Northern Anesthesia & Pain Medicine, LLC, Eagle River, Alaska, USA
WWAMI Program, University of Washington School of Medicine, Anchorage, Alaska, USA
KeywordsOpioidHostMisuseDependenceAddictionOpioid use disorderExposure reductionRisk factorNESARCSubstance use disorderAnxietyPost-traumatic stress disorder (PTSD)Pain catastrophizationDepressionPersonality disordersChildhood abuseSexual abuseChronic painScreeningRisk stratificationRisk assessmentOpioid Risk Tool (ORT)Screener and Opioid Assessment for Patients with Pain (SOAPP)DiagnosisIntractabilityRisk and Efficacy (DIRE) scorePain Medication Questionnaire (PMQ)Prescription Drug Use Questionnaire (PDUQ)Current Opioid Misuse Measure (COMM)Prescription Drug Monitoring Program (PDMP)Urine drug testing (UDT)
While staffing a local urgent care clinic on a Friday evening, you encounter a 46-year-old female who presents mild apparent distress, reporting that she has just moved to town following a “really bad divorce.” She states that she is “really glad to see you” as she has “heard such great things about this clinic” and “can already tell you are a really great doctor.”
She is a somewhat difficult historian but lists complaints of diffuse dorsalgia with bilateral sciatica, polymyalgias, and arthralgias and a history of a “broken neck and back” which she states was sustained in a previous abusive relationship. She has undergone ACDF and also lumbar fusion remotely. She otherwise reports a history of migraine, asthma, and interstitial cystitis. She has also status post hysterectomy and reports a history of endometriosis. Medication list includes oxycodone, alprazolam, carisoprodol, cyclobenzaprine, and albuterol. Allergies include NSAIDs, tramadol, codeine and morphine, bupropion, sertraline, escitalopram, topiramax, lamotrigine, levetiracetam, risperidone, and “steroids.” She admits to tobacco and marijuana use and reports no employment secondary to disability. Family history is notable for paternal alcoholism and hypertension and maternal “rheumatic arthritis” and bipolar disorder. ROS is positive for insomnia, numbness and tingling, cough, abdominal pain, nausea, diarrhea, polyuria and dysuria, and multiple musculoskeletal complaints.
She presents with an out-of-state driver’s license, and the PDMP is negative for any controlled prescriptions.
On exam, you note a thin female appearing older than her stated age, well-groomed and dressed, with a strong odor of tobacco and a frequent dry cough. Her gait is brisk and without compromise. Pupils are 7 mm, and although there is no tremulousness nor evident agitation, she appears fairly anxious. There is a well-healed ACDF scar and a right carpal tunnel release scar. There is yellowish discoloration/clubbing of the fingernails bilaterally, with faint ecchymoses in various stages of resolution over the left humerus, and numerous well-healed linear scars over the left dorsal forearm. There are no track marks nor fresh punctate marks. Her visible joints do not display edema, erythema, and no significant tenderness. She does exhibit fairly pronounced global myofascial tenderness. There is a well-healed midline linear lumbosacral scar. Cervical and lumbar range of motion are both rather limited in flexion and extension by pain. Straight leg raise is positive at 30° bilaterally for sciatica to the ankle. Neurologic exam is otherwise grossly unremarkable with the exception of a positive Hoffman sign on the left.
Opioid-related deaths reached an all-time high in 2015, surpassing 33,000 . We have previously suggested that while not without merit and some degree of benefit, modification of the agent (rendering prescription opioids less addictive) and the vector (improving prescribing practice) does not comprise the most effective means of reducing either individual- or population-level opioid dependence. Adaptation of the host(s) is required if this crisis is to be contained and reversed. Reductions in both vulnerability and exposure behavior must occur.
As also discussed previously, it is only within recent human history that we have been able to engineer immunity with vaccinations, etc.; historically, innate or acquired immunity and natural selection have been the sole mediators of vulnerability reduction. There are always individuals within a population blessed with resistance to specific agents’ pathogenicity, either congenitally or from passive (acquired maternal antibodies) immunity or overcoming the disease themselves. Identifying these individuals a priori is currently impossible however, and as such most population-critical vaccines (e.g., polio, DPT, MMR) are administered to the entire populace indiscriminately. Other vaccines (e.g., Pneumovax, shingles) are currently reserved for high-risk demographics. (Opioid vaccines have been conjectured and even developed, as discussed briefly in Chap. 9; it is unlikely in the author’s opinion that this tactic will yield significant public health benefit.) While general opioid risk reduction strategies should be applied across the board, more intensive preventive measures should be considered for those known to be more vulnerable, which of course assumes such knowledge. The first section of this chapter addresses the known epidemiology of opioid vulnerability.
