Exploring Markets of Data for Personal Health Information

K. Thomas PickardICDM 2014: Designing the Market of Data - for

Practical Data Sharing via Educational and Innovative Communications

December 14, 2014

How I got here

2

My passion

• Not golf

• Research about health information sharing

3

Why health information sharing?

• Implications for improving health

4

Two examples

1. Rare diseases

2. Common diseases

5

Meet the Beery’s

6

Source: http://www.nature.com/news/2011/110615/full/news.2011.368.html

DNA sequencing finds rare disease

7

• Dopa-responsive dystonia in children

• Mom read about condition in newspaper

• Single change on SPR gene

• Improved with serotonin therapy

Chromosome 2

SPR

Rare disease in U.S.

• Defined to be 1 in 1,500 people

Source: https://www.govtrack.us/congress/bills/107/hr4013

8

30M rare disease patients in U.S.

Another ~3,700 single-gene diseases suspected

3,500 single-gene diseases identified

9

Source: http://www.ncbi.nlm.nih.gov/pubmed/23504071

95% of rare diseases have no FDA approved drug

10

Source: http://globalgenes.org/rare-diseases-facts-statistics/

Implications

• Rare disease networks

• Sharing eliminates guesswork

• Solve more cases

11

What about common diseases?

Diabetes

• Disease prevalence:

World: 4% to ~40%

U.S.: ~10%

• Disease types: 30+

• U.S.: ~30M people 30 types 1M per type

12

Sources:http://care.diabetesjournals.org/content/29/suppl_1/s43.full.pdfhttp://healthintelligence.drupalgardens.com/content/prevalence-diabetes-world-2013

Schizophrenia

• Disease prevalence:

World: ~1% (with little variation)

• Disease types: 7 or more

• U.S. ~3.5M people 7 types 500k per type

13

Source: http://www.ncbi.nlm.nih.gov/pubmed/25219520

Nobel laureate John Nash

Autism spectrum disorders

• Disease prevalence:

World: ~1%

• 10% explained by genetics

• U.S. 2M people > 6 types ~300k per type

14

Sources:http://www.cdc.gov/ncbddd/autism/documents/asd_prevalence_table_2013.pdfhttp://www.nature.com/nature/journal/v515/n7526/full/nature13772.html

Autistic activist Temple Grandin

Hospitals and patients

• U.S. hospitals:

~6,000 facilities

• U.S. population: ~320M people

15

Source: http://www.targetmap.com/viewer.aspx?reportId=3065

One hospital per 50,000 people

Patients per U.S. hospital

• 1M diabetes patients

170 patients per hospital 1 in 300

• 500k schizophrenic patients

80 patients per hospital 1 in 625

• 300k autism spectrum disorder patients

50 patients per hospital 1 in 1,000

16

Rare disease: 1 in 1,500

Source: http://www.cdc.gov/datastatistics/

Implications

• Complex and rare disease can look similar

• Hospitals must share to solve diseases

• 5,000 people with a disease are necessary for good genomic results

17

Source: http://jbjs.org/content/96/5/e38

A shift in consumer attitudes

18

“Given the choice between pizza and privacy…a remarkable number will opt for the pizza.”

19

Insights

1. Consumers are willing to share health data under the right conditions.

2. Education seems to play a strong role.

3. Consumers want to be connected to their data.

4. Develop models to encourage sharing.

20

Health Information Sharing Model

Trust Motivation

CommunityInformed Consent

Consumer

21

Can we go faster with MoDAT?

• Markets of Data (MoDAT)

• Will consumers share if paid?

• Online survey

• 400 participants

• 7 questions

22

Ask Your Target Market (aytm.com)

A global survey

Responses by Continent– Asia (40%)

– Europe (35%)

– North America (17%)

– Africa (5%)

– Oceania (2%)

– South America (2%)

59 countries represented

South America2%

Oceania2%

Africa5%

North America17%

Europe35%

Asia40%

Responses by Continent

23

Willing to share

When asked about sharing, 88% responded “Yes” or “In some cases.”

50%

33%

17%

0%

10%

20%

30%

40%

50%

60%

Yes In some cases No

If I could remove my name, age, etc. from my health information, I would share it...

24

Share with financial reward

65%

26%

9%0%

10%

20%

30%

40%

50%

60%

70%

More likely to sharemy health

information

Neither more likelynor less likely to share

my healthinformation

Less likely to sharemy health

information

If I were rewarded financially, then I would be...

25

Share/Sell Data Types

0

20

40

60

80

100

120

140

160

180

200

Re

spo

nse

sI would share/sell these types of health information...

Share

Sell

26

Share/Sell with…

0

20

40

60

80

100

120

140

160

180

Re

spo

nse

s

I would share/sell my health information with...

Share

Sell

27

Financial Reward vs Difference in Average Number of Items Selected

-2.00

0.00

2.00

Yes No

Dif

fere

nce

in A

vera

ge N

um

be

r o

f It

em

s Se

lect

ed

Response to Selling with Financial Reward

"Yes/No" Response to Selling with Financial Reward vs Difference in Average Number of Items

Selected

28

Household Income

0%

10%

20%

30%

40%

50%

60%

Annual Household Income

Over half of the respondents reported a household income of less than $25,000 USD.

29

One-time payment

0%

5%

10%

15%

20%

25%

Re

spo

nse

s (p

erc

en

t)

In exchange for this health information, I would expect to receive a one-time payment of:

30

Age: Sample vs World

Age ranges from 18 to 74.Average age cohort is 30-34 years.

0%

5%

10%

15%

20%

25%

30%

Age Cohort

Age

Samplepopulation

Worldpopulation

Source: U.S. Census Bureau, 2014 World Midyear Population by Age31

Age and reward

0

10

20

30

40

50

60

70

18-24 25-29 30-34 35-39 40-44 45-49

Re

spo

nse

s

Age cohort

Age Cohort vs "Yes" Response to Selling with Financial Reward

R² = 0.87

32

Age and one-time payment

$0

$50

$100

$150

$200

$250

18-24 25-29 30-34 35-39 40-44 45-49

Ave

rage

on

e-t

ime

pay

me

nt

(USD

)

Age cohort

Age vs Average One-time Payment(20 or more responses per cohort)

R² = 0.68

33

GDP and one-time payment

$-

$50

$100

$150

$200

$250

$300

$350

$- $500 $1,000 $1,500 $2,000 $2,500 $3,000

On

e-t

ime

pay

me

nt

(USD

)

GDP in $B

GDP $B vs Average One-time Payment(excluding lowest and highest expected payment amounts)

$56

$169

$250

$304

$489

$878

$1,875

$2,013

$2,471

R² = 0.46

Croatia

Romania

Philippines

Malaysia

Poland

Indonesia

India

Italy

United Kingdom

34

Source: United Nations 2012 GDP Data

Limitations

1. Paid to participate

2. Convenience sample (n=400)

3. Education level

35

Our sample was educated

0%

5%

10%

15%

20%

25%

30%

Education64%Completed at least four years of college

vs

25%Global tertiary education enrollment average

36

Source: UNESCO Institute for Statistics in EdStats. “Teritary education – A global report,” 2012.

Summary

• Rare and complex diseases can look similar

• Share health information to solve disease

• Some consumers view sharing health information like a “free pizza”

37

Thank you

K. Thomas Pickardktpickard [at] startcodon.org@kthomaspickardwww.linkedin.com/in/kthomaspickardBlog: www.genomedad.com

38