1. Educa&on
and
Advocacy
Track:
Using
Data
to
Drive
Down
Prescrip&on
Drug
Abuse
Presenters:
• Lisa
Bullard-­‐Cawthorne,
Public
Health
Madison
&
Dane
County,
WI
• Ellen
Taylor-­‐Powell,
Parent
AddicBon
Network,
Safe
CommuniBes
of
Madison-­‐Dane
County,
WI
• Stefan
Heeke,
SumAll.org
Moderator:
Regina
M.
LaBelle,
White
House
Office
of
NaBonal
Drug
Control
Policy

2. Disclosures
• Lisa
Bullard-­‐Cawthorne
has
disclosed
no
relevant,
real
or
apparent
personal
or
professional
financial
relaBonships.
• Ellen
Taylor-­‐Powell
has
disclosed
no
relevant,
real
or
apparent
personal
or
professional
financial
relaBonships.
• Stefan
Heeke
has
disclosed
no
relevant,
real
or
apparent
personal
or
professional
financial
relaBonships.

3. Learning
ObjecBves
1. Demonstrate
how
“big
data”
can
help
address
the
issue
prescripBon
drug
abuse
more
effecBvely.
2. IdenBfy
types
of
data
that
can
be
used
to
idenBfy
a
problem,
further
invesBgate
an
issue
and
programming,
and
generate
community
interest.
3. Explain
a
mulB-­‐faceted
approach
to
address
prescripBon
drug
poisoning
(overdose
and
death)
and
abuse.
4. IdenBfy
strategies
that
bring
together
mulBdisciplinary
community
partners
and
build
local
municipal
support
to
address
the
prescripBon
drug
overdose
epidemic.

4. Using data to drive down
prescription drug abuse
Lisa
Bullard-­‐Cawthorne,
MS,
MPH
Public
Health
Madison
&
Dane
County,
WI
Ellen
Taylor-­‐Powell,
Ph.D.
Parent
AddicBon
Network,
Safe
CommuniBes
of
Madison-­‐Dane
County,
WI
National Rx Drug Abuse Summit
Atlanta, GA April 22-23, 2014

5. Today’s
presenta&on
DATA
OUTCOMES
ACTIONS
DATA
DATA
DATA
DATA
• Data
used
and
resulBng
acBons
• Examples
• Data
Challenges

6. The
context
http://www.publichealthmdc.com/ www.safercommunity.net

7. The
ini&a&ve:
“Stop
the
overdose
epidemic”
1. Public
health
data
signaled
change
2. Elected
officials
and
implicated
agencies
came
together
3. Lead
agency
appointed
4. Evidence-­‐based
strategy
developed
5. Broad
community
collaboraBve
mobilized

8. L o c a l
i n j u r y
d a t a
Source: Wisconsin Interactive Statistics on Health; Public Health Madison & Dane County
POISONING
VEHICLES

9. Local
data
Source: Office of Health Informatics, DPH, WI DHS; PHMDC
WI Hospital Association; PHMDC
ED visits and
hospitalizations
Poisoning
deaths
82%
from
drugs
s
62%
from
drugs

10. Opioid
specific
local
data
Source: WI Hospital Association; PHMDC
Office of Health Informatics, DPH, WI DHS; PHMDC

11. County
Exec
Mayor
Medical
Examiner
Office
DATA:
Recent
overdose
death
data
Local
Police
&
County
Drug
Task
Force
DATA:
Drug
overdose,
death;
crime
Public
Health
DATA:
hospital
visits
and
deaths
due
to
poisoning
Fire
&
EMS
DATA:
911
Calls
for
Narcan
use
DC
Human
Services
DATA:
AODA
treatment
admissions
District
A^orney,
Courts,
Jail
DATA:
Opiate-­‐
related
arrests
ACTION:
Affected
agencies
brought
together

12. ACTION:
Combine
mul&-­‐agency
data

14. “Stop the overdose epidemic”
ACTION:
Kickoff
Summit,
Jan
2012

15. ACTION:
Gather
community
input
 ParBcipaBng
partners
o NarcoBcs
Task
Force
&
Police
Chiefs
o County
EMS
coordinator
&
EMS
Chiefs
o Needle
exchange
providers
(3),
Methadone
Clinics
(2),
private
treatment
provider,
recovery
organizaBon
1. Opiate
overdose
survey
2. Overdose
discussion
groups