The vast majority of patients seeking opioid prescriptions from providers do so ostensibly for pain complaints, and as such a solid understanding of proper assessment of all major common pain complaints (e.g., lumbago with or without lower extremity symptoms, cervicalgia with or without extremity features, headaches, joint pain, abdominopelvic pain, neuropathic pain, etc.) should be within the scope of any provider planning to prescribe opioids. However, given the complexity of chronic pain, with its broad cognitive and emotional substrate, and tremendously prevalent psychiatric comorbidities, some facility in assessing psychosocial pathology is equally if not more important in keeping with the dictum of primum non nocere. A brief literature survey of risk factors for opioid misuse and dependence shows that the vast majority of identifiable vulnerability has its basis in distress affecting the whole person, rarely confined to (or even generated by) the biological/physical component; presentation of these data forms the first section of this chapter.
The second section examines current risk assessment or stratification tools , the use of which are indicated at every level of the prevention continuum. As discussed in Chap. 8 and as recommended by various advisory bodies (e.g., the recent CDC guidelines  and other professional society guidelines [3, 4]), the use of these instruments (standardized questionnaires, prescription drug monitoring programs, and urine drug testing) forms an essential part of the comprehensive biopsychosocial assessment required for adequate risk assessment when considering opioid prescription.
Risk Factors for Opioid Misuse and Dependence
It had been proposed [5–7] during the late twentieth and first few years of the twenty-first century that the vast majority of patients exposed to short-term prescription opioids for acute pain (postoperative, injury/trauma-related) and even chronic opioid prescription did not go on to develop opioid dependence. One of the more well-known proponents of more aggressive opioid prescription was widely quoted as citing an addiction rate of “less than one percent” .
Descriptive statistics are limited and crude, but current estimates place the number of individuals with an opioid use disorder (OUD) somewhere near 5 million [9, 10], whereas the amount of opioid prescriptions written exceeds that by two to three orders of magnitude over the past two decades [11, 12]. However, high-consumption individuals are receiving not only recurrent (e.g., monthly) but also multiple (e.g., an extended-release/long-acting opioid in conjunction with an immediate-release opioid) thus likely reducing the number of individuals prescribed opioids to something on the order of 20–30 million, assuming high-consumption individuals comprise half of recipients and are issued 20 prescriptions per year. In that case, there are roughly 5 million individuals with OUD in the face of some 20–30 million individuals who have been prescribed opioids in the past couple years, which suggests a much more significant risk of iatrogenic addiction than previously believed. One of the more influential studies referenced above  has been criticized from a methodologic standpoint (including the exclusion of chronic pain patients), and component data show a wide range of addiction and other aberrant behavior from 0 to 45% . Other recent reviews similarly suggest a rate of abuse between 9 and 41% [14, 15].
Nonetheless, not everyone prescribed opioids becomes dependent. Vulnerability to OUD is multifactorial, and as introduced in the previous chapter, risk factors include far more than just genetic predispositions; psychosocial contributors as well as amount and chronicity of exposure play enormous roles as well.
Genetic Risk Factors
While discussed in greater detail in the previous chapter, there are some genetic factors that appear to confer greater vulnerability toward opioid use disorder. Polymorphisms of dopamine receptor and opioid receptor genes , including DRD2, DRD3, DRD4, OPRM1, OPRK1, and OPRD1, have all been implicated in some studies with predisposition to various behavioral disorders and in some cases outright opioid dependence, although evidence is inconsistent . “Poor metabolizers” at the CYP2D6 locus (within the cytochrome P450 family; see Chap. 2) seem to be less vulnerable to opioid dependence than those with more robust activity of this enzyme . This is intuitively logical given that such individuals are exposed to less dynamic and lower plasma levels of active metabolites of tramadol, codeine, hydrocodone, oxycodone, and methadone. However, as discussed below, the only strictly genetic predictor variable shown with any consistency within population-level studies is male gender.
Biological Risk Factors
Most clinicians with any degree of experience in caring for people suffering with chronic pain carry an unspoken list of conditions they associate with increased opioid seeking/OUD. It is well-established, however, that disease and injury confer a highly variable spectrum of pain perception and suffering among different individuals, and to date, there exists no proof of specific physical pathophysiology as an independent risk factor for OUD. Chronic pain is considered independently below.
