Association between treatment setting and outcomes among oregon medicaid patients with opioid use disorder: a retrospective cohort study.

BACKGROUND: Residential treatment is a common approach for treating opioid use disorder (OUD), however, few studies have directly compared it to outpatient treatment. The objective of this study was to compare OUD outcomes among individuals receiving residential and outpatient treatment. METHODS: A retrospective cohort study used linked data from a state Medicaid program, vital statistics, and the Substance Abuse and Mental Health Services Administration (SAMHSA) Treatment Episodes Dataset (TEDS) to compare OUD-related health outcomes among individuals treated in a residential or outpatient setting between 2014 and 2017. Multivariable Cox proportional hazards and logistic regression models examined the association between treatment setting and outcomes (i.e., opioid overdose, non-overdose opioid-related and all-cause emergency department (ED) visits, hospital admissions, and treatment retention) controlling for patient characteristics, co-morbidities, and use of medications for opioid use disorders (MOUD). Interaction models evaluated how MOUD use modified associations between treatment setting and outcomes. RESULTS: Of 3293 individuals treated for OUD, 957 (29%) received treatment in a residential facility. MOUD use was higher among those treated as an outpatient (43%) compared to residential (19%). The risk of opioid overdose (aHR 1.39; 95% CI 0.73-2.64) or an opioid-related emergency department encounter or admission (aHR 1.02; 95% CI 0.80-1.29) did not differ between treatment settings. Independent of setting, MOUD use was associated with a significant reduction in overdose risk (aHR 0.45; 95% CI 0.23-0.89). Residential care was associated with greater odds of retention at 6-months (aOR 1.71; 95% CI 1.32-2.21) but not 1-year. Residential treatment was only associated with improved retention for individuals not receiving MOUD (6-month aOR 2.05; 95% CI 1.56-2.71) with no benefit observed in those who received MOUD (aOR 0.75; 95% CI 0.46-1.29; interaction p = 0.001). CONCLUSIONS: Relative to outpatient treatment, residential treatment was not associated with reductions in opioid overdose or opioid-related ED encounters/hospitalizations. Regardless of setting, MOUD use was associated with a significant reduction in opioid overdose risk.

Evaluation of a Medicaid performance improvement project to reduce high-dose opioid prescriptions.

BACKGROUND: In 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. METHODS: Using 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. RESULTS: There 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. CONCLUSIONS: Although 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.

Factors Associated With Opioid Overdose After an Initial Opioid Prescription.

Importance: The opioid epidemic continues to be a public health crisis in the US. Objective: To assess the patient factors and early time-varying prescription-related factors associated with opioid-related fatal or nonfatal overdose. Design, Setting, and Participants: This cohort study evaluated opioid-naive adult patients in Oregon using data from the Oregon Comprehensive Opioid Risk Registry, which links all payer claims data to other health data sets in the state of Oregon. The observational, population-based sample filled a first (index) opioid prescription in 2015 and was followed up until December 31, 2018. Data analyses were performed from March 1, 2020, to June 15, 2021. Exposures: Overdose after the index opioid prescription. Main Outcomes and Measures: The outcome was an overdose event. The sample was followed up to identify fatal or nonfatal opioid overdoses. Patient and prescription characteristics were identified. Prescription characteristics in the first 6 months after the index prescription were modeled as cumulative, time-dependent measures that were updated monthly through the sixth month of follow-up. A time-dependent Cox proportional hazards regression model was used to assess patient and prescription characteristics that were associated with an increased risk for overdose events. Results: The cohort comprised 236 921 patients (133 839 women [56.5%]), of whom 667 (0.3%) experienced opioid overdose. Risk of overdose was highest among individuals 75 years or older (adjusted hazard ratio [aHR], 3.22; 95% CI, 1.94-5.36) compared with those aged 35 to 44 years; men (aHR, 1.29; 95% CI, 1.10-1.51); those who were dually eligible for Medicaid and Medicare Advantage (aHR, 4.37; 95% CI, 3.09-6.18), had Medicaid (aHR, 3.77; 95% CI, 2.97-4.80), or had Medicare Advantage (aHR, 2.18; 95% CI, 1.44-3.31) compared with those with commercial insurance; those with comorbid substance use disorder (aHR, 2.74; 95% CI, 2.15-3.50), with depression (aHR, 1.26; 95% CI, 1.03-1.55), or with 1 to 2 comorbidities (aHR, 1.32; 95% CI, 1.08-1.62) or 3 or more comorbidities (aHR, 1.90; 95% CI, 1.42-2.53) compared with none. Patients were at an increased overdose risk if they filled oxycodone (aHR, 1.70; 95% CI, 1.04-2.77) or tramadol (aHR, 2.80; 95% CI, 1.34-5.84) compared with codeine; used benzodiazepines (aHR, 1.06; 95% CI, 1.01-1.11); used concurrent opioids and benzodiazepines (aHR, 2.11; 95% CI, 1.70-2.62); or filled opioids from 3 or more pharmacies over 6 months (aHR, 1.38; 95% CI, 1.09-1.75). Conclusions and Relevance: This cohort study used a comprehensive data set to identify patient and prescription-related risk factors that were associated with opioid overdose. These findings may guide opioid counseling and monitoring, the development of clinical decision-making tools, and opioid prevention and treatment resources for individuals who are at greatest risk for opioid overdose.

