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Pain Med. 2014 Apr;15(4):613-24. doi: 10.1111/pme.12263. Epub 2013 Oct 23.

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

Author information

1
University of Derby, Derby, UK.

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.

KEYWORDS:

Addiction; Analgesics; Dependence; Medication; Pain

PMID:
24152117
DOI:
10.1111/pme.12263
[Indexed for MEDLINE]

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