Naloxone Prescriptions Among Commercially Insured Individuals at High Risk of Opioid Overdose.
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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
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]
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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.).
| S-EPMC6487883 | biostudies-literature
Project description:BackgroundSince 2010, heroin-related overdoses have risen sharply, coinciding with policies to restrict access to prescription opioids. It is unknown if patients tapered or discontinued off prescription opioids transitioned to riskier heroin use. This study examined opioid prescribing, including long-term opioid therapy (LTOT) and discontinuation, prior to heroin overdose.MethodsWe used retrospective longitudinal data from a national claims database to identify adults with an emergency or inpatient claim for heroin overdose between January 2010 and June 2017. Receipt of opioid prescription, LTOT episodes, and discontinuation of LTOT were measured for the period of one year prior to heroin overdose.ResultsWe identified 3183 individuals (53.2% age 18-25; 70.0% male) with a heroin overdose (incidence rate 4.20 per 100k person years). Nearly half (42.3%) received an opioid prescription in the prior 12 months, and 10.9% had an active opioid prescription in the week prior to overdose. LTOT at any time in the 12 months prior to overdose was uncommon (12.8%) among those with heroin overdoses, especially among individuals 18-25 years old (3.5%, P < 0.001). LTOT discontinuation prior to overdose was also relatively uncommon, experienced by 6.7% of individuals aged 46 and over and 2.5% of individuals aged 18-25 years (P < 0.001).ConclusionsPrior to heroin overdose, prescription opioid use was common, but LTOT discontinuation was uncommon and observed primarily in older individuals with the lowest heroin overdose rates. Further study is needed to determine if these prescribing patterns are associated with increased heroin overdose.
| S-EPMC7768819 | biostudies-literature
Project description:Objective The increased use of opioids to treat chronic pain in the past 20?years has led to a drastic increase in opioid prescribing in the United States. The Centers for Disease Control and Prevention's (CDC's) Guideline for Prescribing Opioids for Chronic Pain recommends the use of nonopioid therapy as the preferred treatment for chronic pain. This study analyzes the prevalence of nonopioid prescribing among commercially insured patients with chronic pain. Design Data from the 2014 IBM® MarketScan® databases representing claims for commercially insured patients were used. International Classification of Diseases, Ninth Revision, codes were used to identify patients with chronic pain. Nonopioid prescriptions included nonsteroidal anti-inflammatory drugs (NSAIDs), analgesics/antipyretics (e.g., acetaminophen), anticonvulsants, and antidepressant medications. The prevalence of nonopioid and opioid prescriptions was calculated by age, sex, insurance plan type, presence of a depressive or seizure disorder, and region. Results In 2014, among patients with chronic pain, 16% filled only an opioid, 17% filled only a nonopioid prescription, and 28% filled both a nonopioid and an opioid. NSAIDs and antidepressants were the most commonly prescribed nonopioids among patients with chronic pain. Having prescriptions for only nonopioids was more common among patients aged 50-64?years and among female patients. Conclusions This study provides a baseline snapshot of nonopioid prescriptions before the release of the CDC Guideline and can be used to examine the impact of the CDC Guideline and other evidence-based guidelines on nonopioid use among commercially insured patients with chronic pain.
| S-EPMC6536362 | biostudies-literature
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.
| S-EPMC7254182 | biostudies-literature
Project description:To characterize the risk factors associated with overdose or serious opioid-induced respiratory depression (OIRD) among medical users of prescription opioids in a commercially insured population (CIP) and to compare risk factor profiles between the CIP and Veterans Health Administration (VHA) population.Analysis of data from 18,365,497 patients in the IMS PharMetrics Plus health plan claims database (CIP) who were dispensed a prescription opioid in 2009 to 2013. Baseline factors associated with an event of serious OIRD among 7,234 cases and 28,932 controls were identified using multivariable logistic regression. The CIP risk factor profile was compared with that from a corresponding logistic regression among 817 VHA cases and 8,170 controls in 2010 to 2012.The strongest associations with serious OIRD in CIP were diagnosed substance use disorder (odds ratio [OR] = 10.20, 95% confidence interval [CI] = 9.06-11.40) and depression (OR = 3.12, 95% CI?=?2.84-3.42). Other strongly associated factors included other mental health disorders; impaired liver, renal, vascular, and pulmonary function; prescribed fentanyl, methadone, and morphine; higher daily opioid doses; and concurrent psychoactive medications. These risk factors, except depression, vascular disease, and specific opioids, largely aligned with VHA despite CIP being substantially younger, including more females and less chronic disease, and having greater prescribing prevalence of higher daily opioid doses, specific opioids, and most selected nonopioids.Risk factor profiles for serious OIRD among US medical users of prescription opioids with private or public health insurance were largely concordant despite substantial differences between the populations in demographics, clinical conditions, health care delivery systems, and clinical practices.
| S-EPMC5939871 | biostudies-other
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.
| S-EPMC6593630 | biostudies-literature
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.
| S-EPMC6583312 | biostudies-literature
Project description:Background and aimsDespite the growing opioid overdose crisis, medication treatment for opioid use disorder remains uncommon. The comparative effectiveness of buprenorphine and naltrexone treatment in reducing overdose and the comparative risks of discontinuing treatment in the real world, remain uncertain. Our aim was to examine the effectiveness of medications for opioid use disorder in preventing opioid-related overdose.DesignRetrospective cohort study SETTING: United States.Patients46,846 commercially insured individuals diagnosed with opioid use disorder and initiating medication treatment between 2010 and 2016.MeasurementsOpioid-related overdose identified by International Classification of Diseases, Ninth and Tenth Revisions.FindingsIn our sample, 1386 individuals were prescribed extended-release injectable naltrexone (median filled prescriptions = 9 months), 7782 were prescribed oral naltrexone (5 months), and 40,441 were prescribed buprenorphine (19 months) at least once during follow-up. Individuals receiving buprenorphine therapy were at significantly reduced risk of opioid-related overdose compared to no treatment (adjusted hazard ratio (HR) = 0.40, 95% CI 0.35-0.46), while a significant association was not observed in extended-release injectable (HR = 0.74, 95% CI 0.42-1.31) or oral (HR = 0.93, 95% CI 0.71-1.22) naltrexone. We found no association with opioid overdose within four weeks of discontinuation of any medication.ConclusionAmong commercially-insured patients who initiate medications for opioid use disorder, buprenorphine, but not naltrexone, was associated with lower risk of overdose during active treatment compared to post-discontinuation. More research is needed to understand the benefits and risks unique to each treatment option to better tailor therapies to patients with opioid use disorder.
| S-EPMC6613830 | biostudies-literature
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.
| S-EPMC8356065 | biostudies-literature
Project description:Misuse of prescription opioids is a leading cause of premature death in the United States. We use state government administrative data and machine learning methods to examine whether the risk of future opioid dependence, abuse, or poisoning can be predicted in advance of an initial opioid prescription. Our models accurately predict these outcomes and identify particular prior nonopioid prescriptions, medical history, incarceration, and demographics as strong predictors. Using our estimates, we simulate a hypothetical policy which restricts new opioid prescriptions to only those with low predicted risk. The policy's potential benefits likely outweigh costs across demographic subgroups, even for lenient definitions of "high risk." Our findings suggest new avenues for prevention using state administrative data, which could aid providers in making better, data-informed decisions when weighing the medical benefits of opioid therapy against the risks.
| S-EPMC6994994 | biostudies-literature