Dataset Information

Using prescription monitoring program data to characterize out-of-pocket payments for opioid prescriptions in a state Medicaid program.

ABSTRACT:

Background

Out-of-pocket payment for prescription opioids is believed to be an indicator of abuse or diversion, but few studies describe its epidemiology. Prescription drug monitoring programs (PDMPs) collect controlled substance prescription fill data regardless of payment source and thus can be used to study this phenomenon.

Objective

To estimate the frequency and characteristics of prescription fills for opioids that are likely paid out-of-pocket by individuals in the Oregon Medicaid program.

Research design

Cross-sectional analysis using Oregon Medicaid administrative claims and PDMP data (2012 to 2013).

Subjects

Continuously enrolled nondually eligible Medicaid beneficiaries who could be linked to the PDMP with two opioid fills covered by Oregon Medicaid.

Measures

Patient characteristics and fill characteristics for opioid fills that lacked a Medicaid pharmacy claim. Fill characteristics included opioid name, type, and association with indicators of high-risk opioid use.

Results

A total of 33 592 Medicaid beneficiaries filled a total of 555 103 opioid prescriptions. Of these opioid fills, 74 953 (13.5%) could not be matched to a Medicaid claim. Hydromorphone (30%), fentanyl (18%), and methadone (15%) were the most likely to lack a matching claim. The 3 largest predictors for missing claims were opioid fills that overlapped with other opioids (adjusted odds ratio [aOR] 1.37; 95% confidence interval [CI], 1.34-1.4), long-acting opioids (aOR 1.52; 95% CI, 1.47-1.57), and fills at multiple pharmacies (aOR 1.45; 95% CI, 1.39-1.52).

Conclusions

Prescription opioid fills that were likely paid out-of-pocket were common and associated with several known indicators of high-risk opioid use.

SUBMITTER: Hartung DM

PROVIDER: S-EPMC9926937 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Publications

Using prescription monitoring program data to characterize out-of-pocket payments for opioid prescriptions in a state Medicaid program.

Hartung Daniel M DM Ahmed Sharia M SM Middleton Luke L Van Otterloo Joshua J Zhang Kun K Keast Shellie S Kim Hyunjee H Johnston Kirbee K Deyo Richard A RA

Pharmacoepidemiology and drug safety 20170719 9

<h4>Background</h4>Out-of-pocket payment for prescription opioids is believed to be an indicator of abuse or diversion, but few studies describe its epidemiology. Prescription drug monitoring programs (PDMPs) collect controlled substance prescription fill data regardless of payment source and thus can be used to study this phenomenon.<h4>Objective</h4>To estimate the frequency and characteristics of prescription fills for opioids that are likely paid out-of-pocket by individuals in the Oregon Me ...[more]

PMID: 28722211

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Project description:BackgroundIn the past two decades, the U.S. saw an alarmingly increasing trend of benzodiazepine prescribing. Mandatory use of Prescription Drug Monitoring Programs (PDMPs) was suggested to have the potential to reduce opioid prescribing, but little is known about its impacts on benzodiazepines. This study examined whether PDMP data use mandates were associated with changes in benzodiazepine prescribing in the U.S. Methods: Aggregate state quarterly prescription drug records of benzodiazepines for Medicaid enrollees during 2010-2017 were obtained from the U.S. Medicaid State Drug Utilization Data. Three population-adjusted outcome variables were evaluated, including quantity, dosage, and Medicaid spending of benzodiazepine prescriptions per quarter per 100 Medicaid enrollees. The primary policy variable was the state-wide implementation of PDMP data use mandates for benzodiazepines. To account for between-state variations in mandates, an additional policy variable was considered to indicate strong mandates on PDMP data use, which required all prescribers to query a patient's PDMP records for first prescribing and subsequent prescribing at least every 12 months. Linear regressions with difference-in-difference approach were used to assess the associations between PDMP data use mandates and benzodiazepine prescribing, controlling for state-level time-varying policy and socioeconomic covariates. Results: The state-wide implementation of PDMP data use mandates for benzodiazepines was not associated with quantity, dosage, or Medicaid spending of benzodiazepine prescriptions. Strong mandates on PDMP data use were not associated with any benzodiazepine prescribing outcomes, either. Conclusions: There was no evidence for the associations between PDMP data use mandates for benzodiazepines and changes in benzodiazepine prescribing among Medicaid enrollees. Future research is warranted to replicate the study in other populations using individual patient records and continuously monitor the trends in benzodiazepine prescribing in association with PDMPs.