An ambitious attempt to examine biological risk factors for opioid dependence was carried out recently  using data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) Waves 1 (2001–2002) and 2 (2004–2005), a longitudinal survey of adults (18 years or older) conducted by the National Institute on Alcohol Abuse and Alcoholism . Over 43,000 civilian, noninstitutionalized adult individuals were interviewed in Wave 1, with almost 35,000 participants in the follow-up Wave 2. Fifty-three hundred of the original respondents were either non-locatable or refused follow-up interview, and 3100 were excluded due to being “institutionalized, mentally/physically impaired, on active duty in the armed forces throughout the Wave 2 interview period, deceased, or deported” .
By far, the strongest predictor variables in this dataset included sociodemographic and psychiatric ones, namely, male gender, younger age, non-Hispanic white ethnicity, comorbid substance use disorders, and Axis I and II psychiatric disorders [18, 20].
Katz et al. report that after adjusting for sociodemographic variables and both Axis I and II disorders, “cardiovascular disease” was predictive of incident nonmedical prescription opioid use (NMPOU ), whereas “all chronic physical conditions except gastrointestinal disease ” significantly predicted incident OUD, with greater number of conditions correlating with increasing odds. These conditions included atherosclerosis, hypertension, cirrhosis or other hepatic disease, angina pectoris or non-cardiac chest pain, tachycardia, myocardial infarction, “any other form of heart disease,” gastritis or peptic ulcer disease, arthritis, and schizophrenia or other psychotic illness or episode. They report that hepatic disease could not be examined due to limited statistical power.
The authors do note several limitations of the study, including both potential false positives/low specificity due to lay interviewers, rather than clinicians gathering the data; conversely there may be false negatives/low sensitivity due to exclusion of “subclinical manifestations” of psychiatric disease. It is unclear from the report whether tachycardia was included in the “cardiovascular disease” category variable, which would of course invalidate any conclusions drawn regarding the association of cardiovascular disease with NMPOU , as this state may be associated with a host of other conditions both organic and psychiatric. Self-report comprises a very significant weakness of most studies investigating substance use issues; studies relying on NESARC data suffer the additional weakness of self-report of physical conditions as well, with suspect sensitivity/specificity as mentioned previously. The authors also note that NESARC does not include chronic pain data; self-reported pain interference was captured with a single question asking the respondent to identify how much physical pain interferes with normal work and other activities over the previous month.
A recent Australian study  using similar population-based longitudinal (3 months) survey methodology and employing quite complex data analysis strategies sheds a little more light onto the question of whether biological issues confer independent risk for OUD. Specific diagnoses were not captured in this study; however predictor variables included physical condition categories including back and neck problems, arthritis/rheumatism, headaches, and visceral pain conditions. Patients (n = 1514) prescribed opioids for longer than 6 weeks were stratified into four groups:
Those reporting poor physical functioning alone (poor physical functioning only group, 27%)
Those reporting poor physical functioning in combination with poor coping strategies and social support (poor coping and physical functioning group, 35%)
Those reporting a substance use disorder (SUD group, 14%)
Those reporting multiple comorbid problems (multiple comorbid problems group, 25%)
The former (poor physical functioning only) group was chosen to serve as a reference and was statistically significantly more likely to be older and employed. Likelihood of musculoskeletal complaints did not differ significantly among all four groups; the reference group did have statistically significantly lower incidence of headaches than the other three groups. The reference group (poor physical functioning only) showed the lowest incidence of medication noncompliance/aberrant behavior, with rates doubling in the poor coping and physical functioning and the substance use disorder groups, and highest in the multiple comorbid problems group . These data may be interpreted as indicative of relatively less importance of physical pathology compared to psychosocial dysfunction in terms of OUD risk.
In brief, it is highly unlikely that biological factors/disease conditions independently predict OUD; any association thereof must be confounded by pain, which is a nonquantifiable and highly subjective phenomenon with tremendous individual variability and perception of suffering, coping mechanisms, etc. Pain (as well as other psychological variables including emotional suffering/self-medication) is that which drives people to seek opioids in the first place and as discussed below is an independent risk factor for OUD. In conclusion of this section and introduction to the next, Sir William Osler’s adage, “it is much more important to know what sort of a patient has a disease than what sort of a disease a patient has,” remains as insightful today as when he spoke it two centuries ago.
Psychosocial Risk Factors
The vast majority of literature describing risk factors for OUD has focused on psychological/psychiatric and sociological variables, and as is discussed below, these factors show the greatest degree of predictive value in multivariate logistic regression analysis and other models.
Substance Use Disorders
As discussed later, other substance use disorders have been consistently found to confer the highest risk of opioid use disorder [22, 23]. Tobacco and alcohol are the most commonly abused substances in this country with 64 million tobacco users in 2015, 66.7 million “binge,” and 17.3 million “heavy” alcohol users . Numerous large studies over the past couple decades have shown fairly consistently that tobacco use is associated with a higher rate of opioid use and abuse [25–27]. A recent large (n > 24,000) study showed that smokers were greater than three times more likely to report opioid misuse and three to five times more likely to meet OUD criteria relative to non-smokers .
