Hostname: page-component-848d4c4894-ndmmz Total loading time: 0 Render date: 2024-05-20T01:21:27.064Z Has data issue: false hasContentIssue false

The Impact of Prescription Drug Monitoring Programs on U.S. Opioid Prescriptions

Published online by Cambridge University Press:  01 January 2021

Extract

This paper seeks to understand the treatment effect of Prescription Drug Monitoring Programs (PDMPs) on opioid prescription rates. Using county-level panel data on all opioid prescriptions in the U.S. between 2006 and 2015, we investigate whether state interventions like PDMPs have heterogeneous treatment effects at the sub-state level, based on regional and temporal variations in policy design, extent of urbanization, race, and income. Our models comprehensively control for a set of county and time fixed effects, countyspecific and time-varying demographic controls, potentially endogenous time-series trends in prescription rates, and other state-level opioid interventions such as Naloxone Access and Good Samaritan laws, Medicaid expansion, and the provision of Methadone Assistance Treatment. We find that PDMPs are only effective in reducing prescription rates if they obligate doctors to check for patients' history prior to filling out a prescription, but the frequency at which a state requires its PDMP to be updated is irrelevant to its effectiveness. Moreover, the significant treatment effects of PDMPs are almost exclusively driven by urban and predominantly white counties, with the relatively more affluent regions showing greater responsiveness than their less affluent counterparts.

Type
Symposium Articles
Copyright
Copyright © American Society of Law, Medicine and Ethics 2018

