Reversing the Tide: Tracking and Responding to Opioid Misuse State - - PowerPoint PPT Presentation

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Reversing the Tide: Tracking and Responding to Opioid Misuse State - - PowerPoint PPT Presentation

Sep 05, 2023 321 likes •611 views

Reversing the Tide: Tracking and Responding to Opioid Misuse State Mandatory Review of PDMP by Providers and Pain Clinic Law Reduces ED Visits and Inpatient Stays for Opioid Overdose Kun Zhang, Ph.D. Health Scientist 06/27/2017 Annual

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State Mandatory Review of PDMP by Providers and Pain Clinic Law Reduces ED Visits and Inpatient Stays for Opioid Overdose

Kun Zhang, Ph.D. Health Scientist 06/27/2017 Annual Research Meeting

National Center for Injury Prevention and Control

Reversing the Tide: Tracking and Responding to Opioid Misuse

Division of Unintentional Injury Prevention

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SLIDE 2

Collaborators

  • Kun Zhang, PhD, Health Scientist, Division of Unintentional Injury

Prevention (DUIP), NCIPC, CDC

  • Gery Guy, PhD, MPH, Senior Health Economist, DUIP, NCIPC, CDC
  • Rita Noonan, PhD, Branch Chief, DUIP, NCIPC, CDC
  • Chris Jones, PhD, PharmD, MPH, Director, Division of Science Policy,

ASPE, HHS

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SLIDE 3

Disclaimer The results and conclusions in this study are those of the authors and do not necessarily represent the

  • fficial position of the Centers for Disease Control and

Prevention and HHS.

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Overview

  • Background and Literature Review
  • Study Objective
  • Methods
  • Results
  • Conclusions
  • Limitations
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SLIDE 5

Overprescribing of Opioids

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SLIDE 6

Overprescribing of Opioids

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SLIDE 7

SOURCE: CDC/NCHS National Vital Statistics System Mortality File.

2 4 6 8 10 12 14 16

2000 2002 2004 2006 2008 2010 2012 2014

Deaths per 100,000 population

All Drug Overdoses Opioid Overdoses (including Rx and heroin)

Opioids Driving the Epidemic of Overdose Deaths

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SLIDE 8

Opioids-related ED Visits and Inpatient Stays

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SLIDE 9

Promising State Policies to Address the Epidemic by Improving Prescribing

  • Prescription Drug Monitoring Program (PDMP)

– State electronic database that tracks controlled substances dispensing

‒ Implemented in 49 states and D.C. ‒ DEA schedules II-IV such as opioid, benzodiazepine, stimulants, etc. ‒ Collect information of the filled prescription

– PDMPs can help

‒ Identify patients at risk for opioid abuse and overdose ‒ Inform providers of other medications patient receiving that may interact with

those prescribed

  • Pain clinic laws (pill mill laws)

– State law that regulates pain clinics on

– Ownership, licensure, inspection, limited days supply, etc.

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Policy Definition in the Study

  • Mandatory PDMP review

– All prescribers with DEA number required to register with PDMP – All prescribers with DEA number required to check PDMP data prior to

prescribing opioids

– 2011: OH; 2012: KY, WV; 2013: TN, NY; 2014: NM; 2015: NV, OH;

2016: CT, MA, OK, NJ; 2017: CA, WI

  • Pain clinic laws

– Registration, certification, licensing, physician on-site, medical director

required, prescribing restrictions, penalties, etc.

– 2007: LA; 2009: TX; 2010: FL; 2011: OH; 2012: KY, TN; 2013: WV

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Literature Review

  • Mandatory PDMP review

– Reduced overall prescribing of opioids

PDMP Center of Excellence, Brandeis University (2014) Kentucky House Bill 1 Impact Evaluation, University of Kentucky (2015) Rasubala L, Pernapati L, Velasquez X, et al. (2015), Wen H, Schackman BR, Aden B, Bao Y (2017) Dowell D, Zhang K, Noonan R, Hockenberry J (2016)

– Reduced inappropriate prescribing of opioids

PDMP Center of Excellence, Brandeis University (2014) Kentucky House Bill 1 Impact Evaluation (2015) Buchmueller TC, Carey C (2017)

