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The document discusses the use of Prescription Drug Monitoring Programs (PDMPs) and the Prescription Behavior Surveillance System (PBSS) to identify and address problematic prescribing practices. It highlights learning objectives, epidemiological findings, and metrics that can help assess prescription behaviors and risk indicators among providers and patients. Moreover, it addresses the challenges of data quality and the need for a standardized screening tool for evaluating prescribers using PDMP data.
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- 1. PDMP Track Ensuring AppropriatePrescribing: Using PDMPs to Identify and Address Problematic Prescribing Presenters: • Peter W. Kreiner, PhD, Senior Scientist, Institute for Behavioral Health, Brandeis University • Christopher Ringwalt, DrPH, MSW, Senior Scientist, Injury Prevention Center, University of North Carolina at Chapel Hill Moderator: John L. Eadie, Director, Prescription Drug Monitoring Program (PDMP) Center of Excellence, and Member, Rx Summit National Advisory Board
- 2. Dislosures • Peter W.Kreiner, PhD; Chris Ringwalt, DrPH; and John L. Eadie have disclosed no relevant, real or apparent personal or professional financial relationships with proprietary entities that produce health care goods and services.
- 3. Disclosures • All planners/managershereby state that they or their spouse/life partner do not have any financial relationships or relationships to products or devices with any commercial interest related to the content of this activity of any amount during the past 12 months. • The following planners/managers have the following to disclose: – Kelly Clark – Employment: Publicis Touchpoint Solutions; Consultant: Grunenthal US – Robert DuPont – Employment: Bensinger, DuPont & Associates-Prescription Drug Research Center – Carla Saunders – Speaker’s bureau: Abbott Nutrition
- 4. Learning Objectives 1. Advocateuse of PDMPs to identify and address problematic prescribing. 2. Explain the purpose, operation and epidemiological findings of the Prescription Behavior Surveillance System. 3. List metrics that can be used to identify providers manifesting unusual or uncustomary prescribing practices.
- 5. Ensuring Appropriate Prescribing UsingPDMPs to Identify and Address Problematic Prescribing: Epidemiological Findings from the Prescription Behavior Surveillance System April 8, 2015 Peter W. Kreiner, Ph.D. Brandeis University
- 6. Disclosure Statement Peter Kreiner,Ph.D., has disclosed no relevant, real, or apparent personal or professional financial relationships with proprietary entities that produce health care goods and services.
- 7. Learning Objectives 1. Advocateuse of PDMPs to identify and address problematic prescribing. 2. Explain the purpose, operation and epidemiological findings of the Prescription Behavior Surveillance System. 3. List metrics that can be used to identify prescribers manifesting unusual or uncustomary prescribing practices.
- 8. Overview • Development ofthe Prescription Behavior Surveillance System (PBSS): – Federal and state PDMP partners – Data submitted by state partners – Measures of prescribing behavior; and patient, prescriber, and pharmacy risk indicators • Applications of PDMP data: Examples of trends in prescribing behaviors and risk indicators • Issues in data quality and its assessment, including record-matching procedures
- 9. The Prescription Behavior SurveillanceSystem (PBSS) A longitudinal, multi-state database of de-identified PDMP data, to serve as: 1. An early warning public health surveillance tool 2. An evaluation tool, in relation to state and local laws, policies and initiatives, such as prescriber educational initiatives Info available at: http://www.pdmpexcellence.org/content/ prescription-behavior-surveillance-system-0
