Measuring problem prescription opioid use among patients receiving long-term opioid analgesic treatment: development and evaluation of an algorithm for use in EHR and claims data.

Objective: Opioid surveillance in response to the opioid epidemic will benefit from scalable, automated algorithms for identifying patients with clinically documented signs of problem prescription opioid use. Existing algorithms lack accuracy. We sought to develop a high-sensitivity, high-specificity classification algorithm based on widely available structured health data to identify patients receiving chronic extended-release/long-acting (ER/LA) therapy with evidence of problem use to support subsequent epidemiologic investigations. Methods: Outpatient medical records of a probability sample of 2,000 Kaiser Permanente Washington patients receiving ≥60 days' supply of ER/LA opioids in a 90-day period from 1 January 2006 to 30 June 2015 were manually reviewed to determine the presence of clinically documented signs of problem use and used as a reference standard for algorithm development. Using 1,400 patients as training data, we constructed candidate predictors from demographic, enrollment, encounter, diagnosis, procedure, and medication data extracted from medical claims records or the equivalent from electronic health record (EHR) systems, and we used adaptive least absolute shrinkage and selection operator (LASSO) regression to develop a model. We evaluated this model in a comparable 600-patient validation set. We compared this model to ICD-9 diagnostic codes for opioid abuse, dependence, and poisoning. This study was registered with ClinicalTrials.gov as study NCT02667262 on 28 January 2016. Results: We operationalized 1,126 potential predictors characterizing patient demographics, procedures, diagnoses, timing, dose, and location of medication dispensing. The final model incorporating 53 predictors had a sensitivity of 0.582 at positive predictive value (PPV) of 0.572. ICD-9 codes for opioid abuse, dependence, and poisoning had a sensitivity of 0.390 at PPV of 0.599 in the same cohort. Conclusions: Scalable methods using widely available structured EHR/claims data to accurately identify problem opioid use among patients receiving long-term ER/LA therapy were unsuccessful. This approach may be useful for identifying patients needing clinical evaluation.

Using natural language processing of clinical text to enhance identification of opioid-related overdoses in electronic health records data.

PURPOSE: To enhance automated methods for accurately identifying opioid-related overdoses and classifying types of overdose using electronic health record (EHR) databases. METHODS: We developed a natural language processing (NLP) software application to code clinical text documentation of overdose, including identification of intention for self-harm, substances involved, substance abuse, and error in medication usage. Using datasets balanced with cases of suspected overdose and records of individuals at elevated risk for overdose, we developed and validated the application using Kaiser Permanente Northwest data, then tested portability of the application using Kaiser Permanente Washington data. Datasets were chart-reviewed to provide a gold standard for comparison and evaluation of the automated method. RESULTS: The method performed well in identifying overdose (sensitivity = 0.80, specificity = 0.93), intentional overdose (sensitivity = 0.81, specificity = 0.98), and involvement of opioids (excluding heroin, sensitivity = 0.72, specificity = 0.96) and heroin (sensitivity = 0.84, specificity = 1.0). The method performed poorly at identifying adverse drug reactions and overdose due to patient error and fairly at identifying substance abuse in opioid-related unintentional overdose (sensitivity = 0.67, specificity = 0.96). Evaluation using validation datasets yielded significant reductions, in specificity and negative predictive values only, for many classifications mentioned above. However, these measures remained above 0.80, thus, performance observed during development was largely maintained during validation. Similar results were obtained when evaluating portability, although there was a significant reduction in sensitivity for unintentional overdose that was attributed to missing text clinical notes in the database. CONCLUSIONS: Methods that process text clinical notes show promise for improving accuracy and fidelity at identifying and classifying overdoses according to type using EHR data.

Development of an algorithm to identify inpatient opioid-related overdoses and oversedation using electronic data.

