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16 Articles in Volume 20, Issue #5
20/20 with Drs. Carmen R. Green and Johnathan Goree: Racial Disparities in Pain Care
A Kratom Primer: Miracle Medicine or Herb of Abuse?
A Pilot Study: Incidence and Prediction of Diversion among Opioid Therapy Patients
Analgesics of the Future: G-Protein Biased Mu-Opioid Receptor Ligands
Application Note: Decellularized Human Placenta in the Treatment of Infracalcaneal Heel Pain
Are Clinicians Effectively Counseling Patients on Safe Opioid Storage and Disposal? Survey Results
Ask the PharmD: How to Manage Pain Meds During Pregnancy?
Behavioral Medicine: Managing Anxiety and Maladaptive Behaviors
Case Report: Spinal Cord Stimulation for the Treatment of Pain Associated with Chronic Pancreatitis
Differential Diagnoses: Inflammatory or Non-inflammatory Chronic Back Pain?
Pelvic Inflammatory Disease: Diagnosis, Education, and Treatment Options
Product Review: Non-Invasive Neuromodulation for the Treatment of the Most Difficult Pain Conditions
Provider Perspective: Carpal Tunnel's Association with Hypothyroidism
Research Insights: Opioid Use During the Peripartum Period – What to Expect
Special Report: Race, Pain Management, and the System
When Patients Become Pregnant: How to Maintain Chronic Pain Management

A Pilot Study: Incidence and Prediction of Diversion among Opioid Therapy Patients

Environmental risk factors show that diversion is common in individuals taking opioids; the authors provide additional support for the need to generate a new tool to specifically assess risk.

Editors Note: Although opioid-related overdoses appeared to plateau or even decline, recent data suggests that opioid overdoses may again be on the rise. There are many theories about why this may be, including stressors and patient isolation related to the COVID-19 pandemic and increased availability of synthetic and other illicit opioids, particularly fentalogues. There remains an unmet need to develop a clinical risk assessment tool that assesses the environmental risk of diversion, especially in patients with chronic pain and requiring long-term opioid therapy prior to initiation.

The study presented here sought to better understand the performance of existing tools related to diversion. Along these lines, a survey of physician practices regarding opioid storage and disposal counseling is included in this issue). The authors recruited patients who were already undergoing psychological assessment and pulled data from two previous studies. Although it is difficult to compare data from separate studies, this methodology of combining previous retrospective data sets permitted the authors to initiate a proof of concept study. It fostered support of their hypothesis and strengthened their conclusion that “assessing the risk of diversion in the environment around the person for whom opioid therapy is prescribed is a crucial and missing piece of most clinicians’ skillset and armamentarium.”

We commend the authors for their efforts and hope they continue their efforts at developing a tool to assess environmental risk factors in patients taking opioids.


Environmental risk factors have rarely been studied separately from personal risk factors as they relate to prediction of medication aberrant behavior– specifically, diversion, or negative patient outcomes in general. (Image: iStock)


Opioid analgesics have the potential to be incorporated as a part of effective pain management for those individuals with chronic non-cancer pain (CNCP) whose pain cannot be resolved by non-opioid and/or adjuvant analgesics, or other non- or minimally- invasive interventions.However, these analgesics are not without the serious risk of misuse, abuse, diversion, and overdose.2-4 Consequently, risk assessment has become a fundamental part of opioid treatment for CNCP.5


Existing Risk Assessment Tools

Given that so few non-psychiatric clinicians are well trained in addiction and its interface with opioid use for pain, some effort has been devoted to simplifying clinical risk assessment by creating tools for this purpose, specifically ones validated in the chronic pain population. A 2009 review of existing risk assessment tools including the Opioid Risk Tool (ORT), Pain Medication Questionnaire (PMQ), Diagnosis Intractability Risk Efficacy Score (DIRE), and the Screener and Opioid Assessment for Patients with Pain (SOAPP and revision SOAPP-R), found an overall dearth of empirical evidence for the tools’ validity.6Furthermore, existing tools focus almost exclusively on the individual patient’s personal risk factors, with minimal attempts to assess factors related to non-personal risk, such as diversion or unsanctioned use by others in the patient’s proximity. This array of non-personal risk factors potentially surrounding a patient will, for the purposes of this paper, herein be referred to as “environmental risk.”

