Ea 1 bullard cawhtorne_ taylorpowell_heeke


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Ea 1 bullard cawhtorne_ taylorpowell_heeke

  1. 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. 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. 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. 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. 5. Today’s  presenta&on   DATA OUTCOMES ACTIONS DATA DATA DATA DATA •  Data  used  and  resulBng  acBons   •  Examples     •  Data  Challenges    
  6. 6. The  context       http://www.publichealthmdc.com/ www.safercommunity.net
  7. 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. 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. 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. 10. Opioid  specific  local  data     Source: WI Hospital Association; PHMDC Office of Health Informatics, DPH, WI DHS; PHMDC
  11. 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. 12. ACTION:    Combine  mul&-­‐agency  data  
  13. 13. “Stop the overdose epidemic” ACTION:    Kickoff  Summit,  Jan  2012  
  14. 14. 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  
  15. 15. 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
  16. 16. 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
  17. 17. 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
  18. 18. 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  
  19. 19. • 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  
  20. 20. Governor  of     Wisconsin     signs   seven  HOPE     (Heroin  &  Opiate   PrevenBon  and   EducaBon)     bills  on   April  7,  2014  
  21. 21. •  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  
  22. 22. www.parentaddic&onnetwork.org  
  23. 23. •  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
  24. 24. 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  
  25. 25. 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  
  26. 26. 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  
  27. 27. 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.
  29. 29. 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.#
  30. 30. 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.  
  31. 31. 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  
  32. 32. Example:  Family  Homelessness  Preven&on   Data  &  Social  InnovaBon  
  33. 33. 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    
  34. 34. Our  Personal  Data  Footprint...  
  35. 35. ...  can  also  be  used  for  Public  Health.   TransacBon  Pa^erns   Social  Network  Analysis   Search  Trends   Social  Media  Trends   Health  Status  
  36. 36. 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    
  37. 37. 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  
  38. 38. 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  
  39. 39. Poten&al  Use  Cases   RecommendaKons   Message   Message   Message   Public  Health  Related  Recommenda7on  
  40. 40. Rx  Abuse  Data  Mapping     (County  Level  Prototype)     Trending    
  41. 41. College  Tracking  Dashboard   (Concept)    
  42. 42. Rx  Abuse  Related  Open  Data  Plaeorm     (Concept)    
  43. 43. 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