The Prescrip+on Drug Abuse Epidemic and Preven+on: How Prescrip+on Monitoring Programs Can Help

Transcription

1 The Prescrip+on Drug Abuse Epidemic and Preven+on: How Prescrip+on Monitoring Programs Can Help PMP Center of Excellence at Brandeis University September 23, 2010 Webinar

2 Learning Objec+ves To understand the epidemiology of prescripeon drug abuse in the U.S. To have a basic understanding of what a prescripeon monitoring program is, how it works, and differences across states in aceviees To understand how data from a prescripeon monitoring program can be used to address prescripeon drug abuse and its consequences

3 Why Is This Discussion Important? How many people have died from uninteneonal prescripeon opioid poisonings since 9:00 AM this morning? How many people have had overdoses requiring emergency department or inpaeent admissions from prescripeon opioids since this morning? How did this epidemic begin?

4 What is the Epidemiology of Prescrip+on Drug Use and Overdose in the U.S.?

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6 Past Year Ini+ates for Specific Illicit Drugs among Persons Aged 12 or Older - NSDUH 2008

7 Past Month Illicit Drug Use among Persons Aged 12 or Older: 2008 Source: NSDUH Illicit Drugs include marijuana/hashish, cocaine (including crack), heroin, hallucinogens, inhalants, or prescripeon- type psychotherapeuecs used nonmedically.

8 Past Month Nonmedical Use of Types of Psychotherapeu+c Drugs among Persons Aged 12 or Older: Source: NSDUH 2008

9 Emergency Department (ED) Visits Involving the Nonmedical Use of Selected Narco+c Pain Relievers: 2004 to 2008* - DAWN REPORT June 18, 2010

10 Age- specific rates of ED visits for nonmedical use of opioid analgesics (OAs) and benzodiazepines (BZDs) United States, 2004 and 2008 MMWR June 18, 2010

11 Florida Newborns in Narco+c Withdrawal Rate per 1, Rate per 1, Years Source: Florida Dept. of Health NarcoEcs include prescripeon opioids and heroin

12 Epidemics of uninten+onal drug overdose deaths in the United States, Crude rate per 100, Crack cocaine Heroin '70 '72 '74 '76 '78 '80 '82 '84 '86 '88 '90 '92 '94 '96 '98 '00 '02 '04 Year Source: CDC Prescription drugs

13 Uninten+onal drug overdose deaths by major type of drug, United States, ,000 a Number of deaths 12,000 10,000 8,000 6,000 Opioid analgesic Cocaine 4,000 2,000 Heroin 0 '99 '00 '01 '02 '03 '04 '05 '06 07 Source: CDC

14 Uninten+onal overdose deaths involving opioid analgesics parallel per capita sales of opioid analgesics, U.S., Deaths '97 '98 '99 '00 '01 '02 '03 '04 '05 '06 '07 * *Opioid sales (mg/ person) morphine equivalents Source: NaEonal Vital StaEsEcs System, muleple cause of death dataset, and DEA ARCOS * 2007 opioid sales figure is preliminary.

15 Uninten+onal/undetermined intent drug overdose death rates by drug type and urbaniza+on, U.S., 2004 Opioid analgesic Heroin Death rate per 100, Lge Cent Metro Lge Fringe Metro Med Metro Small Metro Micropol Nonmetro Noncore Nonmetro Source: NaEonal Vital StaEsEcs System, accessed through CDC WONDER

16 Uninten+onal and undetermined intent drug overdose death rates by degree of urbaniza+on, U.S., Source: CDC

17 Uninten+onal drug overdose death rates by sex and age group, United States, 2007 Female Male Death rate per 100, <

18 Massachuseds Deaths Benzodiazepines Contributed to Opioid Related Poisoning Deaths 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00%

19 From 2010 Na+onal Drug Control Strategy

20 From 2010 Na+onal Drug Control Strategy

21 Summary of the Epidemiology of Opioid Misuse Risk is equal for women and greater for young people (<26) for nonfatal outcomes. Men and the middle- aged dominate the fatal outcomes. Non- Hispanic whites are at highest risk. Risk might increase with dose, rapid dose escalaeon, number of prescripeons, doctor or pharmacy shopping. Mental illness and history of chronic pain might be risk factors Source - CDC

22 What are Prescrip+on Monitoring Programs (PMPs)?

23 Prescription Monitoring Program GOALS EducaEon & InformaEon Public Health IniEaEves Early IntervenEon& PrevenEon InvesEgaEon & Enforcement

24 Data Collected from Pharmacies PMPs Collect Controlled Substances: 22 collect Schedules II - V 15 collect Schedules II IV 2 collect Schedule II only 1 collect Schedules II & III Some Collect Non- Controlled Substances 13 collect carisoprodol (Soma ) 6 collect tramadol (Ultram ) Electronic Data CollecEon - Batch ReporEng ReporEng Frequency varies - 7 to 30 days

25 PMP System Overview Dispensers Data Submitted Pharmacists Reports Sent State PMP Reports Sent Prescribers Reports Sent Law Enforcement & Professional Licensing Agencies *Other groups may also receive reports other than those listed

26 Prescrip+on Informa+on Collected PaEent ideneficaeon: Name & Address DOB & Gender Prescriber InformaEon Dispensing Pharmacy InformaEon Drug InformaEon, e.g. NDC # = name, type, strength, manufacturer QuanEty & date dispensed

27 Statutorily Authorized State PMPs, Harold Rogers Program Started

28 AK Status of PMPs May 2010 VT WA* OR NV CA ID AZ UT MT WY CO NM ND SD NE KS OK MN WI IA IL MO AR MS NY MI PA OH IN WV VA KY NC TN SC GA AL ME NH MA RI CT NJ DE MD DC HI TX LA FL Operational PDMPs GU Enacted PDMP legislation, but program not yet operational Legislation pending Research is current as of May 28, 2010 *Washington has temporarily suspended its PMP operations due to budgetary constraints.

29 PMP STATISTICS PopulaEon: 273 million - - states with authorized PMPs 89% of the US PopulaEon PrescripEon Records: During OperaEonal PMPs 285 million Controlled Substances PrescripEons

30 Number of PDMP Reports Produced Per Year 4,500,000 4,000,000 3,500,000 3,000,000 Harold Rogers Program Started 2,500,000 2,000,000 1,500,000 1,000, , Est Est. 2008

31 PDMP REPORTS BY TYPE OF USER CA ID KY NV OH OK VA Prescribers & Pharmacists Law Enf. & Regulatory

32 Percent of PMP Reports to Prescribers and Pharmacists by Type of PMP Agency # of PDMPs Type of Agency % Report to Prescribers & Pharmacists 3 Boards of Pharmacy 90%- 96% 3 Law Enforcement 90%- 96%

33 Resources to Learn More About PMPs

34 Alliance of States with Prescrip+on Monitoring Programs To provide a forum for the development, sharing, and exchange of informaeon and ideas about all aspects of prescripeon monitoring programs in an effort to curtail drug diversion and abuse while simultaneously ensuring paeent care.

35 Alliance of States with Prescrip+on Monitoring Programs Alliance founded in 1990 to provide support and informaeon exchange for: ExisEng PMPs States anempeng to establish PMPs States considering establishment of PMPs Partnered with Brandeis University for PMP Training and Technical Assistance Center in 2008

36 Purpose: PMP Training & Technical Assistance Center To provide assistance and training to state PMPs, non- PMP states and other stakeholders (i.e. BJA, DEA) in: Compiling, organizing, assessing and making available PMP materials, research findings, legislaeon, etc. FacilitaEng exchange of knowledge, experese, and tools Developing trainings about emerging needs among PMPs Tailoring technical assistance to PMPs ConducEng Regional and NaEonal MeeEngs for PMPs

37 Sekng Up Interstate Exchanges of PMP Data Ohio PMP authorized User New York PMP System ` Nevada PMP System Ohio PMP System PMIX Hub Server Kentucky PMP System

38 STATE PROFILES TTAC / ALLIANCE ACTIVITIES A State Profile for each PrescripEon Monitoring Program (PMP) Contact informaeon Website addresses Schedules collected Numbers of prescripeons and reports Statutes and regulaeons references Profiles available on the Alliance s website,

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40 Prescrip+on Monitoring Program Center of Excellence Brandeis University Supported by the Bureau of Jus+ce Assistance

41 PMP Center of Excellence Harold Rogers PDMP Grant to Brandeis University Announced by BJA - - Fall 2009 Funds received - - February 2010 Brandeis University formally announced the Center s formaeon - - March 2010 ( Center housed in Schneider InsEtutes for Health Policy, Heller School for Social Policy & Management

42 Web site/clearinghouse Center Components Compile, analyze, improve HR/BJA performance measures Conduct studies to demonstrate effeceveness of PMPs and value of PMP data Conduct case studies of selected start- up PMPs IdenEfy best praceces in selected areas Establish expert panel to guide Center work

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44 PMP Center of Excellence Expert Panel 4 PMP Region RepresentaEves DEA PMP at- large FDA Alliance IJIS InsEtute ATTC NASCSA AATOD NMPI BJA ONDCP CDC SAMHSA Carnevale Associates Tuss Medical School CoaliEon Against Insurance Fraud NaEonal CAPT

45 How can Prescrip+on Monitoring Programs Support Preven+on Efforts?

46 How Can PMP Data Be Used to Support Preven+on? To date, PMP data has not been used to support preveneon Several states have plans to do so VT and ME plan to generate community profiles based on PMP data Panerns of prescribing of selected drugs Panerns of queseonable acevity/doctor shopping Trends in prescribing and/or doctor shopping MA s Strategic PrevenEon Framework State IncenEve Grant (SPF SIG) has a focus on opioid overdose cases State Epi Work Group plans to incorporate PMP data Statewide evaluators plan to use PMP data in evaluaeon