Environmental risk factors have rarely been studied separately from personal risk factors as they relate to prediction of medication aberrant behavior (MAB) specifically, diversion, or negative patient outcomes in general. The literature on diversion of prescription medications finds prevalence rates ranging from 20% to 50% of all patients.7-9 However, most of this research has not incorporated all of the complex individual and environmental factors that could contribute to such diversion.

The current norm is to assess individuals as they are seeking prescriptions and attempt to investigate their individual medical, psychiatric, and social histories and characteristics within measures that minimally incorporate environmental risk. While environmental risk encompasses a range of different variables (eg, proximity of individuals with substance abuse disorders, volume of traffic through the home), environmental risk of diversion (including theft) is of crucial importance when clinicians are considering opioid therapy.

A2010 review noted a lack of research focused on “simple environmental or practical issues” around controlling drug diversion. Despite limited assessment of this risk by existing tools, previous work has demonstrated that risk tools that include items about diversion (ie, Brief Risk Questionnaire [BRQ] and Brief Risk Interview [BRI]) appear to be better predictors of overall MAB than those that do not (SOAPP-R, PMQ, and ORT).10-15 These findings highlight the potential value of expanding research on the impact of environmental risk for diversion assessment on successful pain management.    

As a first step toward creating a tool that specifically assesses the environmental risk of diversion in chronic pain patients on chronic opioid therapy (COT), this pilot study sought to better understand the performance of existing tools related to diversion. Similar to the challenges facing clinicians, our study aimed to act as an initial step in bringing clarity to a challenging, complex, and continually evolving population and problem. The authors evaluated a retrospective clinical sample of chronic pain patients and examined whether commonly used risk assessment tools contained trends or items that could be used to develop a reliable and valid measure for environmental risk of diversion. It was hypothesized that diversion items (ie. borrowing and theft), within and across measures, would be statistically significantly associated with each other, and would correlate with diversion behaviors at 6-month follow-up.

Proposing A New Tool Via a Pilot Study


Study Sample

This pilot study was approved and deemed exempt by the Institutional Review Board (IRB) of the University of Tennessee, Knoxville. Data used were pooled from two previous independent retrospective research studies14,15 across common risk assessment tools. The sample was predominantly white (n = 715 [95.0 %]) and majority female (n = 410 [54.5%]). More than half of participants were prescribed opioid medication (n = 399 [53.1%]), with low back pain reported as the most common pain location (n = 418 [55.9%]). For both original studies, chronic pain patients who had been referred to the psychology practice associated with Pain Consultants of East Tennessee (PCET) in Knoxville, TN, were recruited. The practice at PCET consists of eight nurse practitioners, two board-certified physicians, and two clinical psychologists; they provide treatment for chronic pain conditions, including COT when appropriate.

Data Collected

Patients recruited for the studies were undergoing psychological assessment, including usage of risk assessment tools, as part of an evaluation for initiation of COT at PCET (ie, patients may have been previously managed on opioids/COT at a practice other than PCET). Data used in this study were pooled from two previous studies. In the first study,14 herein “Dataset 1,” patient data was collected using the BRQ and SOAPP-R at initiation of COT. These patients were then followed, and the incidence of MAB was recorded during the first 6 months of COT. 

In the second study,15 herein ”Dataset 2,” patient data was collected using the BRQ and PMQ. In this study patients, prescribed COT were also followed for 6 months and the incidence of MAB was recorded. Patient populations were distinct between studies (ie, no patients participated in both previous studies). 

In the original studies, patients were asked to fill out a set of risk assessment questionnaires (BRQ, SOAPP-R, PMQ) during their first visit with a clinical psychologist, prior to initiation of COT, as a standard clinical practice. Data from these questionnaires along with the occurrence of MABs (see list and definitions of MABs provided below) within the first 6 months that patients were on COT from their electronic medical records were compiled retrospectively into a study database (individual databases for each study) and analyzed. See original publications for additional details.14,15 These retrospective data make up Dataset 1 and Dataset 2 in the current study. To facilitate data analyses, a study dataset was created by combining data from participants in both past studies, across common measures when possible.  

