Opioid-Related Diagnoses and Concurrent Claims for HIV, HBV, or HCV among Medicare Beneficiaries, United States, 2015.
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ABSTRACT:
SUBMITTER: Chang MH
PROVIDER: S-EPMC6912616 | biostudies-literature | 2019 Oct
REPOSITORIES: biostudies-literature
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Opioid-Related Diagnoses and Concurrent Claims for HIV, HBV, or HCV among Medicare Beneficiaries, United States, 2015.
Chang Man-Huei MH Moonesinghe Ramal R Schieber Lyna Z LZ Truman Benedict I BI
Journal of clinical medicine 20191024 11
Unsterile opioid injection increases risk for infection transmission, including HIV, hepatitis B virus (HBV), or hepatitis C virus (HCV). We assess prevalence of and risk factors associated with opioid overdose and infections with HIV, HBV, or HCV among Medicare beneficiaries with opioid-related fee-for-service claims during 2015. We conducted a cross-sectional analysis to estimate claims for opioid use and overdose and HIV, HBV, or HCV infections, using data from US Medicare fee-for-service cla ...[more]
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Project description:ImportanceConcurrent benzodiazepine use is associated with an increased risk of opioid-related overdose; however, it remains unknown how the overdose risk varies with the days of exposure to both medications.ObjectiveTo evaluate the exposure-response association between the days with concurrent prescription opioid and benzodiazepine use and the risk of overdose.Design, setting, and participantsRetrospective cohort study of Medicare Part D claims data from January 1, 2013, to December 31, 2014. Analyses were conducted in fall 2017 and spring 2018. Participants were Medicare Part D beneficiaries who filled at least 1 prescription for an opioid in 2014.ExposuresPatients were divided into 2 groups based on whether they had opioid and benzodiazepine supplies on the day before overdose or censoring. The first group only had a supply of opioids (n?=?50?583) and the second group had supplies of opioids and benzodiazepines (n?=?20?665). The second group was further categorized into 4 subgroups based on the cumulative number of days with overlapping supplies of opioids and benzodiazepines: 1 to 90 days (n?=?3603), 91 to 180 days (n?=?2930), 181 to 270 days (n?=?4082), and 271 days or more (n?=?10?050).Main outcomes and measuresCox proportional hazard models were constructed to compare opioid-related overdose (including fatal and nonfatal overdoses) across time-dependent treatment groups, controlling for demographic characteristics, insurance factors, clinical characteristics, and number of unique opioid and benzodiazepine prescribers.ResultsOf 71?248 total participants, 25?600 (35.9%) were male and 59?532 (83.6%) were white. Mean (SD) age was 66.5 (14.8) years. On the day before overdose or censoring event, 20?665 of 71?248 patients with an opioid prescription (29.0%) were concurrently using benzodiazepines and 14?132 of 20?665 concurrent users (68.4%) had more than 180 days of overlapping supplies of both medications. The risk of overdose was highest on the first days of concurrent opioid and benzodiazepine use and decreased over time; compared with opioid use alone, the hazard ratio for overdose was 5.05 (95% CI, 3.68-6.93) during the first 90 days of concurrent opioid and benzodiazepine use and 1.87 (95% CI, 1.25-2.80) for days 91 to 180 among those who did not have an event before 90 days.Conclusions and relevanceDuring the first 90 days, concurrent benzodiazepine use is associated with a 5-fold increase in the risk of opioid-related overdose. The implementation of policies deterring concurrent opioid and benzodiazepine use is warranted. Patients using both medications should be closely monitored, particularly during the first days of concurrent use.
| S-EPMC6324417 | biostudies-literature
Project description:BackgroundThe Centers for Disease Control and Prevention have declared that the United States is amidst a continuing opioid epidemic, with drug overdose-related death tripling between 1999 and 2014. Among the 47,055 overdose-related deaths that occurred in 2014, 28,647 (60.9%) of them involved an opioid.MethodsTo determine if there are specific trends in opioid prescribing practices of specific groups of surgeons to better describe any regional or subspecialty trends that exist, the Part D Prescriber Public Use File was used to evaluate all prescription drug orders for Medicare beneficiaries with a Part D prescription drug plan for the 2015 calendar year. Only those providers with the specialty description corresponding to a surgical specialty were included in this study, using the provider's Part B claims.ResultsA total of 65,277,932 claims made to Part D by 90,253 surgeons in the 2015 service year were analyzed in this study, demonstrating statistically significant differences in the opioid prescribing practices of surgeons from different states, cities, practice settings, and subspecialties (P < .05). During this year, these surgeons' opioid medication claims cost the health care system $133,091,997.81 in drug benefits.ConclusionAll health professionals with opiate prescribing privileges are entrusted with and responsible for the use of these medications; therefore, physicians have a crucial role in ensuring safe and effective use of this treatment option and the deterrence of its abuse. This is true in particular for surgeons given the acuity level and context of their practice.
