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An Examination of Claims-based Predictors of Overdose from a Large Medicaid Program.

ABSTRACT: Health systems may play an important role in identification of patients at-risk of opioid medication overdose. However, standard measures for identifying overdose risk in administrative data do not exist.Examine the association between opioid medication overdose and 2 validated measures of nonmedical use of prescription opioids within claims data.A longitudinal retrospective cohort study that estimated associations between overdose and nonmedical use.Adult Pennsylvania Medicaid program 2007-2012 patients initiating opioid treatment who were: nondual eligible, without cancer diagnosis, and not in long-term care facilities or receiving hospice.Overdose (International Classification of Disease, ninth edition, prescription opioid poisonings codes), opioid abuse (opioid use disorder diagnosis while possessing an opioid prescription), opioid misuse (a composite indicator of number of opioid prescribers, number of pharmacies, and days supplied), and dose exposure during opioid treatment episodes.A total of 372,347 Medicaid enrollees with 583,013 new opioid treatment episodes were included in the cohort. Opioid overdose was higher among those with abuse (1.5%) compared with those without (0.2%, P<0.001). Overdose was higher among those with probable (1.8%) and possible (0.9%) misuse compared with those without (0.2%, P<0.001). Abuse [adjusted rate ratio (ARR), 1.52; 95% confidence interval (CI), 1.10-2.10), probable misuse (ARR, 1.98; 95% CI, 1.46-2.67), and possible misuse (ARR, 1.76; 95% CI, 1.48-2.09) were associated with significantly more events of opioid medication overdose compared with those without.Claims-based measures can be used by health systems to identify individuals at-risk of overdose who can be targeted for restrictions on opioid prescribing, dispensing, or referral to treatment.

SUBMITTER: Cochran G

PROVIDER: S-EPMC5309160 | biostudies-other | 2017 Mar

REPOSITORIES: biostudies-other

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An Examination of Claims-based Predictors of Overdose from a Large Medicaid Program.

Cochran Gerald G Gordon Adam J AJ Lo-Ciganic Wei-Hsuan WH Gellad Walid F WF Frazier Winfred W Lobo Carroline C Chang Chung-Chou H CH Zheng Ping P Donohue Julie M JM

Medical care 20170301 3

<h4>Background</h4>Health systems may play an important role in identification of patients at-risk of opioid medication overdose. However, standard measures for identifying overdose risk in administrative data do not exist.<h4>Objective</h4>Examine the association between opioid medication overdose and 2 validated measures of nonmedical use of prescription opioids within claims data.<h4>Research design</h4>A longitudinal retrospective cohort study that estimated associations between overdose and ...[more]

PMID: 27984346

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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:BackgroundOpioid overdoses are a growing concern, particularly among people who inject drugs. Sweden, with a comparatively high proportion of drug-related mortality, introduced its first Take-Home Naloxone (THN) program in 2018, at the Stockholm needle and syringe program (NSP). In this study we compare THN participant characteristics regarding refills and overdose reversals as well as investigate predictors associated with number of reversals. We also investigate interventions performed in overdose situations and endpoints for naloxone doses.MethodsThis was a prospective open inclusion cohort study conducted between January 24th 2018 and March 31st 2022 at the Stockholm NSP. Participants received THN, free of charge, after a training session and provided data regarding drug use and overdose experiences. During refill visits, participants reported if the naloxone was used for overdose reversal and, if so, responded to a ten-item questionnaire which included stating whether the naloxone recipient was the participant themselves or somebody else. Questionnaire data was combined with NSP database demographic data. Zero-inflated Poisson regression was applied to analyse predictors for number of reported overdose reversals.ResultsAmong study participants (n = 1,295), 66.5% stated opioids as their primary drug, and 61.4% and 81.0% had previous experience of a personal or witnessed overdose, respectively. Overall, 44.0% of participants reported a total of 1,625 overdose reversals and the victim was known to have survived in 95.6% of cases. Stimulant use (aIRR 1.26; 95% CI 1.01, 1.58), benzodiazepine use (aIRR 1.75; 95% CI 1.1, 2.78) and homelessness (aIRR 1.35; 95% CI 1.06, 1.73) were predictors associated with an increased number of reported overdose reversals. Mortality was higher among those who reported at least one overdose reversal (HR 3.4; 95% CI 2.2, 5.2).ConclusionsAn NSP's existent framework can be utilised to effectively implement a THN program, provide basic training and reach numerous high-risk individuals. During the four-year study, THN participants reversed a sizeable number of potentially fatal overdoses, of which many were reported by participants whose primary drug was not opioids. Naloxone refill rate was high, indicating that participants were motivated to maintain a supply of naloxone in case of future overdose events.

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:ImportanceHealth departments have used a variety of methods for overdose surveillance, and the Centers for Disease Control and Prevention (CDC) is implementing a standardized case definition to improve overdose surveillance nationally. The comparative accuracy of the CDC opioid overdose case definition vs existing state opioid overdose surveillance systems is unknown.ObjectiveTo evaluate the accuracy of the CDC opioid overdose case definition and existing Rhode Island Department of Health (RIDOH) state opioid overdose surveillance system.Design, setting, and participantsThis cross-sectional study of ED opioid overdose visits was conducted at 2 EDs in Providence, Rhode Island, at the state's largest health system from January to May 2021. Electronic health records (EHRs) were reviewed for opioid overdoses identified by the CDC case definition and opioid overdoses reported to the RIDOH state surveillance system. Included patients were those at study EDs whose visit met the CDC case definition, was reported to the state surveillance system, or both. True overdose cases were confirmed by EHR review using a standard case definition; 61 of 460 EHRs (13.3%) were double reviewed to estimate classification accuracy. Data were analyzed from January through May 2021.Main outcome and measureAccurate identification of an opioid overdose was assessed by estimating the positive predictive value of the CDC case definition and state surveillance system using results from the EHR review.ResultsAmong 460 ED visits that met the CDC opioid overdose case definition, were reported to the RIDOH opioid overdose surveillance system, or both (mean [SD] age, 39.7 [13.5] years; 313 males [68.0%]; 61 Black [13.3%], 308 White [67.0%], and 91 other race [19.8%]; and 97 Hispanic or Latinx [21.1%] among each patient visit), 359 visits (78.0%) were true opioid overdoses. For these visits, the CDC case definition and RIDOH surveillance system agreed that 169 visits (36.7%) were opioid overdoses. Of 318 visits meeting the CDC opioid overdose case definition, 289 visits (90.8%; 95% CI, 87.2%-93.8%) were true opioid overdoses. Of 311 visits reported to the RIDOH surveillance system, 235 visits (75.6%; 95% CI, 70.4%-80.2%) were true opioid overdoses.Conclusions and relevanceThis cross-sectional study found that the CDC opioid overdose case definition more often identified true opioid overdoses compared with the Rhode Island overdose surveillance system. This finding suggests that using the CDC case definition for opioid overdose surveillance may be associated with improved data efficiency and uniformity.

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An Examination of Claims-based Predictors of Overdose from a Large Medicaid Program.

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An Examination of Claims-based Predictors of Overdose from a Large Medicaid Program.

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