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105 Sociodemographic and clinical predictors of prescription opioid use in a longitudinal community-based cohort study
  1. Kristin Shiue1,2,
  2. Nabarun Dasgupta2,
  3. Rebecca Naumann1,2,
  4. Amanda Nelson3,4,
  5. Yvonne Golightly1,2,3
  1. 1University of North Carolina at Chapel Hill, Gillings School of Global Public Health
  2. 2University of North Carolina, Chapel Hill, Injury Prevention Research Center
  3. 3Thurston Arthritis Research Center, University of North Carolina at Chapel Hill
  4. 4School of Medicine, University of North Carolina at Chapel Hill


Purpose While opioids have commonly been prescribed for pain management, their use may be more likely in certain patients. The objective of this study was to assess predictors of prescription opioid use in an adult population with a high prevalence of chronic pain.

Methods Data were from a community-based cohort of White and African American adults aged 50–90 years residing in Johnston County, North Carolina. Univariable and multivariable logistic regression models were used to evaluate sociodemographic and clinical factors in non-opioid users (n=795) at baseline (2006–2010) as predictors of opioid use at follow-up (2013–2015). Variables included age, sex, obesity (body mass index≥30 kg/m2), employment status (unemployed, employed/retired), Census block group household poverty rate (<12%, 12%–24%, ≥25%), depressive symptoms (Center for Epidemiologic Studies Depression Scale≥16 and/or depression diagnosis), perceived social support (moderate/poor[<19], strong[≥19]; Strong Ties Measure of Social Support), insurance (uninsured, public, private), polypharmacy (5+ medications), pain sensitivity (sensitive[<4 kg], normal[≥4 kg] pressure-pain threshold), and pain catastrophizing (high[≥15], moderate/low[<15]; Pain Catastrophizing Helplessness Subscale).

Results At follow-up, 13% (n=102) of participants were using prescription opioids. In univariable models, younger age, female sex, obesity, unemployment, higher poverty rate, depressive symptoms, poorer perceived social support, public (vs. private) health insurance, polypharmacy, pain catastrophizing, and elevated pain sensitivity were independently associated (p<0.05) with opioid use. In the multivariable model, younger age (60 vs. 70 years; adjusted odds ratio, 95% confidence interval=1.76, 1.12−2.77), polypharmacy (2.21, 1.27−3.85), high pain catastrophizing (2.12, 1.29−3.48), and depressive symptoms (1.93,1.12−3.31) remained significant independent predictors.

Conclusion/Significance The simultaneous assessment of a breadth of clinical and sociodemographic factors identified polypharmacy, pain catastrophizing, and depressive symptoms as modifiable predictors of prescription opioid use. These findings support the incorporation of behavioral approaches and pharmacological review into chronic pain management strategies. Further research is warranted to track changes in these factors as prescription opioid use declines nationwide.

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