Dataset Information

Does Prescription Opioid Shopping Increase Overdose Rates in Medicaid Beneficiaries?

ABSTRACT: STUDY OBJECTIVE:The link between prescription opioid shopping and overdose events is poorly understood. We test the hypothesis that a history of prescription opioid shopping is associated with increased risk of overdose events. METHODS:This is a secondary analysis of a linked claims and controlled substance dispense database. We studied adult Medicaid beneficiaries in 2014 with prescription opioid use in the 6 months before an ambulatory care or emergency department visit with a pain-related diagnosis. The primary outcome was a nonfatal overdose event within 6 months of the cohort entry date. The exposure of interest (opioid shopping) was defined as having opioid prescriptions by different prescribers with greater than or equal to 1-day overlap and filled at 3 or more pharmacies in the 6 months before cohort entry. We used a propensity score to match shoppers with nonshoppers in a 1:1 ratio. We calculated the absolute difference in outcome rates between shoppers and nonshoppers. RESULTS:We studied 66,328 patients, including 2,571 opioid shoppers (3.9%). There were 290 patients (0.4%) in the overall cohort who experienced a nonfatal overdose. In unadjusted analyses, shoppers had higher event rates than nonshoppers (rate difference of 4.4 events per 1,000; 95% confidence interval 0.8 to 7.9). After propensity score matching, there were no outcome differences between shoppers and nonshoppers (rate difference of 0.4 events per 1,000; 95% confidence interval -4.7 to 5.5). These findings were robust to various definitions of opioid shoppers and look-back periods. CONCLUSION:Prescription opioid shopping is not independently associated with increased risk of overdose events.

SUBMITTER: Sun BC

PROVIDER: S-EPMC5960419 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Does Prescription Opioid Shopping Increase Overdose Rates in Medicaid Beneficiaries?

Sun Benjamin C BC Lupulescu-Mann Nicoleta N Charlesworth Christina J CJ Kim Hyunjee H Hartung Daniel M DM Deyo Richard A RA McConnell K John KJ

Annals of emergency medicine 20171124 6

<h4>Study objective</h4>The link between prescription opioid shopping and overdose events is poorly understood. We test the hypothesis that a history of prescription opioid shopping is associated with increased risk of overdose events.<h4>Methods</h4>This is a secondary analysis of a linked claims and controlled substance dispense database. We studied adult Medicaid beneficiaries in 2014 with prescription opioid use in the 6 months before an ambulatory care or emergency department visit with a p ...[more]

PMID: 29174833

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Project description:BackgroundMedicaid presently insures about one-fourth of the US population and disproportionately insures about 38 % of non-elderly adults with an opioid use disorder (OUD). Owing to Medicaid's prominent role insuring persons with an OUD and that Medicaid coverage includes pharmaceutical benefits, there has been considerable interest in studying potential prescription opioid misuse among Medicaid beneficiaries and identifying subpopulations at higher risk for misuse and possible progression to an OUD.MethodsThe study goals were to explore the associations among prescription opioid misuse, OUD, and co-occurring mental health and other substance use disorders (SUD). We analyzed Illinois Medicaid 2018 claims data for 1102479 adult beneficiaries 18 to 64 years of age. Using algorithms based on previous studies, we first determined either the presence or absence of nine SUDS (including OUD), nine mental health disorders and likely prescription opioid misuse. Then, we subdivided the beneficiary sample into five groups: those who were prescribed opioids and evidenced either no, possible, or probable misuse; those evidencing an OUD; and those evidencing no opioid use or misuse.ResultsBivariate analyses, upset plots, and multinomial logistic regressions were used to compare the five subgroups on the prevalence of co-occurring SUDS and mental health disorders. Those with an OUD or with probable prescription opioid misuse had the highest prevalence of most co-occurring conditions with beneficiaries with an OUD the most likely to evidence co-occurring SUDS, particularly tobacco use disorder, whereas those with probable misuse had elevated prevalence rates of co-occurring mental health disorders comparable to those with an OUD.ConclusionThe medical complexity of persons with an OUD or misusing prescription opioids are considered in light of recent attempts to expand buprenorphine provision as a medication for OUD among Medicaid beneficiaries. Additionally, we consider the possibility of gender, co-occurring mental health disorders, and tobacco use disorder as important risk factors for progressing to prescription opioid misuse and an OUD.

