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Risk factors for clinically recognized opioid abuse and dependence among veterans using opioids for chronic non-cancer pain.

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TLDR
Clinicians need to carefully screen for substance abuse and mental health disorders in candidates for opioid therapy and facilitate appropriate treatment of these disorders.
Abstract
A central question in prescribing opioids for chronic non-cancer pain (CNCP) is how to best balance the risk of opioid abuse and dependence with the benefits of pain relief. To achieve this balance, clinicians need an understanding of the risk factors for opioid abuse, an issue that is only partially understood. We conducted a secondary data analysis of regional VA longitudinal administrative data (years 2000-2005) for chronic users of opioids for CNCP (n=15,160) to investigate risk factors for the development of clinically recognized (i.e., diagnosed) opioid abuse or dependence among these individuals. We analyzed four broad groups of possible risk factors: (i) non-opioid substance abuse disorders, (ii) painful physical health disorders, (iii) mental health disorders, and (iv) socio-demographic factors. In adjusted models, a diagnosis of non-opioid substance abuse was the strongest predictor of opioid abuse/dependence (OR=2.34, p<0.001). Mental health disorders were moderately strong predictors (OR=1.46, p=0.005) of opioid abuse/dependence. However, the prevalence of mental health disorders was much higher than the prevalence of non-opioid substance abuse disorders (45.3% vs. 7.6%) among users of opioids for CNCP, suggesting that mental health disorders account for more of the population attributable risk for opioid abuse than does non-opioid substance abuse. Males, younger adults, and individuals with greater days supply of prescription opioids dispensed in 2002 were more likely to develop opioid abuse/dependence. Clinicians need to carefully screen for substance abuse and mental health disorders in candidates for opioid therapy and facilitate appropriate treatment of these disorders.

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Journal ArticleDOI

CDC Guideline for Prescribing Opioids for Chronic Pain—United States, 2016

TL;DR: This guideline is intended to improve communication about benefits and risks of opioids for chronic pain, improve safety and effectiveness of pain treatment, and reduce risks associated with long-term opioid therapy.
Journal ArticleDOI

CDC Guideline for Prescribing Opioids for Chronic Pain - United States, 2016.

TL;DR: This guideline is intended to improve communication between clinicians and patients about the risks and benefits of opioid therapy for chronic pain, improve the safety and effectiveness of pain treatment, and reduce the risks associated with long-term opioid therapy, including opioid use disorder, overdose, and death.
Journal ArticleDOI

New Persistent Opioid Use After Minor and Major Surgical Procedures in US Adults.

TL;DR: New persistent opioid use after surgery is common and is not significantly different between minor and major surgical procedures but rather associated with behavioral and pain disorders, which suggests its use is not due to surgical pain but addressable patient-level predictors.
References
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Journal ArticleDOI

Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases

TL;DR: It is concluded that the adapted comorbidity index will be useful in studies of disease outcome and resource use employing administrative databases.
Journal ArticleDOI

Comorbidity measures for use with administrative data.

TL;DR: The present method addresses some of the limitations of previous measures and produces an expanded set of comorbidities that easily is applied without further refinement to administrative data for a wide range of diseases.
Journal ArticleDOI

Validation and utility of a self-report version of PRIME-MD : The PHQ Primary Care Study

TL;DR: The study suggests that the PHQ has diagnostic validity comparable to the original clinician-administered PRIME-MD, and is more efficient to use.

The behavior of maximum likelihood estimates under nonstandard conditions

TL;DR: In this paper, the authors prove consistency and asymptotic normality of maximum likelihood estimators under weaker conditions than usual, such that the true distribution underlying the observations belongs to the parametric family defining the estimator, and the regularity conditions do not involve the second and higher derivatives of the likelihood function.
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