– Reduced prescription opioid overdose deaths

Dowell D, Zhang K, Noonan R, Hockenberry J (2016)

  • Pain clinic laws

– Reduced overall prescribing of opioids

Rutkow L, Chang H, Daubresse M, et al. (2015), Lyapustina T, Rutkow L, Chang H, et al. (2016)

– Reduced diversion of prescription opioids

Surratt HL, O’Grady C, Kurtz SP, et al. (2014)

– Reduced prescription opioid overdose deaths

Hendricks A, Richey M, McGinty EE, et al. (2015)

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Objective and Selection of State

  • Study the impact of mandatory PDMP review and pill mill law
  • n opioid overdose related ED visits and inpatient stays
  • Select the states of study

– Mandatory review and pill mill law: state-level policy – State-level data availability – Literature and institutional knowledge – Intervention state: Kentucky – Control state: North Carolina

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Policies in Kentucky

State Effective date Policy detail Drugs included Timing triggers Exceptions Kentucky July, 2012 Mandatory review of PDMP by prescribers prior to prescribing DEA Schedules II-IV Initial prescription, every 90 days End of life care; cancer pain; inpatient at hospital or long-term care facility; during an emergency or following surgery; single-dose treatments to relieve symptoms from a procedure.

  • Pain clinic law: registration, certification, licensing, physician
  • n-site, medical director required, prescribing restrictions,

penalties, etc.

  • Mandatory review of PDMP:
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SLIDE 14

Percent of Active Kentucky Prescribers Registered in PDMP

20 30 40 50 60 70 80 90 100

Implementation of mandate in Kentucky

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Reports Requested by Kentucky Prescribers: per 1,000 Residents

50 100 150 200 250 300

Implementation of mandate in Kentucky

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Data

  • Data source

– HCUP

  • State Inpatient Databases (SID), State Emergency Department Databases (SEDD)
  • All payers, entire sample of discharges from community-based hospitals in a state

– States: KY, NC – Years: 2011-2013

  • Drug overdose definitions using ICD9-CM

– Prescription opioid overdose

  • 965.00, 965.02, 965.09, E850.1, E850.2

– Heroin overdose

  • 965.01, E850.0

– Exclusion

  • Deceased
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SLIDE 17

Analytical Approach

  • Outcome variable

– Overdose visits aggregated at monthly level: prescription opioids, heroin – Converted to rate per 100,000 population

  • Interrupted time series analysis (ITSA)

– Intervention state (KY) and control state (NC): comparative ITSA – 18 pre-intervention time points, 17 post-intervention time points – Nonequivalent dependent variable: other type of overdose (cocaine overdose)

  • Model specification

൯ 𝒁𝒖𝒌 = 𝜸𝟏 + 𝜸𝟐(𝒏𝒑𝒐𝒖𝒊 𝒖𝒌 + 𝜸𝟑 𝒒𝒑𝒎𝒋𝒅𝒛 𝒖𝒌 + 𝜸𝟒 𝒒𝒑𝒎𝒋𝒅𝒛 × 𝒏𝒑𝒐𝒖𝒊 𝒖𝒌 + 𝜸𝟓 𝒕𝒖𝒃𝒖𝒇 𝒖𝒌 + 𝜸𝟔 𝒕𝒖𝒃𝒖𝒇 × 𝒏𝒑𝒐𝒖𝒊 𝒖𝒌 + 𝜸𝟕 𝒕𝒖𝒃𝒖𝒇 × 𝒒𝒑𝒎𝒋𝒅𝒛 𝒖𝒌 + 𝜸𝟖 𝒕𝒖𝒃𝒖𝒇 × 𝒒𝒑𝒎𝒋𝒅𝒛 × 𝒏𝒑𝒐𝒖𝒊 𝒖𝒌 + 𝜻𝒖𝒌

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Results: Prescription Opioid Overdose

4 5 6 7 8 2011m1 2012m1 2013m1 2014m1 Month Kentucky: Actual Predicted Controls average: Actual Predicted

Prais-Winsten and Cochrane-Orcutt regression - lag(1)

