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: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: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.

Project description:BackgroundThe Pharmacy Quality Alliance (PQA) recently developed 3 quality measures for prescribing opioids: high dosages, multiple providers and pharmacies, and concurrent use of opioids and benzodiazepines.ObjectiveTo examine the prevalence of the PQA measures and identify the patient demographic and health characteristics associated with the measures.MethodsWe conducted a cross-sectional analysis using Pennsylvania Medicaid data (2013-2015). We limited our analyses to noncancer patients who were aged 18-64 years and not dual-eligible for Medicare/Medicaid. Per PQA specifications, patients were required to possess ≥ 2 opioid prescriptions for ≥ 15 days annual supply each year. Outcome measures included (a) high dosages, defined as > 120 morphine milligram equivalents for ≥ 90 consecutive days; (b) multiple providers/pharmacies, defined as receiving opioid prescriptions from ≥ 4 providers and ≥ 4 pharmacies; and (c) concurrent use of opioids and benzodiazepines, defined as ≥ 30 cumulative days of overlapping opioids and benzodiazepines among individuals having ≥ 2 opioid and ≥ 2 benzodiazepine fills. Patient characteristics assessed included demographics; other medication use; and physical, mental, and behavioral health comorbidities. We present descriptive and multivariable statistical analyses of the data to describe trends in quality measure prevalence and associations with enrollee health characteristics.ResultsNumbers of enrollees meeting inclusion criteria ranged from 73,082 in 2013 to 85,710 in 2015. From 2013 to 2015, high dosage prevalence increased from 5.1% to 5.5%; the use of multiple providers/pharmacies decreased from 7.1% to 5.0%; and concurrent use of opioids and benzodiazepines decreased from 29.1% to 28.4% (all P < 0.05). A substantial portion of patients with > 1 PQA measure from 2013 to 2015 was eligible for Medicaid because of disability (41.8%-81.9%). Enrollees with opioid use disorder were more likely to have high dosages (adjusted odds ratio [AOR] = 2.01, 95% CI = 1.83-2.21). Enrollees with anxiety and mood disorders were more likely to have multiple providers/pharmacies (anxiety: AOR = 1.54, 95% CI = 1.43-1.65; mood: AOR = 1.15, 95% CI = 1.06-1.25) and concurrent use of opioids and benzodiazepines (anxiety: AOR = 3.50, 95% CI = 3.38-3.63; mood: AOR = 1.42, 95% CI = 1.36-1.48).ConclusionsGiven high levels of eligibility based on disability and the prevalence of mood, anxiety, and opioid use disorders among those identified by the quality measures, providers may require additional support to care for this patient population.DisclosuresThis project was supported by a grant from the Centers for Disease Control and Prevention and was also supported by an intergovernmental agreement between the Pennsylvania Department of Human Services and the University of Pittsburgh. Lo-Ciganic was supported by the University of Arizona Health Sciences Career Development Award. The other authors have nothing to disclose. The conclusions, findings, and opinions expressed by authors contributing to this article do not necessarily reflect the official position of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the Commonwealth of Pennsylvania. A portion of these results was presented at the Association for Medical Education and Research in Substance Abuse 41st National Conference; November 2-4, 2017; Washington, DC.

Project description:Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk. This prognostic study included Medicaid beneficiaries (n = 237,259) in Allegheny County, Pennsylvania enrolled between 2015 and 2018, randomly divided into training, testing, and validation samples. We measured 290 potential predictors (239 derived from Medicaid claims data) in 30-day periods, beginning with the first observed Medicaid enrollment date during the study period. Using a gradient boosting machine, we predicted a composite outcome (i.e., fatal or nonfatal opioid overdose constructed using medical examiner and claims data) in the subsequent month. We compared prediction performance between a Medicaid claims only model to one integrating human services and criminal justice data with Medicaid claims (i.e., integrated model) using several metrics (e.g., C-statistic, number needed to evaluate [NNE] to identify one overdose). Beneficiaries were stratified into risk-score decile subgroups. The samples (training = 79,087, testing = 79,086, validation = 79,086) had similar characteristics (age = 38±18 years, female = 56%, white = 48%, having at least one overdose = 1.7% during study period). Using the validation sample, the integrated model slightly improved on the Medicaid claims only model (C-statistic = 0.885; 95%CI = 0.877-0.892 vs. C-statistic = 0.871; 95%CI = 0.863-0.878), with small corresponding improvements in the NNE and positive predictive value. Nine of the top 30 most important predictors in the integrated model were human services and criminal justice variables. Using the integrated model, approximately 70% of individuals with overdoses were members of the top risk decile (overdose rates in the subsequent month = 47/10,000 beneficiaries). Few individuals in the bottom 9 deciles had overdose episodes (0-12/10,000). Machine-learning algorithms integrating claims and social service and criminal justice data modestly improved opioid overdose prediction among Medicaid beneficiaries for a large U.S. county heavily affected by the opioid crisis.

Project description:INTRODUCTION:Millions of Americans are affected by acute or chronic pain every year. This study investigates opioid prescription patterns for acute and chronic pain management among U.S. Medicaid patients. METHODS:The study used medical and pharmacy claims data obtained from the multistate Truven MarketScan Medicaid Database from 2013 to 2015 for Medicaid patients receiving health care. Medicaid beneficiaries who utilized an outpatient healthcare facility for back pain, neck pain (cervicalgia), joint pain (osteoarthritis and rheumatoid arthritis), orthopedics (simple/closed fractures and muscle strains/sprains), headache (cluster headaches and migraines), dental conditions, or otorhinolaryngologic (otalgia) diagnoses, based on the International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes, and received an opioid prescription within 14 days of diagnosis were included in this study. RESULTS:There were 5,051,288 patients with 1 of the 7 diagnostic groupings; 18.8% had an opioid prescription filled within 14 days of diagnosis. Orthopedic pain (34.8%) was the primary reason for an opioid prescription, followed by dental conditions (17.3%), back pain (14.0%), and headache (12.9%). Patients receiving an opioid for conditions associated with acute pain management, such as otorhinolaryngologic (OR=1.93, 95% CI=1.85, 2.0), dental (OR=1.50, 95% CI=1.48, 1.53), or orthopedic conditions (OR=1.31, 95% CI=1.29, 1.32), were more likely to receive the prescription from an emergency department provider versus a general practitioner. However, compared with general practitioners, other providers were more likely to prescribe opioids for conditions associated with chronic pain management. CONCLUSIONS:More than half of Medicaid beneficiaries receiving an opioid for pain management do so for orthopedic- and dental-related reasons, with emergency department providers more likely to prescribe opioids. Modifications to the guidelines addressing temporary acute pain management practices with opioids would be likely to benefit emergency department providers the most.

Dataset Information

Does Prescription Opioid Shopping Increase Overdose Rates in Medicaid Beneficiaries?

Publications

Does Prescription Opioid Shopping Increase Overdose Rates in Medicaid Beneficiaries?

Similar Datasets

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