PURPOSE: To facilitate surveillance and evaluate interventions addressing opioid-related overdoses, algorithms are needed for use in large health care databases to identify and differentiate community-occurring opioid-related overdoses from inpatient-occurring opioid-related overdose/oversedation. METHODS: Data were from Kaiser Permanente Northwest (KPNW), a large integrated health plan. We iteratively developed and evaluated an algorithm for electronically identifying inpatient overdose/oversedation in KPNW hospitals from 1 January 2008 to 31 December 2014. Chart audits assessed accuracy; data sources included administrative and clinical records. RESULTS: The best-performing algorithm used these rules: (1) Include events with opioids administered in an inpatient setting (including emergency department/urgent care) followed by naloxone administration within 275 hours of continuous inpatient stay; (2) exclude events with electroconvulsive therapy procedure codes; and (3) exclude events in which an opioid was administered prior to hospital discharge and followed by readmission with subsequent naloxone administration. Using this algorithm, we identified 870 suspect inpatient overdose/oversedation events and chart audited a random sample of 235. Of the random sample, 185 (78.7%) were deemed overdoses/oversedation, 37 (15.5%) were not, and 13 (5.5%) were possible cases. The number of hours between time of opioid and naloxone administration did not affect algorithm accuracy. When "possible" overdoses/oversedations were included with confirmed events, overall positive predictive value (PPV) was very good (PPV = 84.0%). Additionally, PPV was reasonable when evaluated specifically for hospital stays with emergency/urgent care admissions (PPV = 77.0%) and excellent for elective surgery admissions (PPV = 97.0%). CONCLUSIONS: Algorithm performance was reasonable for identifying inpatient overdose/oversedation with best performance among elective surgery patients.

Identifying and classifying opioid-related overdoses: A validation study.

PURPOSE: The study aims to develop and validate algorithms to identify and classify opioid overdoses using claims and other coded data, and clinical text extracted from electronic health records using natural language processing (NLP). METHODS: Primary data were derived from Kaiser Permanente Northwest (2008-2014), an integrated health care system (~n > 475 000 unique individuals per year). Data included International Classification of Diseases, Ninth Revision (ICD-9) codes for nonfatal diagnoses, International Classification of Diseases, Tenth Revision (ICD-10) codes for fatal events, clinical notes, and prescription medication records. We assessed sensitivity, specificity, positive predictive value, and negative predictive value for algorithms relative to medical chart review and conducted assessments of algorithm portability in Kaiser Permanente Washington, Tennessee State Medicaid, and Optum. RESULTS: Code-based algorithm performance was excellent for opioid-related overdoses (sensitivity = 97.2%, specificity = 84.6%) and classification of heroin-involved overdoses (sensitivity = 91.8%, specificity = 99.0%). Performance was acceptable for code-based suicide/suicide attempt classifications (sensitivity = 70.7%, specificity = 90.5%); sensitivity improved with NLP (sensitivity = 78.7%, specificity = 91.0%). Performance was acceptable for the code-based substance abuse-involved classification (sensitivity = 75.3%, specificity = 79.5%); sensitivity improved with the NLP-enhanced algorithm (sensitivity = 80.5%, specificity = 76.3%). The opioid-related overdose algorithm performed well across portability assessment sites, with sensitivity greater than 96% and specificity greater than 84%. Cross-site sensitivity for heroin-involved overdose was greater than 87%, specificity greater than or equal to 99%. CONCLUSIONS: Code-based algorithms developed to detect opioid-related overdoses and classify them according to heroin involvement perform well. Algorithms for classifying suicides/attempts and abuse-related opioid overdoses perform adequately for use for research, particularly given the complexity of classifying such overdoses. The NLP-enhanced algorithms for suicides/suicide attempts and abuse-related overdoses perform significantly better than code-based algorithms and are appropriate for use in settings that have data and capacity to use NLP.

Patient perspectives on how living with a mental illness affects making and maintaining healthy lifestyle changes.