While numerous investigations suggest increased risk of OUD among patients with comorbid alcohol use disorder, large-scale epidemiologic data and even smaller individual retrospective or prospective studies are very scarce. This is likely due in part to a number of factors, including ubiquity of alcohol use in the general population, well-documented underreporting of alcohol use, and also lack of consensus for alcohol use disorder definition/cutoffs.
Among 1883 patients using opioids daily in a managed care environment on the West Coast, 12.4% admitted to concurrent use of alcohol, defined as having a drink within 2 h of consuming opioids . Within this study, however, there was no difference between concurrent drinkers vs. nondrinkers in terms of diagnosis or self-report of substance use disorder, other than concurrent drinkers being statistically significantly more likely to show an AUDIT-C (a self-report tool for quantitation of alcohol consumption) score consistent with alcohol use disorder. Coexisting alcohol use disorder has been reported to be as prevalent as one-third among patients undergoing opioid maintenance therapy . Daily alcohol use has been associated with increased risk of prescription drug misuse in general , and an early investigation using NESARC data reported a 5% and 15% prevalence of prescription opioid misuse among alcohol-abusing and alcohol-dependent individuals, respectively, compared to 0.6% for those who had abstained from alcohol in the previous year . A recent study using data from NESARC Waves 1 and 2 shows an association between early-onset alcohol abuse and later development of prescription drug, including opioid misuse,  and makes a strong case for the frequently cited “gateway” phenomenon of drugs such as alcohol and marijuana leading to OUD and other substance use disorders.
Cannabis is the most commonly illicit substance of abuse in this country, and its use has been shown in several recent investigations to be strongly predictive of opioid use [33–35].
Benzodiazepines are frequently co-prescribed with opioids; the ever-growing body of evidence [36–38] from emergency department visit and postmortem analyses of overdose victims demonstrates the tremendous and generally unacceptable risk of this combination. Nonetheless, the practice often continues, not infrequently due to ignorance of (hopefully not apathy concerning) other providers’ treatment plans that may include benzodiazepines. The use of benzodiazepines has been shown in multiple investigations to confer increased risk of opioid use disorder [39, 40], and a large (n = 17,074) Norwegian study published at the beginning of this decade showed an unadjusted odds ratio of 7:7 for chronic opioid use from previous benzodiazepine prescription . After adjusting for alcohol and tobacco use, chronic pain, and socioeconomic variables, the effect was reduced by a little over 50% (odds ratio 3.1); nonetheless this sample suggested that benzodiazepine use exceeds even chronic pain as a risk factor for opioid use.
Benzodiazepine use of course probably bears significant association with opioid use in that it is a surrogate for anxiety and a predilection for “chemical coping” with distress. The association between anxiety disorders and opioid use disorders has been reported for decades [42–45]. Whether pre-existing anxiety (or other psychiatric disorder) precedes and predicts the development of OUD or vice versa has been debated in the literature, and support for both pathways exists, as discussed below. “Self-medication” of emotional distress including anxiety by opioids has long been recognized as a significant issue driving OUD; conversely, anxiety almost universally accompanies the development of dependence and withdrawal states. A common underlying vulnerability to both issues has also been postulated but awaits proof. The recent availability of large-scale population databases (e.g., NESARC) has allowed for some attempt to investigate the temporal directions/progression of these associations. One investigation examining the question of whether psychiatric disorders including anxiety preceded the development of OUD or vice versa showed support for both directional hypotheses . In this study using NESARC Wave 1 data, nonmedical use of opioids was associated with a threefold higher rate of development of anxiety, and on the other hand, the odds of developing OUD was 6-fold higher in patients with anxiety disorders in general and nearly 11-fold higher in patients with generalized anxiety disorder. A follow-up study using NESARC Waves 1 and 2 showed similar results .
A cognitive distortion common to anxiety disorders (and also PTSD, discussed below, which was previously categorized within the anxiety disorders in the DSM-IV) is pain catastrophization , defined as a negative perceptual filter applied to actual or anticipated pain. Components include feelings and thoughts of inevitability of and helplessness about pain, rumination on pain, and magnification of pain. Pain catastrophization has been associated in numerous studies with increased incidence and severity of chronic pain [48–50] and also independently with increased risk of opioid misuse and OUD [51–53]. In the study by Martel et al., catastrophizing conferred statistically significant risk for opioid misuse even after controlling for pain severity, anxiety, and depressive symptoms .
