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Opioid Prescriptions for Acute and Chronic Pain Management Among Medicaid Beneficiaries.

ABSTRACT: INTRODUCTION:Millions of Americans are affected by acute or chronic pain every year. This study investigates opioid prescription patterns for acute and chronic pain management among U.S. Medicaid patients. METHODS:The study used medical and pharmacy claims data obtained from the multistate Truven MarketScan Medicaid Database from 2013 to 2015 for Medicaid patients receiving health care. Medicaid beneficiaries who utilized an outpatient healthcare facility for back pain, neck pain (cervicalgia), joint pain (osteoarthritis and rheumatoid arthritis), orthopedics (simple/closed fractures and muscle strains/sprains), headache (cluster headaches and migraines), dental conditions, or otorhinolaryngologic (otalgia) diagnoses, based on the International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes, and received an opioid prescription within 14 days of diagnosis were included in this study. RESULTS:There were 5,051,288 patients with 1 of the 7 diagnostic groupings; 18.8% had an opioid prescription filled within 14 days of diagnosis. Orthopedic pain (34.8%) was the primary reason for an opioid prescription, followed by dental conditions (17.3%), back pain (14.0%), and headache (12.9%). Patients receiving an opioid for conditions associated with acute pain management, such as otorhinolaryngologic (OR=1.93, 95% CI=1.85, 2.0), dental (OR=1.50, 95% CI=1.48, 1.53), or orthopedic conditions (OR=1.31, 95% CI=1.29, 1.32), were more likely to receive the prescription from an emergency department provider versus a general practitioner. However, compared with general practitioners, other providers were more likely to prescribe opioids for conditions associated with chronic pain management. CONCLUSIONS:More than half of Medicaid beneficiaries receiving an opioid for pain management do so for orthopedic- and dental-related reasons, with emergency department providers more likely to prescribe opioids. Modifications to the guidelines addressing temporary acute pain management practices with opioids would be likely to benefit emergency department providers the most.

SUBMITTER: Janakiram C

PROVIDER: S-EPMC6713282 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Opioid Prescriptions for Acute and Chronic Pain Management Among Medicaid Beneficiaries.

Janakiram Chandrashekar C Fontelo Paul P Huser Vojtech V Chalmers Natalia I NI Lopez Mitnik Gabriela G Brow Avery R AR Iafolla Timothy J TJ Dye Bruce A BA

American journal of preventive medicine 20190801 3

<h4>Introduction</h4>Millions of Americans are affected by acute or chronic pain every year. This study investigates opioid prescription patterns for acute and chronic pain management among U.S. Medicaid patients.<h4>Methods</h4>The study used medical and pharmacy claims data obtained from the multistate Truven MarketScan Medicaid Database from 2013 to 2015 for Medicaid patients receiving health care. Medicaid beneficiaries who utilized an outpatient healthcare facility for back pain, neck pain ...[more]

PMID: 31377093

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Project description:ImportanceExpanding Medicaid eligibility could affect prescriptions of buprenorphine with naloxone, an established treatment for opioid use disorder, and opioid pain relievers (OPRs).ObjectiveTo examine changes in prescriptions of buprenorphine with naloxone and OPRs after the US Affordable Care Act Medicaid expansion.Design, setting, and participantsIn this cohort study, longitudinal, patient-level, retail pharmacy claims were extracted from IQVIA real-world data from an anonymized, longitudinal, prescription database. The sample included 11.9 million individuals who filled 2 or more prescriptions for a prescription opioid during at least 1 year between January 1, 2010, and December 31, 2015, from California, Maryland, and Washington (expansion states) and Florida and Georgia (nonexpansion states). Data analysis was conducted from August 1, 2017, to May 31, 2018. Data were aggregated to county-year observations (N = 2082) and linked to county-level covariates. For each outcome, a difference-in-differences regression model was estimated comparing changes before and after expansion in expansion vs nonexpansion counties. Models were adjusted for county demographics, uninsured rate, and overdose mortality in the baseline year (2010).ExposuresPresence of Medicaid expansion in the year.Main outcomes and measuresFor buprenorphine with naloxone and OPRs, rates per 100 000 county residents were calculated separately for any prescriptions overall and by different payment sources. Mean days of medication per county among people filling prescriptions for these agents were also determined.ResultsThe study sample included 11.9 million individuals (expansion states: 40.9% men; mean [SD] age, 44.1 [13.8] years; nonexpansion states: 41.0% men; mean [SD] age, 43.7 [13.7] years). In expansion counties, 68.8 individuals per 100 000 county residents filled buprenorphine with naloxone and 5298.3 filled OPR prescriptions in 2010. After expansion, buprenorphine with naloxone fills per 100 000 county residents increased significantly in expansion relative to nonexpansion counties (8.7; 95% CI, 1.7 to 15.7). Opioid pain reliever fills per 100 000 county residents did not significantly change in expansion counties relative to nonexpansion counties (327.4; 95% CI -202.5 to 857.4). The rate of OPRs per 100 000 county residents paid for by Medicaid significantly increased (374.0; 95% CI, 258.3 to 489.7). There were no significant changes in days per 100 000 county residents of either medication after expansion.Conclusions and relevanceMedicaid expansion significantly increased buprenorphine with naloxone prescriptions per 100 000 county residents in expansion counties, suggesting that expansion improved access to opioid use disorder treatment. Expansion did not significantly increase the overall rate per 100 000 county residents of OPR prescriptions, but increased the population with OPRs paid for by Medicaid. This finding therefore suggests the growing importance of Medicaid in pain management and addiction prevention.

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:BackgroundIn 2015, Oregon's Medicaid program implemented a performance improvement project to reduce high-dose opioid prescribing across its 16 coordinated care organizations (CCOs). The objective of this study was to evaluate the effect of that program on prescription opioid use and outcomes.MethodsUsing Medicaid claims data from 2014 to 2017, we conducted interrupted time-series analyses to examine changes in the prescription opioid use and overdose rates before (July 2014 to June 2015) and after (January 2016 to December 2017) implementation of Oregon's high-dose policy initiative (July 2015 to December 2015). Prescribing outcomes were: 1) total opioid prescriptions 2) high-dose [> 90 morphine milligram equivalents per day] opioid prescriptions, and 3) proportion of opioid prescriptions that were high-dose. Opioid overdose outcomes included emergency department visits or hospitalizations that involved an opioid-related poisoning (total, heroin-involved, non-heroin involved). Analyses were performed at the state and CCO level.ResultsThere was an immediate reduction in high dose opioid prescriptions after the program was implemented (- 1.55 prescription per 1000 enrollee; 95% CI - 2.26 to - 0.84; p < 0.01). Program implementation was also associated with an immediate drop (- 1.29 percentage points; 95% CI - 1.94 to - 0.64 percentage points; p < 0.01) and trend reduction (- 0.23 percentage point per month; 95% CI - 0.33 to - 0.14 percentage points; p < 0.01) in the monthly proportion of high-dose opioid prescriptions. The trend in total, heroin-involved, and non-heroin overdose rates increased significantly following implementation of the program.ConclusionsAlthough Oregon's high-dose opioid performance improvement project was associated with declines in high-dose opioid prescriptions, rates of opioid overdose did not decrease. Policy efforts to reduce opioid prescribing risks may not be sufficient to address the growing opioid crisis.

Dataset Information

Opioid Prescriptions for Acute and Chronic Pain Management Among Medicaid Beneficiaries.

Publications

Opioid Prescriptions for Acute and Chronic Pain Management Among Medicaid Beneficiaries.

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