| S-EPMC8083011 | biostudies-literature
Project description:ImportanceThe frequency of anti-vascular endothelial growth factor (VEGF) injections has grown exponentially with the introduction of bevacizumab, ranibizumab, and most recently aflibercept. The cost associated with these medications has garnered significant national attention, warranting a granular analysis of their use.ObjectiveTo analyze trends in anti-VEGF injections for US Medicare Part B beneficiaries from 2012 to 2015.Design, setting, and participantsThis observational cohort study used 2012-2015 data from the Centers for Medicare & Medicaid Services Medicare Part B Provider Utilization Files to analyze trends in intravitreal injections of anti-VEGF medications among Medicare Part B beneficiaries and their health care professionals.Main outcomes and measuresThe primary outcome measure was distribution of and change over time in the number of anti-VEGF injections performed for ranibizumab, aflibercept, and bevacizumab.ResultsA total of 2 574 124 intravitreal injections were performed by 3348 ophthalmologists in the outpatient setting for Medicare Part B beneficiaries during the 2015 calendar year; 100 ophthalmologists (3.0%) performed the highest volume of intravitreal injections. The total number of intravitreal injections administered in 2015 was 870 843 for aflibercept, 697 412 for ranibizumab, and 1 147 432 for bevacizumab. Ranibizumab injections decreased by 7.1% from 2012 to 2015 and bevacizumab injections decreased by 17.1%. From 2013 to 2015, aflibercept injections increased by 69.4%. The 100 ophthalmologists performing the highest volume of ranibizumab injections, as gauged by number of injections administered, accounted for 31.0% (95% CI, 30.994%-30.997%) of all ranibizumab injections nationally. The 100 ophthalmologists performing the highest volume of aflibercept injections accounted for 17.6% (95% CI, 17.638%-17.641%) of all aflibercept injections and the 100 ophthalmologists performing the highest volume of bevacizumab injections accounted for 19.6% (95% CI, 19.649%-19.653%) of all bevacizumab injections administered nationally to Medicare Part B beneficiaries. The highest number of injections per 1000 Medicare Part B beneficiaries occurred in Nebraska (aflibercept), Tennessee (ranibizumab), and South Dakota (bevacizumab).Conclusions and relevanceA total of 3.0% of ophthalmologists account for 17.6% to 31.0% of the total number of anti-VEGF injections administered nationally in the Medicare Part B population. Overall, bevacizumab and ranibizumab injections have decreased, coinciding with a 69.4% increase in aflibercept injections.
| S-EPMC6587152 | biostudies-literature
Project description:To assess the feasibility of using existing claims-based algorithms to identify community-dwelling Medicare beneficiaries with disability based solely on the conditions for which they are being treated, and improving on these algorithms by combining them in predictive models.Data on 12,415 community-dwelling fee-for-service Medicare beneficiaries who first responded to the Medicare Current Beneficiary Survey (MCBS) in 2003-2006.Logistic regression models in which six claims-based disability indicators are used to predict self-reported disability. Receiver operating characteristic (ROC) curves were used to assess the performance of the predictive models.The predictive performance of the regression-based models is better than that of the individual claims-based indicators. At a predicted probability threshold chosen to maximize the sum of sensitivity and specificity, sensitivity is 0.72 for beneficiaries age 65 or older and specificity is 0.65. For those under 65, sensitivity is 0.54 and specificity is 0.67. The findings also suggest ways to improve predictive performance for specific disability populations of interest to researchers.Predictive models that incorporate multiple claims-based indicators provide an improved tool for researchers seeking to identify people with disabilities in claims data.