OBJECTIVE: To understand the ways that mental health symptoms interfere with achieving health goals. METHODS: Individuals with mental illness diagnoses and varying levels of preventive service use were recruited from federally qualified health centers and an integrated health care delivery system and interviewed. Thematic analysis was used to characterize descriptions of how mental illness experiences influenced lifestyle change efforts. RESULTS: Three themes described patients' (n = 163) perspectives on barriers to making healthy lifestyle changes: 1) Thinking about making lifestyle changes is overwhelming for individuals already managing the burdens of mental illnesses; 2) Depression makes it difficult to care about a healthy future; and 3) When mental illness symptoms are not adequately treated unhealthy behaviors that provide relief are unlikely to be discontinued. Participants also made suggestions for improving health care delivery to facilitate positive behavior change. CONCLUSION: Patients with mental illnesses need their clinicians to be empathic, help them envision a healthier future, address unmet mental health needs, and provide resources. PRACTICE IMPLICATIONS: Primary care clinicians should encourage their patients with mental illnesses to make healthy lifestyle changes within the context of a supportive relationship. Lifestyle change can be overwhelming; clinicians should acknowledge progress and provide ongoing tangible support.

Benefits and challenges of using service dogs for veterans with posttraumatic stress disorder.

OBJECTIVE: Veterans with posttraumatic stress disorder (PTSD) are increasingly seeking service dogs to help them manage trauma-related symptoms, yet literature describing service dog use in this population is scant. The goal of this study was to document the benefits and challenges experienced by veterans with service dogs trained to assist with PTSD-related needs. METHOD: Participants were veterans (N = 41) with service dogs, and their caregivers (n = 8), recruited through community-based service dog training agencies. We conducted in-depth interviews and observed training sessions as part of a larger study, and used thematic analysis to characterize data. RESULTS: Veterans reported that service dogs reduced hypervigilance by alerting and creating boundaries, and disrupted nightmares, improving sleep quality and duration. Dogs also helped veterans turn their attention away from invasive trauma-related thoughts. Additional reported benefits included improved emotional connections with others, increased community participation and physical activity, and reduced suicidal impulses and medication use. Demands of training, adjustment to life with a service dog, and delayed benefits were challenging for many veterans and caregivers. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Veterans report that service dogs help reduce PTSD symptoms and facilitate recovery and realization of meaningful goals. Service dogs may be a reasonable option for veterans who are reluctant to pursue or persist with traditional evidence-based treatments. Additional rigorous research on the effectiveness of service dogs for this population is warranted. (PsycINFO Database Record

Perspectives on Providing And Receiving Preventive Health Care From Primary Care Providers and Their Patients With Mental Illnesses.

PURPOSE: Individuals with mental illnesses have higher morbidity rates and reduced life expectancy compared to the general population. Understanding how patients and providers perceive the need for prevention, as well as the barriers and beliefs that may contribute to insufficient care, are important for improving service delivery tailored to this population. DESIGN: Cross-sectional; mixed methods. SETTING: An integrated health system and a network of federally qualified health centers and safety net clinics. PARTICIPANTS: Interviews (n = 30) and surveys (n = 249) with primary care providers. Interviews (n = 158) and surveys (n = 160) with patients diagnosed with schizophrenia, bipolar, anxiety, or major depressive disorders. MEASURES: Semi-structured interviews and surveys. ANALYSIS: Thematic analysis for qualitative data; frequencies for quantitative data. RESULTS: More than half (n = 131, 53%) of clinicians believed patients with mental illnesses care less about preventive care than the general population, yet 88% (n = 139) of patients reported interest in improving health. Most providers (n = 216, 88%) lacked confidence that patients with mental illnesses would follow preventive recommendations; 82% (n = 129) of patients reported they would try to change lifestyles if their doctor recommended. Clinicians explained that their perception of patients' chaotic lives and lack of interest in preventive care contributed to their fatalistic attitudes on care delivery to this population. Clinicians and patients agreed on substantial need for additional support for behavior changes. Clinicians reported providing informational support by keeping messages simple; patients reported a desire for more detailed information on reasons to complete preventive care. Patients also detailed the need for assistive and tangible support to manage behavioral health changes. CONCLUSIONS: Our results suggest a few clinical changes could help patients complete preventive care recommendations and improve health behaviors: improving clinician-patient collaboration on realistic goal setting, increasing visit time or utilizing behavioral health consultants that bridge primary and specialty mental health care, and increasing educational and tangible patient support services.