Post-Traumatic Stress Disorder
Post-traumatic stress disorder (PTSD) as defined in the DSM-5 is a syndrome of persistent reexperiencing of distress (including nightmares, intrusive thoughts, flashbacks, etc.) and other symptom clusters of avoidance, negative alterations in cognition and mood, and alterations in arousal and reactivity following exposure to actual or threatened death, serious injury, or sexual violence .
PTSD has strong independent associations with chronic pain [55–57] and has been shown for decades to be associated with higher rates of substance abuse. PTSD confers a higher risk of heroin use , and those with this dual diagnosis are more prone to overdose and otherwise show worse treatment outcomes . More recently, specific association of PTSD with general opioid misuse and OUD has been shown in both military and civilian populations [60–63], although the latter two studies, both using NESARC data, showed that full-blown OUD (again, subject to that database’s limitations of self-report and lay interviewers) occurred in the PTSD population only among females.
Depression /Bipolar Disorder
Moderate to severe depression has long been understood to both contribute to and also stem from chronic pain [64–66], and, similarly, earlier literature on the association between depression and opioid misuse could not clearly identify/support directionality of risk [67, 68]. As discussed in Chap. 4, more recent data support a causal association between chronic opioid use and resultant depression [69, 70], whereas the converse (depression increasing risk for opioid misuse/OUD) has also been shown [46, 47]. Given the depressant effects of opioids, it seems intuitively less likely that patients suffering with depressive disorders would self-medicate with this drug class compared to those with anxiety and PTSD, and the odds ratios bear that out. Complex comorbidities abound however, with depression often intertwined with chronic pain, substance use disorders in general, anxiety, and other risk factors. Fink et al. used data from the 2011–2012 National Survey of Drug Use and Health (n = 113,665) to show that patients suffering from comorbid depression and OUD were more likely to be female, of low annual income, not currently married, and to report an alcohol use disorder or other drug use ; numerous confounders between these variable exist of course, rendering conclusions difficult.
Associations between bipolar disorder and chronic pain are far less frequently reported, although some reports exist . Substance use disorders on the other hand are extremely comorbid with bipolar disorder; their coexistence is regarded by many as “the norm” . While opioids do not appear to be the drug of choice among this population, with international literature showing a preference for alcohol, cannabis, and cocaine all greater than opioids , the condition does remain a significant risk factor for the development of OUD [46, 47].
Personality disorders have been associated with OUD; borderline personality disorder (BPD ) in particular has shown some consistency as a predictive variable within the literature [75–77]. Heightened sensitivity to physical pain sensation in addition to lowered emotional distress threshold and heightened impulsivity have both been suggested as contributors to this association. A recent population-based review using NESARC data identified borderline, schizotypal, and antisocial personality disorders as all being predictive of opioid misuse and OUD .
Various personality factors have also been reported to confer both increased and decreased risk for opioid misuse and OUD. As indicated above, impulsivity appears to be associated with OUD irrespective of DSM diagnosis [79–81]. A study of 312 opioid addicts in Serbia (compared to 346 controls) linked high novelty seeking and low reward dependence, as well as self-transcendence to OUD . A Dutch study comparing 161 opioid misusers without a diagnosis of OUD to 402 methadone- or heroin-maintained addicts (and a third group of 135 non-heroin users) found that the misusers were more likely to report increased novelty seeking, self-transcendence, harm avoidance, and less self-directedness than healthy non-using controls. Conversely, they reported greater social reward dependence and self-directedness than diagnosed addicts .
Other Sociodemographic Risk Factors
Domestic Developmental Factors
Disruption of normal childhood development by various aberrancies has long been held to confer later-life psychological/psychiatric dysfunction. Plentiful evidence of childhood sexual and physical abuse leading to various disturbances including substance abuse and dependence exist and are considered in a later section. Harm needs not occur solely in the context of these gross violations; however, verbal and emotional abuse and neglect may also predict opioid misuse and OUD. An Australian study of nearly 1000 opioid-dependent individuals showed an odds ratio of 1.9 for frequent emotional abuse in male OUD subjects (no difference for female OUD subjects vs. controls) . Outright child neglect, as well as detached/disengaged parenting styles, has been shown to increase the risk of substance abuse in general [85, 86] as has a less encouraging/more rejecting parental role . (Significant overlap between nonphysical forms of abuse with physical and sexual abuse exists of course, which confounds the associations.) Parental loss through death or divorce and even family discord seem to increase the risk as well [85, 88], and adoptee status is associated with nearly a twofold increase in substance use disorder risk, with an adjusted odds ratio of 2.2 for opioids specifically . A fascinating link between childhood parental loss (and separation anxiety disorder in general) with disruption of the endogenous opioid system has been elucidated by the panic disorder research community [90, 91]. This deficiency in endogenous opioid activity, with its complex ramifications upon the neuroimmunoendocrine system, may explain in part the vulnerability of these individuals to exogenous opioids in later life.
