State-level prescription drug monitoring program mandates and adolescent injection drug use in the United States, 1995–2017: A difference-in-differences analysis
Autoři:
Joel J. Earlywine aff001; Scott E. Hadland aff003; Julia Raifman aff001
Působiště autorů:
Department of Health Law, Policy, and Management, Boston University School of Public Health, Boston, Massachusetts, United States of America
aff001; Department of Health Services, University of Washington School of Public Health, Seattle, Washington, United States of America
aff002; Grayken Center for Addiction/Department of Pediatrics, Boston Medical Center, Boston, Massachusetts, United States of America
aff003; Division of General Pediatrics, Department of Pediatrics, Boston University School of Medicine, Boston, Massachusetts, United States of America
aff004
Vyšlo v časopise:
State-level prescription drug monitoring program mandates and adolescent injection drug use in the United States, 1995–2017: A difference-in-differences analysis. PLoS Med 17(9): e32767. doi:10.1371/journal.pmed.1003272
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pmed.1003272
Souhrn
Background
Prescription opioid misuse is an ongoing crisis and a risk factor for injection drug use (IDU). Few studies have evaluated strategies for preventing opioid or IDU initiation among adolescents. We evaluated changes in the proportion of adolescents reporting IDU before and after prescription drug monitoring program (PDMP) mandates were implemented in 18 states compared to 29 states without such mandates.
Methods and findings
This difference-in-differences analysis used biannual Youth Risk Behavioral Surveillance System (YRBSS) data representative of adolescents 17 to 18 years old across 47 states from 1995 to 2017. We compared changes in adolescent IDU in 18 states with and 29 states without PDMP mandates. Among 331,025 adolescents, 51.7% identified as male, 62.1% as non-Hispanic white, 17.4% as non-Hispanic black, and 14.6% as Hispanic. Overall, 3.5% reported IDU during the 2 years prior to PDMP mandates. In the final multivariable difference-in-differences model, we included individual age, sex, and race/ethnicity, as well as state and year as covariates from the YRBSS. We also included state- and year-specific poverty rates based on US Census Bureau data. Additionally, we controlled for state implementation of (non-mandated) PDMPs before states subsequently implemented mandates and pill mill laws. We conducted several sensitivity analyses, including repeating our main analysis using a logistic, rather than linear, model, and with a lead indicator on PDMP mandate implementation, a lag indicator, and alternative policy implementation dates. PDMP mandates were associated with a 1.5 percentage point reduction (95% CI −2.3 to −0.6 percentage points; p = 0.001) in adolescent IDU, on average over the years following mandate implementation, a relative reduction of 42.9% (95% CI −65.7% to −17.1%). The association of PDMP mandates with this reduction persisted at least 4 years beyond implementation. Sensitivity analyses were consistent with the main results. Limitations include the multi-stepped causal pathway from PDMP mandate implementation to changes in IDU and the potential for omitted state-level time-varying confounders.
Conclusions
Our analysis indicated that PDMP mandates were associated with a reduction in adolescent IDU, providing empirical evidence that such mandates may prevent adolescents from initiating IDU. Policymakers might consider PDMP mandates as a potential strategy for preventing adolescent IDU.
Klíčová slova:
Adolescents – Census – Heroin – Opioids – Schools – State law – United States – Prescription drug addiction
Zdroje
1. National Institute on Drug Abuse. Overdose death rates. Bethesda (MD): National Institute on Drug Abuse; 2019 [cited 2019 Jun 17]. Available from: https://www.drugabuse.gov/related-topics/trends-statistics/overdose-death-rates.
