Journal of General Internal Medicine

, Volume 29, Issue 3, pp 491–499 | Cite as

Prescription Opioid Analgesics Increase the Risk of Depression

  • Jeffrey F. Scherrer
  • Dragan M. Svrakic
  • Kenneth E. Freedland
  • Timothy Chrusciel
  • Sumitra Balasubramanian
  • Kathleen K. Bucholz
  • Elizabeth V. Lawler
  • Patrick J. Lustman
Original Research



Prescription opioid analgesic use has quintupled recently. Evidence linking opioid use with depression emanates from animal models and studies of persons with co-occurring substance use and major depression. Little is known about depressogenic effects of opioid use in other populations.


The purpose of this study was to determine whether prescription opioids are associated with increased risk of diagnosed depression.


Retrospective cohort study, new user design.


Medical record data from 49,770 US Department of Veterans Affairs (VA) health care system patients with no recent (24-month) history of opioid use or a diagnosis of depression in 1999 and 2000.


Propensity scores were used to control for bias by indication, and the data were weighted to balance the distribution of covariates by duration of incident opioid exposure. Cox proportional hazard models with adjustment for painful conditions were used to estimate the association between duration of prescription opioid use and the subsequent risk of development of depression between 2001 and 2007.


Of 49,770 patients who were prescribed an opioid analgesic, 91 % had a prescription for < 90 days, 4 % for 90–180 days, and 5 % for > 180 days. Compared to patients whose prescription was for < 90 days, the risk of depression increased significantly as the duration of opioid prescription increased (HR = 1.25; 95 % CI: 1.05–1.46 for 90–180 days, and HR = 1.51; 95  % CI:1.31–1.74 for > 180 days).


In this sample of veterans with no recent (24-month) history of depression or opioid analgesic use, the risk of development of depression increased as the duration of opioid analgesic exposure increased. The potential for depressogenic effect should be considered in risk-benefit discussions, and patients initiating opioid treatment should be monitored for development of depression.


prescription opioid analgesics depression propensity score epidemiology administrative medical records veteran 



This work was supported by a VA HSR&D Career Development Award-2 granted to Jeffrey F. Scherrer, Ph.D. The funding agency had no role in the design or conduct of the study, data analysis and interpretation, preparation of the manuscript, and review or approval of the manuscript. Dr. Scherrer had full access to all the data in the study and takes responsibility for data integrity and the accuracy of the data analysis.

Conflict of Interest

The authors declare that they do not have a conflict of interest.

Funding Source

Veteran’s Administration Career Development Award to Dr. Scherrer

Statement of Authorship

All authors had access to aggregate data and contributed to writing the manuscript.

Supplementary material

11606_2013_2648_MOESM1_ESM.docx (16 kb)
e-Table 1 ICD-9-CM codes used to define diagnoses (DOCX 16 kb)


