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Association of Opioid Overdose With Opioid Prescriptions to Family Members.

ABSTRACT: Importance:Prescription opioid misuse is a public health problem that leads to overdose. Although existing interventions focus on limiting prescribing to patients at high risk, individuals may still access prescription opioids dispensed to family members. Objective:To determine whether opioid prescriptions to family members were associated with overdose for individuals who themselves did not have an opioid prescription. Design, Setting, and Participants:We conducted a 1:4 matched case-control study using health care utilization data from 2004 through 2015 from a large US commercial insurance company. Eligible individuals were required to have at least 12 months of continuous enrollment and 1 or more family members in the database. Individuals who experienced overdose were identified by their first opioid overdose after the baseline period and matched to control participants by time in the database, calendar time, age, sex, and number of individuals in the family unit. Both groups were restricted to individuals with no prior opioid dispensing of their own. Data analysis was conducted from January 2018 to August 2018. Exposures:Any prior opioid dispensing to a family member, total morphine milligram equivalents dispensed to family members, and the type of opioid product dispensed. Main Outcomes and Measures:Individual odds of opioid overdose resulting in an emergency department visit or hospitalization were the primary end point. The primary analysis evaluated the odds of overdose among individuals whose family members had been dispensed an opioid. Sensitivity analyses examined the odds stratified by age and timing relative to the dispensing of opioids to family members. Results:A total of 2303 individuals who experienced opioid overdose and 9212 matched control individuals were identified. The mean (SD) age was 23.2 (18.1) years; 1158 affected individuals and 4632 control individuals (50.3%) were female. The mean (SD) time in the database before an overdose case was 3.2 (3.3) years. Prior opioid dispensing to family members was associated with individual overdose (odds ratio [OR], 2.89 [95% CI, 2.59-3.23]). There was a significant dose-response association between increasing amounts of opioids dispensed to family members and odds of overdose (>0-<50 morphine milligram equivalents per day: OR, 2.71 [95% CI, 2.42-3.03]; 50-<90 morphine milligram equivalents per day: OR, 7.80 [95% CI, 3.63-16.78]; ?90 morphine milligram equivalents per day: OR, 15.08 [95% CI, 8.66-26.27]). Conclusions and Relevance:In this analysis, opioid prescriptions to family members were associated with overdose among individuals who do not receive opioid prescriptions. Interventions may focus on expanding access to opioid antagonists, locking prescription opioids in the home, and providing greater patient education to limit fatal overdose among family members.

SUBMITTER: Khan NF

PROVIDER: S-EPMC6593630 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Association of Opioid Overdose With Opioid Prescriptions to Family Members.

Khan Nazleen F NF Bateman Brian T BT Landon Joan E JE Gagne Joshua J JJ

JAMA internal medicine 20190901 9

<h4>Importance</h4>Prescription opioid misuse is a public health problem that leads to overdose. Although existing interventions focus on limiting prescribing to patients at high risk, individuals may still access prescription opioids dispensed to family members.<h4>Objective</h4>To determine whether opioid prescriptions to family members were associated with overdose for individuals who themselves did not have an opioid prescription.<h4>Design, setting, and participants</h4>We conducted a 1:4 m ...[more]

PMID: 31233088

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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.

Project description:ImportanceThe receipt of overlapping opioid and benzodiazepine prescriptions is associated with increased overdose risk. It is unknown whether this increase in risk varies when overlapping prescriptions are written by multiple prescribers vs 1 prescriber.ObjectiveTo evaluate the association between receipt of overlapping opioid and benzodiazepine prescriptions from multiple prescribers and overdose risk.Design, setting, and participantsThis cohort study was conducted using 2017 to 2018 claims from the Optum deidentified Clinformatics Data Mart. Participants were patients with private insurance or Medicare Advantage aged 12 years or older with overlapping opioid and benzodiazepine prescriptions. Data were analyzed from March through November 2020.ExposuresFor each patient, person-days on which opioid and benzodiazepine prescriptions overlapped were identified. The exposure was whether these prescriptions were written by multiple prescribers vs 1 prescriber.Main outcomes and measuresThe outcome was a treated overdose, defined as the occurrence of 1 or more claims containing a diagnosis code for opioid or benzodiazepine poisoning on a person-day of opioid-benzodiazepine overlap. The association between exposure and outcome at the person-day level was estimated using logistic regression, controlling for opioid and benzodiazepine prescribing patterns, demographics, and comorbidities. The average marginal effect (AME) of the exposure, defined as the absolute difference in the probability of a treated overdose if all person-days of overlap involved prescriptions from multiple prescribers vs 1 prescriber, was calculated.ResultsAmong 529 053 patients, the mean (SD) age was 61.2 (15.6) years and 350 857 (66.3%) were female patients. Mean (SD) follow-up was 198.7 (249.8) days. During follow-up, overdose occurred on 1 or more person-days of opioid-benzodiazepine overlap for 2288 patients (0.4%, or 1 in 231 patients). There were 52 989 316 person-days of opioid-benzodiazepine overlap. Among 19 895 457 person-days (37.5%) involving prescriptions from multiple prescribers, there were 1390 overdoses (7.0 per 100 000 person-days), and among 33 093 859 person-days (62.5%) involving prescriptions from 1 prescriber, there were 1302 overdoses (3.9 per 100 000 person-days). Overdose risk was increased 1.8-fold (95% CI, 1.6-1.9) on person-days of overlap involving prescriptions from multiple prescribers vs 1 prescriber. The association between multiple prescribers and increased risk of overdose persisted in adjusted analyses (adjusted odds ratio, 1.20; 95% CI, 1.10-1.31; AME, 0.91 per 100 000 person-days of overlap; 95% CI, 0.46-1.37).Conclusions and relevanceThis study found that among patients already at increased risk of overdose owing to concurrent treatment with opioids and benzodiazepines, overdose risk was increased further when multiple prescribers were responsible for this treatment regimen compared with 1 prescriber. This increased risk was not fully accounted for by differences in prescribing patterns, demographics, or comorbidities. This finding suggests that other factors, such as poor care coordination, may be associated with the increase in risk.