2. Zibbell JE, Asher AK, Patel RC, Kupronis B, Iqbal K, Ward JW, et al. Increases in acute hepatitis C virus infection related to a growing opioid epidemic and associated injection drug use, United States, 2004 to 2014. Am J Public Health. 2018;108(2):175–81. doi: 10.2105/AJPH.2017.304132 29267061
3. Gaither JR, Shabanova V, Leventhal JM. US national trends in pediatric deaths from prescription and illicit opioids, 1999–2016. JAMA Netw Open. 2018;1(8):e186558. doi: 10.1001/jamanetworkopen.2018.6558 30646334
4. Cranston K, Alpren C, John B, Dawson E, Roosevelt K, Burrage A, et al. Notes from the field: HIV diagnoses among persons who inject drugs—northeastern Massachusetts, 2015–2018. MMWR Morb Mortal Wkly Rep. 2019;68(10):253–4. doi: 10.15585/mmwr.mm6810a6 30870405
5. Peters PJ, Pontones P, Hoover KW, Patel MR, Galang RR, Shields J, et al. HIV infection linked to injection use of oxymorphone in Indiana, 2014–2015. N Engl J Med. 2016;375(3):229–39. doi: 10.1056/NEJMoa1515195 27468059
6. Liebling EJ, Green TC, Hadland SE, Marshall BDL. Injection drug use and overdose among young adults who use prescription opioids non-medically. Addict Behav. 2018;76:20–6. doi: 10.1016/j.addbeh.2017.07.017 28735037
7. Degenhardt L, Mathers B, Vickerman P, Rhodes T, Latkin C, Hickman M. Prevention of HIV infection for people who inject drugs: why individual, structural, and combination approaches are needed. Lancet. 2010;376(9737):285–301. doi: 10.1016/S0140-6736(10)60742-8 20650522
8. Liang TJ, Ward JW. Hepatitis C in injection-drug users—a hidden danger of the opioid epidemic. N Engl J Med. 2018;378(13):1169–71. doi: 10.1056/NEJMp1716871 29601263
9. Center for Behavioral Health Statistics and Quality. Treatment Episode Data Set (TEDS): 2003–2013. National admissions to substance abuse treatment services. Rockville (MD): Substance Abuse and Mental Health Services Administration; 2015.
10. Johnston LD, Miech RA, O’Malley PM, Bachman JG, Schulenberg JE, Patrick ME. Monitoring the Future national survey results on drug use: 1975–2017: overview, key findings on adolescent drug use. Ann Arbor (MI): Institute for Social Research; 2018.
11. Cerdá M, Santaella J, Marshall BDL, Kim JH, Martins SS. Nonmedical prescription opioid use in childhood and early adolescence predicts transitions to heroin use in young adulthood: a national study. J Pediatr. 2015;167(3):605–12.e2. doi: 10.1016/j.jpeds.2015.04.071 26054942
12. Kelley-Quon LI, Cho J, Strong DR, Miech RA, Barrington-Trimis JL, Kechter A, et al. Association of nonmedical prescription opioid use with subsequent heroin use initiation in adolescents. JAMA Pediatr. 2019;173(9):e191750.
13. Rajan S, Ruggles K V, Guarino H, Mateu-Gelabert P. Heroin use and drug injection among youth also misusing prescription drugs. Am J Health Behav. 2018;42(1):144–55. doi: 10.5993/AJHB.42.1.14 29320347
14. Substance Abuse and Mental Health Services Administration. Key substance use and mental health indicators in the United States: results from the 2017 National Survey on Drug Use and Health. Rockville (MD): Substance Abuse and Mental Health Services Administration; 2018.
15. Paulozzi LJ, Mack KA, Hockenberry JM. Vital signs: variation among states in prescribing of opioid pain relievers and benzodiazepines—United States, 2012. MMWR Morb Mortal Wkly Rep. 2014;63(26):563–8. 24990489
16. Rutkow L, Chang H-Y, Daubresse M, Webster DW, Stuart EA, Alexander GC. Effect of Florida’s prescription drug monitoring program and pill mill laws on opioid prescribing and use. JAMA Intern Med. 2015;175(10):1642. doi: 10.1001/jamainternmed.2015.3931 26280092
17. Reisman RM, Shenoy PJ, Atherly AJ, Flowers CR. Prescription opioid usage and abuse relationships: an evaluation of state prescription drug monitoring program efficacy. Subst Abuse. 2009;3:41–51. doi: 10.4137/sart.s2345 24357929
18. Surratt HL, O’Grady C, Kurtz SP, Stivers Y, Cicero TJ, Dart RC, et al. Reductions in prescription opioid diversion following recent legislative interventions in Florida. Pharmacoepidemiol Drug Saf. 2014;23(3):314–20. doi: 10.1002/pds.3553 24677496
19. Reifler LM, Droz D, Bailey JE, Schnoll SH, Fant R, Dart RC, et al. Do prescription monitoring programs impact state trends in opioid abuse/misuse? Pain Med. 2012;13(3):434–42. doi: 10.1111/j.1526-4637.2012.01327.x 22299725
20. Grecu AM, Dave DM, Saffer H. Mandatory access prescription drug monitoring programs and prescription drug abuse. J Policy Anal Manage. 2019;38(1):181–209. 30572414
21. Haffajee RL, Jena AB, Weiner SG. Mandatory use of prescription drug monitoring programs. JAMA. 2015;313(9):891–2. doi: 10.1001/jama.2014.18514 25622279
22. Birk EG, Waddell GR. The mitigating role of prescription drug monitoring programs in the abuse of prescription drugs. Bonn: IZA Institute of Labor Economics; 2017.
