Trajectories of prescription opioids filled over time


Autoři: Jonathan Elmer aff001;  Riccardo Fogliato aff002;  Nikita Setia aff003;  Wilson Mui aff003;  Michael Lynch aff004;  Eric Hulsey aff005;  Daniel Nagin aff002
Působiště autorů: Departments of Emergency Medicine, Critical Care Medicine and Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America aff001;  Department of Statistics and Data Science, Carnegie Mellon University, PA, United States of America aff002;  Heinz College, Carnegie Mellon University, Pittsburgh, PA, United States of America aff003;  Department of Emergency Medicine, Division of Medical Toxicology, University of Pittsburgh School of Medicine, Pittsburgh PA, United States of America aff004;  Allegheny County Department of Human Services, Pittsburgh, PA, United States of America aff005
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222677

Souhrn

We performed a retrospective cohort study that aimed to identify one or more groups that followed a pattern of chronic, high prescription use and quantify individuals’ time-dependent probabilities of belonging to a high-utilizer group. We analyzed data from 52,456 adults age 18–45 who enrolled in Medicaid from 2009–2017 in Allegheny County, Pennsylvania who filled at least one prescription for an opioid analgesic. We used group-based trajectory modeling to identify groups of individuals with distinct patterns of prescription opioid use over time. We found the population to be comprised of three distinct trajectory groups. The first group comprised 83% of the population and filled few, if any, opioid prescriptions after their index prescription. The second group (12%) initially filled an average of one prescription per month, but declined over two years to near-zero. The third group (6%) demonstrated sustained high opioid prescriptions utilization. Using individual patients’ posterior probability of membership in the high utilization group, which can be updated iteratively over time as new information become available, we defined a sensitive threshold predictive of sustained future opioid utilization. We conclude that individuals at risk of sustained opioid utilization can be identified early in their clinical course from limited observational data.

Klíčová slova:

African American people – Criminal justice system – Epidemiology – Mental health and psychiatry – Opioids – Pain management – Public and occupational health – Pennsylvania


Zdroje

1. Blendon RJ, Benson JM. The Public and the Opioid-Abuse Epidemic. N Engl J Med. 2018;378(5):407–11. doi: 10.1056/NEJMp1714529 29298128

2. Vivolo-Kantor AM, Seth P, Gladden RM, Mattson CL, Baldwin GT, Kite-Powell A, et al. Vital Signs: Trends in Emergency Department Visits for Suspected Opioid Overdoses—United States, July 2016-September 2017. MMWR Morb Mortal Wkly Rep. 2018;67(9):279–85. doi: 10.15585/mmwr.mm6709e1 29518069

3. Centers for Disease C, Prevention. Vital signs: overdoses of prescription opioid pain relievers—United States, 1999–2008. MMWR Morb Mortal Wkly Rep. 2011;60(43):1487–92. 22048730

4. Soelberg CD, Brown RE Jr., Du Vivier D, Meyer JE, Ramachandran BK. The US Opioid Crisis: Current Federal and State Legal Issues. Anesth Analg. 2017;125(5):1675–81. doi: 10.1213/ANE.0000000000002403 29049113

5. National Academies of Sciences E, and Medicine; Health and Medicine Division; Board on Health Sciences Policy; Committee on Pain Management and Regulatory Strategies to Address Prescription Opioid Abuse;. Evidence on Strategies for Addressing the Opioid Epidemic. In: Phillips JK FM, Bonnie RJ, editor. Pain Management and the Opioid Epidemic: Balancing Societal and Individual Benefits and Risks of Prescription Opioid Use. Washington (DC): National Academies Press (US); 2017.

6. Prevention CfDCa. Understanding the epidemic Atlanta, GA: Center for Disease Control and Prevention; 2017 [12/01/2018]. Available from: https://www.cdc.gov/drugoverdose/epidemic/index.html.

7. White AG, Birnbaum HG, Schiller M, Tang J, Katz NP. Analytic models to identify patients at risk for prescription opioid abuse. Am J Manag Care. 2009;15(12):897–906. 20001171

8. Rice JB, White AG, Birnbaum HG, Schiller M, Brown DA, Roland CL. A model to identify patients at risk for prescription opioid abuse, dependence, and misuse. Pain Med. 2012;13(9):1162–73. doi: 10.1111/j.1526-4637.2012.01450.x 22845054

9. Shah A, Hayes CJ, Martin BC. Characteristics of Initial Prescription Episodes and Likelihood of Long-Term Opioid Use—United States, 2006–2015. MMWR Morb Mortal Wkly Rep. 2017;66(10):265–9. doi: 10.15585/mmwr.mm6610a1 28301454

10. Butler SF, Fernandez K, Benoit C, Budman SH, Jamison RN. Validation of the revised Screener and Opioid Assessment for Patients with Pain (SOAPP-R). J Pain. 2008;9(4):360–72. doi: 10.1016/j.jpain.2007.11.014 18203666

11. Belgrade MJ, Schamber CD, Lindgren BR. The DIRE score: predicting outcomes of opioid prescribing for chronic pain. J Pain. 2006;7(9):671–81. doi: 10.1016/j.jpain.2006.03.001 16942953

12. Compton PA, Wu SM, Schieffer B, Pham Q, Naliboff BD. Introduction of a self-report version of the Prescription Drug Use Questionnaire and relationship to medication agreement noncompliance. J Pain Symptom Manage. 2008;36(4):383–95. doi: 10.1016/j.jpainsymman.2007.11.006 18508231

13. Passik SD, Kirsh KL, Whitcomb L, Schein JR, Kaplan MA, Dodd SL, et al. Monitoring outcomes during long-term opioid therapy for noncancer pain: results with the Pain Assessment and Documentation Tool. J Opioid Manag. 2005;1(5):257–66. 17319559

14. Paulozzi LJ, Strickler GK, Kreiner PW, Koris CM, Centers for Disease C, Prevention. Controlled Substance Prescribing Patterns—Prescription Behavior Surveillance System, Eight States, 2013. MMWR Surveill Summ. 2015;64(9):1–14.