- 10. PBSS Continued • Beganin FY2012 with support from CDC and FDA, administered through BJA • Guided by Oversight Committee: – Federal partners: CDC, FDA, BJA, SAMHSA – State partners to date: CA, DE, FL, ID, KY, LA, ME, OH, TX, WA, WV – Additional state partners in process – Adjunct state partners (MA, OK, TN) – unable to share data but may be willing to provide PBSS surveillance measures – No release of data or findings without Oversight Committee approval
- 11. PBSS Continued • De-identifieddata from each participating state – Data use agreements tailored to each state’s laws and requirements – Beginning with 2010 or 2011, initial 2 – 4 years of data – Data updated quarterly (including prior 12 months) – Project-specific ID #’s for patients, prescribers, pharmacies • Maintained for the duration of the data – Data housed in secure IT environment at Brandeis University
- 12. PBSS Measures • Prescribingmeasures – Rates of opioid, benzodiazepine, stimulant prescriptions • By quarter and year, by drug class, sex, and age group • By quarter and year, by major opioid, benzodiazepine, and stimulant drug category • Patient risk indicators – Average daily dosage of opioids (MMEs) – Days of overlapping prescriptions – Multiple provider episode rates • By drug class, age group, and drug category
- 13. PBSS Measures Continued •Prescriber risk indicators – Prescriber percentile ranking, based on daily prescribing volume • By quarter, year, and drug class – Average daily dosage for opioid patients (MMEs) – Median distance in miles, patient to prescriber – Percentage of patients with MPE – Percentage of prescriptions by payment type – Percentage of patients prescribed LA/ER opioids who were opioid-naïve • Pharmacy risk indicators – Analogous to prescriber risk indicators
- 14. Some Examples • Trendsin prescribing rates, by state – Opioids in general – Hydrocodone in particular • Trends in patient/prescriber risk indicators, by state – Overlapping opioid and benzodiazepine prescriptions – High average daily dosage of opioids – Multiple provider episodes (MPEs) • Framework for validation studies of prescriber risk indicators
- 15. 0 50 100 150 200 250 300 350 400 2010 Q1 2010 Q2 2010 Q3 2010 Q4 2011 Q1 2011 Q2 2011 Q3 2011 Q4 2012 Q1 2012 Q2 2012 Q3 2012 Q4 2013 Q1 2013 Q2 2013 Q3 2013 Q4 2014 Q1 2014 Q2 2014 Q3 2014 Q4 Opioid Prescriptions per1,000 State Residents By State and Quarter CA DE FL ID KY LA ME OH WA WV Tramadol becomes Sch. IV
- 16. 0 50 100 150 200 250 2010 Q1 2010 Q2 2010 Q3 2010 Q4 2011 Q1 2011 Q2 2011 Q3 2011 Q4 2012 Q1 2012 Q2 2012 Q3 2012 Q4 2013 Q1 2013 Q2 2013 Q3 2013 Q4 2014 Q1 2014 Q2 2014 Q3 2014 Q4 Hydrocodone Prescriptions per1,000 State Residents By State and Quarter CA DE FL ID KY LA ME OH WA WV Hydrocodone rescheduled to Sch. II
- 17. 0.00 5.00 10.00 15.00 20.00 25.00 2010 Q1 2010 Q2 2010 Q3 2010 Q4 2011 Q1 2011 Q2 2011 Q3 2011 Q4 2012 Q1 2012 Q2 2012 Q3 2012 Q4 2013 Q1 2013 Q2 2013 Q3 2013 Q4 2014 Q1 2014 Q2 2014 Q3 2014 Q4 Percent of OpioidPatients Receiving an Average Dosage > 100 MME per Day CA DE FL ID KY LA ME OH WA WV
- 18. 0.00 5.00 10.00 15.00 20.00 25.00 2010 Q1 2010 Q2 2010 Q3 2010 Q4 2011 Q1 2011 Q2 2011 Q3 2011 Q4 2012 Q1 2012 Q2 2012 Q3 2012 Q4 2013 Q1 2013 Q2 2013 Q3 2013 Q4 2014 Q1 2014 Q2 2014 Q3 2014 Q4 Percent of OpioidPrescription Days with Overlapping Benzodiazepine Prescriptions, by State and Quarter CA DE FL ID KY LA ME OH WA WV
- 19. Multiple Provider Episodes •Defined as the number of patients with CS prescriptions from 5 or more prescribers and 5 or more pharmacies in a 3-month period, per 100,000 state residents • Differences in how states determine which prescription records belong to the same patient preclude comparisons between states • We can, however, compare state MPE trends – Simeone reported decreasing trends nationally 2008 - 2012
- 20. 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 2010 Q1 2010 Q2 2010 Q3 2010 Q4 2011 Q1 2011 Q2 2011 Q3 2011 Q4 2012 Q1 2012 Q2 2012 Q3 2012 Q4 2013 Q1 2013 Q2 2013 Q3 2013 Q4 2014 Q1 2014 Q2 2014 Q3 2014 Q4 MPEratesper1,000stateresidents Multiple Provider Episodesby State and Quarter Rates per 1,000 Residents CA DE FL ID KY LA ME OH WA WV Kentucky mandatory PDMP use law implemented
