Exercising more Health behavior in response to DTC test

27 Similar pattern observed with other behaviors Those who said that knowing Mike s 11% colorectal cancer risk, they would consider themselves to be at: Low Risk Moderate Risk High Risk p=0.12 p=0.69 p= Follow-up lab test Changed RX regimen Changed OTC regimen Exercising more Health behavior in response to DTC test results

28 Conclusions Demographics of early DTC customers skewed Use of additional health care resources may add value and utility to the test Adjusting for demographic factors, subjective interpretation of risk seems to strongly influence follow-up behaviors Suggests careful attention needed to reports, baseline risks, how relative and absolute risk are relayed

29 Conclusions Some of what companies try to communicate is understood Whether information given is accurate or valid is a different question Behavioral changes observed could be positive or negative, transient or lasting Are not the same as outcomes Longitudinal studies w/richer data needed

30 Thanks to NCHPEG Juli Murphy Kim Kreger Gail Javitt GPPC/JHU Survey Participants Amy DuRoss Brenna Sweeney Navigenics Kathy Hudson Stephanie Devaney NIH Joanna Mountain Amy Keifer 23andMe Robert Green Sara Katsanis Boston U. Duke Jeffrey Gulcher Jónheiður Ísleifsdóttir decodeme Joan Scott NCHPEG Work funded by NHGRI Grant 1R1HG