  1. 1.
    Hall AJ, Logan JE, Toblin RL, et al. Patterns of abuse among unintentional pharmaceutical overdose fatalities. JAMA. 2008;300:2613–2620.PubMedCrossRefGoogle Scholar
  2. 2.
    Caudill-Slosberg MA, Schwartz LM, Woloshin S. Office visits and analgesic prescriptions for musculoskeletal pain in US: 1980 vs 2000. Pain. 2004;109:514–9.PubMedCrossRefGoogle Scholar
  3. 3.
    Ballantyne JC, Mao J. Opioid therapy for chronic pain. N Engl J Med. 2003;349:1943–53.PubMedCrossRefGoogle Scholar
  4. 4.
    Cicero T, Inciardi JA, Munoz A. Trends in abuse of Oxycontin and other opioid analgesics in the United States 2002–2004. J Pain. 2005;6:662–72.PubMedCrossRefGoogle Scholar
  5. 5.
    Kluger J. The new drug crisis: addiction by prescription. Time 2010:46–9.Google Scholar
  6. 6.
    Cole BE. The need for chronic opioids to treat persistent noncancer pain. Gen Hosp Psychiatry. 2011;33:419–22.PubMedCrossRefGoogle Scholar
  7. 7.
    Morrison RS, Magaziner J, Gilbert M, Koval KJ, McLaughlin MA, Orosz G, Strauss E, Siu AL. Relationship between pain and opioid analgesics on the development of delirium following hip fracture. J Gerontol A Biol Sci Med Sci. 2003;58:76–81.PubMedCrossRefGoogle Scholar
  8. 8.
    Results from the 2010 National Survey on Drug Use and Health. In: Administration SAMHSA, ed. Rockville, MD: Substance Abuse and Mental Health Services Administration; 2011.Google Scholar
  9. 9.
    Dodrill CL, Helmer DA, Kosten TR. Prescription pain medication dependence. Am J Psychiatry. 2011;168:466–71.PubMedCrossRefGoogle Scholar
  10. 10.
    Department of Health & Human Services CfDCaP. Unintentional Drug Poisoning in the United States; 2010.Google Scholar
  11. 11.
    Jackson SW. Melancholia and depression: from hippocratic times to modern times. New Haven, CT: Yale University Press; 1986.Google Scholar
  12. 12.
    Pfeiffer A, Brantl V, Herz A, Emrich HM. Psychotomimesis mediated by kappa opiate recepters. Science. 1986;233:774–6.PubMedCrossRefGoogle Scholar
  13. 13.
    Grattan A, Sullivan M, Saunders K, Campbell C, Von Korff M. Depression and prescription opioid misuse among chronic opioid therapy recipients with no history of substance abuse. Ann Fam Med. 2012;10:304–11.PubMedCentralPubMedCrossRefGoogle Scholar
  14. 14.
    Reid MC, Engles-Horton LL, Weber MB, Kerns RD, Rogers EL, O’Connor PG. Use of opioid medications for chronic noncancer pain syndromes in primary care. J Gen Intern Med. 2002;17:173–9.PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Sullivan MD, Edlund MJ, Zhang L, Unutzer J, Wells KB. Association between mental health disorders, problem drug use, and regular prescription opioid use. Arch Intern Med. 2006;166:2087–93.PubMedCrossRefGoogle Scholar
  16. 16.
    Braden JB, Sullivan MD, Ray GT, et al. Trends in long-term opioid therapy for noncancer pain among persons with a history of depression. Gen Hosp Psych. 2009;31:564–70.CrossRefGoogle Scholar
  17. 17.
    Volkow ND. Drug abuse and mental illness: progress in understanding comorbidity. Am J Psychiatry. 2001;158:1181–3.PubMedCrossRefGoogle Scholar
  18. 18.
    Edlund M, Martine BC, Fan MY, Brennan Braden J, Deveries A, Sullivan MD. An analysis of heavy utilizers of opioids for chronic noncancer pain in the TROUP study. J Pain Symptom Manage. 2010;40:279–89.PubMedCentralPubMedCrossRefGoogle Scholar
  19. 19.
    Volkow ND. The reality of comorbidity: depression and drug abuse. Biol Psychiatry. 2004;56:714–7.PubMedCrossRefGoogle Scholar
  20. 20.
    Oxycodone and acetaminophen: drug information. In: Lexicomp, Inc; 2012.Google Scholar
  21. 21.
    Diagnostic and Statistical Manual of Mental Disorders, Text Revision (DSM-IV-TR). Fourth Edition. Washington DC: American Psychiatric Association; 2000.Google Scholar
  22. 22.
    Fishbain D, Cutler R, Rosomoff H, Rosomoff RS. Chronic pain-associated depression: antecedent or consequence of chronic pain? A review. Clin J Pain. 1997;13:116–37.PubMedCrossRefGoogle Scholar
  23. 23.
    Heinze G, Juni P. An overview of the objectives and the approaches to propensity score analysis. Eur Heart J. 2011;32:1704–8.PubMedCrossRefGoogle Scholar
  24. 24.
