Brief Opioid Overdose Knowledge (BOOK): A Questionnaire to Assess Overdose Knowledge in Individuals Who Use Illicit or Prescribed Opioids.
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ABSTRACT: Background
Opioid overdose is a public health crisis. This study describes efforts to develop and validate the Brief Opioid Overdose Knowledge (BOOK) questionnaire to assess patient knowledge gaps related to opioid overdose risks.Methods
Two samples of illicit opioid users and a third sample of patients receiving an opioid for the treatment of chronic pain (total N = 848) completed self-report items pertaining to opioid overdose risks.Results
A 3-factor scale was established, representing Opioid Knowledge (4 items), Opioid Overdose Knowledge (4 items), and Opioid Overdose Response Knowledge (4 items). The scale had strong internal and face validity. Patients with chronic pain performed worse than illicit drug users in almost all items assessed, highlighting the need to increase knowledge of opioid overdose risk to this population.Conclusions
This study sought to develop a brief, internally valid method for quickly assessing deficits in opioid overdose risk areas within users of illicit and prescribed opioids, to provide an efficient metric for assessing and comparing educational interventions, facilitate conversations between physicians and patients about overdose risks, and help formally identify knowledge deficits in other patient populations.
SUBMITTER: Dunn KE
PROVIDER: S-EPMC5042823 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:After decades of increased opioid pain reliever prescribing, providers are rapidly reducing prescribing. We hypothesized that reduced access to prescribed opioid pain relievers among patients previously reliant upon opioid pain relievers would result in increased illicit opioid use. METHODS AND FINDINGS:We conducted a retrospective cohort study among 602 publicly insured primary care patients who had been prescribed opioids for chronic non-cancer pain for at least three consecutive months in San Francisco, recruited through convenience sampling. We conducted a historical reconstruction interview and medical chart abstraction focused on illicit substance use and opioid pain reliever prescriptions, respectively, from 2012 through the interview date in 2017-2018. We used a nested-cohort design, in which patients were classified, based on opioid pain reliever dose change, into a series of nested cohorts starting with each follow-up quarter. Using continuation-ratio models, we estimated associations between opioid prescription discontinuation or 30% increase or decrease in dose, relative to no change, and subsequent frequency of heroin and non-prescribed opioid pain reliever use, separately. Models controlled for demographics, clinical and behavioral characteristics, and past use of heroin or non-prescribed opioid pain relievers. A total of 56,372 and 56,484 participant-quarter observations were included from the 597 and 598 participants available for analyses of heroin and non-prescribed opioid pain reliever outcomes, respectively. Participants discontinued from prescribed opioids were more likely to use heroin (Adjusted Odds Ratio (AOR) = 1.57, 95% CI: 1.25-1.97) and non-prescribed opioid pain relievers (AOR = 1.75, 1.45-2.11) more frequently in subsequent quarters compared to participants with unchanged opioid prescriptions. Participants whose opioid pain reliever dose increased were more likely to use heroin more frequently (AOR = 1.67, 1.32-2.12). Results held throughout sensitivity analyses. The main limitations were the observational nature of results and limited generalizability beyond safety-net settings. CONCLUSIONS:Discontinuation of prescribed opioid pain relievers was associated with more frequent non-prescribed opioid pain reliever and heroin use; increased dose was also associated with more frequent heroin use. Clinicians should be aware of these risks in determining pain management approaches.
