Open Access Open Access  Restricted Access Subscription or Fee Access

Long-term opioid users with chronic noncancer pain: Assessment of opioid abuse risk and relationship with healthcare resource use

Anna D. Coutinho, BPharm, PhD, Kavita Gandhi, BPharm, MS, Rupali M. Fuldeore, BAMS, MS, Pamela B. Landsman-Blumberg, MPH, DrPH, Sanjay Gandhi, PhD


Objective: Identify opioid abuse risk factors among chronic noncancer pain (CNCP) patients receiving long-term opioid therapy and assess healthcare resource use (HRU) among patients at elevated abuse risk.

Design: Data were obtained from an integrated administrative claims database. Classification and Regression Tree (CART) analysis identified risk factors potentially predictive of opioid abuse, which were used to classify the overall population into cohorts defined by levels of abuse risk. Multivariable logistic regression compared HRU across risk cohorts.

Setting: Retrospective cohort study.

Patients, participants: 21,072 patients aged 18 years diagnosed with 1 of 5 types of CNCP and a prescription for Schedule II or III/IV opioid medication used long-term (90 days).

Main outcome measures: (1) Opioid abuse risk factors; (2) HRU differences between risk cohorts.

Results: CART analysis identified four groups at elevated opioid abuse risk defined by three factors (age, daily opioid dose, and total days’ supply of opioids); sensitivity: 70.3 percent, specificity: 74.1 percent, and positive predictive value: 5.6 percent. The analysis results were used to classify patients into low-risk (72.5 percent), at-risk (25.4 percent), and opioid-abuser (2.2 percent) cohorts. In multivariable analysis, emergency department (ED) use was higher among at-risk vs low-risk patients (odds ratio [OR]: 1.14; p < 0.05); hospitalization and ED visits were higher for opioid-abusers vs low-risk patients (OR: 2.33 and 2.14, respectively; p < 0.05).

Conclusions: This study identifies a subpopulation of CNCP patients at risk of opioid abuse. However, limited sensitivity and specificity of criteria defining this subpopulation reinforce the importance of physician discretion in patient-level treatment decisions.


opioid analgesics, chronic pain, opioid abuse

Full Text:



Nahin RL: Estimates of pain prevalence and severity in adults: United States, 2012. J Pain. 2015; 16(8): 769-780.

Gaskin DJ, Richard P: The economic costs of pain in the United States. J Pain. 2012; 13(8): 715-724.

Conaghan PG, Peloso PM, Everett SV, et al.: Inadequate pain relief and large functional loss among patients with knee osteoarthritis: Evidence from a prospective multinational longitudinal study of osteoarthritis real-world therapies. Rheumatology. 2015; 54(2): 270-277.

Ziegler D: Painful diabetic neuropathy: Advantage of novel drugs over old drugs? Diabetes Care. 2009; 32(suppl 2): S414-S419.

American Academy of Pain Medicine: Use of opioids for the treatment of chronic pain. A statement from the American Academy of Pain Medicine. 2013. Available at Accessed July 18, 2017.

Birnbaum HG, White AG, Schiller M, et al.: Societal costs of prescription opioid abuse, dependence, and misuse in the United States. Pain Med. 2011; 12(4): 657-667.

Birnbaum HG, White AG, Reynolds JL, et al.: Estimated costs of prescription opioid analgesic abuse in the United States in 2001: A societal perspective. Clin J Pain. 2006; 22(8): 667-676.

Breiman L, Friedman RA, Olshen RA, et al.: Classification and Regression Trees. New York: Kluwer Academic Publishers, 1984.

Lewis RJ: An introduction to classification and regression tree (CART) analysis [presentation paper]. Presented at: Annual Meeting of the Society for Academic Emergency Medicine; 2000; San Francisco, CA. Available at Accessed July 18, 2017.

Fonarow GC, Adams KF, Jr., Abraham WT, et al.: Risk stratification for in-hospital mortality in acutely decompensated heart failure: Classification and regression tree analysis. JAMA. 2005; 293(5): 572-580.

Garzotto M, Beer TM, Hudson RG, et al.: Improved detection of prostate cancer using classification and regression tree analysis. J Clin Oncol. 2005; 23(19): 4322-4329.

Edlund MJ, Steffick D, Hudson T, et al.: Risk factors for clinically recognized opioid abuse and dependence among veterans using opioids for chronic non-cancer pain. Pain. 2007; 129(3): 355-362.

Edlund MJ, Martin BC, Fan MY, et al.: Risks for opioid abuse and dependence among recipients of chronic opioid therapy: Results from the TROUP study. Drug Alcohol Depend. 2010; 112(1-2): 90-98.

Ciesielski T, Iyengar R, Bothra A, et al.: A tool to assess risk of de novo opioid abuse or dependence. Am J Med. 2016; 129(7): 699-705(e694).

Fishbain DA, Cole B, Lewis J, et al.: What percentage of chronic nonmalignant pain patients exposed to chronic opioid analgesic therapy develop abuse/addiction and/or aberrant drug-related behaviors. A structured evidence-based review. Pain Med. 2008; 9(4): 444-459.

Sehgal N, Manchikanti L, Smith HS: Prescription opioid abuse in chronic pain: A review of opioid abuse predictors and strategies to curb opioid abuse. Pain Physician. 2012; 15(3 suppl): ES67-ES92.

Bohnert AS, Logan JE, Ganoczy D, et al.: A detailed exploration into the association of prescribed opioid dosage and overdose deaths among patients with chronic pain. Med Care. 2016; 54(5): 435-441.

Dilokthornsakul P, Moore G, Campbell JD, et al.: Risk factors of prescription opioid overdose among Colorado Medicaid beneficiaries. J Pain. 2016; 17(4): 436-443.

Jamison RN, Edwards RR: Risk factor assessment for problematic use of opioids for chronic pain. Clin Neuropsychol. 2013; 27(1): 60-80.



  • There are currently no refbacks.
This site uses cookies to maintain session information critical to the user's experience and environment on this system. Click "Accept Cookies" to continue.
For more details please visit our privacy statement at: Privacy & GDPR