Dataset Information

The Association Between Local Economic Conditions and Opioid Prescriptions Among Disabled Medicare Beneficiaries.

ABSTRACT: 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.

SUBMITTER: Zhou C

PROVIDER: S-EPMC6300050 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Association Between Local Economic Conditions and Opioid Prescriptions Among Disabled Medicare Beneficiaries.

Zhou Chao C Yu Ning Neil NN Losby Jan L JL

Medical care 20180101 1

<h4>Background</h4>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.<h4>Objective</h4>We examined the association between county-level socioeconomic factors (median household income, unemployment rate, Gini index) and opioid prescribing.<h ...[more]

PMID: 29227444

Similar Datasets

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.

Project description:ObjectivesLittle is known about relationships between opioid- and gabapentinoid-use patterns and healthcare expenditures that may be affected by pain management and risk of adverse outcomes. This study examined the association between patients' opioid and gabapentinoid prescription filling/refilling trajectories and direct medical expenditures in US Medicare.MethodsThis cross-sectional study included a 5% national sample (2011-2016) of fee-for-service beneficiaries with fibromyalgia, low back pain, neuropathy, or osteoarthritis newly initiating opioids or gabapentinoids. Using group-based multitrajectory modeling, this study identified patients' distinct opioid and gabapentinoid (OPI-GABA) dose and duration patterns, based on standardized daily doses, within a year of initiating opioids and/or gabapentinoids. Concurrent direct medical expenditures within the same year were estimated using inverse probability of treatment weighted multivariable generalized linear regression, adjusting for sociodemographic and health status factors.ResultsAmong 67 827 eligible beneficiaries (mean age ± SD = 63.6 ± 14.8 years, female = 65.8%, white = 77.1%), 11 distinct trajectories were identified (3 opioid-only, 4 gabapentinoid-only, and 4 concurrent OPI-GABA trajectories). Compared with opioid-only early discontinuers ($13 830, 95% confidence interval = $13 643-14 019), gabapentinoid-only early discontinuers and consistent low-dose and moderate-dose gabapentinoid-only users were associated with 11% to 23% lower health expenditures (adjusted mean expenditure = $10 607-$11 713). Consistent low-dose opioid-only users, consistent high-dose opioid-only users, consistent low-dose OPI-GABA users, consistent low-dose opioid and high-dose gabapentinoid users, and consistent high-dose opioid and moderate-dose gabapentinoid users were associated with 14% to 106% higher healthcare expenditures (adjusted mean expenditure = $15 721-$28 464).ConclusionsDose and duration patterns of concurrent OPI-GABA varied substantially among fee-for-service Medicare beneficiaries. Consistent opioid-only users and all concurrent OPI-GABA users were associated with higher healthcare expenditures compared to opioid-only discontinuers.

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.

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.

Project description:ImportanceThe measured severity of illness of hospitalized Medicare beneficiaries has increased. Whether this change is associated with payment reforms, concentrated among hospitalizations with principal diagnoses targeted by payment reform, and reflective of true increases in severity of illness is unknown.ObjectivesTo assess whether the expansion of secondary diagnosis codes in January 2011 and the incentive payments for health information technology under the US Health Information Technology for Economic and Clinical Health Act were associated with changes in measured severity of illness and whether those changes are reflective of true increases in underlying patient severity.Design, setting, and participantsThis cohort study of Medicare fee-for-service beneficiary discharges (N = 47 951 443) between January 1, 2008, and August 31, 2015, used a regression-discontinuity design to evaluate changes in measured severity of illness after the expansion of secondary diagnoses. Discharge-level linear regression model with hospital fixed effects was used to evaluate changes in measured severity of illness after hospitals' receipt of incentives for health information technology. The change in predictive accuracy of measured severity of illness on 30-day readmissions after the implementation of both policies was evaluated. Data analysis was performed from November 1, 2018, to March 5, 2019.Main outcomes and measuresThe primary outcome was patients' measured severity of illness determined by the number of condition categories from secondary discharge diagnosis codes. Measured severity of illness for diagnoses commonly targeted by Medicare policies and untargeted diagnoses was assessed.ResultsIn total, 47 951 443 discharges at 2850 hospitals were included. In 2008, these beneficiaries included 3 882 672 women (58.5%) with a mean (SD) age of 78.5 (8.4) years. In 2014, the discharges included 3 377 137 women (57.8%) with the mean (SD) age of 78.4 (8.7) years. The Centers for Medicare & Medicaid Services expansion of secondary diagnoses was associated with a 0.348 (95% CI, 0.328-0.367; P < .001) change in condition categories for all diagnoses, 0.445 (95% CI, 0.419-0.470; P < .001) for targeted diagnoses, and 0.321 (95% CI, 0.302-0.341; P < .001) for untargeted diagnoses. Health information technology incentives were associated with a 0.013 (95% CI, 0.004-0.022; P = .005) change in condition categories for all diagnoses, 0.195 (95% CI, 0.184-0.207; P < .001) for targeted diagnoses, and -0.016 (95% CI, -0.025 to -0.007; P < .001) for untargeted diagnoses. Minimal improvements in predictive accuracy were observed.Conclusions and relevanceChanges in Centers for Medicare & Medicaid Services policies appear to be associated with increases in measured severity of illness; these increases do not appear to reflect substantive changes in true patient severity.

Dataset Information

The Association Between Local Economic Conditions and Opioid Prescriptions Among Disabled Medicare Beneficiaries.

Publications

The Association Between Local Economic Conditions and Opioid Prescriptions Among Disabled Medicare Beneficiaries.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets