Presentation is loading. Please wait.

Presentation is loading. Please wait.

Prescription Behavior Surveillance Using PDMP Data Dagan Wright, PhD, MSPH (Oregon Health Authority) Denise Penone, PhD (New York City Department of Health)

Similar presentations

Presentation on theme: "Prescription Behavior Surveillance Using PDMP Data Dagan Wright, PhD, MSPH (Oregon Health Authority) Denise Penone, PhD (New York City Department of Health)"— Presentation transcript:

1 Prescription Behavior Surveillance Using PDMP Data Dagan Wright, PhD, MSPH (Oregon Health Authority) Denise Penone, PhD (New York City Department of Health) Special thanks and acknowledgement to Len Paulozzi who could not attend as all contributors

2 Outline of the PDMP Talk What is PMP or PDMP? Why so important? What are general characteristics and data elements? What are questions that can be answered? Examples of data Examples of outreach and evaluation

3 What is PMP or PDMP? Tool utilized for reducing prescription drug misuse and diversion –Drug Epidemic Warning System –Drug Diversion & Fraud Investigative Tool Public Health Surveillance tool to collect, monitor, and analyze dispensing data –Avoidance of Drug Interactions –Patient Care Tool –Identification & Prevention of “Doctor Shopping”* Data now can used to support states’ efforts in education, research, quality assurance (better healthcare), enforcement and abuse prevention Not meant to infringe on the legitimate prescribing of controlled substances *Doctor Shopping: Practice of obtaining multiple controlled substance prescriptions from multiple doctors Source:


5 Why so Important? 5

6 Opioid analgesic overdose deaths increased 65% Opioid analgesic overdose deaths, NYC, 2005-2011 Source: New York City Office of the Chief Medical Examiner & New York City Department of Health and Mental Hygiene 2005-2011

7 Oregon Drug Related Trends Counts and rates/100,00 7


9 Methadone Death Rates Parallel Methadone Sales 9

10 More Drug Overdose Deaths than Motor Vehicle Crash Deaths 10 Source: Oregon Vital Records Year

11 Oregon Hospitalization Rate/10,000 residents 11

12 What are General Characteristics and Data Elements? 12

13 PDMP: General Characteristics Typically require monthly or bi-weekly reporting –Some States require weekly reporting i.e., Florida, Oregon –Oklahoma, requires reporting at time of sale Reactive vs. Proactive –Reactive: Generate solicited reports only in response to a specific inquiry –Proactive: Generate unsolicited reports whenever suspicious or potentially at risk to the patient behavior is detected Drug Schedules Monitored by states: –24 collect Schedules II -V –17 collect Schedules II –IV –1 collect Schedule II only –2 collect Schedules II & III Source:

14 PDMP: Information Collected Patient identification –Name & Address –DOB & Gender Prescriber Information & Dispenser Information –DEA number Drug Information –National Drug Code (NDC) Info: Name Type Strength Manufacturer –Quantity & date dispensed Source:

15 PDMP Attributes As a Surveillance System Simplicity: single data source, few data elements, drug code (NDC) is complicated Flexibility: limited fields Data quality: insurance and system error checks Acceptability: mandatory See: Lee et al, eds., Principles and Practice of Public Health Surveillance, 3 rd edition, 2010.

16 PDMP Attributes As a Surveillance System Sensitivity: high, required by law Predictive value positive: metrics untested Representativeness: population-based Timeliness: days to weeks Stability: in most cases operating for years Cost: support for many is inadequate for most PDMPs – Other sources Oregon uses a provider licensing fee to support the PDMP See: Lee et al, eds., Principles and Practice of Public Health Surveillance, 3 rd edition, 2010.

17 Model Act 2010 Revision Data Elements for PDMPs PrescriptionNumber, Date issued by prescriber, Date filled, New or refill, Number of refills, State-issued serial number (optional) DrugNDC code for drug, Quantity dispensed, Days’ supply dispensed

18 Model Act 2010 Revision Data Elements for PDMPs PatientIdentification number Name, Address, Date of birth, Sex Source of payment Name of person who receives prescription if other than patient PrescriberIdentification number DispenserIdentification number

19 Descriptive Measures: Prescription Counts Specific compound, formulation Drug class – Opioids, benzodiazepines, stimulants, etc. – All extended-release formulations of opioids – Class within a schedule, e.g., Schedule II opioids Daily dosage of an opioid prescription

20 Questions that can be Answered 20

21 Descriptive Measures: Denominators Person, e.g., rx per 1,000 people (most common) Patient, e.g., rx per 1,000 patients Prescriber, e.g., mean daily dose/prescriber Pharmacy, e.g., rx/pharmacy Time period is specified: e.g., in 2012, in past quarter

22 Descriptive Measures: “By” Variables Patient sex, age group Patient/prescriber/pharmacy by county or zip code Month, year (prescribed or dispensed) Prescriber specialty (requires linkage based on prescriber number) Source of payment (where collected) Patient type, e.g., opioid-naive

23 Risk Measures: Daily Dose for Opioids Converted to morphine milligram equivalents (MME) Usually categorized, e.g., – High, e.g., >100 MME/day – Going beyond specific dosing guidelines e.g., more than 30 mg of methadone per day for an opioid-naïve person Also quantified by measures of central tendency: mean, median, quartiles dose SAS coding to do MME conversions available from CDC