Intervention starts: 630

Kentucky and average of controls

  • Same pre-policy trend
  • Level change

immediately following the intervention: –1.16**

  • Pre and post intervention

slope difference: –0.09*

  • Monthly change post

intervention: –0.08*** ***P<0.01 **P<0.05 *P<0.1

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Results: Heroin Overdose

1 2 3 4 5 2011m1 2012m1 2013m1 2014m1 Month Kentucky: Actual Predicted Controls average: Actual Predicted

Prais-Winsten and Cochrane-Orcutt regression - lag(1)

Intervention starts: 630

Kentucky and average of controls

  • Different pre-policy trend
  • Level change

immediately following the intervention: 0.95**

  • Pre and post intervention

slope difference: –0.07*

  • Monthly change post

intervention: 0.027 ***P<0.01 **P<0.05 *P<0.1

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Results: Benzodiazepine Overdose

5 6 7 8 9 10 2011m1 2012m1 2013m1 2014m1 Month Kentucky: Actual Predicted Controls average: Actual Predicted

Prais-Winsten and Cochrane-Orcutt regression - lag(1)

Intervention starts: 630

Kentucky and average of controls

  • Same pre-policy trend
  • Level change

immediately following the intervention: –1.46**

  • Pre and post intervention

slope difference: –0.09

  • Monthly change post

intervention: –0.1** ***P<0.01 **P<0.05 *P<0.1

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Results: Cocaine Overdose

.5 1 1.5 2 2011m1 2012m1 2013m1 2014m1 Month Kentucky: Actual Predicted Controls average: Actual Predicted

Prais-Winsten and Cochrane-Orcutt regression - lag(1)

Intervention starts: 630

Kentucky and average of controls

  • Same pre-policy trend
  • Nothing significant

***P<0.01 **P<0.05 *P<0.1

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Conclusions

  • Mandatory PDMP review + pain clinic laws

– Decreased ED visits and inpatient stays of prescription opioid overdose

  • Both level change and slope change
  • Level change is immediate; sustained decrease in the study period

– Mixed results for ED visits and inpatient stays of heroin overdose

  • Violation of ITSA assumption, significant increasing trend pre-policy in Kentucky
  • Immediate level increase, but reversed the trend of increasing

– No impact on cocaine overdose

  • Consistent with previous findings

– Decreased overall opioid prescribing and prescription opioid overdose deaths – Associated with decrease in high-risk prescribing of opioids

  • Promising interventions for states to consider, particularly those

with low PDMP use, high rates of risk indicators for prescribing, and high rates of opioid overdose

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Limitations

  • Cannot differentiate the effect of mandatory PDMP review from

the effect of pain clinic laws

– Implemented simultaneously – No data on pain clinics – Pain clinic law alone insufficient to impact overall prescribing

  • The regression method used assumes linear trends over time
  • Single comparison group
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THANK YOU

Contact Information: Kun Zhang: kzhang@cdc.gov

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4 5 6 7 8 2011m1 2012m1 2013m1 2014m1 Month Kentucky: Actual Predicted Controls average: Actual Predicted

Prais-Winsten and Cochrane-Orcutt regression - lag(1)

Intervention starts: 630

Kentucky and average of controls

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Overall Trends in ED Visits and Inpatient Stays

4000 4500 5000 5500 6000 6500 7000 7500

2011/1 2011/2 2011/3 2011/4 2011/5 2011/6 2011/7 2011/8 2011/9 2011/10 2011/11 2011/12 2012/1 2012/2 2012/3 2012/4 2012/5 2012/6 2012/7 2012/8 2012/9 2012/10 2012/11 2012/12 2013/1 2013/2 2013/3 2013/4 2013/5 2013/6 2013/7 2013/8 2013/9 2013/10 2013/11 2013/12

Total ED visits and inpatient stays per 100,000 population

Kentucky North Carolina

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Comparison of Population Characteristics, 2013

Kentucky North Carolina P-value

Female 51% 52% >0.1 Age group 0-18 19-34 35-64 65+ 25% 20% 39% 16% 24% 22% 40% 14% >0.1 Race White Black Hispanic Asian 85% 7% 3% N/A 62% 21% 11% 2% <0.01 Insurance Employer Medicaid Medicare Other Uninsured 47% 20% 14% 6% 14% 45% 19% 14% 7% 15% >0.1