Literature DB >> 24152117

Predictors of painkiller dependence among people with pain in the general population.

James Elander1, Joana Duarte, Frances A Maratos, Paul Gilbert.   

Abstract

OBJECTIVES: Self-medication with painkillers is widespread and increasing, and evidence about influences on painkiller dependence is needed to inform efforts to prevent and treat problem painkiller use.
DESIGN: Online questionnaire survey. PARTICIPANTS: People in the general population who had pain and used painkillers in the last month (N = 112). MEASUREMENTS: Pain frequency and intensity, use of over-the-counter and prescription painkillers, risk of substance abuse (Screener and Opioid Assessment for Patients with Pain [SOAPP] scale), depression, anxiety, stress, alexithymia, pain catastrophizing, pain anxiety, pain self-efficacy, pain acceptance, mindfulness, self-compassion, and painkiller dependence (Leeds Dependence Questionnaire).
RESULTS: In multiple regression, the independent predictors of painkiller dependence were prescription painkiller use (β 0.21), SOAPP score (β 0.31), and pain acceptance (β -0.29). Prescription painkiller use mediated the influence of pain intensity. Alexithymia, anxiety, and pain acceptance all moderated the influence of pain.
CONCLUSIONS: The people most at risk of developing painkiller dependence are those who use prescription painkillers more frequently, who have a prior history of substance-related problems more generally, and who are less accepting of pain. Based on these findings, a preliminary model is presented with three types of influence on the development of painkiller dependence: 1) pain leading to painkiller use, 2) risk factors for substance-related problems irrespective of pain, and 3) psychological factors related to pain. The model could guide further research among the general population and high-risk groups, and acceptance-based interventions could be adapted and evaluated as methods to prevent and treat painkiller dependence. Wiley Periodicals, Inc.

Entities:  

Keywords:  Addiction; Analgesics; Dependence; Medication; Pain

Mesh:

Substances:

Year:  2013        PMID: 24152117     DOI: 10.1111/pme.12263

Source DB:  PubMed          Journal:  Pain Med        ISSN: 1526-2375            Impact factor:   3.750


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