24 Examples of Data 24

25 Number of Patients Receiving Opioid Dosages > 100 MME/day, Tennessee, 2007‒2011 Number of Patients Baumblatt J. Prescription Opioid Use and Opioid-Related Overdose Death TN, 2009–2010, CDC EIS Tuesday Morning Seminar, 1/8/2013

26 Opioid Prescriptions Filled by Staten Islanders Are More Frequently High Dose Schedule II opioids + hydrocodone, New York State Prescription Drug Monitoring Program

27 27 Number of people/1,000 residents receiving an opioid Oct 1, 2011 to March 31, 2012

28 28 Number of people/1,000 residents receiving an opioid and benzodiazepine Oct 1, 2011 to March 31, 2012

29 29 Number of people/10,000 residents using 4 or more prescribers and 4 or more pharmacies Oct 1, 2011 to March 31, 2012

30 Rates of Unintentional Poisoning Mirrors Rates of Dispensed Prescriptions Source:

31 Use of PMP Data by MA Dept. of Public Health “Shopping” as a portion of all prescriptionsOverdoses in ED Data Slide provided courtesy of Peter Kreiner, PMP Center of Excellence at Brandeis. Doctor shopping, the questionable activity, was defined as 4+ prescriber s and 4+ pharmacies for CSII in six months.

32 Measures of “Shopping” or “Multiple Provider Episodes”

33 Patient vs. Provider Metrics? Top 1% of prescribers based on number of prescriptions might account for 33% of the morphine equivalents (MME) in your state.(1) Top 1% of patients might account for 40% of MME.(2) 1. Swedlow 2011; 2. Edlund 2010

34 15% of prescribers write 82% of opioid analgesic prescriptions Prescriptions filled by NYC residents, 2010 15% 82% Percent Source: New York State Department of Health, Bureau of Narcotic Enforcement, Prescription Drug Monitoring Program, 2008-2010 34

35 Distribution of CS II-IV prescriptions to prescribers, Oregon, 1/12 to 9/12 % of Prescribers% of CS Prescriptions Oregon Health Authority. Prescription Drug Dispensing in Oregon, October 1, 2011 – March 31, 2012

36 Examples of Outreach and Evaluation 36

37 Patient vs. Provider Metrics? 100 patients in the PMP for every prescriber It takes roughly 100 times more effort to address the same fraction of problematic prescriptions. For interventions, provider case-finding is preferred based on efficiency.

38 1 st Evaluation of Oregon PDMP soon followed by NIH study – survey use 65% say it is very helpful to monitor patients’ prescriptions for controlled substances 64% report it is very helpful to control “doctor shopping” 78% have spoken with patient about controlled substance use after using system 59% reduced or eliminated prescriptions for a patient after using system 49% contacted other providers or pharmacies 38 Source: Oregon Prescription Drug Monitoring Program Evaluation

39 Avoid prescribing opioids for chronic non-cancer, non- end-of-life pain E.g. low back pain, arthritis, headache, fibromyalgia When opioids are warranted for acute pain, 3-day supply usually sufficient Avoid whenever possible prescribing opioids in patients taking benzodiazepines If dosing reaches 100 MED, reassess and reconsider other approaches to pain management NYC Opioid Treatment Guidelines

40 References Cited Cepeda, M., D. Fife, et al. (2012). "Assessing opioid shopping behavior." Drug Safety. Edlund, M. J., B. C. Martin, et al. (2010). "Risks for opioid abuse and dependence among recipients of chronic opioid therapy: results from the TROUP study." Drug Alcohol Depend 112(1-2): 90-98. Forrester, M. B. (2011). "Ingestions of hydrocodone, carisoprodol, and alprazolam in combination reported to Texas poison centers." Journal of Addictive Diseases 30: 110-115. Hall, A. J., J. E. Logan, et al. (2008). "Patterns of abuse among unintentional pharmaceutical overdose fatalities." JAMA 300: 2613-2620. Katz, N., L. Panas, et al. (2010). "Usefulness of prescription monitoring programs for surveillance---analysis of Schedule II opioid prescription data in Massachusetts, 1996--2006." Pharmacoepidemiol Drug Safety 19: 115-123. Ohio Department of Health. (2010). "Epidemic of prescription drug overdoses in Ohio." Retrieved September 1, 2010, from Peirce, G., M. Smith, et al. (2012). "Doctor and pharmacy shopping for controlled substances." Med Care. Swedlow, A., J. Ireland, et al. (2011). Prescribing patterns of schedule II opioids in California Workers' Compensation, California Workers' Compensation Institute. White, A. G., H. G. Birnbaum, et al. (2009). "Analytic models to identify patients at risk for prescription opioid abuse." Am J Manag Care 15(12): 897-906. Wilsey, B. L., S. M. Fishman, et al. (2010). "Profiling multiple provider prescribing of opioids, benzodiazepines, stimulants, and anorectics." Drug Alcohol Depend 112: 99-106.

Download ppt "Prescription Behavior Surveillance Using PDMP Data Dagan Wright, PhD, MSPH (Oregon Health Authority) Denise Penone, PhD (New York City Department of Health)"

Similar presentations

Ads by Google