| S-EPMC7197848 | biostudies-literature
Project description:ImportanceFamily members are cited as a common source of prescription opioids used for nonmedical reasons. However, the overdose risk associated with exposure to opioids prescribed to family members among adolescents and young adults is not well established.ObjectiveTo assess the association of opioids prescribed to family members with pharmaceutical opioid overdose among youth.Design, setting, and participantsThis cohort study included 45 145 family units with a total of 72 040 adolescents and young adults aged 11 to 26 years enrolled in a Kaiser Permanente Colorado health plan in 2006 and observed through June 2018.ExposuresOpioid prescriptions and dosage dispensed to family members and youth in the past month.Main outcomes and measuresFatal pharmaceutical opioid overdoses identified in vital records and nonfatal pharmaceutical opioid overdoses identified in emergency department and inpatient settings. Time to first overdose was modeled using Cox regression.ResultsThe study population consisted of 72 040 adolescents and young adults (mean [SD] age across follow-up, 19.3 [3.7] years; 36 646 [50.9%] girls and women) nested in 45 145 family units. Youth were more commonly exposed to prescription opioids dispensed to a family member than through their own prescriptions. During follow-up, 26 284 youth (36.5%) filled at least 1 opioid prescription, and 47 461 youth (65.9%) had at least 1 family member with a prescription. Exposure to family members with opioid prescriptions in the past month was associated with increased risk of pharmaceutical opioid overdose (adjusted hazard ratio [aHR], 2.17; 95% CI, 1.24-3.79) independent of opioids prescribed to youth (aHR, 6.62; 95% CI, 3.39-12.91). Concurrent exposure to opioid prescriptions from youth and family members was associated with substantially increased overdose risk (aHR, 12.99; 95% CI, 5.08-33.25). High dosage of total morphine milligram equivalents (MME) prescribed to family members in the past month was associated with youth overdose (0 MME vs >0 to <200 MME: aHR, 1.39; 95% CI, 0.51-3.81; 0 MME vs 200 to <600 MME: aHR, 1.49; 95% CI, 0.59-3.77; 0 MME vs ≥600 MME: aHR, 2.93; 95% CI, 1.55-5.56).Conclusions and relevanceIn this study of youth linked to family members, exposure to family members' prescribed opioids was associated with increased risk of pharmaceutical opioid overdose, independent of opioids prescribed to youth. Further interventions targeting youth and families are needed, including counseling patients about the risks of opioids to youth in their families.
| S-EPMC7462253 | biostudies-literature
Project description:Opioid-related mortality continues to increase in the United States. The current study assesses demographic and behavioral predictors of perceived overdose risk among individuals who use opioids illicitly. By examining these correlates in the context of established overdose risk factors, we aim to assess whether characteristics and behaviors that have been associated with actual overdose risk translate to higher perception of risk.We conducted a cross-sectional survey of 172 adult illicit opioid users in San Francisco, CA and used multivariable logistic regression to identify predictors of perception of high risk for opioid overdose.Age (aOR=0.96, 95%CI=0.93-1.00) and number of injection days per month (0.91, 0.86-0.97) were associated with a lower odds of perceived high overdose risk. There was no independent association between use of opioid analgesics, concurrent use of opioids and benzodiazepines or cocaine, or HIV status and overdose risk perception.Opioid users who injected more frequently and those who were older were less likely to perceive themselves as being at risk of overdose, notwithstanding that those who inject more are at higher risk of overdose and those who are older are at higher risk overdose mortality. In addition, despite being established overdose risk factors, there was no relationship between use of opioid analgesics, concurrent use of opioids and cocaine or benzodiazepines, or self-reported HIV status and overdose risk perception. These findings highlight key populations of opioid users and established risk factors that may merit focused attention as part of education-based overdose prevention and opioid management strategies.