Defining Medication Aberrant Behavior (MAB)

From the data previously collected on MABs, three were chosen to represent diversion:

  • Patient report of theft of medication
  • Patient report of a patient giving his/her medication to someone else (almost always a spouse or family member)
  • Urine drug test (UDT) positive for a non-prescribed opioid

The first two MABs represent clear diversion. The third MAB chosen is not usually thought of diversion, however. In contrast to the usual idea of “diversion” referring to “outgoing” opioids (opioids prescribed by a provider are going to someone else), a UDT positive for a non-prescribed opioid is usually indicative of “incoming” diverted opioids – the patient is the recipient of someone else’s medication. Not only does this meet the definition of diversion, it also stands to reason that if someone can get opioid medication from someone else, that social channel is open and likely represents an increased risk of the prescribed opioid leaving the patient’s control as well. 

There are potential flaws with the choice of these MABs. A patient report of theft or giving away medication is diversion, but is very likely underreported by patients – leaving room for inaccurate data. UDT results are more objective but finding an unexpected opioid in a UDT in of itself may or may not represent diversion. This may be indicative of “doctor shopping” (receiving an opioid prescription from two or more providers in the same time frame), which is MAB but not diversion.

In the authors’ experience using state prescription drug monitoring programs (PDMPs), the programs have greatly reduced doctor shopping and, most of the time, the identification of an unexpected non-prescribed opioid in a drug monitoring screen represents diversion and not doctor shopping. Other indicators of diversion that we could have used, such as a failed pill count or a UDT negative for prescribed opioids, are also flawed. These may indicate diversion but could also indicate the overtaking of opioid medication. As the latter option seemed quite common in our experience, we chose not to use these as indicators of diversion in an attempt to isolate as best we could the specific diversion phenomenon and not combine it with the overtaking of opioid medication.

Thus, each of these three MABs chosen appears to be flawed indicators of diversion and we had to make difficult choices about what to use as criteria for MAB measurement. However, while imperfect, we determined these MABs could be used as a starting point for investigational study.

Data Analysis

The primary outcomes of interest were:

  • Incidence of past diversion (borrowing and theft)
  • Extent to which common risk items were correlated with diversion at baseline
  • Incidence of diversion MABs at 6-month follow-up
  • Correlates of diversion MABs at 6-month follow-up

Bivariate correlations were used to assess the associations with past diversion as well as diversion MABs at 6-month follow-up. Only statistically significant correlations above P = 0.20 were interpreted.


Sample Characteristics

Dataset 1 contained data from the BRQ and SOAPP-R on 454 total patients, of which a total of 257 patients were prescribed opioids and the incidence of MAB was recorded over time.14 Dataset 2 contained data from the BRQ and PMQ on 299 total subjects, of which a total of 142 patients were prescribed opioids and the incidence of MAB was recorded over time.15 In the current study, data were combined from Dataset 1 and Dataset 2 across similar measures (ie, for the BRQ and associated MABs as it was the only common measure). In the current study, patient data were available for a total of 753 subjects, of which 399 patients were prescribed opioids and had available data on the occurrence of diversion MABs (see Table I).  Results presented are pooled data from Dataset 1 and Dataset 2.



Table II describes the combined demographics of all patients included in this study.  Participants’ ages ranged from 20 to 89 years (m = 52.3, SD = 13.4). Participants were predominately white (n = 715 [95.0 %]) and female (n = 410 [54.5%]), with lower back representing the most commonly reported pain location (n = 418 [55.9%]). The majority (n = 399 [53.1%]) of participants were prescribed opioid medication, with 310 (41.4%) receiving short-acting opioid medication.

Incidence of Past Diversion: Borrowing and Theft

Currently available risk assessment tools contain few questions related to environmental diversion; all available items were related to borrowing and theft behaviors. The BRQ item 3 (“How often have you ever had to get pain medications from family, friends, or the street?”), SOAPP-R item 23 (“How often have you had to borrow pain medications from your family or friends?”), and the PMQ item 14 (“At times, I need to borrow pain medication from friends or family to get relief”) were the only questions that were indicative of past borrowing of an opioid medication; only the BRQ contained a question related to theft of an opioid medication (item 7: “Has any of your pain medication ever been stolen?”).