16. Opiate
Overdose
Survey
Overdose
Discussion
Group
Purposeful sample. N= 1100
• 504 current & past drug
users
• 597 first responders
(police & EMS)
• 30 people: ½ people in
recovery; ½ allies
• Group met twice

17. Key
results
from
community
input
Opiate overdose is common
• 75% of respondents witnessed opiate
overdose
• 33% had personal overdose
experience; 65% more than once
Increased
understanding
of
nature
and
scope
of
the
local
problem
SBmulated
discussion
within
and
across
agencies
and
groups
Corrected
misconcepBons
IdenBfied
gaps
in
service
delivery
Raised
a^enBon
of
law
enforcement
Use of 911
• Majority do not call 911
• Reason: Fear of arrest
Missed opportunity
• 74% report treatment was not
discussed at overdose scene
• Treatment or support needs not
discussed at ED
Misconceptions
• Calling 911 brings only the police

18. Key
results
–con’t
Event or circumstance that was
turning point
• #1: withdrawal (75%)
• Many SOCIAL issues: financial concerns,
loss of relationships, loss of employment,
legal consequences, lack of stable
housing
• Someone died or personal OD
experience
Barriers to or challenges with
maintaining treatment
• Lack of insurance or funding for services
• Lack of transportation
Increased understanding of
what motivates people to seek
help
Stimulated discussion among
service providers
Encouraged treatment providers
to collaborate with community
partners to discuss opportunities
to raise funds for treatment

19. Mix
of
data
• Public
health
data
• State
health
data
• Local
agency
data
• Community
survey
• Community
discussion
groups
• Ongoing
informal
data
collecBon
• Best
pracBce
literature
and
science

20. • Overdose
experience
• Calling
911
aaer
overdose
&
reasons
not
calling
• View
of
Naloxone
expansion
• #
lives
saved
by
non-­‐medical
person
Strategy
component:
Improve
overdose
intervenBon
Naloxone
pilot
-­‐ Police
-­‐ EMS
-­‐ Hospital
ED
DATA ACTIONS OUTCOMES

Overdose
deaths

911
calls
in
event
of
overdose
Jail
pilot
project
Good
Samaritan
Law
Naloxone
First
Responder
Law
Policy
Environment
Opioid
diversion
program

Overdose
in
community

21. Governor
of
Wisconsin
signs
seven
HOPE
(Heroin
&
Opiate
PrevenBon
and
EducaBon)
bills
on
April
7,
2014

22. • Inadequate
support
and
resources
for
families
and
friends
• RecommendaBon:
“one
stop”
shop
• Parent
experiences
&
frustraBons
Strategy
component:
Increase
treatment
and
recovery
DATA ACTIONS OUTCOMES

SBgma

Awareness
and
knowledge

Use
of
local
services

Overdose
deaths

Family
stress

23. www.parentaddic&onnetwork.org

24. • Drug
poisoning
exceeds
traffic
deaths
• #
of
deaths
• Nature
and
scope
of
drug
poisonings
CollaboraBon
DATA ACTIONS OUTCOMES

Awareness
and
knowledge

Overdose
deaths

Community
building

Agencies
working
together
in
new
ways
C O M M U N I T Y
C O L L A B O R A T I O N

26. Data
challenges
• Unreported
data
• DifficulBes
obtaining
certain
types
of
data
• Timeliness
of
data
• CompeBng
demands
on
data
providers
• Inconsistencies
across
different
sources/
agencies
• Reliability
of
data,
e.g.
911
Narcan
calls
• StandardizaBon

27. Individual
Community
Social
&
Economic
• Loss
of
rela&onships
• Loss
of
tangibles
• Financial
costs
• Lost
produc&vity
• Crime
• Family
adversi&es
Deaths
Hospital
Visits
Overdoses
in
Community
Opioid
Abuse
&
Dependence
Physical
Psychological
Physical
• Injury
Opioid
Burden

28. Wrap-­‐up
• Mix
of
data
• local,
state,
naBonal
• mix
of
perspecBves;
mulB-­‐agency
• quanBtaBve
and
qualitaBve
• Know
what
data
will
resonate
with
audience;
and
how
to
present
• Share
data
broadly
• Engage
those
who
provide
the
data
• Partner
with
others
• Ongoing
data
collecBon
and
analysis
for
conBnuous
improvement
and
accountability

29. Thank
you!
“This
is
absolutely
the
right
thing
for
us
to
do
as
a
community.
The
solu7on
does
not
come
from
a
single
office
or
person.
It
has
to
be
a
community-­‐wide
approach.”
h^p://www.youtube.com/watch?
feature=player_detailpage&v=7bOgx_ACKk4
Lisa Bullard-Cawthorne, MS, MPH Ellen Taylor-Powell, Ph.D.