Patient and System Characteristics Associated with Performance on the HEDIS Measures of Alcohol and Other Drug Treatment Initiation and Engagement.

OBJECTIVES: Understand patient and system characteristics associated with performance on the Healthcare Effectiveness Data and Information Set (HEDIS) Alcohol and Other Drug (AOD) Initiation and Engagement of Treatment (IET) measures. METHODS: This mixed-methods study linked patient and health system data from four Kaiser Permanente regions to HEDIS performance measure data for 44,320 commercially or Medicare-insured adults with HEDIS-eligible AOD diagnoses in 2012. Characteristics associated with IET were examined using multilevel logistic regression models. Key informant interviews (n = 18) focused on opportunities to improve initiation and engagement. RESULTS: Non-white race/ethnicity, alcohol abuse, or nonopioid drug abuse diagnoses were associated with lower odds of treatment initiation among commercially insured. For both insurance groups, those diagnosed in healthcare departments other than specialty AOD treatment were less likely to initiate or engage in treatment. Being diagnosed in facilities with co-located AOD/primary care clinics, and those with medications for addiction treatment available, was each associated with higher odds of initiation and engagement for both commercially and Medicare-insured. Having behavioral medicine specialists or clinical health educators in primary care increased initiation and engagement odds among commercially insured. Key informants recommended were as follows: patient-centered care; increased treatment choices; cross-departmental patient identification, engagement, and coordination; provider education; and use of informatics/technology. CONCLUSIONS: Tailoring treatment, enhancing treatment motivation among individuals with lower severity diagnoses, offering medication treatment of addiction, clinician education, care coordination, co-located AOD and primary care departments, and behavioral medicine specialists in primary care may improve rates of initiation and engagement in AOD treatment.

Effects of primary care clinician beliefs and perceived organizational facilitators on the delivery of preventive care to individuals with mental illnesses.

BACKGROUND: Although many studies have documented patient-, clinician-, and organizational barriers/facilitators of primary care among people with mental illnesses, few have examined whether these factors predict actual rates of preventive service use. We assessed whether clinician behaviors, beliefs, characteristics, and clinician-reported organizational characteristics, predicted delivery of preventive services in this population. METHODS: Primary care clinicians (n = 247) at Kaiser Permanente Northwest (KPNW) or community health centers and safety-net clinics (CHCs), in six states, completed clinician surveys in 2014. Using electronic health record data, we calculated preventive care-gap rates for patients with mental illnesses empaneled to survey respondents (n = 37,251). Using separate multi-level regression models for each setting, we tested whether survey responses predicted preventive service care-gap rates. RESULTS: After controlling for patient-level characteristics, patients of clinicians who reported a greater likelihood of providing preventive care to psychiatrically asymptomatic patients experienced lower care-gap rates (KPNW γ= - .05, p = .041; CHCs γ= - .05, p = .033). In KPNW, patients of female clinicians had fewer care gaps than patients of male clinicians (γ= - .07, p = .011). In CHCs, patients of clinicians who had practiced longer had fewer care gaps (γ= - .004, p = .010), as did patients whose clinicians believed that organizational quality goals facilitate preventive service provision (γ= - .06, p = .006). Case manager availability in CHCs was associated with higher care-gap rates (γ=.06, p = .028). CONCLUSIONS: Clinicians who report they are likely to address preventive concerns when their mentally ill patients present without apparent psychiatric symptoms had patients with fewer care gaps. In CHCs, care quality goals may facilitate preventive care whereas case managers may not.

Preventive Service Use Among People With and Without Serious Mental Illnesses.