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Kolodny, A. et al., “The Prescription Opioid and Heroin Crisis: A Public Health Approach to an Epidemic of Addiction,” Annual Review of Public Health 36 (2015): 559-574, at 560.Google Scholar
Paulozzi, L. J. “Prescription Drug Overdoses: A Review,” Journal of Safety Research 43, no. 4 (2012): 283-289; R. A. Rudd et al., “Fatal Unintentional Injuries in the Home in the US, 2000—2008,” American Journal of Preventive Medicine 44, no. 3 (2013): 239-246.CrossRefGoogle Scholar
See Kolodny et al., supra note 1; Centers for Disease Control and Prevention, “Vital Signs: Overdoses of Prescription Opioid Pain Relievers — United States, 1999—2008,” Morbidity and Mortality Weekly Report 60 no.43 (2011): 1487.Google Scholar
Centers for Disease Control and Prevention (CDC), “Annual Surveillance Report of Drug-Related Risks and Outcomes — United States, 2017,” Surveillance Special Report 1 (2017): 10; Centers for Disease Control and Prevention (CDC) on the internet: Opioid Prescribing, available at <https://www.cdc.gov/vitalsigns/opioids/infographic.html> (last visited May 7, 2018).+(last+visited+May+7,+2018).>Google Scholar
Paulozzi, L. J. et al., “Vital Signs: Variation Among States in Prescribing of Opioid Pain Relievers and Benzodiazepines — United States, 2012,” Morbidity and Mortality Weekly Report 63 no. 26 (2014): 563-568.Google Scholar
See CDC, supra note 4.Google Scholar
Frakt, A., "A Helpful Tool to Combat the Opioid Crisis," New York Times, September, 11, 2017, available at <https://www.nytimes.com/2017/09/11/upshot/a-helpful-tool-to-combat-the-opioid-crisis.html?_r=0> (last visited May 7, 2018).+(last+visited+May+7,+2018).>Google Scholar
Prescription Drug Abuse Policy System (PDAPs), Naloxone Overdose Prevention Laws, available at <http://pdaps.org/datasets/laws-regulating-administration-ofnaloxone-1501695139> (last visited May 7, 2018); PDAPs, Good Samaritan Overdose Prevention Laws, available at <http://pdaps.org/datasets/good-samaritan-overdose-laws-1501695153> (last visited May 7, 2018).+(last+visited+May+7,+2018);+PDAPs,+Good+Samaritan+Overdose+Prevention+Laws,+available+at++(last+visited+May+7,+2018).>Google Scholar
Rees, D. I. et al., “With a Little Help from My Friends: The Effects of Naloxone Access and Good Samaritan Laws on Opioid-Related Deaths,” National Bureau of Economic Research, No. w23171 (2017).Google Scholar
See PDAPs, Naloxone Overdose Prevention Laws, supra note 8.Google Scholar
Based on an estimate by health care economists Richard G. Frank and Sherry Glied, about 1.3 million people now receive substance abuse treatment due to Medicaid expansion. Zezima, K. and Ingraham, C., “GOP Health-Care Bill Would Drop Addiction Treatment Mandate Covering 1.3 million Americans,” Washington Post, March 9, 2017, available at <https://www.washingtonpost.com/news/wonk/wp/2017/03/09/gop-health-care-bill-would-drop-mental-health-coverage-mandate-covering-1-3-million-americans/?utm_term=.75d1717f8a2b> (last visited May 7, 2018).+(last+visited+May+7,+2018).>Google Scholar
Substance Abuse and Mental Health Services Administration (SAMHSA), Number of DATA-Certified Physicians, available at <https://www.samhsa.gov/medication-assisted-treatment/physician-program-data/certified-physicians> (last visited May 7, 2018). As of 2016 (after our sample ends), the regulations were amended to allow certified physicians to treat treat up to 275 patients. Substance Abuse and Mental Health Services Administration (SAMHSA), Apply to Increase Patient Limits, available at <https://www.samhsa.gov/medication-assisted-treatment/buprenorphine-waivermanagement/increase-patient-limits> (last visited May 7, 2018).+(last+visited+May+7,+2018).+As+of+2016+(after+our+sample+ends),+the+regulations+were+amended+to+allow+certified+physicians+to+treat+treat+up+to+275+patients.+Substance+Abuse+and+Mental+Health+Services+Administration+(SAMHSA),+Apply+to+Increase+Patient+Limits,+available+at++(last+visited+May+7,+2018).>Google Scholar
Alpert, A. et al., “Supply-Side Drug Policy in the Presence of Substitutes: Evidence from the Introduction of Abuse-Deterrent Opioids,” National Bureau of Economic Research, no. w23031 (2017).Google Scholar
Hollingsworth, A. et al., “Macroeconomic Conditions and Opioid Abuse,” National Bureau of Economic Research, No. w23192 (2017).Google Scholar
See Rees et al., supra note 9.Google Scholar
Maclean, J. C. and Saloner, B.. “The Effect of Public Insurance Expansions on Substance Use Disorder Treatment: Evidence from the Affordable Care Act,” National Bureau of Economic Research, no. w23342 (2017).Google Scholar
Meara, E. et al., “State Legal Restrictions and Prescription-Opioid Use among Disabled Adults,” New England Journal of Medicine 375, no. 1 (2016): 4453; L. J. Paulozzi et al., “Prescription Drug Monitoring Programs and Death Rates from Drug Overdose,” Pain Medicine 12, no. 5 (2011): 747—754; L. M. Reifler et al., “Do Prescription Monitoring Programs Impact State Trends in Opioid Abuse/Misuse?” Pain Medicine 13 (2012); A. B. Jena et al., "Opioid Prescribing by Multiple Providers in Medicare: Retrospective Observational Study of Insurance Claims," British Medical Journal 348 (2014); G. Li et al., “Prescription Drug Monitoring and Drug Overdose Mortality,” Injury Epidemiology 1, no. 1 (2014): 1—8; J. E. Brady et al., “Prescription Drug Monitoring and Dispensing of Prescription Opioids," Public Health Reports 129 (2014); T. M. Haegerich et al., "What We Know, and Don’t Know, About the Impact of State Policy and Systems-Level Interventions on Prescription Drug Overdose,” Drug and Alcohol Dependence 145 (2014): 34–47.Google Scholar