Project description:BackgroundA large proportion of individuals who use heroin report initiating opioid use with prescription opioids. However, patterns of prescription opioid use preceding heroin-related overdose have not been described.ObjectiveTo describe prescription opioid use in the year preceding heroin overdose.DesignCase-control study comparing prescription opioid use with a heroin-involved overdose, non-heroin-involved opioid overdose, and non-overdose controls from 2015 to 2017.ParticipantsOregon Medicaid beneficiaries with linked administrative claims, vital statistics, and prescription drug monitoring program data.Main measuresOpioid, benzodiazepine, and other central nervous system depressant prescriptions preceding overdose; among individuals with one or more opioid prescription, we assessed morphine milligram equivalents per day, overlapping prescriptions, prescriptions from multiple prescribers, long-term use, and discontinuation of long-term use.Key resultsWe identified 1458 heroin-involved overdoses (191 fatal) and 2050 non-heroin-involved opioid overdoses (266 fatal). In the 365 days prior to their overdose, 45% of individuals with a heroin-involved overdose received at least one prescribed opioid compared with 78% of individuals who experienced a non-heroin-involved opioid overdose (p < 0.001). For both heroin- and non-heroin-involved overdose cases, the likelihood of receiving an opioid increased with age. Among heroin overdose cases with an opioid dispensed, the rate of multiple pharmacy use was the only high-risk opioid pattern that was greater than non-overdose controls (adjusted odds ratio 3.2; 95% confidence interval 1.48 to 6.95). Discontinuation of long-term opioid use was not common prior to heroin overdose and not higher than discontinuation rates among non-overdose controls.ConclusionsAlthough individuals with a heroin-involved overdose were less likely to receive prescribed opioids in the year preceding their overdose relative to non-heroin opioid overdose cases, prescription opioid use was relatively common and increased with age. Discontinuation of long-term prescription opioid use was not associated with heroin-involved overdose.

Project description:ImportanceThe opioid epidemic continues to be a public health crisis in the US.ObjectiveTo assess the patient factors and early time-varying prescription-related factors associated with opioid-related fatal or nonfatal overdose.Design, setting, and participantsThis cohort study evaluated opioid-naive adult patients in Oregon using data from the Oregon Comprehensive Opioid Risk Registry, which links all payer claims data to other health data sets in the state of Oregon. The observational, population-based sample filled a first (index) opioid prescription in 2015 and was followed up until December 31, 2018. Data analyses were performed from March 1, 2020, to June 15, 2021.ExposuresOverdose after the index opioid prescription.Main outcomes and measuresThe outcome was an overdose event. The sample was followed up to identify fatal or nonfatal opioid overdoses. Patient and prescription characteristics were identified. Prescription characteristics in the first 6 months after the index prescription were modeled as cumulative, time-dependent measures that were updated monthly through the sixth month of follow-up. A time-dependent Cox proportional hazards regression model was used to assess patient and prescription characteristics that were associated with an increased risk for overdose events.ResultsThe cohort comprised 236 921 patients (133 839 women [56.5%]), of whom 667 (0.3%) experienced opioid overdose. Risk of overdose was highest among individuals 75 years or older (adjusted hazard ratio [aHR], 3.22; 95% CI, 1.94-5.36) compared with those aged 35 to 44 years; men (aHR, 1.29; 95% CI, 1.10-1.51); those who were dually eligible for Medicaid and Medicare Advantage (aHR, 4.37; 95% CI, 3.09-6.18), had Medicaid (aHR, 3.77; 95% CI, 2.97-4.80), or had Medicare Advantage (aHR, 2.18; 95% CI, 1.44-3.31) compared with those with commercial insurance; those with comorbid substance use disorder (aHR, 2.74; 95% CI, 2.15-3.50), with depression (aHR, 1.26; 95% CI, 1.03-1.55), or with 1 to 2 comorbidities (aHR, 1.32; 95% CI, 1.08-1.62) or 3 or more comorbidities (aHR, 1.90; 95% CI, 1.42-2.53) compared with none. Patients were at an increased overdose risk if they filled oxycodone (aHR, 1.70; 95% CI, 1.04-2.77) or tramadol (aHR, 2.80; 95% CI, 1.34-5.84) compared with codeine; used benzodiazepines (aHR, 1.06; 95% CI, 1.01-1.11); used concurrent opioids and benzodiazepines (aHR, 2.11; 95% CI, 1.70-2.62); or filled opioids from 3 or more pharmacies over 6 months (aHR, 1.38; 95% CI, 1.09-1.75).Conclusions and relevanceThis cohort study used a comprehensive data set to identify patient and prescription-related risk factors that were associated with opioid overdose. These findings may guide opioid counseling and monitoring, the development of clinical decision-making tools, and opioid prevention and treatment resources for individuals who are at greatest risk for opioid overdose.