INTRODUCTION: People with serious mental illnesses experience excess morbidity and premature mortality resulting from preventable conditions. The goal was to examine disparities in preventive care that might account for poor health outcomes. METHODS: In this retrospective cohort study, adults (N=803,276) served by Kaiser Permanente Northwest and federally qualified health centers/safety-net community health clinics were categorized into five groups: schizophrenia spectrum disorders, bipolar disorders/affective psychoses, anxiety disorders, nonpsychotic unipolar depression, and reference groups with no evidence of these specific mental illnesses. The primary outcome was overall preventive care-gap rate, the proportion of incomplete preventive services for which each patient was eligible in 2012-2013. Secondary analyses examined Kaiser Permanente Northwest data from 2002 to 2013. Data were analyzed in 2015. RESULTS: Controlling for patient characteristics and health services use, Kaiser Permanente Northwest mean care-gap rates were significantly lower for bipolar disorders/affective psychoses (mean=18.6, p<0.001) and depression groups (mean=18.6, p<0.001) compared with the reference group. Schizophrenia (mean=19.4, p=0.236) and anxiety groups (mean=19.9, p=0.060) did not differ from the reference group (mean=20.3). In community health clinics, schizophrenia (mean=34.1, p<0.001), bipolar/affective psychosis (mean=35.7, p<0.001), anxiety (mean=38.5, p<0.001), and depression groups (mean=36.3, p<0.001) had significantly lower care-gap rates than those in the reference group (mean=40.0). Secondary analyses of diabetes and dyslipidemia screening trends in Kaiser Permanente Northwest showed diagnostic groups consistently had fewer care gaps than patients in the reference group. CONCLUSIONS: In vastly different settings, individuals with serious mental illnesses received preventive services at equal or better rates than the general population.

Colorectal Cancer Screening Completion Among Individuals With and Without Mental Illnesses: A Comparison of 2 Screening Methods.

PURPOSE: Cancer mortality is worse among people with psychiatric disorders. The purpose of this study was to compare facilitators and rates of colorectal cancer (CRC) screening between people with and without mental illnesses. DESIGN: We conducted a secondary analysis using data from a general population cohort study (N = 92 445) that assessed effects of 2 types of CRC screening test kits-guaiac fecal occult blood testing (gFOBT) and fecal immunochemical testing (FIT)-on CRC screening completion. SETTING: The setting was a health system that served approximately 485 000 members in urban and suburban Oregon and Washington. PARTICIPANTS: Participants were health system members, categorized by mental illness diagnosis (psychotic disorders, non-psychotic unipolar depression, and no mental illness), who were age-eligible, at average risk of CRC, and were at least 366 days past their last gFOBT with no evidence of other CRC screening. MEASURES: The outcome was time until completion of CRC screening. ANALYSIS: We used Cox proportional hazard models. RESULTS: FIT reduced CRC screening barriers for all the groups. Compared to people without mental illness diagnoses, those with psychotic disorders were equally likely to screen using FIT (hazard ratio [HR] = .95, p = .679) and those with depression were more likely (HR = 1.17, p = .006). CONCLUSIONS: FIT can improve CRC screening rates among people with mental illnesses, particularly depression.

Patterns and correlates of medical cannabis use for pain among patients prescribed long-term opioid therapy.

