Barriers to the Adoption and Implementation of Genomic Technologies - - PowerPoint PPT Presentation

Barriers to the Adoption and Implementation of Genomic Technologies Among Underserved Patient Populations

Catharine Wang, PhD, MSc

Department of Community Health Sciences Boston University School of Public Health

Annual Meeting of the New England Regional Genetics Network April 6, 2018

Outline for Today:

▪ Public health genomics translation

▪ Public awareness/understanding of genomics ▪ Direct-to-consumer testing landscape

▪ Equity in translation

▪ Background on health and genetic literacy ▪ Overview of family health history (fhx) efforts ▪ Using virtual counselors to overcome literacy-related barriers: VICKY ▪ Preliminary data from ongoing VICKY trial

Providing the right intervention to the right population at the right time Identify population subgroups likely to respond differently to interventions ...ensuring that all people have access to the intended benefits… Shifting focus from treatment to prevention (targeting preventive strategies)

2016

Public Awareness and Understanding of Genetics Scientists Discover The Couch Potato Gene

Public Understanding of Genetics

Lanie et al., J Genetic Counseling, 2004

Framing of Genomic Advances: Destiny

Framing of Genomic Advances: Prediction/Precision

Outside Magazine Oct 2005

Framing of Genomic Advances: Empowerment

Knowledge is power.

Juthe, Zaharchuk & Wang (2014) Genet Med

Celebrity disclosures and information seeking

PDQ Genetics of Breast and Ovarian Cancer: 5-fold increase in page views on May 14 compared to the previous Tuesday. Preventive Mastectomy Fact Sheet: <200 page views/day - jumped to more than 69,000 page views on May 14.

Preventative Mastectomy: 69,225 page views BRCA1 & BRCA2: 57,616 page views Genetics Services Search Results: 2,685 page views Genetics of Breast and Ovarian Cancer: 1,608 page views Breast Reconstruction: 1,229 page views

The Angelina Effect: Milestone in Public Awareness

▪ Single most-blogged-about medical topic in the past 5 years (BMJ Commentary, 2013; 346:f3340) ▪ ~ Doubling of (appropriate) referral rates to clinics

(Canada; UK-Evans et al., 2014). Demand for testing also

almost doubled (UK). ▪ 40% increase in actual testing (US – AARP study)

▪ Testing rates remained elevated for rest of 2013 ▪ Increase in testing among unaffected twice that of affected women

The Angelina Effect: Milestone in Public Awareness

▪ Single most-blogged-about medical topic in the past 5 years (BMJ Commentary, 2013; 346:f3340) ▪ ~ Doubling of (appropriate) referral rates to clinics

(Canada; UK-Evans et al., 2014). Demand for testing also

almost doubled (UK). ▪ 40% increase in actual testing (US – AARP study)

▪ Testing rates remained elevated for rest of 2013 ▪ Increase in testing among unaffected twice that of affected women

Why did it take Angelina? What now?

Cancer Moonshot to accelerate cancer research

Aims to make more therapies available to more patients and improve our ability to prevent cancer and detect it at an early age Blue Ribbon Panel (2016 report)

• G. Expand use of proven prevention and early detection strategies

Several cancer prevention and risk-reduction strategies have proven to be highly effective, including tobacco control, colorectal cancer screening, and HPV vaccination. Boosting prevention research to identify ways to increase uptake of these strategies, especially in medically underserved populations, could greatly reduce incidence and death from lung and other tobacco-related cancers, colorectal cancer, and cervical and other HPV-related cancers.

Direct to Consumer Genetic Testing

Premature translation? Potential for harm?

Direct to Consumer Genetic Testing

Premature translation? Potential for harm?

23andMe CEO and co-founder Anne Wojcicki: Giving women (and men) the freedom to test for BRCA1/BRCA2 is important… Under the current system, there are specific guidelines for BRCA screening that limit who has access to BRCA testing. (Meaning insurers will generally only cover people for testing if they’re of Ashkenazi Jewish descent or they have a family history of cancers related to the mutations.) So, many people fall through the cracks in the current screening system leaving them unaware of their risk.