15. Kitzmiller EM. IDS Case Study: Allegheny County’s Data Warehouse: Leveraging Data to Enhance Human Service Programs and Policies. Actionable Intelligence for Social Policy (AISP), University of Pennsylvania. 2013.

16. Nagin D. Group-based modeling of development. Cambridge, Mass.: Harvard University Press; 2005. x, 201 p. p.

17. Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annual review of clinical psychology. 2010;6:109–38. doi: 10.1146/annurev.clinpsy.121208.131413 20192788

18. Nagin DS, Tremblay RE. Analyzing developmental trajectories of distinct but related behaviors: a group-based method. Psychol Methods. 2001;6(1):18–34. 11285809

19. Centers for Disease Control and Prevention. Data Resources 2018 [cited 2019 Feb 22]. Available from: https://www.cdc.gov/drugoverdose/resources/data.html.

20. Kass RE, Raftery AE. Bayes Factors. Journal of the American Statistical Association. 1995;90(430):773–95.

21. Klijn SL, Weijenberg MP, Lemmens P, van den Brandt PA, Lima Passos V. Introducing the fit-criteria assessment plot—A visualisation tool to assist class enumeration in group-based trajectory modelling. Stat Methods Med Res. 2017;26(5):2424–36. doi: 10.1177/0962280215598665 26265768

22. Jones BL, Nagin DS. Advances in Group-Based Trajectory Modeling and an SAS Procedure for Estimating Them. Sociological Methods & Research. 2007;35(4):542–71.

23. Bohnert AS, Valenstein M, Bair MJ, Ganoczy D, McCarthy JF, Ilgen MA, et al. Association between opioid prescribing patterns and opioid overdose-related deaths. JAMA. 2011;305(13):1315–21. doi: 10.1001/jama.2011.370 21467284

24. Han B, Compton WM, Jones CM, Cai R. Nonmedical Prescription Opioid Use and Use Disorders Among Adults Aged 18 Through 64 Years in the United States, 2003–2013. JAMA. 2015;314(14):1468–78. doi: 10.1001/jama.2015.11859 26461997

25. Miller M, Barber CW, Leatherman S, Fonda J, Hermos JA, Cho K, et al. Prescription opioid duration of action and the risk of unintentional overdose among patients receiving opioid therapy. JAMA Intern Med. 2015;175(4):608–15. doi: 10.1001/jamainternmed.2014.8071 25686208

26. Scholl L, Seth P, Kariisa M, Wilson N, Baldwin G. Drug and Opioid-Involved Overdose Deaths—United States, 2013–2017. MMWR Morb Mortal Wkly Rep. 2018;67(5152):1419–27. doi: 10.15585/mmwr.mm675152e1 30605448

27. Ringwalt C, Roberts AW, Gugelmann H, Skinner AC. Racial disparities across provider specialties in opioid prescriptions dispensed to medicaid beneficiaries with chronic noncancer pain. Pain Med. 2015;16(4):633–40. doi: 10.1111/pme.12555 25287703

28. Serdarevic M, Striley CW, Cottler LB. Sex differences in prescription opioid use. Curr Opin Psychiatry. 2017;30(4):238–46. doi: 10.1097/YCO.0000000000000337 28426545

29. VanHouten JP, Rudd RA, Ballesteros MF, Mack KA. Drug Overdose Deaths Among Women Aged 30–64 Years—United States, 1999–2017. MMWR Morb Mortal Wkly Rep. 2019;68(1):1–5. doi: 10.15585/mmwr.mm6801a1 30629574


Článek vyšel v časopise

PLOS One


2019 Číslo 10

Nejčtenější v tomto čísle

Tomuto tématu se dále věnují…


Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Léčba bolesti v ordinaci praktického lékaře
nový kurz
Autoři: MUDr. PhDr. Zdeňka Nováková, Ph.D.

Revmatoidní artritida: včas a k cíli
Autoři: MUDr. Heřman Mann

Jistoty a nástrahy antikoagulační léčby aneb kardiolog - neurolog - farmakolog - nefrolog - právník diskutují
Autoři: doc. MUDr. Štěpán Havránek, Ph.D., prof. MUDr. Roman Herzig, Ph.D., doc. MUDr. Karel Urbánek, Ph.D., prim. MUDr. Jan Vachek, MUDr. et Mgr. Jolana Těšínová, Ph.D.

Léčba akutní pooperační bolesti
Autoři: doc. MUDr. Jiří Málek, CSc.

Nové antipsychotikum kariprazin v léčbě schizofrenie
Autoři: prof. MUDr. Cyril Höschl, DrSc., FRCPsych.

Všechny kurzy
Kurzy Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Nemáte účet?  Registrujte se

Zapomenuté heslo

Zadejte e-mailovou adresu se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se