OBJECTIVE: Little is known about co-occurring long-term opioid therapy (LTOT) and medical cannabis use. We compared characteristics of patients prescribed LTOT who endorsed using medical cannabis for pain to patients who did not report cannabis use. METHOD: Participants (n=371) prescribed LTOT completed self-report measures about pain, substance use, and mental health. RESULTS: Eighteen percent of participants endorsed using medical cannabis for pain. No significant differences were detected on pain-related variables, depression, or anxiety between those who endorsed medical cannabis use and those who did not. Medical cannabis users had higher scores of risk for prescription opioid misuse (median=17.0 vs. 11.5, p<0.001), rates of hazardous alcohol use (25% vs. 16%, p<0.05), and rates of nicotine use (42% vs. 26%, p=0.01). Multivariable analyses indicated that medical cannabis use was significantly associated with risk of prescription opioid misuse (ß=0.17, p=0.001), but not hazardous alcohol use (aOR=1.96, 95% CI=0.96-4.00, p=0.06) or nicotine use (aOR=1.61, 95% CI=0.90-2.88, p=0.11). CONCLUSION: There are potential risks associated with co-occurring LTOT and medical cannabis for pain. Study findings highlight the need for further clinical evaluation in this population. Future research is needed to examine the longitudinal impact of medical cannabis use on pain-related and substance use outcomes.

Factors influencing the long-term sustainment of quality improvements made in addiction treatment facilities: a qualitative study.

BACKGROUND: A greater understanding of the factors that influence long-term sustainment of quality improvement (QI) initiatives is needed to promote organizational ability to sustain QI practices over time, help improve future interventions, and increase the value of QI investments. METHODS: We approached 83 of 201 executive sponsors or change leaders at addiction treatment organizations that participated in the 2007-2009 NIATx200 QI intervention. We completed semi-structured interviews with 33 individuals between November 2015 and April 2016. NIATx200 goals were to decrease wait time, increase admissions and improve retention in treatment. Interviews sought to understand factors that either facilitated or impeded long-term sustainment of organizational QI practices made during the intervention. We used thematic analysis to organize the data and group patterns of responses. We assessed available quantitative outcome data and intervention engagement data to corroborate qualitative results. RESULTS: We used narrative analysis to group four important themes related to long-term sustainment of QI practices: (1) finding alignment between business- and client-centered practices; (2) staff engagement early in QI process added legitimacy which facilitated sustainment; (3) commitment to integrating data into monitoring practices and the identification of a data champion; and (4) adequate organizational human resources devoted to sustainment. We found four corollary factors among agencies which did not sustain practices: (1) lack of evidence of impact on business practices led to discontinuation; (2) disengaged staff and lack of organizational capacity during implementation period led to lack of sustainment; (3) no data integration into overall business practices and no identified data champion; and (4) high staff turnover. In addition, we found that many agencies' current use of NIATx methods and tools suggested a legacy effect that might improve quality elsewhere, even absent overall sustainment of original study outcome goals. Available quantitative data on wait-time reduction demonstrated general concordance between agency perceptions of, and evidence for, sustainment 2 years following the end of the intervention. Additional quantitative data suggested that greater engagement during the intervention period showed some association with sustainment. CONCLUSIONS: Factors identified in QI frameworks as important for short-term sustainment-organizational capacity (e.g. staffing and leadership) and intervention characteristics (e.g. flexibility and fit)-are also important to long-term sustainment.

An Observational Study of Service Dogs for Veterans With Posttraumatic Stress Disorder.

OBJECTIVES: This study examined needs related to posttraumatic stress disorder (PTSD), assistance by service dogs, and feasibility of data collection among veterans receiving service dogs. METHODS: Questionnaires assessed PTSD-related needs and services performed or expected to be performed by service dogs among 78 veterans who had or were on a wait list for a service dog (average age, 42; women, 31%). Analyses compared pre-post characteristics among 22 veterans who received a service dog as part of the study (91% follow-up; average follow-up=3.37±2.57 months). RESULTS: Veterans reported that the most important services performed were licking or nudging veterans to help them "stay present," preventing panic, and putting space between veterans and strangers. High follow-up rates and improvements in outcomes with moderate to large effect sizes among recipients of study-provided dogs suggest further study is warranted. CONCLUSIONS: Service dogs may be feasible supports for veterans with PTSD; randomized clinical trials are needed to assess effectiveness.

Assessing the accuracy of opioid overdose and poisoning codes in diagnostic information from electronic health records, claims data, and death records.