    Frayne SM, Miller DR, Sharkansky EJ, Jackson VW, Wang F, Halanych JH, Berlowitz DR, Kader B, Rosen CS, Keane TM. Using administrative data to identify mental illness: what approach is best? American Journal of Medical Quality 2010;25[1]:42–50.Google Scholar
  25. 25.
    Solberg LI, Engebretson KI, Sperl-Hillen JM, Hroscikoski MC, O'Connor PJ. Are claims data accurate enough to identify patients for performance measures or quality improvement? The case of diabetes, heart disease, and depression. American Journal of Medical Quality 2006;21[4]:238-245.Google Scholar
  26. 26.
    Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41–55.CrossRefGoogle Scholar
  27. 27.
    Williamson E, Morley R, Lucas A, Carpenter J. Propensity scores: From naive enthusiasm to inituitive understanding. Stat Methods Med Res. 2012;21:273–93.PubMedCrossRefGoogle Scholar
  28. 28.
    Cole SR, Hernan MA. Constructing inverse probability weights for marginal structural models. Am J Epidemiol. 2008;168:656–64.PubMedCentralPubMedCrossRefGoogle Scholar
  29. 29.
    Curtis LH, Hammill BG, Eisenstein EL, Kramer JM, Anstrom KJ. Using inverse probability-weighted estimators in comparative effectiveness analysis with observational databases. Med Care. 2007;45:S103–7.PubMedCrossRefGoogle Scholar
  30. 30.
    Kilpatrick RD, Gilberston D, Brookhart MA, Polley E, Rothman KJ, Bradbury BD. Exploring large weight deletion and the ability to balance confounders when using inverse probability of treatment weighting in the presence of rate treatment decisions. Pharmacoepidemiol Drug Saf. 2013;22:111–21.PubMedCrossRefGoogle Scholar
  31. 31.
    Seal KH, Shi Y, Cohen G, et al. Association of mental health disorders with prescription opioids and high-risk opioid use in US Veterans of Iraq and Afghanistan. JAMA. 2012;307:940–7.PubMedGoogle Scholar
  32. 32.
    Healy D. Pharmageddon. Berkley and Los Angeles: University of California Press; 2010.Google Scholar
  33. 33.
    Hyman SE, Malenka RC, Nestler EJ. Neural mechanisms of addiction: the role of reward-related learning and memory. Annu Rev Neurosci. 2006;29:565–98.PubMedCrossRefGoogle Scholar
  34. 34.
    Robinson TE, Kolb B. Structural plasticity associated with exposure to drugs of abuse. Neuropharmacology. 2004;47:33–46.PubMedCrossRefGoogle Scholar
  35. 35.
    Spanagel R, Shipenberg TS. Modulation of morphine-induced sensitization by endogenous kappa opioid systems in the rat. Neurosci Lett. 1993;153:232–6.PubMedCrossRefGoogle Scholar
  36. 36.
    George S, Murali V, Pullickal R. Review of neuroendocrine correlates of chronic opiate misuse: dysfunctions and pathophysiological mechanisms. Addic Dis Treat. 2005;4:99–109.CrossRefGoogle Scholar
  37. 37.
    Schaffer CB, Nordahl TE, Schaffer LC, Howe J. Mood-elevating effects of opioid analgesics in patients with bipolar disorder. J Neuropsychiatry Clin Neurosci. 2007;19:449–52.PubMedCrossRefGoogle Scholar
  38. 38.
    Schneeweiss S. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Saf. 2006;15:291–303.PubMedCrossRefGoogle Scholar
  39. 39.
    Psaty BM, Koepsell TD, Lin D, et al. Assessment and control for confounding by indication in obsrvational studies. J Am Geriatr Soc. 1999;47:749–54.PubMedGoogle Scholar

Copyright information

© Society of General Internal Medicine 2013

Authors and Affiliations

  • Jeffrey F. Scherrer
    • 1
    • 2
    • 3
  • Dragan M. Svrakic
    • 3
    • 4
  • Kenneth E. Freedland
    • 3
  • Timothy Chrusciel
    • 1
  • Sumitra Balasubramanian
    • 1
    • 3
  • Kathleen K. Bucholz
    • 2
  • Elizabeth V. Lawler
    • 5
    • 6
  • Patrick J. Lustman
    • 3
    • 4
  1. 1.Research Service, Clinical Research and Epidemiology WorkgroupSt. Louis VA Medical CenterSt. LouisUSA
  2. 2.Department of Family and Community MedicineSaint Louis University School of MedicineSt. LouisUSA
  3. 3.Department of PsychiatryWashington University School of MedicineSt. LouisUSA
  4. 4.The Bell Street Clinic, John Cochran HospitalSt. Louis VA Medical CenterSt. LouisUSA
  5. 5.Massachusetts Veterans Epidemiology Research and Information Center, VA Cooperative Studies ProgramVA Boston Healthcare SystemJamaica PlainUSA
  6. 6.Harvard Medical School and Division of AgingBrigham and Women’s HospitalBostonUSA

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