| S-EPMC4722209 | biostudies-literature
Project description:Importance:Although there are many pharmacologic alternatives to opioids, it is unclear whether the structure of Medicare Part D formularies discourages use of the alternatives. Objectives:To quantify the coverage of opioid alternatives and prevalence of prior authorization, step therapy, quantity limits, and tier placement for these drugs, and test whether these formulary exclusions and restrictions are associated with increased opioid prescribing to older adults at the county level. Design, Setting, and Participants:County fixed-effect models were estimated using a panel of counties across the 50 US states and the District of Columbia over calendar years 2015 and 2016. Data analysis was conducted from July 1 to September 23, 2019. The sample included 2721 counties in 2015 and 2671 counties in 2016 with sufficient data on Medicare Part D formulary design and opioid prescribing. Main Outcomes and Measures:County-level opioid prescribing rate (number of opioid claims divided by the number of overall claims) and counts of excluded opioid alternatives and opioid alternatives with prior authorization, step therapy, quantity limits, and high-tier placements. Results:A total of 30 nonopioid analgesics were examined across 28 997 Medicare plans in 2015 and 30 390 plans in 2016. Medicare plans did not cover a mean of 7% of these drugs (interquartile range, 10%; lower to upper limit, 0%-23%). Among covered nonopioids, prior authorization and step therapy were uncommon, with fewer than 5% affected by prior authorization and 0% by step therapy. However, 13% of covered nonopioids had quantity limits (interquartile range, 10%; lower to upper limit, 0%-31%) and 22% were in high-cost tiers (interquartile range, 38%; lower to upper limit, 0%-50%). Increases in the number of nonopioids excluded on Medicare plans in a county were associated with increased opioid prescribing (effect size relative to mean, 2.2%-3.7%; P = .004). Conversely, increases in the number of opioids not covered on Medicare plans in a county was found to be associated with decreased opioid prescribing (effect size relative to mean, 0.8%-1.5%; P = .02). None of the utilization management strategies (prior authorization, step therapy, and quantity limits) examined or high-cost tier placements of nonopioids were associated with increased opioid prescribing. Conclusions and Relevance:Lack of Medicare coverage for pharmacologic alternatives to opioids may be associated with increased opioid prescribing.
| S-EPMC7052746 | biostudies-literature
Project description:Prescription opioid use and overdose deaths are increasing in the United States. Among disabled Medicare beneficiaries under the age of 65, the rise in musculoskeletal conditions as qualifying diagnoses suggests that opioid analgesic use may be common and increasing, raising safety concerns.From a 40% random-sample Medicare denominator, we identified fee-for-service beneficiaries under the age of 65 and created annual enrollment cohorts from 2007 to 2011 (6.4 million person-years). We obtained adjusted, annual opioid use measures: any use, chronic use (? 6 prescriptions), intensity of use [daily morphine equivalent dose (MED)], and opioid prescribers per user. Geographic variation was studied across Hospital Referral Regions.Most measures peaked in 2010. The adjusted proportion with any opioid use was 43.9% in 2007, 44.7% in 2010, and 43.7% in 2011. The proportion with chronic use rose from 21.4% in 2007 to 23.1% in 2011. Among chronic users: mean MED peaked at 81.3 mg in 2010, declining to 77.4 mg in 2011; in 2011, 19.8% received ? 100 mg MED; 10.4% received ? 200 mg. In 2011, Hospital Referral Region-level measures varied broadly (5th-95th percentile): any use: 33.0%-58.6%, chronic use: 13.9%-36.6%; among chronic users, mean MED: 45 mg-125 mg; mean annual opioid prescribers: 2.4-3.7.Among these beneficiaries, opioid use was common. Although intensity stabilized, the population using opioids chronically grew. Variation shows a lack of a standardized approach and reveals regions with mean MED at levels associated with overdose risk. Future work should assess outcomes, chronic use predictors, and policies balancing pain control and safety.
| S-EPMC4151179 | 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:This paper concerns public health crises today-the problem of opioid prescription access and related abuse. Inspired by Case and Deaton's seminal work on increasing mortality among white Americans with lower education, this paper explores the relationship between opioid prescribing and local economic factors. OBJECTIVE:We examined the association between county-level socioeconomic factors (median household income, unemployment rate, Gini index) and opioid prescribing. SUBJECTS:We used the complete 2014 Medicare enrollment and part D drug prescription data from the Center for Medicare and Medicaid Services to study opioid prescriptions of disabled Medicare beneficiaries without record of cancer treatment, palliative care, or end-of-life care. MEASURES AND RESEARCH DESIGN:We summarized the demographic and geographic variation, and investigated how the local economic environment, measured by county median household income, unemployment rate, Gini index, and urban-rural classification correlated with various measures of individual opioid prescriptions. Measures included number of filled opioid prescriptions, total days' supply, average morphine milligram equivalent (MME)/day, and annual total MME dosage. To assess the robustness of the results, we controlled for individual and other county characteristics, used multiple estimation methods including linear least squares, logistic regression, and Tobit regression. RESULTS AND CONCLUSIONS:Lower county median household income, higher unemployment rates, and less income inequality were consistently associated with more and higher MME opioid prescriptions among disabled Medicare beneficiaries. Geographically, we found that the urban-rural divide was not gradual and that beneficiaries in large central metro counties were less likely to have an opioid prescription than those living in other areas.