| S-EPMC4734628 | biostudies-literature
Project description:BackgroundOpioid prescribing for a range of health issues is increasing globally. The risk of fatal and non-fatal overdose is increased among people prescribed strong opioids: in high doses in the context of polypharmacy (the use of multiple medications at the same time), especially with other sedatives; and among people with multiple morbidities including cardiorespiratory, hepatic and renal conditions. This study described and quantified the prescribing of strong opioids, comorbidities and other overdose risk factors among those prescribed strong opioids, and factors associated with high/very high opioid dosage in a regional health authority in Scotland as part of a wider service improvement exercise.MethodsParticipating practices ran searches to identify patients prescribed strong opioids and their characteristics, polypharmacy, and other overdose risk factors. Data were anonymised before being analysed at practice and patient-level. Morphine Equivalent Doses were calculated for patients based on drug/dose information and classed as Low/Medium/High/Very High. Descriptive statistics were generated on the strong opioid patient population and overdose risk factors. The relationship between the prescribing of strong opioids and practice/patient-level factors was investigated using linear and logistic regression models.ResultsEighty-five percent (46/54) of GP practices participated. 12.4% (42,382/341,240) of individuals in participating practices were prescribed opioids and, of these, one third (14,079/42,382) were prescribed strong opioids. The most common comorbidities and overdose risk factors among strong opioid recipients were pain (67.2%), cardiovascular disease (43.2%), and mental health problems (39.3%). There was a positive significant relationship between level of social deprivation among practice caseload and level of strong opioid prescribing (p < 0.001). People prescribed strong opioids tended to be older (mean 59.7 years) and female (8638, 61.4%) and, among a subset of patients, age, gender and opioid drug class were significantly associated with prescribing of High/Very High doses.ConclusionsOur findings have identified a large population at potential risk of prescription opioid overdose. There is a need to explore pragmatic models of tailored interventions which may reduce the risk of overdose within this group and clinical practice may need to be tightened to minimise overdose risk for individuals prescribed high dose opioids.
| S-EPMC8223343 | biostudies-literature
Project description:IntroductionWith increasing rates of opioid overdoses in the US, a surveillance tool to identify high-risk patients may help facilitate early intervention.ObjectiveTo develop an algorithm to predict overdose using routinely-collected healthcare databases.MethodsWithin a US commercial claims database (2011-2015), patients with ≥1 opioid prescription were identified. Patients were randomly allocated into the training (50%), validation (25%), or test set (25%). For each month of follow-up, pooled logistic regression was used to predict the odds of incident overdose in the next month based on patient history from the preceding 3-6 months (time-updated), using elastic net for variable selection. As secondary analyses, we explored whether using simpler models (few predictors, baseline only) or different analytic methods (random forest, traditional regression) influenced performance.ResultsWe identified 5,293,880 individuals prescribed opioids; 2,682 patients (0.05%) had an overdose during follow-up (mean: 17.1 months). On average, patients who overdosed were younger and had more diagnoses and prescriptions. The elastic net model achieved good performance (c-statistic 0.887, 95% CI 0.872-0.902; sensitivity 80.2, specificity 80.1, PPV 0.21, NPV 99.9 at optimal cutpoint). It outperformed simpler models based on few predictors (c-statistic 0.825, 95% CI 0.808-0.843) and baseline predictors only (c-statistic 0.806, 95% CI 0.787-0.26). Different analytic techniques did not substantially influence performance. In the final algorithm based on elastic net, the strongest predictors were age 18-25 years (OR: 2.21), prior suicide attempt (OR: 3.68), opioid dependence (OR: 3.14).ConclusionsWe demonstrate that sophisticated algorithms using healthcare databases can be predictive of overdose, creating opportunities for active monitoring and early intervention.
| S-EPMC7575098 | biostudies-literature
Project description:ImportanceAttempts to discontinue opioid therapy to reduce the risk of overdose and adhere to prescribing guidelines may lead patients to be exposed to variability in opioid dosing. Such dose variability may increase the risk of opioid overdose even if therapy discontinuation is associated with a reduction in risk.ObjectiveTo examine the association between opioid dose variability and opioid overdose.Design, setting, and participantsA nested case-control study was conducted in a large Colorado integrated health plan and delivery system from January 1, 2006, through June 30, 2018. Cohort members were individuals prescribed long-term opioid therapy.ExposuresDose variability was defined as the SD of the milligrams of morphine equivalents across each patient's follow-up and categorized based on the quintile distribution of the SD in the cohort (0-5.3, 5.4-9.1, 9.2-14.6, 14.7-27.2, and >27.2 mg of morphine equivalents).Main outcomes and measuresOpioid overdose cases were identified using International Classification of Diseases, Ninth Revision and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes. Each case patient with overdose was matched to up to 20 control patients using risk set sampling. Conditional logistic regression models were used to generate matched odds ratios and 95% CIs, adjusted for age, sex, race/ethnicity, drug or alcohol use disorder, tobacco use, benzodiazepine dispensings, medical comorbidities, mental health disorder, opioid dose, and opioid formulation.ResultsIn a cohort of 14 898 patients (mean [SD] age, 56.3 [16.0] years; 8988 [60.3%] female) prescribed long-term opioid therapy, 228 case patients with incident opioid overdose were matched to 3547 control patients. The mean (SD) duration of opioid therapy was 36.7 (33.7) months in case patients and 33.0 (30.9) months in control patients. High-dose variability (SD >27.2 mg of morphine equivalents) was associated with a significantly increased risk of overdose compared with low-dose variability (matched odds ratio, 3.32; 95% CI, 1.63-6.77) independent of opioid dose.Conclusions and relevanceVariability in opioid dose may be a risk factor for opioid overdose, suggesting that practitioners should seek to minimize dose variability when managing long-term opioid therapy.