PURPOSE: The purpose of this study is to assess positive predictive value (PPV), relative to medical chart review, of International Classification of Diseases (ICD)-9/10 diagnostic codes for identifying opioid overdoses and poisonings. METHODS: Data were obtained from Kaiser Permanente Northwest and Northern California. Diagnostic data from electronic health records, submitted claims, and state death records from Oregon, Washington, and California were linked. Individual opioid-related poisoning codes (e.g., 965.xx and X42), and adverse effects of opioids codes (e.g., E935.xx) combined with diagnoses possibly indicative of overdoses (e.g., respiratory depression), were evaluated by comparison with chart audits. RESULTS: Opioid adverse effects codes had low PPV to detect overdoses (13.4%) as assessed in 127 charts and were not pursued. Instead, opioid poisoning codes were assessed in 2100 individuals who had those codes present in electronic health records in the period between the years 2008 and 2012. Of these, 10/2100 had no available information and 241/2100 were excluded potentially as anesthesia-related. Among the 1849 remaining individuals with opioid poisoning codes, 1495 events were accurately identified as opioid overdoses; 69 were miscodes or misidentified, and 285 were opioid adverse effects, not overdoses. Thus, PPV was 81%. Opioid adverse effects or overdoses were accurately identified in 1780 of 1849 events (96.3%). CONCLUSIONS: Opioid poisoning codes have a predictive value of 81% to identify opioid overdoses, suggesting ICD opioid poisoning codes can be used to monitor overdose rates and evaluate interventions to reduce overdose. Further research to assess sensitivity, specificity, and negative predictive value are ongoing. Copyright © 2017 John Wiley & Sons, Ltd.

Higher Prescription Opioid Dose is Associated With Worse Patient-Reported Pain Outcomes and More Health Care Utilization.

Some previous research has examined pain-related variables on the basis of prescription opioid dose, but data from studies involving patient-reported outcomes have been limited. This study examined the relationships between prescription opioid dose and self-reported pain intensity, function, quality of life, and mental health. Participants were recruited from 2 large integrated health systems, Kaiser Permanente Northwest (n = 331) and VA Portland Health Care System (n = 186). To be included, participants had to have musculoskeletal pain diagnoses and be receiving stable doses of long-term opioid therapy. We divided participants into 3 groups on the basis of current prescription opioid dose in daily morphine equivalent dose (MED): low dose (5-20 mg MED), moderate dose (20.1-50 mg MED), and higher dose (50.1-120 mg MED) groups. A statistically significant trend emerged where higher prescription opioid dose was associated with moderately sized effects including greater pain intensity, more impairments in functioning and quality of life, poorer self-efficacy for managing pain, greater fear avoidance, and more health care utilization. Rates of potential alcohol and substance use disorders also differed among groups. Findings from this evaluation reveal significant differences in pain-related and substance-related factors on the basis of prescription opioid dose. PERSPECTIVE: This study included 517 patients who were prescribed long-term opioid therapy and compared differences on pain- and mental health-related variables on the basis of prescription opioid dose. Findings reveal small- to medium-sized differences on pain-related variables, alcohol and substance use, and health care utilization on the basis of the dose of opioid prescribed.

Patient-reported pathways to opioid use disorders and pain-related barriers to treatment engagement.

BACKGROUND: Risk factors associated with developing opioid use disorders (OUD) are documented, but less is known about different pathways to initiation of opioids or opioid dependence, or how such pathways affect treatment engagement. METHODS: We recruited 283 adults with electronic medical record (EMR) evidence of opioid dependence diagnoses. Open-ended and structured interview items focused on prior opioid treatment experiences, barriers to and knowledge of treatment options. Interviews were audio-recorded, transcribed, and coded. In exploratory analyses, we used a modified grounded theory approach to organize emergent, patient-reported themes describing participants' perceived pathways to opioid dependence. RESULTS: 121 participants described one or more pathways to OUD. Qualitative analyses revealed five pathway themes. Three pathways were related to pain control: inadequately controlled chronic pain, exposure to opioids during acute pain episodes, and chronic pain among individuals with prior substance use disorders. A fourth pathway included individuals for whom opioids provided relief from emotional distress; the fifth related to recreational or non-medically supervised opioid use. We identified pain-related barriers to reducing/stopping opioids and treatment engagement barriers among individuals who perceived themselves solely as pain patients. CONCLUSION: Patients' perceptions of inadequately controlled pain, patients' previous substance use disorders, and the relief from emotional distress that some patients feel while using opioids are relevant when making clinical decisions about whether to initiate or sustain opioid therapy, and for how to monitor certain individuals. Among individuals with pain and OUD, treatment barriers include fear of uncontrolled pain, and stigmatization of being treated alongside people with non-medical opioid use.