- 21. 0.00 5.00 10.00 15.00 20.00 25.00 30.00 2010 2011 20122013 2014 MPErateper100,000stateresidents Multiple Provider Episodes by State and Year Rates per 100,000 Residents, Annual Averages CA DE FL ID KY LA ME OH WA WV
- 22. Prescriber Risk Indicators •Prescribing volume by prescriber decile: Proportion of total prescriptions accounted for by prescriber 10% groupings • Average daily opioid dosage (MMEs) by prescriber decile (volume) • Distance patients travel to prescriber and proportion of prescriber practice who meet MPE threshold
- 23. 0 10 20 30 40 50 60 70 1st 2nd 3rd4th 5th 6th 7th 8th 9th 10th California, 2012: Proportion of Total Opioid, Stimulant, and Benzodiazepine Prescriptions Written by Prescriber Deciles Percent of total opioid prescriptions Percent of total stimulant prescriptions Percent of total benzodiazepine prescriptions
- 24. 0 10 20 30 40 50 60 70 80 1st 2nd 3rd4th 5th 6th 7th 8th 9th 10th AveragedailydosageofpatientsinMMEs Prescriber decile based on volume of opioid prescriptions California 2012: Average Daily Dosage of Patients by Prescriber Decile Based on Volume of Opioid Prescriptions
- 25. 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0 20 4060 80 100 120 140 PercentageofprescriberpatientswithMPE Average distance patients travel in miles California 2012: Prescriber Deciles Based on Average Distance Patients Travel, Compared with Percentage of Prescriber Patients with an MPE Percentage of patients with MPE Log. (Percentage of patients with MPE)
- 26. Validation Studies Purpose: 1. Examinefrequency of prescribers highest on prescriber risk indicators having actions taken against them (Medical Board, DEA, law enforcement) – Vs. prescribers lower on these indicators 2. Develop predictive models of actions taken to estimate relative effects of different prescriber behaviors
- 27. Validation Studies: AnalyticStrategy • Prescriber outcomes – Identify prescribers against whom actions have been taken • By the state Medical Board/Board of Osteopathic Medicine • By the DEA • By other law enforcement – Categorize types of offense and types of action taken – Examine/take into account prescriber license type and physician specialty
- 28. Validation Studies: AnalyticStrategy • Predictor variables – Prescriber risk indicators • Yearly, prior to year of action(s) taken • Trajectory analysis: identify different groups/patterns over time – Measure of prescribing complexity? • Pattern of drugs prescribed, in relation to peers • Control variables – Prescriber age, sex, location
- 30. Limitations of PDMPData for Surveillance and Evaluation • No unique identifier for patients: record linking procedures vary by PDMP – Probabilistic vs. deterministic record linking • PDMP relies on submitting pharmacies for data accuracy • Practices to assess and ensure data quality vary by PDMP • Recording of PRN prescriptions subject to pharmacist discretion (e.g., 30 pills may be recorded as 30 days’ supply)
- 31. Contact Information Peter Kreiner,Ph.D. Principal Investigator PDMP Center of Excellence Brandeis University 781-736-3945 pkreiner@brandeis.edu www.pdmpexcellence.org
- 32. Chris Ringwalt, DrPH* SharonSchiro, PhD** Meghan Shanahan, PhD* Scott Proescholdbell, MPH*** Harold Meder, MBA* Anna Austin, MPH,*** Nidhi Sachdeva, MPH *** *UNC Injury Prevention Research Center **UNC Department of Surgery ***NC Division of Public Health Using the NC Controlled Substances Reporting System to Identify Providers Manifesting Unusual Prescribing Practices 32
- 33. Disclosure Statement • ChrisRingwalt, DrPH, has disclosed no relevant, real or apparent personal or professional financial relationships with proprietary entities that produce health care goods and services.
- 34. Learning Objectives 1. Advocateuse of PDMPs to identify and address problematic prescribing. 2. Explain the purpose, operation and epidemiological findings of the Prescription Behavior Surveillance System. 3. List metrics that can be used to conduct an initial screen of providers manifesting unusual or uncustomary prescribing practices.