| S-EPMC6481879 | biostudies-literature
Project description:ObjectiveTo develop a validated instrument that measures knowledge about prescription opioid overdose.MethodsWithin an integrated health care system, we adapted, piloted, and tested the reliability and predictive validity of a modified Opioid Overdose Knowledge Scale (OOKS) instrument specific to prescription opioids (Rx-OOKS) with a patient population prescribed long-term opioid therapy and potentially at risk of opioid overdose. We used an interdisciplinary team approach and patient interviews to adapt the instrument. We then piloted the survey on a patient sample and assessed it using Cronbach's alpha and logistic regression.ResultsRx-OOKS (N = 56) resulted in a three-construct, 25-item instrument. Internal consistency was acceptable for the following constructs: "signs of an overdose" (10 items) at α = 0.851, "action to take with opioid overdose" (seven items) at α = 0.692, and "naloxone use knowledge" (eight items) at α = 0.729. One construct, "risks of an overdose" (three items), had an α of 0.365 and was subsequently eliminated from analysis due to poor performance. We conducted logistic regression to determine if any of the constructs was strongly associated with future naloxone receipt. Higher scores on "actions to take in an overdose" had nine times the odds of receiving naloxone (odds ratio [OR] = 9.00, 95% confidence interval [CI] = 1.42-57.12); higher "naloxone use knowledge" scores were 15.8 times more likely to receive naloxone than those with lower scores (OR = 15.83, 95% CI = 1.68-149.17).ConclusionsThe Rx-OOKS survey instrument can reliably measure knowledge about prescription opioid overdose recognition and naloxone use. Further, knowledge about actions to take during an opioid overdose and naloxone use were associated with future receipt of naloxone.
| S-EPMC7593800 | biostudies-literature
Project description:Background: Individuals with pain prescribed opioids experience high rates of comorbid depression. The aim of this study was to characterize pain, substance use, and health status as a function of depressive symptom level in individuals filling an opioid prescription at a community pharmacy. Methods: Participants (N = 1268) filling an opioid prescription enrolled in a study validating a prescription drug monitoring metric completed an online survey assessing sociodemographics, depressive symptoms, substance use, prescription opioid misuse, overdose history, general health, and pain severity and interference. Results: Approximately one-fifth (19.3%) had a positive depression screen result. In covariate-adjusted logistic regression analyses, individuals with a positive depression screen result were more likely to have moderate/high substance use risk scores for prescription opioids (adjusted odds ratio [AOR] = 2.06; 95% confidence interval [CI], 1.51-2.79); street opioids (AOR = 7.18; 95% CI, 2.57-20.01); cannabis (AOR = 2.00; 95% CI, 1.34-3.00); cocaine (AOR = 3.46; 95% CI, 1.46-8.22); tobacco (AOR = 1.59; 95% CI, 1.18-2.15); methamphetamine (AOR = 7.59; 95% CI, 2.58-22.35); prescription stimulants (AOR = 2.95; 95% CI, 1.59-5.49); and sedatives (AOR = 3.41; 95% CI, 2.43-4.79). Individuals with a positive depression screen were more likely to misuse prescription opioids (AOR = 3.46; 95% CI, 2.33-5.15), experience a prior overdose (AOR = 2.69; 95% CI, 1.76-4.11), report poorer general health (AOR = 0.25, 95% CI, 0.18-0.35), and report moderate/severe pain severity (AOR = 4.36, 95% CI, 2.80-6.77) and interference (AOR = 6.47, 95% CI, 4.08-10.26). Conclusions: Individuals prescribed opioids with heightened depression were more likely to report other substance use, prescription opioid misuse, prior overdose, greater pain, and poorer health.