Project description:ImportanceAs opioid-related mortality continues to increase, naloxone remains a critical intervention in preventing overdose death. Opportunities to expand access through the health care setting should be optimized.ObjectiveTo determine the characteristics of naloxone prescribing for US patients at high risk of opioid overdose.Design, setting, and participantsThis retrospective cohort study used Truven Health MarketScan data from October 1, 2015, through December 31, 2016, of individuals with International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes related to opioid use, misuse, dependence, and overdose. The cohort included 138 108 commercially insured individuals aged 15 years or older in the United States with claims related to opioid misuse or dependence, opioid-related overdose, or both.ExposuresOutpatient naloxone pharmacy claims.Main outcomes and measuresDemographic characteristics, clinical characteristics, health care service use, and proportion prescribed naloxone were included in multivariable logistic regression analyses to test the association of opioid risk group with naloxone claim.ResultsOf 138 108 high-risk individuals (mean [SD] age, 43.4 [0.4] years; 72 435 [52.4%] men), 2135 (1.5%) were prescribed naloxone. Having prior diagnoses of both opioid misuse or dependence and overdose was associated with a greater likelihood of receiving naloxone (odds ratio [OR], 2.32; 95% CI, 1.98-2.72; P < .001) compared with having a prior diagnosis of opioid misuse or dependence without overdose. Having a prior diagnosis of opioid overdose alone was associated with a decreased likelihood of receiving naloxone (OR, 0.73; 95% CI, 0.57-0.94; P = .01) compared with having a prior diagnosis of opioid misuse or dependence without overdose. Factors associated with lower naloxone prescription included being aged 30 to 44 years (OR, 0.72; 95% CI, 0.62-0.84; P < .001) and being from the Midwest (OR, 0.62; 95% CI, 0.54-0.71; P < .001) or West (OR, 0.85; 95% CI, 0.74-0.98; P = .03). Opioid use disorder treatment, such as use of medication-assisted therapy (OR, 1.68; 95% CI, 1.53-1.86; P < .001), visiting a detoxification facility (OR, 1.51; 95% CI, 1.31-1.76; P < .001), or receiving other substance use disorder treatment (OR, 1.16; 95% CI, 1.04-1.30; P = .01), were associated with increased likelihood of receiving naloxone, as were receiving outpatient care from a pain specialist (OR, 1.57; 95% CI, 1.40-1.76; P < .001), psychologist (OR, 1.49; 95% CI, 1.29-1.70; P < .001), or surgeon (OR, 1.19; 95% CI, 1.08-1.32; P < .001). Overall, 98.5% (n = 135 973) of high-risk patients did not received naloxone, despite many interactions with the health care system, including 88 618 hospitalizations, 229 680 emergency department visits, 298 058 internal medicine visits, and 568 448 family practice visits.Conclusions and relevancePatients at high risk of opioid overdose rarely received prescriptions for naloxone despite numerous interactions with the health care system. Prescribing in emergency, inpatient, and outpatient settings represents an opportunity to improve access.