- 35. 35 Introduction • In 2012,the percent of the population admitting to the misuse of prescriptions drugs in the past 12 months was: – 5.3% of youth aged 12-17 – 10.1% of young adults aged 18-25 – 3.8% of adults >25 • In 10 years, the annual number of prescriptions for opioid analgesics has increased from 76 to 210 million • 1.2 million visits to EDs for the nonmedical use of prescription drugs in 2009 • 11,700 deaths were attributed to the nonmedical use of prescription drugs in 2011 • Total cost to society in 2007: $55.4 million
- 36. 36 Prescription Drug MonitoringPrograms: A Powerful Clinical and Research Tool • Registries of all scheduled drug (controlled substances) prescriptions filled in a given state • Typically include: – Date dispensed – Type, strength, and duration of each prescription – Identifying information relating to each patient, prescriber, and dispenser (pharmacy) • Designed to be used for multiple purposes: – Querying by registered providers and pharmacies of active patients to promote appropriate prescribing practices and prevent fraud – Detect inappropriate prescribing (or dispensing) practices – (Occasionally) research • Now in all states but Missouri
- 37. 37 Problems with Useof PDMPs to Detect Inappropriate Prescribing • Lack of clarity as to which indicators may serve as a good screening tool • Concerns about the potential for many false positives • Lack of resources to investigate providers identified by these screens • Lack of information in PDMPs concerning provider specialty (e.g., oncologists, end-of-life treatment specialists) • Concern that providers treating chronic patients may: – Dismiss those prematurely – Treat them sub-optimally – Decline to accept these patients into their practices
- 38. 38 How do RegulatoryAuthorities Detect Inappropriate Prescribing Now? • Complaints from patients and colleagues • Audits of medical records • Investigations by coroners or chief medical examiners However, currently, there is no standardized screening tool to apply to Prescription Drug Monitoring Programs for this purpose
- 39. 39 Project Goal To developand validate a set of algorithms from metrics that utilize data from North Carolina’s PDMP to develop a screening tool to identify prescribers who manifest unusual and uncustomary prescribing patterns
- 40. 40 Candidates for Metrics Providerswho Write the Highest: • Rates of prescriptions for daily doses of opioids >100 milligrams of morphine equivalents (MMEs) • Average daily dose of MMEs • Total MMEs for each prescription • Rates of prescriptions for following drug classes, irrespective of dose: – Benzodiazepines – Opioids – Stimulants • Rates of co-prescribed benzodiazepines + opioids >100 MMEs • Temporally overlapping prescriptions
- 41. 41 Candidates for Metrics Providerswith patients who: • Travel long distances from their homes to their: – Providers – Pharmacies • Fill prescriptions received from multiple providers (doctor shopping) for: – Opioids – Stimulants – Benzodiazepines – Any controlled substance • Fill prescriptions at multiple pharmacies (pharmacy hopping)
- 42. 42 Example of metricdistribution 0 200 400 600 800 1000 1200 1400 1600 1800 2000 0 5 10 15 20 25 30 35 NumberofProviders Average daily rate that providers write opioids for >100 MMEs Average daily rate that NC providers write opioid prescriptions for >100 MMEs
- 43. 43 Example: Distribution tail 47 34 8 10 11 2 4 2 1 2 00 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 5 10 15 20 25 30 35 40 45 50 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 NumberofProviders Average daily rate that providers write opioids for >100 MMEs Average daily rate that NC providers write opioid prescriptions for >100 MMEs
- 44. 44 SO WHAT? SoNothing, until Each Metric is Validated Initial Validation Strategy: • Combed NC Vital Statistics records for deaths (N=465) in 2012 related to opioid overdose – used t-codes representing drug-related poisonings • Recorded DEA #s of providers who had prescribed opioids to these patients within 30 days of their death. • Any given decedent could have received prescriptions from multiple providers (N=651) • Matched these to metrics relating to: – List 1: Top 1% of prescribers of controlled substances in each tail – List 2: Top 1% of prescribers in each tail + top 1% of prescribers for all controlled substances – Thus List 2 is a subset of List 1 • Note that because the number of providers in each full distribution varies, the number in the top 1% will also