Buchmueller, T. C. and Carey, C., “The Effect of Prescription Drug Monitoring Programs on Opioid Utilization in Medicare,” National Bureau of Economic Research, no. w23148 (2017).Google Scholar
Olsen, Y. et al., “Opioid Prescriptions by US Primary Care Physicians From 1992 To 2001,” The Journal of Pain 7, no. 4 (2006): 225-235.CrossRefGoogle Scholar
Curtis, L. H. et al., “Geographic Variation in The Prescription of Schedule II Opioid Analgesics Among Outpatients in The United States,” Health Services Research 41, no. 3, p. 1 (2006): 837-855.Google Scholar
Carlson, K. et al., “Geographic Variation in Opioid Prescribing in the US,” The Journal of Pain 13, no. 10 (2012): 988-996.Google Scholar
Currie, J. and Schnell, M., “Addressing the Opioid Epidemic: Is There a Role for Physician Education?” National Bureau of Economic Research, no. w23645 (2017).Google Scholar
Bao, Y. et al., “Prescription Drug Monitoring Programs are Associated with Sustained Reductions in Opioid Prescribing by Physicians,” Health Affairs 35, no. 6, (2016).Google Scholar
Wen, H. et al., “States with Prescription Drug Monitoring Mandates Saw a Reduction in Opioids Prescribed to Medicaid Enrollees,” Health Affairs 36, no. 4 (2017): 733-741.Google Scholar
Frosch, D., “Prescription Drug Overdoses Plague: New Mexico,” New York Times, June 8, 2012; S. Hiaasen, PPain Pills from South Florida Flood Appalachian States,” Miami Herald, April 8, 2009; see Carlson supra note 23; L. M. Rossen. “Trends and Geographic Patterns in Drug-Poisoning Death Rates in the US, 1999—2009,” American Journal of Preventive Medicine 45, no. 6 (2013): e19-e25.Google Scholar
Centers for Disease Control and Prevention (CDC), U.S. Prescribing Rate Maps, available at <https://www.cdc.gov/drugoverdose/maps/rxrate-maps.html> (last visited May 7, 2018).+(last+visited+May+7,+2018).>Google Scholar
To calculate prescription rates per 100 persons, CDC used the total number of opioid prescriptions dispensed in a given year and county as the numerator, and divided it by the annual resident population, estimates of which were procured from the Population Estimates Program, U.S. Census Bureau.Google Scholar
Prescription Drug Monitoring Program Training and Technical Assistance Center, PDMP Legislation & Operational Dates, available at <http://www.pdmpassist.org/content/pdmp-legislation-operational-dates> (last visited May 7, 2018).+(last+visited+May+7,+2018).>Google Scholar
Urahn, S. K. at al., “Prescription Drug Monitoring Programs, Evidence-Based Practices to Optimize Prescriber Use,” Pew Charitable Trusts, December 2016, available at <http://www.pewtrusts.org/~/media/assets/2016/12/prescription_drug_monitoring_programs.pdf> (last visited May 7, 2018).+(last+visited+May+7,+2018).>Google Scholar
See J. C. Maclean and B. Saloner, supra note 16.Google Scholar
See Prescription Drug Abuse Policy System (PDAPs), supra note 8.Google Scholar
See D. I. Rees et al., supra note 9.Google Scholar
U.S. Department of Agriculture Economic Research Service, Rural-Urban Continuum Codes, available at <https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/> (last visited May 7, 2018).+(last+visited+May+7,+2018).>Google Scholar
Substance Abuse and Mental Health Services Administration (SAMHSA), Number of DATA-Certified Physicians, available at <https://www.samhsa.gov/medication-assisted-treatment/physician-program-data/certified-physicians> (last visited May 7, 2018).+(last+visited+May+7,+2018).>Google Scholar
U.S. Census Bureau, American Community Survey, 2010-2015, available at <https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS155YRDP05&prodType=table> (last visited May 7, 2017); U.S. Census Bureau, CenStats USA Counties Database 2000-2009, available at: <https://www.census.gov/support/USACdataD-ownloads.html> (last visited May 7, 2018).+(last+visited+May+7,+2017);+U.S.+Census+Bureau,+CenStats+USA+Counties+Database+2000-2009,+available+at:++(last+visited+May+7,+2018).>Google Scholar
Bureau of Labor Statistics, The Quarterly Census of Employment and Wages (QCEW) 2000-2015, available at <https://www.bls.gov/cew/datatoc.htm> (last visited May 7, 2018).+(last+visited+May+7,+2018).>Google Scholar
Substance Abuse and Mental Health Services Administration, National Survey of Substance Abuse Treatment Services (N-SSATS) 2000-2016, available at <https://wwwdasis.samhsa.gov/dasis2/nssats.htm>>Google Scholar
Figure 1 of the appendix maps the concentration of prescriptions in counties over time as measured by the Herfindahl—Hirschman Index. We can see from the graph that the decline in prescriptions has been accompanied by a decreased concentration of prescriptions at the county level. Tables 2 and 3 of the appendix show the summary statistics for our data.Google Scholar
They find that while the coefficient of variation (COV) for opioids is 1.09, the COV for total state level health care spending per capita in the U.S. during 2004 was 0.123. See Carlson et al., supra note 21.Google Scholar