Data show that women receive more opioid prescriptions than men [92, 93] perhaps associated with a higher incidence of severe pain complaints [94, 95]. Numerous studies over the past decade have investigated whether gender is associated with opioid misuse and dependence [24, 25, 96–101]. Despite variability in settings, most show a consistent pattern of increased prescription opioid misuse and dependence in men compared to women. A recent large investigation using NESARC data  revealed greater prevalence of opioid misuse among men than women but no difference in the prevalence of OUD between the sexes.
Jamison et al. reported similar degrees of aberrant opioid use between men and women, but a greater association among women to misuse opioids is “due to emotional issues and affective distress while men tend to misuse opioids due to legal and problematic behavioral issues .”
Human Rights Violations
Among numerous tragic consequences of childhood physical and sexual abuse, a substantial body of literature bears witness to markedly increased rates of substance abuse in victims [102–105]. More recently childhood physical and sexual abuse has been linked with increased risk of opioid abuse and dependence, specifically [84, 106]. So well known is this association that screening for preadolescent sexual abuse is a component of opioid misuse risk stratification instruments, such as the Opioid Risk Tool (ORT) discussed below. While self-medication of physical pain symptoms (not uncommon in abusive relationships, which tend to correlate with substance abuse) is certainly present in many situations, the literature on the subject draws considerable attention/lends support to the very plausible theory that a tremendous amount of opioid misuse stems from an attempt to long-standing emotional wounds and distress.
While not as well represented in the literature, there is growing awareness that post-childhood sexual abuse [107–109] and other interpersonal violence particularly toward women  are associated with an increased incidence of opioid misuse and dependence. Unidirectional association of abuse preceding opioid misuse/OUD clearly seen in children (who do not use opioids for the most part) is not as clear with adults, in whom there is likely a circular pattern of abuse preceding opioid misuse and that misuse likely facilitating further abuse, etc.
Chronic Pain and Opioid Use Disorder
Not surprisingly, chronic pain is associated with opioid misuse and the development of OUD, as borne out in multiple studies [15, 111, 112]. Greater severity of pain rating, number of complaints, and reported impairments also correlate with increased risk of misuse [113, 114]. The potential bidirectionality of the association however must be considered; it has been shown for some time that opioid-dependent patients display reduced pain tolerance [115, 116], possibly owing to opioid-induced hyperalgesia . Furthermore, given the complexities of chronic pain as discussed in Chap. 6, and the disproportionately high degree of comorbid psychiatric conditions among chronic pain patients [117, 118], significant confounding almost certainly exists within these associations. As such, any conclusions regarding chronic pain and opioid misuse/OUD risk must be interpreted cautiously and with a full biopsychosocial “filter.”
Prescription Factors and Opioid Use Disorder Risk
Numerous studies consistently cite high-dose [119–122], higher abuse liability as predicted by controlled substance schedule  escalation of dose [123, 124] and duration of therapy [119, 121, 125] as risk factors for the development of OUD. There is less agreement in the literature regarding the association of immediate-release/short-acting opioids vs. extended-release/long-acting opioids as risk factors, although there is some evidence implicating the former [120, 125] and the Food and Drug Administration recently released an “enhanced” warning indicating higher risk of misuse, abuse, and dependence with immediate-release opioids .
There exist within the literature a handful of large, primarily retrospective case-control studies comparing individuals with known OUD to those for whom that diagnosis has not been established. One of the earlier investigations of OUD risk factors was the TROUP (Trends and Risks of Opioid Use for Pain) study, which analyzed patients receiving chronic opioid therapy (COT) excluding buprenorphine for chronic non-cancer pain (CNPC) between 2000 and 2005. The base population comprised over 46,000 nationwide privately insured and Arkansas Medicaid populations, and roughly 3% of this population had documented OUD diagnostic codes. Predictor variables assessed included both physical and mental health/substance abuse diagnoses, sociodemographic factors, and prescription factors. Statistically significant risk factors included age younger than 65 and especially younger than age 50, pre-existing substance abuse diagnoses, other pre-existing mental health disorders, chronic back pain and headaches, and increasing dose and duration of COT .
White et al.  examined 875 patients with an OUD diagnosis in Maine between 2005 and 2006 and compared them to over 15,000 patients without that diagnosis who also received opioid prescriptions that year. Younger age, male gender, multiple prescriptions, multiple pharmacies, escalating doses, and other evidences of aberrancy including early refills were all associated with OUD. Substance abuse and other psychiatric diagnoses and history of viral hepatitis were also associated.