DTC Landscape

▪ Commercial DNA testing for ancestry available since 2000 ▪ Three companies control vast majority of ancestry genetic testing market

▪ Family Tree DNA, 23andMe, AncestryDNA ▪ All make raw DNA data files available to consumers

▪ Proliferation of third-party companies to analyze and interpret raw DNA for health purposes

Raw DNA Interpretation Service Used By Consumers

22 7 4 59 6 9 16 20 36 62 81 20 40 60 80

DNA.Land Interpretome LiveWello Genetic Genie Family Tree DNA GEDmatch Promesthease

Consumer G/C 73% of consumers reported using more than 1

% reported

(Wang et al., 2018; Allen et al., 2018)

5% sought advice before using service

Consumer - Health seeking/results sharing

Family Friend s Other Medical provider

83% 8% 62% 30%

Sha hared ed r resu esults lts wi with th

80% 25% 14% 10%

Other specialist PCP Genetic counselor Nurse practitioner

(Wang et al., 2018)

Counselor - Requested Counseling Specialty Areas

57% 57% 21% 21% 18% 18% 11% 11% 11% 11% 7% 4% 7% 4%

% % of cases

(Allen et al., 2018)

(Flynn et al., under review)

(Allen et al., 2018)

Undermining the effective translation of genomic technologies?

Equity in Translation

Who has access? What is access?

Literacy Skills of U.S. Adults

▪ average reading level in U.S. is 8th - 9th grade

SOURCE: U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, 2003 National Assessment of Adult Literacy

The role of health literacy

▪ Health literacy: degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions

▪ ~ 1/3 of U.S. adults have limited health literacy ▪ disproportionally affects less educated, elderly, poor, or have limited English proficiency

Facilitating genetic literacy: Family history tools

Family health history is simplest genomic test available and remains the gold standard for clinical risk assessment Underutilized in primary care

“Genomic tool” for prevention

National Family History Day Launched in Nov 2004 My Family Health Portrait

https://familyhistory.hhs.gov/

Electronic tools to collect cancer family history

▪ Cancer in the Family (Rupert et al., 2013) ▪ CRA Health (HughesRiskApps, Ozanne et al., 2009) ▪ Family HealthLink (JamesLink, Sweet et al.,2002; 2014) ▪ Family Healthware (Yoon et al.,2009) ▪ GREAT (Acheson et al., 2006) ▪ GRACE (Braithwaite et al., 2005) ▪ Health Heritage (Cohn et al., 2010) ▪ ItRunsInMyFamily (Welch et al., 2015) ▪ Me Tree (Orlando et al., 2013) ▪ My Family Health Portrait (Guttmacher et al., 2004) ▪ MyLegacy (MyFamily, Doerr et al., 2014)

Family history tools to increase genetic literacy

• If you build it, will they come?
• If they come, can they use the tool the way

you expect them to?

Literacy Assessment of Family History Tools

Wang et al (2011) Public Health Genomics

Validity of family history assessment

Sensitivity results (1st/2nd degree relatives) MFHP NHGRI validation

(Facio et al., 2010; GIM) ▪ N=150, 95% white, 67% >college grad, 57% >100K ▪ Heart Disease: 78% ▪ Stroke: 87% ▪ Diabetes: 82% ▪ Breast Cancer: 84%

MFHP BU pilot validation

(Wang et al., 2015; GIM) ▪ N=35, 60% black, 51% <HS, 51% <25K

Validity of family history assessment

Sensitivity results (1st/2nd degree relatives) MFHP NHGRI validation

(Facio et al., 2010; GIM) ▪ N=150, 95% white, 67% >college grad, 57% >100K ▪ Heart Disease: 78% ▪ Stroke: 87% ▪ Diabetes: 82% ▪ Breast Cancer: 84%

MFHP BU pilot validation

(Wang et al., 2015; GIM) ▪ N=35, 60% black, 51% <HS, 51% <25K ▪ Heart Disease: 51% ▪ Stroke: 50% ▪ Diabetes: 22% ▪ Breast Cancer: 33%

Virtual Counselors

▪ Computer-animated characters that simulate face-to- face conversation between a patient and a health provider ▪ Requires minimal language / computer skills ▪ Use of nonverbal conversational behaviors ▪ Flexible and responsive, tailored to individual ▪ Prototype developed to collect family health history information from patients

VIrtual Counselor for Knowing Your Family History (VICKY)

VICKY pilot study

Wang et al (2015) Genet in Med

Wang et al (2015) Genet in Med

49 31 55 51 47 15 54 22 42 50 33 33

10 20 30 40 50 60

Sensitivity % (tool/gc)

Total HD HBP T2D Stroke Breast C

Sensitivity of Identified Health Conditions N=70 (1st/2nd degree relatives)

VICKY MFHP

p=.008 p=.001 p=.004

VICKY 2.0 - Conditions

VICKY 2.0

VICKY 2.0 Trial – Launched Fall 2016

MFHP vs VICKY

▪ N=151 currently enrolled (Target: 352) ▪ English and Spanish* speaking patients ▪ Block randomized (health literacy) to use a tool, followed by genetic counselor intake ▪ Outcomes:

