ea 1 bullard cawhtorne_ taylorpowell_heeke
TRANSCRIPT
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
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.
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.
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
Today’s presenta&on
DATA
OUTCOMES
ACTIONS
DATA
DATA
DATA
DATA
• Data used and resulBng acBons • Examples
• Data Challenges
The context
http://www.publichealthmdc.com/ www.safercommunity.net
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
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
Local data
Source: Office of Health Informatics, DPH, WI DHS; PHMDC WI Hospital Association; PHMDC
ED visits and hospitalizations
Poisoning deaths
82% from drugss
62% from drugs
Opioid specific local data
Source: WI Hospital Association; PHMDC Office of Health Informatics, DPH, WI DHS; PHMDC
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
ACTION: Combine mul&-‐agency data
“Stop the overdose epidemic”
ACTION: Kickoff Summit, Jan 2012
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
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
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
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
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
• 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
Governor of Wisconsin
signs seven HOPE
(Heroin & Opiate PrevenBon and EducaBon) bills on
April 7, 2014
• 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
www.parentaddic&onnetwork.org
• 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
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
Individual Community
Soc ia l & 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
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
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.
PRESCRIPTION DRUG ABUSE RISK DETECTION WITH BIG DATA
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.#
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.
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
Example: Family Homelessness Preven&on Data & Social InnovaBon
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
Our Personal Data Footprint...
... can also be used for Public Health.
TransacBon Pa^erns
Social Network Analysis
Search Trends
Social Media Trends
Health Status
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
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
Search
Twi^er
Wearable Devices
Risk Factors Data Source
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
Poten&al Use Cases
RecommendaKons
Message
Message
Message
Public Health Related Recommenda7on
Rx Abuse Data Mapping (County Level Prototype)
Trending
College Tracking Dashboard (Concept)
Rx Abuse Related Open Data Plaeorm (Concept)
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