Boscarino et al.  evaluated 705 of 2139 patients receiving COT within the Geisinger Health System in Pennsylvania and found similar results, with age less than 65, history of opioid abuse, “pain impairment,” major depression, and psychotropic medication use all conferring statistically significant risk for OUD.
Rice et al.  compared 6380 patients with a diagnosis of OUD within a large compilation of privately insured patients to over 800,000 patients without this diagnosis, between the years of 2007 and 2009. Statistically significant risk factors included prior opioid prescriptions, at least one prior prescription of buprenorphine or methadone, non-opioid drug abuse, or other psychiatric diagnosis, hepatitis, and family history of opioid abuse. Of note, patients with OUD were also far more likely than those without that diagnosis to have received a prescription for oxycodone (40.8% vs 13.4%).
Dufour et al.  evaluated 3500 cases of OUD identified within the Humana database from 2010–2011 and determined that both younger age and male gender were independent risk factors for OUD, as were substance abuse and other psychological disorders and a history of viral hepatitis.
Cochran et al.  examined nearly 3000 commercially and Medicare-insured patients (geographic locations not disclosed) with OUD for individual predictor variables and subsequently applied multivariate risk models based on these factors to both cases and controls (n = 2.8 million). Individual risk factors included younger age and male gender, economic dependent status, increased healthcare utilization variables, substance abuse and other mental health diagnoses, and psychotropic medication use. Predictive multivariate models were constructed based on diagnostic codes, medical utilization, pharmacy data, and mental health variables; the latter provided nearly an 80% positive predictive value for OUD diagnostic codes. Within this category, substance dependence (especially benzodiazepines/barbiturate dependence) conferred by far the highest risk of opioid misuse, followed by mood disorders, anxiety disorders, and chronic pain diagnoses.
A recent study  of 2067 OUD patients drawn from a sample of nearly 700,000 patients receiving at least one opioid prescription via a nationwide pharmacy benefit manager (excluding those with a prior diagnosis of OUD or cancer) found both chronic and high-dose usage, non-opioid substance (including alcohol) use, mental illness, younger age, and male gender to all confer increased risk.
Turk et al.  reviewed 15 well-conducted English language studies for risk factors for OUD. They reported that a personal history of substance abuse has been the most consistent predictor found in the literature and also noted strong correlations with other psychiatric diagnoses and history of legal troubles in multiple studies. Family history of drug abuse, personal history of childhood sexual abuse, and other aberrant prescription-related behaviors showed strongly positive association but were not widely investigated.
A recent large study by Quinn et al. examining over 10 million patients receiving chronic opioid therapy without a diagnosis of cancer showed statistically significant increased risk of chronic opioid use (not necessarily OUD) with common psychiatric diagnoses including anxiety, depression, and substance use disorders . Despite the lack of specific OUD outcome data, the known association between chronic opioid use and OUD along with the immense statistical power of this study further highlights the need for comprehensive assessment and careful therapeutic planning among patients suffering with psychological and emotional distress issues and disorders.
All of these studies suffer from the common limitation of reliance upon reported diagnostic codes, with insensitivity of diagnosis certainly diluting the strength of statistical associations.
Primary Preventive Risk Assessment Approaches
Beyond thorough history (including psychosocial assessment), examination, and corroborating diagnostic imaging and laboratory tests, numerous clinical screening approaches have been developed and published. Atlari and Sudarshan  published a set of six criteria (in non-standardized instrument format) that were developed for pain management settings in particular and shown to be highly predictive of opioid misuse in the chronic pain patient population [131–133]. They include focus on opioids, opioid overuse, other substance use, nonfunctional status, unclear etiology of pain, and exaggeration of pain. These criteria should be considered by anyone prescribing opioids for pain but are not easily defined in many cases and are open to highly subjective interpretation.
Standardized instruments designed for stratification of patients’ risk for opioid misuse, abuse, and dependence have proliferated within the literature recently as awareness of the problem expands. Three of these tools are reviewed below.