23. Meinhofer A. Prescription drug monitoring programs: the role of asymmetric information on drug availability and abuse. Am J Heal Econ. 2018;4(4):1–24.
24. Buchmueller TC, Carey C. The effect of prescription drug monitoring programs on opioid utilization in Medicare. Am Econ J Econ Policy. 2018;10(1):77–112.
25. Kilby AE. Opioids for the masses: welfare tradeoffs in the regulation of narcotic pain medications. Cambridge (MA): Massachusetts Institute of Technology Department of Economics; 2015 [cited 2020 Aug 28]. Available from: http://economics.mit.edu/files/11150.
26. Delcher C, Wang Y, Wagenaar AC, Goldberger BA, Cook RL, Maldonado-Molina MM. Prescription and illicit opioid deaths and the prescription drug monitoring program in Florida. Am J Public Health. 2016;106(6):e10–1. doi: 10.2105/AJPH.2016.303104 27153025
27. Haffajee RL. Prescription drug monitoring programs—friend or folly in addressing the opioid-overdose crisis? N Engl J Med. 2019;381(8):699–701. doi: 10.1056/NEJMp1904714 31433916
28. Kann L, Kinchen S, Shanklin SL, Flint KH, Kawkins J, Harris WA, et al. Youth risk behavior surveillance—United States, 2013. MMWR Suppl. 2014;63(4):1–168.
29. Guarino H, Mateu-Gelabert P, Teubl J, Goodbody E. Young adults’ opioid use trajectories: from nonmedical prescription opioid use to heroin, drug injection, drug treatment and overdose. Addict Behav. 2018;86:118–23. doi: 10.1016/j.addbeh.2018.04.017 29747875
30. Horwitz J, Davis CS, McClelland LS, Fordon RS, Meara E. The problem of data quality in analyses of opioid regulation: the case of prescription drug monitoring programs. NBER Working Paper No. 24947. Cambridge (MA): National Bureau of Economic Research; 2018.
31. Prescription Drug Monitoring Program Training and Technical Assistance Center. Prescription Drug Monitoring Program Training and Technical Assistance Center; 2019 [cited 2019 Jun 25]. Available from: https://www.pdmpassist.org/.
32. National Alliance for Model State Drug Laws. National Alliance for Model State Drug Laws; 2019 [cited 2019 Jun 25]. Available from: https://namsdl.org/.
33. Chang HY, Murimi I, Faul M, Rutkow L, Alexander GC. Impact of Florida’s prescription drug monitoring program and pill mill law on high-risk patients: a comparative interrupted time series analysis. Pharmacoepidemiol Drug Saf. 2018;27(4):422–9. doi: 10.1002/pds.4404 29488663
34. Brighthaupt SC, Stone EM, Rutkow L, McGinty EE. Effect of pill mill laws on opioid overdose deaths in Ohio & Tennessee: a mixed-methods case study. Prev Med. 2019;126:105736. doi: 10.1016/j.ypmed.2019.05.024 31152831