Project description:Background and aimsSome adolescents and young adults (termed "youth") prescribed an opioid will develop opioid use disorder or experience overdose. This study aimed to identify patient and prescription characteristics associated with subsequent risk of opioid use disorder or overdose during the year after an opioid is first dispensed.DesignRetrospective cohort study.SettingCommercial health insurance claims in a large United States (US) database from 2006 to 2016.ParticipantsYouth age 11 to 25 years filling an initial opioid prescription (n = 3 278 990).MeasurementsThe primary outcome was development of an 'opioid-related complication' (a diagnosis of opioid use disorder or opioid-related overdose) during the subsequent 12 months. Exposures of interest were patient (sociodemographic information, and physical and mental health diagnoses) and prescription characteristics (opioid formulation, dose, and duration).FindingsAmong youth filling an initial opioid prescription, median age was 18 years (interquartile range [IQR] = 16-21) and 56.1% were female. During the subsequent 12 months, 10 405 (0.3%) youth experienced an opioid-related complication. Conditions associated with increased risk included mood/anxiety disorders (adjusted relative risk [aRR] = 4.45; 95% CI = 4.25-4.66) and substance use (aRR = 20.77; 95% CI = 19.74-21.84). Comorbid substance use disorders were present among 72.8% of youth experiencing an opioid-related complication and included alcohol (33.4%), cannabis (33.0%), nicotine (43.2%), and other substance use disorders (75.5%). Long-acting opioids (aRR = 2.59; 95% CI = 2.18-3.09) and longer durations were associated with increased risk (7-14 days: aRR = 1.15; 95% CI = 1.08-1.22; ≥15 days: aRR = 1.96; 95% CI = 1.80-2.12) compared with short-acting formulations and durations ≤3 days, respectively.ConclusionsAmong United States youth, complications after an initial opioid prescription appear to be relatively rare and appear to be associated with mood/anxiety disorders, substance use, comorbid substance use disorders, and prescriptions involving long-acting opioids or long durations.

Project description:BackgroundLittle is known about whether machine-learning algorithms developed to predict opioid overdose using earlier years and from a single state will perform as well when applied to other populations. We aimed to develop a machine-learning algorithm to predict 3-month risk of opioid overdose using Pennsylvania Medicaid data and externally validated it in two data sources (ie, later years of Pennsylvania Medicaid data and data from a different state).MethodsThis prognostic modelling study developed and validated a machine-learning algorithm to predict overdose in Medicaid beneficiaries with one or more opioid prescription in Pennsylvania and Arizona, USA. To predict risk of hospital or emergency department visits for overdose in the subsequent 3 months, we measured 284 potential predictors from pharmaceutical and health-care encounter claims data in 3-month periods, starting 3 months before the first opioid prescription and continuing until loss to follow-up or study end. We developed and internally validated a gradient-boosting machine algorithm to predict overdose using 2013-16 Pennsylvania Medicaid data (n=639 693). We externally validated the model using (1) 2017-18 Pennsylvania Medicaid data (n=318 585) and (2) 2015-17 Arizona Medicaid data (n=391 959). We reported several prediction performance metrics (eg, C-statistic, positive predictive value). Beneficiaries were stratified into risk-score subgroups to support clinical use.FindingsA total of 8641 (1·35%) 2013-16 Pennsylvania Medicaid beneficiaries, 2705 (0·85%) 2017-18 Pennsylvania Medicaid beneficiaries, and 2410 (0·61%) 2015-17 Arizona beneficiaries had one or more overdose during the study period. C-statistics for the algorithm predicting 3-month overdoses developed from the 2013-16 Pennsylvania training dataset and validated on the 2013-16 Pennsylvania internal validation dataset, 2017-18 Pennsylvania external validation dataset, and 2015-17 Arizona external validation dataset were 0·841 (95% CI 0·835-0·847), 0·828 (0·822-0·834), and 0·817 (0·807-0·826), respectively. In external validation datasets, 71 361 (22·4%) of 318 585 2017-18 Pennsylvania beneficiaries were in high-risk subgroups (positive predictive value of 0·38-4·08%; capturing 73% of overdoses in the subsequent 3 months) and 40 041 (10%) of 391 959 2015-17 Arizona beneficiaries were in high-risk subgroups (positive predictive value of 0·19-1·97%; capturing 55% of overdoses). Lower risk subgroups in both validation datasets had few individuals (≤0·2%) with an overdose.InterpretationA machine-learning algorithm predicting opioid overdose derived from Pennsylvania Medicaid data performed well in external validation with more recent Pennsylvania data and with Arizona Medicaid data. The algorithm might be valuable for overdose risk prediction and stratification in Medicaid beneficiaries.FundingNational Institute of Health, National Institute on Drug Abuse, National Institute on Aging.

Dataset Information

Does Prescription Opioid Shopping Increase Overdose Rates in Medicaid Beneficiaries?

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Does Prescription Opioid Shopping Increase Overdose Rates in Medicaid Beneficiaries?

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