30. PRESCRIPTION
DRUG
ABUSE
RISK
DETECTION
WITH
BIG
DATA

31. Clinton
Founda&on’s
Health
Maaers
In&a&ve
Vision
• To improve the health and well-being of all people no matter
where they live, work
or play. #
• We know that better health is contagious – people,
#communities and organizations have solutions to share and
#we are the platform for elevating their collective successes.#

32. Clinton
Founda&on’s
Health
Maaers
Ini&a&ve:
What
We
Do
• Build
strategic
partnerships
that
will
help
facilitate
the
development
and
scaling
of
health
promoBng
soluBons.
• Work
across
sectors
to
develop
and
implement
coordinated,
systemic
approaches
to
creaBng
healthier
communiBes.
• Leverage
technology
and
digital
innovaBon
to
help
advance
health
and
wellness
at
the
naBonal
and
community
levels
by
disseminaBng
evidence-­‐based
individual,
systems,
and
investment
strategies.

33. Leveraging
the
Power
of
Data
for
Social
Innova&on
• Solve
specific
data-­‐related
problems
with
partners,
measure
impact,
share
soluBons
• Explore
scalable,
data-­‐driven
social
innovaBon
opportuniBes
with
partners

34. Example:
Family
Homelessness
Preven&on
Data
&
Social
InnovaBon

35. Over
2,000
4
year
Degree
Gran&ng
Ins&tu&ons….
1/2
of
these
college
students:
• Will
be
asked
to
trade
or
give
away
their
medica&on
•
Will
have
been
offered
the
opportunity
to
misuse
prescrip&on
drugs

36. Our
Personal
Data
Footprint...

37. ...
can
also
be
used
for
Public
Health.
TransacBon
Pa^erns
Social
Network
Analysis
Search
Trends
Social
Media
Trends
Health
Status

38. Why
is
Data
valuable
for
Public
Health?
• More
Granular,
Real-­‐Time
InformaBon
• IntervenBon
(Micro)
TargeBng
• Resource
AllocaBon
• Visualize
Public
Health
Issue
• Storytelling
• Enable
Scalable
SoluBons

39. Examples
for
Data-­‐Driven
Risk
Detec&on
Doctor
Shopping
Demand
for
Drugs
Search
for
Emergency
Treatment
Search
for
Professional
Help
Culture
of
Drug
Abuse
Risky
Behaviors
Emergency
Alerts
Healthy
Lifestyle
Expressions
of
EmoKonal
Stress
Credit
Cards
Facebook
Search
Twi^er
Wearable
Devices
Risk
Factors
Data
Source

40. Monitoring
Risk
via
Twiaer
Keyword
Examples
duragesic
diazepam
downers
sleepingpills
benzos
valium
xanax
klonopin
aBvan
librium
hillbillyheroin
oyco^on
percs
oxycodone
hydrocodone
lomoBl
Demerol
dilaudid
vicodin
lortab
OxyconBn
Percocet
ambien
lunesta
Adderall
aderall
ritalin
concerta
dexedrine

41. Poten&al
Use
Cases
RecommendaKons
Message
Message
Message
Public
Health
Related
Recommenda7on

42. Rx
Abuse
Data
Mapping
(County
Level
Prototype)
Trending

43. College
Tracking
Dashboard
(Concept)

44. Rx
Abuse
Related
Open
Data
Plaeorm
(Concept)

45. Next
Steps
• Working
with
technology
companies
and
data
providers
on
Rx
abuse
related
to
data
sharing
&
visualizaBon
• ImplemenBng
Rx
abuse
dashboard
on
college
campuses
to
be
used
in
2015
Like
to
get
involved?
Please
contact
us:
Lexie
Komisar,
Clinton
FoundaBon
Stefan
Heeke,
SumAll.org