Instruments for Initial Risk Assessment
Opioid Risk Tool
The Opioid Risk Tool (ORT), published in 2005 by Dr. Lynn Webster, was designed specifically “to predict the probability of a patient displaying aberrant behaviors when prescribed opioids for chronic pain” . The ORT is a self-report questionnaire designed specifically for new (not at all synonymous with opioid-naïve) patient screening and assesses both personal and family history of substance abuse including alcohol, prescription and illicit drugs, age, history of preadolescent sexual abuse, and specific psychiatric diagnoses. Positive answers are assigned a weighted point value for overall risk contribution, and the sum is tabulated and categorized into low-risk (score 0–3), moderate-risk (score 4–7), and high-risk (score >7) groups. This original publication reported results from a sample of 185 patients new to the authors’ pain clinic. Subsequent aberrant drug-related behaviors (including soliciting prescriptions from other providers, using unauthorized/illicit opioids, abnormal drug screening, unsanctioned dose escalation, missing visits) were identified after a 12-month period in 6% of patients categorized as low risk, 28% of patients categorized as moderate risk, and 91% of those categorized as high risk. Increasing number of aberrant behaviors also correlated with increasing score.
Moore et al.  conducted a small but important study of 48 patients new to their pain clinic who were initially prescribed opioids but subsequently had opioid therapy terminated for aberrant behaviors. Besides an initial interview by one of the psychologists, the sample was subjected to the ORT, and two other risk assessment tools described below in a head-to-head comparison of the instruments’ sensitivity for predicting aberrancy. In this analysis, when evaluating patients assigned an initial ORT risk category of moderate or high risk, the instrument showed a sensitivity value of 0.45 (21 of the 48 patients accurately identified a priori.) The sensitivity was reduced to 0.10 when evaluating only patients categorized as high risk by the instrument.
Two follow-up studies comparing the same instruments in larger samples (n = 132 and 263) and using the same methodology  showed similar low sensitivity for the ORT (0.10 and 0.18, respectively) but superior specificity (0.88) among the screening tools. Thus, while more likely to miss patients at risk for opioid misuse and abuse, the ORT has the lowest likelihood of false positives among commonly used tools.
A German study evaluating a cancer population (n = 114) compared the predictive value of the ORT for urine drug testing (UDT) abnormalities . This study found a higher proportion of aberrant UDT (positive primarily for cannabis) in patients categorized as moderate risk (69%) and high risk (59%) compared to those categorized as low risk (7%). Significant limitations of this analysis however included the fact that some patients did not fill out the ORT themselves, with questionnaire completed retrospectively by staff. Furthermore, not all patients underwent urine drug screening, with the test biased toward those patients assigned a higher-risk categorization (79% of high-risk patients undergoing UDT compared to 52% in the moderate-risk group and 21% in the low-risk group).
Screener and Opioid Assessment for Patients with Pain
The Screener and Opioid Assessment for Patients with Pain (SOAPP) is a self-report questionnaire designed to predict opioid misuse and abuse among chronic pain patients considered for long-term opioid therapy. The original instrument (SOAPP) was designed using eight concept clusters listed here in descending order of predictive importance: antisocial behaviors/history, substance abuse history, medication-related behaviors, doctor-patient relationship factors, psychiatric history, emotional attachment to pain medicine, personal care and lifestyle issues, and finally psychosocial problems .
An initial validation study by the developers  reported good sensitivity (0.91) and specificity (0.69) at a cutoff score of 7 or greater in predicting aberrant drug-related behaviors after 6 months. A second validation study by the same group  showed markedly lower performance, with sensitivity of 0.68 and specificity of 0.39 for a cutoff score of 8 or greater; the authors reported however that 10% of the sample did not complete the form and were excluded, and the comparison standard (UDT) was not applied to all patients in this sample.
In the analysis of Moore et al. , the SOAPP achieved a relatively high sensitivity value (0.73), with 35 of 48 of the aberrancy-displaying/discharged patients having received a high-risk rating (score greater than 6) at baseline.
The initial version, however, was perceived to be excessively vulnerable to deceptive answers and furthermore was conceptually flawed in that predictive validity which was tested primarily against self-reported aberrant behaviors at follow-up . As such, SOAPP subsequently underwent revision (SOAPP-R) which included a focus on eliminating “admission of socially unacceptable behaviors” and incorporating more “subtle” predictors (deemed less transparent to respondents) from the literature such as impulsivity, anger, resentment, and boredom to complement or update the initial instrument (Fig. 10.1). Scoring categories based upon subsequent analysis introduced below include low-risk (score <10), moderate-risk (score 10–21), and high-risk (score >21).
Screener and Opioid Assessment for Patients in Pain (SOAPP). Copyright 2015 Inflexxion, Inc. Used with permission
In the initial validation study , an outcome measure (“aberrant drug behavior index” or ADBI) was created with score based upon self-report, physician assessment, and UDT. In the final analysis, 223 patients’ SOAPP-R data were compared to their ADBI score, and at the recommended cutoff score (18) for positivity, sensitivity was shown to be 0.81 with a specificity of 0.68. Subsequent cross-validation analysis in a different sample showed comparable predictive value and reliability .
WordPress theme by UFO themes