Opioid use behaviors, mental health and pain—Development of a typology of chronic pain patients

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Abstract

Background

The intersection of pain, addiction and mental health has not been adequately described. We describe the roles of these three conditions in a chronic pain patient population using opioid analgesics. Aims were to improve our understanding of this population as well as to explore ways of identifying different types of patients.

Methods

We conducted a retrospective cohort study in a large integrated group medical practice in Washington State with persons using opioids chronically (n = 704). Patient classes were derived with latent class analysis using factors representing DSM-IV opioid abuse and dependence, opioid misuse, pain, anxiety and depression. Regression analyses explored the utility of automated and interview data to distinguish the empirically derived patient groups.

Results

Three classes were identified: a Typical group, the substantial majority that had persistent, moderate mental health and pain symptoms; an Addictive Behaviors group with elevated mental health symptoms and opioid problems, but pain similar to the Typical class; and a Pain Dysfunction class with significantly higher pain interference as well as elevated mental health and opioid problems. Prescribed average daily dose of opioids was three times higher for those in the two atypical groups and was strongly associated with class membership after adjusting for other variables.

Conclusion

We describe three distinct types of patient classes as well as data elements that could help identify the two atypical types. Further research is needed to confirm these findings and determine the utility of this approach in other clinical settings.

Introduction

Opioid addiction among chronic pain patients prescribed opioids is a vexing problem that is compounded by complex clinical presentations that often include mental health and persistent pain problems. Concerns about inappropriate opioid use have increased as the incidence and prevalence of long term opioid prescribing for non-cancer pain have increased (Boudreau et al., submitted for publication). Also concerning is evidence that rates of long term opioid use are significantly higher for those with mental health and substance abuse problems than those without (Sullivan et al., 2006). The intersection of pain, addiction and mental health, though increasingly recognized as important, has not been adequately described in a general patient population of chronic pain patients using opioids chronically. Neither conceptual frameworks nor appropriate integrated assessment approaches appear to exist that might guide research or practice in this area. As a result, clinicians are faced with evaluating multiple symptoms and behaviors without knowledge of any typical patterns of patient presentation in this setting.

Problematic opioid use patterns among patients with chronic pain have been described (Kouyanou et al., 1997, Jonasson et al., 1998, Chabal et al., 1997, Reid et al., 2002, Compton et al., 1998, Butler et al., 2008, Adams et al., 2004, Wu et al., 2006) but these studies, while informative, are based on relatively small numbers of subjects, an average of 152 subjects with a range from 52 to 283, seen mostly in specialty pain management settings. Fleming et al. (2007) described the prevalence of substance use disorders in a large (n = 801) population of patients taking opioids daily and being managed in primary care settings, reporting a low prevalence of opioid use disorders (4%) and a significant association between opioid use disorders and four opioid use behaviors: purposeful over-sedation, using for non-analgesic effect, non-sanctioned dose increases and feeling intoxicated. Further empirical understanding of relationships among opioid misuse, mental health and pain issues is required.

Opioid addiction, mental health and pain symptoms and diagnoses often overlap. Chronic pain conditions are found in a substantial proportion of those presenting for addiction treatment (Mertens et al., 2003, Rosenblum et al., 2003, Sproule et al., 1999). Substance use outcomes among drug treatment patients with pain conditions are mixed, with no differences for alcohol, heroin or cocaine use, but worse outcomes for marijuana, prescription opioids and sedative medicines (Trafton et al., 2004). Mental health symptoms are more common among those with chronic pain, those who use opioids regularly, and those with problematic opioid use. A general population survey sample found that those reporting regular use of prescribed opioids were much more likely to report a mental disorder after adjusting for socio-demographic and clinical characteristics (Sullivan et al., 2005). Findings of a multivariate analysis of the National Survey on Drug Use and Health indicated that those who reported non-medical use of opioids (4.5% of the U.S. population) were more likely to have panic, depressive or social phobic symptoms and that those who met opioid abuse/dependence criteria (0.6% of the U.S. population) had significantly higher odds of symptoms of panic and social phobic disorders (Becker et al., 2008). These studies point to the overlapping issues of pain, addiction and mental health that make identification of these co-morbid problems both complex and necessary for appropriate clinical care. These findings also suggest variables that might be of value to health plans or clinicians in identifying those with potentially problematic use of opioids.

To improve our understanding of these challenging clinical scenarios, information about pain, mental health and opioid use behaviors were integrated to categorize types of patients who are prescribed opioids chronically in a large integrated group practice (also known as a health maintenance organization). In addition, we sought to explore whether automated health plan data and simple screening tools could potentially be used to identify these different patient types.

Section snippets

Methods

In order to meet the aims of the study we used a two step analytic approach: (1) a latent class analysis to determine the classes of patients and (2) regression analyses to explore the utility of automated and interview data elements for identifying patient classes (Fig. 1).

Respondent characteristics

A total of 778 interviews were conducted, for a response rate of 57% among eligible patients. The three most common reasons for being ineligible, among those determined to likely be eligible via automated data, were being physically or mentally unable to participate in the phone interview, having a non-working phone number, and using opioid medicines for reasons other than pain. Among those who met eligibility criteria, a lack of participation in the study was most commonly due to active

Discussion

We sought to comprehensively describe the population of pain patients prescribed opioids chronically in a general medical population served by an integrated group practice. Latent class analysis was used to identify response patterns across seven factors to identify the class structure that best fit the data and was of value clinically. Three classes were identified: (1) a Typical group, the great majority, which had moderate levels of pain and mental health symptoms, but very low levels of

Conflict of interest

The authors report no conflicts of interest.

Role of funding source

Funding for this study was provided by NIH NIDA grant R21 DA018695-01A2. The NIDA had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.

Contributors

Authors Banta-Green, Merrill, Calsyn and Boudreau designed the study. Banta-Green created the analytic datasets. Doyle conducted statistical analyses. Banta-Green wrote the first draft of the manuscript, all authors contributed to and approved the final manuscript.

Acknowledgments

We would like to thank Group Health's Center for Health Studies Survey Research Program for their diligence and care in recruiting and interviewing patients. Our sincere thanks to the patients who participated in this study for their time and energy. Thanks also to Drs. Peggy Compton, Mark Sullivan and Michael von Korff who provided feedback on drafts of this manuscript.

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