35. Wooldridge JM. Introductory econometrics: a modern approach. 7th edition. Boston: Cengage; 2019. 826 p.
36. Dimick JB, Ryan AM. Methods for evaluating changes in health care policy. JAMA. 2014;312(22):2401. doi: 10.1001/jama.2014.16153 25490331
37. Sommers BD, Baicker K, Epstein AM. Mortality and access to care among adults after state Medicaid expansions. N Engl J Med. 2012;367(11):1025–34. doi: 10.1056/NEJMsa1202099 22830435
38. Venkataramani AS, Cook E, O’Brien RL, Kawachi I, Jena AB, Tsai AC. College affirmative action bans and smoking and alcohol use among underrepresented minority adolescents in the United States: a difference-in-differences study. PLOS Med. 2019;16(6):e1002821. doi: 10.1371/journal.pmed.1002821 31211777
39. Kahn-Lang A, Lang K. The promise and pitfalls of differences-in-differences: reflections on 16 and Pregnant and other applications. J Bus Econ Stat. 2019;38(3):613–20.
40. Greene W. The behaviour of the maximum likelihood estimator of limited dependent variable models in the presence of fixed effects. Econ J. 2004;7(1):98–119.
41. Bertrand M, Duflo E, Mullainathan S. How much should we trust differences-in-differences estimates? Q J Econ. 2004;119(1):249–75.
42. Goodman-Bacon A. Difference-in-differences with variation in treatment timing. NBER Working Paper No. 25018. Cambridge (MA): National Bureau of Economic Research; 2018.
43. Venkataramani AS, Bair EF, O’Brien RL, Tsai AC. Association between automotive assembly plant closures and opioid overdose mortality in the United States: a difference-in-differences analysis. JAMA Intern Med. 2020;180(2):254–62. doi: 10.1001/jamainternmed.2019.5686 31886844
44. Haffajee RL, Bohnert ASB, Lagisetty PA. Policy pathways to address provider workforce barriers to buprenorphine treatment. Am J Prev Med. 2018;54(6):S230–42.
45. Samet JH, Botticelli M, Bharel M. Methadone in primary care—one small step for Congress, one giant leap for addiction treatment. N Engl J Med. 2018;379:7–8. doi: 10.1056/NEJMp1803982 29972744
46. Abouk R, Pacula RL, Powell D. Association between state laws facilitating pharmacy distribution of naloxone and risk of fatal overdose. JAMA Intern Med. 2019;179(6):805–11. doi: 10.1001/jamainternmed.2019.0272 31058922
47. Follman S, Arora VM, Lyttle C, Moore PQ, Pho MT. Naloxone prescriptions among commercially insured individuals at high risk of opioid overdose. JAMA Netw Open. 2019;2(5):e193209. doi: 10.1001/jamanetworkopen.2019.3209 31050777
48. Mauri AI, Townsend TN, Haffajee RL. The association of state opioid misuse prevention policies with patient‐ and provider‐related outcomes: a scoping review. Milbank Q. 2020;98(1):57–105. doi: 10.1111/1468-0009.12436 31800142
49. Kishore S, Hayden M, Rich J. Lessons from Scott County—progress or paralysis on harm reduction? N Engl J Med. 2019;380(21):1988–90. doi: 10.1056/NEJMp1901276 31042821
Článek vyšel v časopise
PLOS Medicine
2020 Číslo 9
- FDA varuje před selfmonitoringem cukru pomocí chytrých hodinek. Jak je to v Česku?
- Proč se v Česku šíří několik „vymýcených“ infekcí najednou? Ptáme se odborníků
- Prof. Jan Škrha: Metformin je bezpečný, ale je třeba jej bezpečně užívat a léčbu kontrolovat
- Jak souvisí postcovidový syndrom s poškozením mozku?
- Umělá inteligence pomůže s výběrem chřipkových kmenů pro výrobu vakcín
Nejčtenější v tomto čísle
- Interventions for treatment of COVID-19: A living systematic review with meta-analyses and trial sequential analyses (The LIVING Project)
- COVID-19 prevention and treatment: A critical analysis of chloroquine and hydroxychloroquine clinical pharmacology
- Comorbidities associated with mortality in 31,461 adults with COVID-19 in the United States: A federated electronic medical record analysis
- Antibiotic prescribing for lower UTI in elderly patients in primary care and risk of bloodstream infection: A cohort study using electronic health records in England