Dataset Information

Naloxone Prescriptions Among Commercially Insured Individuals at High Risk of Opioid Overdose.

ABSTRACT:

Importance

As 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.

Objective

To determine the characteristics of naloxone prescribing for US patients at high risk of opioid overdose.

Design, setting, and participants

This 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.

Exposures

Outpatient naloxone pharmacy claims.

Main outcomes and measures

Demographic 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.

Results

Of 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?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.

SUBMITTER: Follman S

PROVIDER: S-EPMC6503491 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Naloxone Prescriptions Among Commercially Insured Individuals at High Risk of Opioid Overdose.

Follman Sarah S Arora Vineet M VM Lyttle Chris C Moore P Quincy PQ Pho Mai T MT

JAMA network open 20190503 5

<h4>Importance</h4>As 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.<h4>Objective</h4>To determine the characteristics of naloxone prescribing for US patients at high risk of opioid overdose.<h4>Design, setting, and participants</h4>This retrospective cohort study used Truven Health MarketScan data from October 1, 2015, through December 31, 2 ...[more]

PMID: 31050777

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Project description:BackgroundThe United States is undergoing a crippling opioid epidemic, spurred in part by overuse of prescription opioids by adults 25 to 64 years of age. Of concern are long-duration and high-dose initial prescriptions, which place the patients and their friends and relatives at heightened risk for long-term opioid use, misuse, overdose, and death.MethodsWe estimated the incidence of initial opioid prescriptions in each month between July 2012 and December 2017 using administrative-claims data from across the United States (accessed through Blue Cross-Blue Shield [BCBS] Axis); monthly incidence was estimated as the percentage of enrollees who received an initial opioid prescription among those who had not used opioids (i.e., no opioid prescription or a diagnosis of opioid use disorder in the 6 months before a given month). We then estimated the percentage of enrollees initiating opioid therapy who received a long-duration or high-dose initial opioid prescription in each month during this period. We also calculated the number of providers who initiated opioid therapy in any patient who had not used opioids in each month and examined monthly trends in the duration and dose of initial opioid prescriptions in prescriber and patient subgroups. Our study sample included 63,817,512 enrollees who had not used opioids (mean, 15,897,673 per month).ResultsThe monthly incidence of initial opioid prescriptions among enrollees who had not used opioids declined by 54%, from 1.63% in July 2012 to 0.75% in December 2017. This decline was accompanied by a decreasing number of providers (from 114,043 in July 2012 to 80,462 in December 2017) who initiated opioid therapy in any patient who had not used opioids. Nonetheless, among the shrinking subgroup of physicians who initiated opioid therapy in such patients, high-risk prescribing (i.e., prescriptions for more than a 3-day supply or for a dose of 50 morphine milligram equivalents per day or higher) persisted at a monthly rate of 115,378 prescriptions per 15,897,673 enrollees who had not used opioids.ConclusionsAs the opioid crisis progressed between July 2012 and December 2017, many providers stopped initiating opioid therapy. Although the number of initial opioid prescriptions declined, a subgroup of providers continued to write high-risk initial opioid prescriptions. (Funded by the National Institute on Aging and a gift from Owen and Linda Robinson.).

Project description:Importance:Timely initiation and referral to treatment for patients with opioid use disorder seen in the emergency department is associated with reduced mortality. It is not known how often commercially insured adults obtain follow-up treatment after nonfatal opioid overdose. Objective:To investigate the incidence of follow-up treatment following emergency department discharge after nonfatal opioid overdose and patient characteristics associated with receipt of follow-up treatment. Design, Setting, and Participants:A retrospective cohort study was conducted using an administrative claims database for a large US commercial insurer, from October 1, 2011, to September 30, 2016. Data analysis was performed from May 1, 2019, to September 26, 2019. Adult patients discharged from the emergency department after an index opioid overdose (no overdose in the preceding 90 days) were included. Patients with cancer and without continuous insurance enrollment were excluded. Main Outcomes and Measures:The primary outcome was follow-up treatment in the 90 days following overdose, defined as a combined outcome of claims for treatment encounters or medications for opioid use disorder (buprenorphine and naltrexone). Analysis was stratified by whether patients received treatment for opioid use disorder in the 90 days before the overdose. Logistic regression models were used to identify patient characteristics associated with receipt of follow-up treatment. Marginal effects were used to report the average adjusted probability and absolute risk differences (ARDs) in follow-up for different patient characteristics. Results:A total of 6451 patients were identified with nonfatal opioid overdose; the mean (SD) age was 45.0 (19.3) years, 3267 were women (50.6%), and 4676 patients (72.5%) reported their race as non-Hispanic white. A total of 1069 patients (16.6%; 95% CI, 15.7%-17.5%) obtained follow-up treatment within 90 days after the overdose. In adjusted analysis of patients who did not receive treatment before the overdose, black patients were half as likely to obtain follow-up compared with non-Hispanic white patients (ARD, -5.9%; 95% CI, -8.6% to -3.6%). Women (ARD, -1.7%; 95% CI, -3.3% to -0.5%) and Hispanic patients (ARD, -3.5%; 95% CI, -6.1% to -0.9%) were also less likely to obtain follow-up. For each additional year of age, patients were 0.2% less likely to obtain follow-up (95% CI, -0.3% to -0.1%). Conclusions and Relevance:Efforts to improve the low rate of timely follow-up treatment following opioid overdose may seek to address sex, race/ethnicity, and age disparities.

Project description:ImportancePrescription 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.ObjectiveTo determine whether opioid prescriptions to family members were associated with overdose for individuals who themselves did not have an opioid prescription.Design, setting, and participantsWe 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.ExposuresAny 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 measuresIndividual 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.ResultsA 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 relevanceIn 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.

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.

Dataset Information

Naloxone Prescriptions Among Commercially Insured Individuals at High Risk of Opioid Overdose.

Importance

Objective

Design, setting, and participants

Exposures

Main outcomes and measures

Results

Publications

Naloxone Prescriptions Among Commercially Insured Individuals at High Risk of Opioid Overdose.

Similar Datasets

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