- 45. 46% 77% n=57 n=31 0 10 20 30 40 50 60 Highest 1% ofthis metric Highest 1% of this metric + 1% of prescribers Co-prescribed benzodiazepines + opioids >100MMEs Providers who did not prescribe opioids to a decedent Providers who prescribed opioids to a decedent
- 46. 10% 61% n=165 n=18 0 20 40 60 80 100 120 140 160 180 Highest 1%of this metric Highest 1% of this metric + 1% of prescribers Temporally overlapping prescriptions Providers who did not prescribe opioids to a decedent Providers who prescribed opioids to a decedent
- 47. 34% 43% n=157 n=96 0 20 40 60 80 100 120 140 160 180 Highest 1% ofthis metric Highest 1% of this metric + 1% of prescribers Prescriptions for opioids >100MMEs Providers who did not prescribe opioids to a decedent Providers who prescribed opioids to a decedent
- 48. 36% 42% n=290 n=176 0 50 100 150 200 250 300 350 Highest 1% ofthis metric Highest 1% of this metric + 1% of prescribers Prescriptions for any opioids Providers who did not prescribe opioids to a decedent Providers who prescribed opioids to a decedent
- 49. 30% 32% n=271 n=167 0 50 100 150 200 250 300 Highest 1% ofthis metric Highest 1% of this metric + 1% of prescribers Prescriptions for any benzodiazepine Providers who did not prescribe opioids to a decedent Providers who prescribed opioids to a decedent
- 50. 50 Key Metrics Validatedby this Mechanism Metric label Highest 1% of metric (Proportion, %) Highest 1% of metric + 1% of prescribers (Proportion, %) Co-prescribed benzodiazepines + opioids >100 MMEs 26/57 (46%) 24/31 (77%) Temporally overlapping prescriptions 16/165 (10%) 11/18 (61%) Prescriptions for opioids >100 MMEs 54/157 (34%) 41/96 (43%) Prescriptions for any opioids 105/290 (36%) 74/176 (42%) Prescriptions for any benzodiazepines 80/271 (30%) 54/167 (32%)
- 51. 51 Non-Performing Metrics*: Providers withPatients who • Travel long distances to their – Providers – Pharmacies • Are doctor shoppers • Are pharmacy shoppers * With this validation effort, at least
- 52. 52 Discussion • Some ofthese metrics performed remarkably well • However, prescribing opioid analgesics within a month of a patient’s death does not constitute causality • Further, attributing deaths to opioid overdoses is not a perfect science • Thus we assessed concurrent, not criterion, validity • And findings from these metrics only represent an initial screen • Sensitivity analyses may be helpful: nothing magical about top 1% of providers • Greater concurrent validity related to providers in top 1% of all prescribers of a controlled substance (2nd bar) may be a function of greater exposure – i.e., they write the most prescriptions • Our PDMD: – Lacks specialty information – Lacked (until last year) payer information • Further validation required, ideally within the context of a longitudinal study that examines the results of screening metrics relative to investigative outcomes
- 53. Conclusions • A fewmetrics show considerable promise as a screening tool for aberrant prescribing • Others await further validation before they should be employed • Appropriate regulatory bodies (law enforcement, medical boards) can now open investigations for proactive in addition to reactive reasons • Potential for metric placement (rate & rank) to assist investigations by demonstrating to providers exactly where they lie on these distributions • Effects of use of screening mechanisms like this should be carefully evaluated to determine potential for “chilling” effects on prescribing behaviors
- 54. PDMP Track Ensuring AppropriatePrescribing: Using PDMPs to Identify and Address Problematic Prescribing Presenters: • Peter W. Kreiner, PhD, Senior Scientist, Institute for Behavioral Health, Brandeis University • Christopher Ringwalt, DrPH, MSW, Senior Scientist, Injury Prevention Center, University of North Carolina at Chapel Hill Moderator: John L. Eadie, Director, Prescription Drug Monitoring Program (PDMP) Center of Excellence, and Member, Rx Summit National Advisory Board
Editor's Notes
- #24 This distribution – approximately 80% of total prescriptions accounted for by the top 20% of prescribers, is quite consistent across states.
- #25 Note the association between average volume of opioid prescriptions prescribed and average daily dosage of patients.
- #26 This graph shows an association between the average distance patients travel to prescribers (aggregated into prescriber deciles based on distance patients travel) and the percentage of the prescribers’ patients who meet the multiple provider episode criteria (MPE: 5 or more prescribers and 5 or more pharmacies in a 3-month period). The association appears to be logarithmic.