Project description:STUDY OBJECTIVE:We assess whether an automated prescription drug monitoring program intervention in emergency department (ED) settings is associated with reductions in opioid prescribing and quantities. METHODS:We performed a retrospective cohort study of ED visits by Medicaid beneficiaries. We assessed the staggered implementation (pre-post) of automated prescription drug monitoring program queries at 86 EDs in Washington State from January 1, 2013, to September 30, 2015. The outcomes included any opioid prescribed within 1 day of the index ED visit and total dispensed morphine milligram equivalents. The exposure was the automated prescription drug monitoring program query intervention. We assessed program effects stratified by previous high-risk opioid use. We performed multiple sensitivity analyses, including restriction to pain-related visits, restriction to visits with a confirmed prescription drug monitoring program query, and assessment of 6 specific opioid high-risk indicators. RESULTS:The study included 1,187,237 qualifying ED visits (898,162 preintervention; 289,075 postintervention). Compared with the preintervention period, automated prescription drug monitoring program queries were not significantly associated with reductions in the proportion of visits with opioid prescribing (5.8 per 1,000 encounters; 95% confidence interval [CI] -0.11 to 11.8) or the amount of prescribed morphine milligram equivalents (difference 2.66; 95% CI -0.15 to 5.48). There was no evidence of selective reduction in patients with previous high-risk opioid use (1.2 per 1,000 encounters, 95% CI -9.5 to 12.0; morphine milligram equivalents 1.22, 95% CI -3.39 to 5.82). The lack of a selective reduction in high-risk patients was robust to all sensitivity analyses. CONCLUSION:An automated prescription drug monitoring program query intervention was not associated with reductions in ED opioid prescribing or quantities, even in patients with previous high-risk opioid use.

Project description:BackgroundA large proportion of individuals who use heroin report initiating opioid use with prescription opioids. However, patterns of prescription opioid use preceding heroin-related overdose have not been described.ObjectiveTo describe prescription opioid use in the year preceding heroin overdose.DesignCase-control study comparing prescription opioid use with a heroin-involved overdose, non-heroin-involved opioid overdose, and non-overdose controls from 2015 to 2017.ParticipantsOregon Medicaid beneficiaries with linked administrative claims, vital statistics, and prescription drug monitoring program data.Main measuresOpioid, benzodiazepine, and other central nervous system depressant prescriptions preceding overdose; among individuals with one or more opioid prescription, we assessed morphine milligram equivalents per day, overlapping prescriptions, prescriptions from multiple prescribers, long-term use, and discontinuation of long-term use.Key resultsWe identified 1458 heroin-involved overdoses (191 fatal) and 2050 non-heroin-involved opioid overdoses (266 fatal). In the 365 days prior to their overdose, 45% of individuals with a heroin-involved overdose received at least one prescribed opioid compared with 78% of individuals who experienced a non-heroin-involved opioid overdose (p < 0.001). For both heroin- and non-heroin-involved overdose cases, the likelihood of receiving an opioid increased with age. Among heroin overdose cases with an opioid dispensed, the rate of multiple pharmacy use was the only high-risk opioid pattern that was greater than non-overdose controls (adjusted odds ratio 3.2; 95% confidence interval 1.48 to 6.95). Discontinuation of long-term opioid use was not common prior to heroin overdose and not higher than discontinuation rates among non-overdose controls.ConclusionsAlthough individuals with a heroin-involved overdose were less likely to receive prescribed opioids in the year preceding their overdose relative to non-heroin opioid overdose cases, prescription opioid use was relatively common and increased with age. Discontinuation of long-term prescription opioid use was not associated with heroin-involved overdose.

Dataset Information

Using prescription monitoring program data to characterize out-of-pocket payments for opioid prescriptions in a state Medicaid program.

Background

Objective

Research design

Subjects

Measures

Results

Conclusions

Publications

Using prescription monitoring program data to characterize out-of-pocket payments for opioid prescriptions in a state Medicaid program.

Similar Datasets

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