Dataset Information

Evaluating short- and long-term impacts of a Medicaid "lock-in" program on opioid and benzodiazepine prescriptions dispensed to beneficiaries.

ABSTRACT: BACKGROUND:Insurance-based "lock-in" programs (LIPs) have become a popular strategy to address controlled substance (CS) (e.g., opioid) misuse. However, little is known about their impacts. We examined changes in CS dispensing to beneficiaries in the 12-month North Carolina Medicaid LIP. METHODS:We analyzed Medicaid claims linked to Prescription Drug Monitoring Program (PDMP) records for beneficiaries enrolled in the LIP between October 2010 and September 2012 (n=2702). Outcomes of interest were 1) number of dispensed CS prescriptions and 2) morphine milligram equivalents (MMEs) of dispensed opioids while a) locked-in and b) in the year following release. RESULTS:Compared to a period of stable CS dispensed prior to LIP enrollment, numbers of dispensed CS during lock-in and post-release were lower (count difference per person-month: -0.05 (95% CI: -0.11, 0.01); -0.23 (95% CI: -0.31, -0.15), respectively). However, beneficiaries' average daily MMEs of opioids were elevated during both lock-in and post-release (daily mean difference per person: 18.7 (95% CI: 13.9, 23.6); 11.1 (95% CI: 5.1, 17.1), respectively). Stratification by payer source revealed increases in using non-Medicaid (e.g., out-of-pocket) payment during lock-in that persisted following release. CONCLUSION:While the LIP reduced the number of CS dispensed, the program was also associated with increased acquisition of CS prescriptions using non-Medicaid payment. Moreover, beneficiaries acquired greater dosages of dispensed opioids from both Medicaid and non-Medicaid payment sources during lock-in and post-release. Refining LIPs to increase beneficiary access to substance use disorder screening and treatment services and provider use of PDMPs may address important unintended consequences.

SUBMITTER: Naumann RB

PROVIDER: S-EPMC7475002 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Evaluating short- and long-term impacts of a Medicaid "lock-in" program on opioid and benzodiazepine prescriptions dispensed to beneficiaries.

Naumann Rebecca B RB Marshall Stephen W SW Lund Jennifer L JL Gottfredson Nisha C NC Ringwalt Christopher L CL Skinner Asheley C AC

Drug and alcohol dependence 20171114

<h4>Background</h4>Insurance-based "lock-in" programs (LIPs) have become a popular strategy to address controlled substance (CS) (e.g., opioid) misuse. However, little is known about their impacts. We examined changes in CS dispensing to beneficiaries in the 12-month North Carolina Medicaid LIP.<h4>Methods</h4>We analyzed Medicaid claims linked to Prescription Drug Monitoring Program (PDMP) records for beneficiaries enrolled in the LIP between October 2010 and September 2012 (n=2702). Outcomes o ...[more]

PMID: 29150151

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Project description: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.

Project description:BackgroundLock-in programs are proliferating among private and public payers to restrict access to controlled substance prescriptions and enhance care coordination for patients exhibiting high-risk use of, primarily, opioids. Patients enrolled in lock-in programs are required to seek opioids from a designated provider and pharmacy for insurance coverage of their opioid and benzodiazepine prescriptions. Lock-in program restrictions are often circumvented by patients through out-of-pocket cash purchases of opioid prescriptions, undermining the program's intended function. This study sought to construct and explain trajectories of Medicaid-covered and cash pay opioid prescription fills among adults enrolled in an opioid lock-in program. Methods: We used sequential explanatory mixed methods, which involved a quantitative retrospective cohort analysis of opioid fill trajectories using North Carolina Medicaid administrative claims data linked with state prescription drug monitoring program data, followed by qualitative semi-structured interviews with North Carolina pharmacists. The quantitative component included adults enrolled in the North Carolina Medicaid lock-in program between 10/1/2010-3/31/2012. The qualitative component included a maximum variation sample of community pharmacists in North Carolina delivering care to lock-in patients. Quantitative outcomes included group-based trajectories of monthly Medicaid-covered and cash pay opioid prescription fills six months before and after LIP enrollment, and qualitative analyses generated themes explaining observed trajectories. Results: Two-thirds of subjects exhibited reduced Medicaid-covered opioid prescription fills and no increase in cash pay fills after lock-in enrollment, with one-third exhibiting increased cash pay fills after lock-in. Pharmacists attributed increases in cash pay fills primarily to illicit behaviors, while some cash pay behavior likely reflected new unintended barriers to care. Conclusions: Lock-in programs appear to reduce prescription opioid use for most enrolled patients. However, lock-in programs may have limited capacity to deter illicit behaviors among patients intent on abusing, misusing, or diverting these medications and may introduce new access barriers to necessary care for some.

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

Evaluating short- and long-term impacts of a Medicaid "lock-in" program on opioid and benzodiazepine prescriptions dispensed to beneficiaries.

Publications

Evaluating short- and long-term impacts of a Medicaid "lock-in" program on opioid and benzodiazepine prescriptions dispensed to beneficiaries.

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