▪ Accuracy (sensitivity) ▪ Communication with family members, clinicians (3 month follow-up)

MFHP (N=76) VICKY (N=75) Gender Male 19 (25%) 27 (36%) Female 57 (75%) 48 (64%) Age range 45-54 22 (29%) 19 (25%) 55-64 37 (49%) 36 (48%) 65+ 4 (5%) 3 (4%) Education <High school degree/G.E.D. 35 (46%) 37 (49%) Some college, no degree 24 (32%) 16 (21%) Race/Ethnicity Hispanic/Latino 17 (23%) 18 (24%) African American 48 (63%) 49 (65%) Income <25K year 39 (51%) 35 (47%)

VICKY 2.0 Trial – Initial 151 patients

Demographics

MFHP (N=76) VICKY (N=75) Health Literacy Limited literacy 34 (45%) 32 (43%) Possibility of limited literacy 26 (34%) 26 (35%) Adequate literacy 16 (21%) 17 (23%) Computer Literacy MFHP (N=76) VICKY (N=75) Never used one 9 (12%) 8 (11%) Tried one a few times 20 (26%) 19 (25%) Use one regularly 39 (51%) 41 (55%) I’m an expert 8 (11%) 7 (9%)

VICKY 2.0 Trial – Initial 151 patients

Demographics

VICKY 2.0 Trial – Initial 151 patients

Tool not completed: 43/151 – 28%

VICKY 7% incomplete (5/75) 93% complete MFHP 50% incomplete (38/76) 50% complete

38 70 38 5

10 20 30 40 50 60 70 80

MFHP VICKY

Complete Incomplete

VICKY 2.0 Trial – Initial 151 patients

5 10 15 20 25 30

Tool crashed Could not use computer Chose not to continue Could not navigate tool

VICKY

(N=75)

5 10 15 20 25 30

Tool crashed Could not use computer Chose not to continue Could not navigate tool

MFHP

(N=76)

VICKY 2.0 Trial – Initial 151 patients

2 5 10 15 20 25 30

Tool crashed Could not use computer Chose not to continue Could not navigate tool

VICKY

(N=75)

14 5 10 15 20 25 30

Tool crashed Could not use computer Chose not to continue Could not navigate tool

MFHP

(N=76)

VICKY 2.0 Trial – Initial 151 patients

1 5 10 15 20 25 30

Tool crashed Could not use computer Chose not to continue Could not navigate tool

VICKY

(N=75)

10 5 10 15 20 25 30

Tool crashed Could not use computer Chose not to continue Could not navigate tool

MFHP

(N=76)

Reason participant chose not to continue

1 2 3 4 5 6 7 8 9 10

MFHP

(n=10)

1 2 3 4 5 6 7 8 9 10

VICKY

(n=1)

VICKY 2.0 Trial – Initial 151 patients

1 5 10 15 20 25 30

Tool crashed Could not use computer Chose not to continue Could not navigate tool

VICKY

28 5 10 15 20 25 30

Tool crashed Could not use computer Chose not to continue Could not navigate tool

MFHP

MFHP Navigation issues preventing tool completion

2 4 6 8 10 12 14 Could not navigate from home screen DOB entry self Entering gender Adding/saving conditions for self Entering # of relatives Unsure what to do with relative list DOB entry for relative Adding/saving conditions for relative Other

Navigation issue preventing completion (n=28)

Ongoing Lessons and Next Challenge

Spanish* VICKY

▪ Complete removal of Spanish MFHP for updates ▪ Length of script (~10-20 min longer to complete same fhx) ▪ Larger family sizes (45 min English-language patients vs 60 min Spanish-language) ▪ Cultural differences

▪ privacy concerns ▪ hesitation to include family medical information

Barriers to adoption/implementation

▪ Be mindful of the public’s source of genetic knowledge

▪ Popular press ▪ Aggressive marketing from companies

Barriers to adoption/implementation

▪ Consumers are savvy and aware, but may be inaccurate in their assumptions about genetic tests

▪ Address misconceptions ▪ Efforts to de-implement?

▪ Building a tool does not guarantee everyone who can “access” can actually use it

▪ Garbage in, Garbage out ▪ Test. Revise. Test again on a different population.

Email: clwang@bu.edu Twitter: @CatharineWang Acknowledgements:

Supported by NIH grants R01HG007746, R03HG004216, K07CA131103, UL1RR025771, BUSPH pilot grant, and a Peter T. Paul career development professorship from Boston University.

Thank You

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MFHP Disease Entry

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