47 Example 1: Community- Level Doctor Shopping Rates Associated with Community- Level Rates of Opioid Overdoses in Massachuseds

48 What is Doctor Shopping? PaEent seeks prescripeon without informing a prescriber that he/she has obtained prescripeons from other prescribers How Can PMP Data be Used to Measure it? PaEents with prescripeons from muleple prescribers, filled at muleple pharmacies, can be idenefied in PMP data States use different thresholds

49 2005 Prescriptions Associated with Questionable Activity (Rates per 100,000 Prescriptions) by Pharmacy Town Questionable activity rates

50 2005 Opioid-related Health Overdose Problems Rate per 100,000 by Town Rate per 100,000 Quintiles

51 Example 1 con+nued Rates of doctor shopping: prescripeons associated with individuals who obtained Schedule II opioid prescripeons from 4 or more prescribers and filled them at 4 or more pharmacies in a 12 month period, by town of pharmacy Rates of doctor shopping were significantly associated with rates of fatal and non- fatal opioid overdoses, controlling for populaeon density, poverty rate, ethnic diversity, populaeon mobility, age distribueon, and spaeal associaeon Rates of doctor shopping were higher in suburban, middle class, predominantly white communiees, in contrast to typical risk factors for substance abuse

52 Example 1 con+nued: Preven+on Implica+ons Data from PMPs is available in a much more Emely way than death cereficate or hospital discharge data Mapping of doctor shopping rates can help target take- back programs, prescriber outreach and educaeon (promote prescriber use of PMP online portal if one exists in the state), and preveneon programs for prescripeon drug abuse

53 Example 1 con+nued: Preven+on Implica+ons CommuniEes osen cluster with respect to doctor shopping rates and prescribing panerns. PrevenEon efforts targeeng a single community in a cluster may be undermined by influence of surrounding communiees Isolate individual community with low rates in a cluster of communiees with high rates. How are isolates able to resist the influence of neighbors? What can be learned from a community with high rates in a cluster of those with low rates that could assist preveneon?

54 Example 2: Geographic Spread of OxyCon+n Prescribing and OxyCon+n- Related Doctor Shopping in Massachuseds,

55 OxyContin Prescriptions in MA, 1996 Number of prescriptions, by quintile

56 OxyContin Prescriptions in MA, 1998 Number of prescriptions, by quintile

57 Rates of Doctor Shopping Associated with OxyContin in MA, 1996 Rates per 100 of doctor shopping, by quintile

58 Rates of Doctor Shopping Associated with OxyContin in MA, 1998 Rates of doctor shopping x 100, per quintile

59 Example 2: Preven+on Implica+ons Knowledge of the diffusion panerns of problemaec prescripeon drugs can help inform preveneon of future prescripeon drug problems Did prescribing of the drug and problems associated with the drug start from a single or muleple origins? What was the panern of diffusion? Which communiees were more likely to have problems early on in the history of a new prescripeon drug with abuse poteneal? Which communiees were able to resist?

60 Example 3: Geographic Paderns of Prescrip+ons Wriden in Georgia and Filled in One of Four Neighboring States

61 Controlled Substances Dosage Units Prescribed by Georgia Prescribers but Dispensed in Other States AK VT WA* OR NV CA ID AZ UT MT WY CO NM ND MN SD WI NE IA IL KS MO OK AR MS NY MI PA OH IN WV VA KY TN GA AL NC SC ME NH MA RI CT NJ DE MD DC HI TX LA FL 0.4 Million Dosage Units 3.0 Million Dosage Units 9.8 million Dosage Units *Washington has temporarily suspended its PMP operations due to budgetary constraints Million Dosage Units

62 Average Number of Prescriptions Per Patient, 2009 Prescriptions Originating in GA and Filled in AL by GA Zip Code Legend GA prescribers: Average # prescriptions per patient

63 Average Number of Prescriptions Per Patient, 2009 Prescriptions Originating in GA and Filled in NC by GA Zip Code Legend GA prescribers: Average # prescriptions per patient

64 Average Number of Prescriptions Per Patient, 2009 Prescriptions Originating in GA and Filled in AL, LA, NC, or SC by GA Zip Code Legend GA prescribers: Average # prescriptions per patient

65 Example 3: Preven+on Implica+ons In some Georgia Zip Codes, between 15 and 54 prescripeons per paeent were wrinen, which were filled in North Carolina, South Carolina, Alabama, or Louisiana PMP data from neighboring states can inform prescripeon drug abuse needs in your state. A complete needs assessment may involve assembling data from muleple state PMPs. Data from neighboring states can also reveal aspects of the problem not visible with data from within the state (e.g., border communiees)

66 Ques+ons?

67 Contact Informa+on John Eadie, MPA Director, PMP Center of Excellence Kristen Clements- Nolle, Ph.D CAPT West Epidemiologist Peter Kreiner, Ph.D. Principal InvesEgator, PMP Center Of Excellence Wendy Baumbach CAPT West T/TA Specialist