At intake, the endorsement of an event indicative of past diversion or theft was reported by a minority, but meaningful portion, of patients (Diversion: BRQ item 3: 143 [19.2%]; SOAPP-R item 23: 76 [17.1%]; PMQ item 14: 34 [12.3%]; Theft: BRQ item 7: 106 [14.2%]). 

Item Correlates of Past Borrowing

The BRQ represents the only risk assessment questionnaire with available data from the combined population (ie, from Dataset 1 and Dataset 2) and an item indicative of past opioid medication borrowing (item 3: “How often have you ever had to get pain medications from family, friends, or the street?”), thus it was the only borrowing question that correlations were assessed for. Correlations at a level of  ≥ 0.2 between BRQ item 3 and items from other questionnaires are presented in Table III.

Items at a level of ≥ 0.2 were selected for the table as they are more likely to represent a clinically meaningful association. Items that correlated of note are, SOAPP-R item 2 (“How often have you felt a need for higher doses of medication to treat your pain?; r = 0.22), SOAPP-R item 17 (“How often have you been treated for an alcohol or drug problem?”; r = 0.23), PMQ item 7 (“Family members seem to think that I may be too dependent on my pain medication.”; r = 0.21), and PMQ item 12 (“I find it necessary to go to the emergency room to get treatment for my pain”; r = 0.25).  Furthermore, the other borrowing items from the SOAPP-R (item 23 “How often have you had to borrow pain medications from your family or friends?”, r = 0.80) and PMQ (item 14 (“At times, I need to borrow pain medication from friends or family to get relief”; r = 0.60) were highly correlated, as expected (Table III).


Item Correlates of Past Theft

In addition to examining questionnaire items that correlated with past borrowing, it was also determined what questionnaire items correlated (at a level of ≥ 0.2) with past theft (BRQ item 7: “Has any of your pain medication ever been stolen?”). The BRQ item 1 (“Have you ever been discharged from medical practice?”; r = 0.21), BRQ item 12 (“Does someone help you with storing or taking your pain medication?”; r = 0.21), SOAPP-R item 6 (“How often have you counted pain pills to see how many are remaining?”; r = 0.30), and PMQ item 25 (”How many times in the past year have you run out of pain medication early and had to request an early refill?”; r = 0.21) all correlated with the BRQ item 7 (Table III). 

Incidence of Diversion at 6-month Follow-up

Among the 399 total patients prescribed opioids, 60 (15.0%) patients showed MABs that indicated diversion at the 6-month follow-up; 36 [9.0%] the presence of non-prescribed opioids in a UDT: 14 [3.5%] reported theft of medication; or 1 [0.3%] a report of giving medication to someone else.

Item Correlates of Diversion MAB at the 6-month Follow-up

The presence of MABs that were indicative of diversion at the 6-month follow-up were used to create a binary variable (yes/no) and then correlated using point biserial correlation with questionnaire items. Only two items yielded a correlation ≥ 0.2 with an observed diversion behavior: BRQ item 8 (“Have you ever had a drinking or drug abuse problem?”; r = 0.20) and PMQ item 22 (“How often have others suggested that you have a drug or alcohol problem?”; r = 0.24).        



The presented pilot study represents initial efforts (ie, proof of concept) in working toward the creation of a validated assessment tool that can be used to quantify the environmental risk of diversion related to an individual with chronic pain. Data from patients with chronic pain based on three opioid risk assessment tools (BRQ, SOAPP-R, and PMQ) were examined to determine what questionnaire items related to environmental diversion and, further, to determine which of these items correlated with reports or observations of diversion at a 6-month follow-up. Findings indicate that diversion (ie, past borrowing and/or theft of opioid medications) is common among patients with chronic pain.