| S-EPMC9487888 | biostudies-literature
Project description:BackgroundNaloxone is a life-saving opioid antagonist. Chronic pain guidelines recommend that physicians co-prescribe naloxone to patients at high risk for opioid overdose. However, clinical tools to efficiently identify patients who could benefit from naloxone are lacking.ObjectiveTo develop and validate an overdose predictive model which could be used in primary care settings to assess the need for naloxone.DesignRetrospective cohort.SettingDerivation site was an integrated health system in Colorado; validation site was a safety-net health system in Colorado.ParticipantsWe developed a predictive model in a cohort of 42,828 patients taking chronic opioid therapy and externally validated the model in 10,708 patients.Main measuresPotential predictors and outcomes (nonfatal pharmaceutical and heroin overdoses) were extracted from electronic health records. Fatal overdose outcomes were identified from state vital records. To match the approximate shelf-life of naloxone, we used Cox proportional hazards regression to model the 2-year risk of overdose. Calibration and discrimination were assessed.Key resultsA five-variable predictive model showed good calibration and discrimination (bootstrap-corrected c-statistic?=?0.73, 95% confidence interval [CI] 0.69-0.78) in the derivation site, with sensitivity of 66.1% and specificity of 66.6%. In the validation site, the model showed good discrimination (c-statistic?=?0.75, 95% CI 0.70-0.80) and less than ideal calibration, with sensitivity and specificity of 82.2% and 49.5%, respectively.ConclusionsAmong patients on chronic opioid therapy, the predictive model identified 66-82% of all subsequent opioid overdoses. This model is an efficient screening tool to identify patients who could benefit from naloxone to prevent overdose deaths. Population differences across the two sites limited calibration in the validation site.
| S-EPMC6153224 | biostudies-literature
Project description:BackgroundOpioid overdose prevention education and naloxone distribution (OEND) programs include information on general risk factors, overdose recognition, and naloxone utilization. This study evaluated a personally-tailored OEND (PTOEND) intervention designed to promote harm reduction and treatment readiness for illicit opioid users by also including education about personal overdose-risk factors and medication for opioid use disorder (MOUD).MethodA secondary analysis of a randomized controlled trial testing a Peer recovery support service (PRSS) intervention, relative to Control, in adult illicit opioid users reporting treatment for an overdose in the prior 6 months. PTOEND, a 30-minute computer-guided intervention, was administered by a research assistant at the randomization visit to all participants (N = 80). Participants completed a telephone visit 3 weeks post-randomization (n = 74) to assess changes in opioid overdose/MOUD knowledge and treatment readiness. Participants completed in-person visits at 3 (n = 66), 6 (n = 58), and 12 (n = 44) months post-randomization to assess illicit opioid use and naloxone utilization (all time points) and overdose-risk behaviors (12 months). We conducted pre-post analyses of the impact of PTOEND controlling for the PRSS effect.ResultsPTOEND increased knowledge of overdose (79.8% to 81.5%, p < 0.05) and MOUD (66.9% to 75.0%, p < 0.01) and decreased perceived treatment barriers (2.1 to 1.9, p < 0.01); desire to quit all substances increased (7.2 to 7.8, p = 0.05). Self-reported opioid use was significantly decreased at each follow-up (all p < 0.01). Self-reported overdose-risk behaviors decreased significantly (6.2 to 2.4, p < 0.01). A majority of participants (65 %) reported naloxone utilization.ConclusionsPTOEND may be effective for promoting harm reduction and treatment readiness.
| S-EPMC7458095 | biostudies-literature