Project description:ImportanceCurrent approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk.ObjectiveTo develop and validate a machine-learning algorithm to predict opioid overdose risk among Medicare beneficiaries with at least 1 opioid prescription.Design, setting, and participantsA prognostic study was conducted between September 1, 2017, and December 31, 2018. Participants (n = 560 057) included fee-for-service Medicare beneficiaries without cancer who filled 1 or more opioid prescriptions from January 1, 2011, to December 31, 2015. Beneficiaries were randomly and equally divided into training, testing, and validation samples.ExposuresPotential predictors (n = 268), including sociodemographics, health status, patterns of opioid use, and practitioner-level and regional-level factors, were measured in 3-month windows, starting 3 months before initiating opioids until loss of follow-up or the end of observation.Main outcomes and measuresOpioid overdose episodes from inpatient and emergency department claims were identified. Multivariate logistic regression (MLR), least absolute shrinkage and selection operator-type regression (LASSO), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN) were applied to predict overdose risk in the subsequent 3 months after initiation of treatment with prescription opioids. Prediction performance was assessed using the C statistic and other metrics (eg, sensitivity, specificity, and number needed to evaluate [NNE] to identify one overdose). The Youden index was used to identify the optimized threshold of predicted score that balanced sensitivity and specificity.ResultsBeneficiaries in the training (n = 186 686), testing (n = 186 685), and validation (n = 186 686) samples had similar characteristics (mean [SD] age of 68.0 [14.5] years, and approximately 63% were female, 82% were white, 35% had disabilities, 41% were dual eligible, and 0.60% had at least 1 overdose episode). In the validation sample, the DNN (C statistic = 0.91; 95% CI, 0.88-0.93) and GBM (C statistic = 0.90; 95% CI, 0.87-0.94) algorithms outperformed the LASSO (C statistic = 0.84; 95% CI, 0.80-0.89), RF (C statistic = 0.80; 95% CI, 0.75-0.84), and MLR (C statistic = 0.75; 95% CI, 0.69-0.80) methods for predicting opioid overdose. At the optimized sensitivity and specificity, DNN had a sensitivity of 92.3%, specificity of 75.7%, NNE of 542, positive predictive value of 0.18%, and negative predictive value of 99.9%. The DNN classified patients into low-risk (76.2% [142 180] of the cohort), medium-risk (18.6% [34 579] of the cohort), and high-risk (5.2% [9747] of the cohort) subgroups, with only 1 in 10 000 in the low-risk subgroup having an overdose episode. More than 90% of overdose episodes occurred in the high-risk and medium-risk subgroups, although positive predictive values were low, given the rare overdose outcome.Conclusions and relevanceMachine-learning algorithms appear to perform well for risk prediction and stratification of opioid overdose, especially in identifying low-risk subgroups that have minimal risk of overdose.

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.

Project description:Importance:In 2014, the US Drug Enforcement Administration moved hydrocodone-containing analgesics from schedule III to the more restrictive schedule II to limit prescribing and decrease nonmedical opioid use. The association of this policy change with postoperative prescribing is not well understood. Objective:To examine the hypothesis that the amount of opioids prescribed following surgery is associated with the rescheduling of hydrocodone. Design, Setting, and Participants:An interrupted time series analysis of outpatient opioid prescriptions was conducted to examine the trends in the amount of postoperative opioids filled before and after the schedule change. Opioid prescriptions filled between January 2012 and October 2015 were analyzed using insurance claims data from the Michigan Value Collaborative, which includes data from 75 hospitals across Michigan. A total of 21 955 adult inpatients 18 to 64 years of age who underwent 1 of 19 common elective surgical procedures and filled an opioid prescription within 14 days of discharge to home were eligible for inclusion. Main Outcomes and Measures:The primary outcome was the trends in the mean amount of opioids filled in oral morphine equivalents (OMEs) for the initial postoperative prescriptions before and after the schedule change date of October 6, 2014, compared using interrupted time series and multivariable regression analyses. Secondary outcomes included the total amount of opioids filled and the refill rate for the 30-day postoperative period. Subgroup analyses were performed by hydrocodone prescriptions, nonhydrocodone prescriptions, surgical procedure, and prior opioid use. Results:Data from 21 955 patients undergoing surgical procedures across 75 hospitals and 5120 prescribers were analyzed. Cohorts before and after the schedule change were equivalent with respect to sex (10 197 of 15 791 [64.6%] vs 3966 of 6169 [64.3%] female; P = .69) and mean (SE) age (47.9 [11.2] vs 47.7 [11.3] years; P = .19). After the schedule change, the mean OMEs filled in the initial opioid prescription increased by approximately 35 OMEs (β = 35.1 [13.2]; P < .01), equivalent to 7 tablets of hydrocodone (5 mg). There were no significant differences in the total OMEs filled during the 30-day postoperative period before and after the schedule change (β = 18.3 [30.5]; P = .55), but there was a significant decrease in the refill rate (β = -5.2% [1.3%]; P < .001). Conclusions and Relevance:Changing hydrocodone from schedule III to schedule II was associated with an increase in the amount of opioids filled in the initial prescription following surgery. Opioid-related policies require close follow-up to identify and address early unintended effects given the multitude of competing factors that influence health care professional prescribing behaviors.

Dataset Information

Association of Opioid Overdose With Opioid Prescriptions to Family Members.

Publications

Association of Opioid Overdose With Opioid Prescriptions to Family Members.

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