These results indicate that there are correlations of reported past diversion, both borrowing and theft, within and across existing questionnaires. Items correlated with past borrowing reflect some common themes:

  • Overtaking pain medication
  • Desire for more medication
  • Mood problems/disorders
  • Past or present substance abuse disorder

Further, there appears to be a slightly different set of item correlates for theft, including:

  • Counting medication
  • Needing help with medication
  • Running out of medication early
  • Presence of a past or present substance abuse disorder

While reports of past diversion had multiple correlates, there were very few items from current risk assessment tools that predicted observed diversion while in treatment. Finally, it is important to note that current assessment tools focus on the actual occurrence of diversion rather than the risk for diversion. In other words, they ask “Has your medication every been stolen?” rather than “How safe are medications in your home from theft?” 

Previous studies have found that diversion of prescribed medications is common. A national community survey done across 2012 to 2014 found that 4.3% of all those 12 years or older have used pain relievers non-medicinally.16However, a community survey in 2006 found that 22.9% of those interviewed reported loaning their medication to someone else and 26.9% reported having borrowed someone else’s medication.8

Surveys of those prescribed opioid pain medication also found substantial rates of diversion. A systematic review of the literature on non-recreational prescription medication sharing found that the rates of borrowing someone’s prescription medication ranged from 5% to 51%; the rate of lending medication to others ranged from 6% to 22%.7A 2012 study of over 350 pain clinic patients found that of reported medication diversions, 30% resulted from stolen medications and 20% due to lost medications.9A 2006-2007 survey of veterans found that 16% shared their medication with others.17Similarly, high rates of diversion have been found among college students prescribed any psychoactive medications (23.7%),18and opioid maintenance treatment patients (50.5% shared and 28% sold).19

Walker and Webster9point out that deterrents, such as locking medication away, were not found to impact the likelihood of diversion, but the risk for drug diversion did increase with a family history of drug abuse and of criminal behavior. Furthermore, despite the instrumental role of physicians and healthcare providers in safe and effective pain management, research suggests that the majority do not receive training in identifying diversion as medical students, and approximately 43% report avoiding inquiring into patients’ prescription drug abuse history altogether.20

It is important to note that the vast majority of existing data related to diversion relies on surveys and self-report, which have known limitations (eg, erroneous reporting, dishonesty, sampling biases). However, this limitation exists as diversion is difficult to study since it is often not disclosed and the usual methods of monitoring patients (UDTs, pill counts, and pharmacy reports) rarely detect diversion, at least not directly. In other words, there are few to none non-subjective, quantitative measures to collect data on diversion. Thus, the problem of obtaining accurate self-report about embarrassing and/or criminal behavior is compounded by the difficulty of creating a criterion validator. However, findings from this study suggest that diversion might be predicted through indirect questioning about other interpersonal characteristics. As it is clear that diversion is a significant problem and not well-assessed by current risk assessment tools, new tools may be needed to be developed to assess this multifaceted problem.

Assessing the risk of diversion in the environment around the person for whom opioid therapy is prescribed is a crucial and missing piece of most clinicians’ skillset and armamentarium. If the considerable harm to third parties that stems from diversion is to be minimized, it must be appreciated, assessed, and managed. As important as it is clinically it is small wonder that, to date, the field does not have a validated tool to quantify the risk around the person with pain. This is in part because methodological issues complicate nearly every facet of what constitutes the process of instrument development. The lack of truthfulness when assessing clinically relevant behavior limits the reliability and validity of the assessment, and the inability to specify a definitive criterion for which to predict is another substantial barrier. Thus, it is no surprise that the present study met with mixed results and only partial success. Existing tools contain some proxy items that can be used in the future, but overall, it is clear that our further research will likely need to focus on the development of a new scale.


The results of this study appear to be aligned with other published literature (ie, that diversion of opioid medications is common among patients on COT) and suggest that predicting diversion with any accuracy is likely outside the scope of current assessment tools. While these preliminary results do not provide conclusive data on how to assess the environmental risk of diversion, they do signal potential factors that may predict diversion, which provides a starting point for the development of a future tool. Furthermore, these results provide additional scientific evidence to support the need to generate and validate a new tool that specifically assesses this risk.


Disclsoures: Dr. Passik serves as the vice president of scientific affairs, education, and policy for Collegium Pharmaceuticals. Dr. Meske served as senior manager of clinical development and scientific affairs at Collegium until January 2020 (This paper was submitted in April 2020.

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