Understanding opioid overdose characteristics involving prescription and illicit opioids: A mixed methods analysis.

BACKGROUND: Opioid abuse and misuse are significant public health issues. The CDC estimated 72% of pharmaceutical-related overdose deaths in the US in 2012 involved opioids. While studies of opioid overdoses have identified sociodemographic characteristics, agents used, administration routes, and medication sources associated with overdoses, we know less about the context and life circumstances of the people who experience these events. METHODS: We analyzed interviews (n=87) with survivors of opioid overdoses or family members of decedents. Individuals experiencing overdoses were members of a large integrated health system. Using ICD codes for opioid overdoses and poisonings, we identified participants from five purposefully derived pools of health-plan members who had: 1) prescriptions for OxyContin(®) or single-ingredient sustained-release oxycodone, 2) oxycodone single-ingredient immediate release, 3) other long-acting opioids, 4) other short-acting opioids, or 5) no active opioid prescriptions. RESULTS: Individuals who experienced opioid overdoses abused and misused multiple medications/drugs; experienced dose-related miscommunications or medication-taking errors; had mental health and/or substance use conditions; reported chronic pain; or had unstable resources or family/social support. Many had combinations of these risks. Most events involved polysubstance use, often including benzodiazepines. Accidental overdoses were commonly the result of abuse or misuse, some in response to inadequately treated chronic pain or, less commonly, medication-related mistakes. Suicide attempts were frequently triggered by consecutive negative life events. CONCLUSIONS: To identify people at greater risk of opioid overdose, efforts should focus on screening for prescribed and illicit polysubstance use, impaired cognition, and changes in life circumstances, psychosocial risks/supports, and pain control.

Protocol: changes in rates of opioid overdose and poisoning events in an integrated health system following the introduction of a formulation of OxyContin® with abuse-deterrent properties.

BACKGROUND: Addiction, overdoses and deaths resulting from prescription opioids have increased dramatically over the last decade. In response, several manufacturers have developed formulations of opioids with abuse-deterrent properties. For many of these products, the Food and Drug Administration (FDA) recognized the formulation with labeling claims and mandated post-marketing studies to assess the abuse-deterrent effects. In response, we assess differences in rates of opioid-related overdoses and poisonings prior to and following the introduction of a formulation of OxyContin® with abuse-deterrent properties. METHODS/DESIGN: To assess effects of this formulation, electronic medical record (EMR) data from Kaiser Permanente Northwest (KPNW) and Kaiser Permanente Northern California (KPNC) are linked to state death data and compared to chart audits. Overdose and poisoning events will be categorized by intentionality and number of agents involved, including illicit drugs and alcohol. Using 6-month intervals over a 10-year period, trends will be compared in rates of opioid-related overdoses and poisoning events associated with OxyContin® to rates of events associated with other oxycodone and opioid formulations. Qualitative interviews with patients and relatives of deceased patients will be conducted to capture circumstances surrounding events. DISCUSSION: This study assesses and tracks changes in opioid-related overdoses and poisoning events prior to and following the introduction of OxyContin® with abuse-deterrent properties. Public health significance is high because these medications are designed to reduce abuse-related behaviors that lead to important adverse outcomes, including overdoses and deaths.