| S-EPMC6300050 | biostudies-literature
Project description:Background: Pain is common among patients with cardiopulmonary conditions; however, there are increasing concerns, but limited research, regarding use of opioids for pain in patients with noncancer conditions. Objective: To compare patterns of opioid prescribing among older adults reporting pain with cardiopulmonary conditions and/or cancer. Design: Observational study using data from the Surveillance, Epidemiology, and End Results-Medicare Health Outcomes Survey resource linked to Medicare Part D prescription claims. Setting/Subjects: We identified patients who self-reported moderate-to-severe pain interference with daily activities. Patients were stratified by (1) self-reported history of cardiopulmonary conditions; (2) were within five years of cancer diagnosis; (3) had both conditions; or (4) neither. Measurements: We characterized opioid prescribing within 30 days of survey and one-year follow-up using logistic regression and Cox proportional hazard time-to-event analyses. Results: Of 10,516 patients with moderate-to-severe pain (1758 cardiopulmonary conditions, 3383 cancer, 2861 both, 2514 neither), 46% were aged ≥75 years, 65% were non-Hispanic white, and 10% non-Hispanic black. At survey, 1627 (15.5%) received opioids. Adjusted proportions of opioid use were lower for patients with cardiopulmonary conditions only (14%) compared with cancer only (17%; p < 0.001) and both conditions (17%; p < 0.001) but higher than patients with neither condition (13.1%; p < 0.001). There was no difference in time to initiation of opioids at follow-up among patients with cardiopulmonary conditions only, relative to cancer only (adjusted hazard ratio 1.03; 95% confidence interval 0.88-1.21). Conclusions: Opioid use is lower among patients with pain and cardiopulmonary conditions relative to patients with cancer. Findings emphasize the importance of pain assessment and management for patients with cardiopulmonary conditions.
| S-EPMC7840305 | biostudies-literature
Project description:BackgroundLong-term opioid therapy was prescribed with increasing frequency over the past decade. However, factors surrounding long-term use of opioids in older adults remains poorly understood, probably because older people are not at the center stage of the national opioid crisis.ObjectivesTo estimate the annual utilization and trends in long-term opioid use among older adults in the United States.Study designRetrospective cohort study.SettingData from Medicare-enrolled older adults.MethodsThis study utilized a nationally representative sample of Medicare administrative claims data from the years 2012 to 2016 containing records of health care services for more than 2.3 million Medicare beneficiaries each year. Medicare beneficiaries who were 65 years of age or older and who were enrolled in Medicare Parts A, B, and D, but not Part C, for at least 10 months in a year were included in the study. We measured annual utilization and trends in new long-term opioid use episodes over 4 years (2013-2016). We examined claims records for the demographic characteristics of the eligible individuals and for the presence of chronic non-cancer pain (CNCP), cancer, and other comorbidities.ResultsFrom 2013 to 2016, administrative claims of approximately 2.3 million elderly Medicare beneficiaries were analyzed in each year with a majority of them being women (~56%) and white (~82%) with a mean age of approximately 75 years. The proportion of all eligible beneficiaries with at least one new opioid prescription increased from 6.64% in 2013, peaked at 10.32% in 2015, and then decreased to 8.14% in 2016. The proportion of individuals with long-term opioid use among those with a new opioid prescription was 12.40% in 2013 and 10.20% in 2016. Among new long-term opioid users, the proportion of beneficiaries with a cancer diagnosis during the study years increased from 13.30% in 2013 to 15.67% in 2016, and the proportion with CNCP decreased from 30.25% in 2013 to 27.36% in 2016. Across all years, long-term opioid use was consistently high in the Southern states followed by the Midwest region.LimitationsThis study used Medicare fee-for-service administrative claims data to capture prescription fill patterns, which do not allow for the capture of individuals enrolled in Medicare Advantage plans, cash prescriptions, or for the evaluation of appropriateness of prescribing, or the actual use of medication. This study only examined long-term use episodes among patients who were defined as opioid-naive. Finally, estimates captured for 2016 could only utilize data from 9 months of the year to capture 90-day long-term-use episodes.ConclusionsUsing a national sample of elderly Medicare beneficiaries, we observed that from 2013 to 2016 the use of new prescription opioids increased from 2013 to 2014 and peaked in 2015. The use of new long-term prescription opioids peaked in 2014 and started to decrease from 2015 and 2016. Future research needs to evaluate the impact of the changes in new and long-term prescription opioid use on population health outcomes.
| S-EPMC7789048 | biostudies-literature