Prescription Opioid Dispensing Patterns Prior to Heroin Overdose in a State Medicaid Program: a Case-Control Study.

BACKGROUND: A 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. OBJECTIVE: To describe prescription opioid use in the year preceding heroin overdose. DESIGN: Case-control study comparing prescription opioid use with a heroin-involved overdose, non-heroin-involved opioid overdose, and non-overdose controls from 2015 to 2017. PARTICIPANTS: Oregon Medicaid beneficiaries with linked administrative claims, vital statistics, and prescription drug monitoring program data. MAIN MEASURES: Opioid, 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 RESULTS: We 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. CONCLUSIONS: Although 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.

Opioid-Prescribing Continuity and Risky Opioid Prescriptions.

We aimed to better understand the association between opioid-prescribing continuity, risky prescribing patterns, and overdose risk. For this retrospective cohort study, we included patients with long-term opioid use, pulling data from Oregon's Prescription Drug Monitoring Program (PDMP), vital records, and hospital discharge registry. A continuity of care index (COCI) score was calculated for each patient, and we defined metrics to describe risky prescribing and overdose. As prescribing continuity increased, likelihood of filling risky opioid prescriptions and overdose hospitalization decreased. Prescribing continuity is an important factor associated with opioid harms and can be calculated using administrative pharmacy data.

A statewide effort to reduce high-dose opioid prescribing through coordinated care organizations.

BACKGROUND: Oregon's Medicaid program is delivered through 16 Coordinated Care Organizations (CCOs) participating in a statewide performance improvement program to reduce high-dose opioid prescribing. CCOs were allowed flexibility to develop their own dose targets and any policies, trainings, guidelines, and/or materials to meet these targets. In this study, we characterize CCO strategies to reduce high-dose opioid prescribing across the 16 CCOs. METHODS: We reviewed relevant CCO documents and conducted semi-structured interviews with CCO administrators to acquire opioid-related policies, practices, timelines and contextual factors. We applied a systematic coding procedure to develop a comprehensive description of each CCO's strategy. We used administrative data from the state to summarize contextual utilization data for each CCO. RESULTS: Most CCOs selected a target daily morphine milligram equivalent (MME) dose of 90 mg. Sixteen issued quantity limits related to dose, eight restricted specific drug formulations (short-acting or long-acting), and 11 allowed for time-limited taper plan periods for patients over threshold. Many CCOs also employed provider trainings, feedback reports, and/or onsite technical assistance. Other innovations included incentive measures, electronic health record alerts, and toolkits with materials on local alternative therapy resources and strategies for patient communication. CCOs leveraging collaborations with regional partners appeared to mount a greater intensity of interventions than independently operating CCOs. CONCLUSIONS: CCOs developed a diversity of interventions to confront high-risk opioid prescribing within their organization. As healthcare systems mount interventions to reduce risky opioid prescribing, it is critical to carefully describe these activities and examine their impact on process and health outcomes.

Association of Prescription Drug Monitoring Program Use With Opioid Prescribing and Health Outcomes: A Comparison of Program Users and Nonusers.

Prescription drug monitoring programs (PDMPs) are a response to the prescription opioid epidemic, but their effects on prescribing and health outcomes remain unclear, with conflicting reports. We sought to determine if prescriber use of Oregon's PDMP led to fewer high-risk opioid prescriptions or overdose events. We conducted a retrospective cohort study from October 2011 through October 2014, using statewide PDMP data, hospitalization registry, and vital records. Early PDMP registrants (n = 927) were matched with clinicians who never registered during the study period, using baseline prescribing metrics in a propensity score. Generalized estimating equations were used to examine prescribing trends after PDMP registration, using 2-month intervals. We found a statewide decline in measures of per capita opioid prescribing. However, compared with nonregistrants, PDMP registrants did not subsequently have significantly fewer patients receiving high-dose prescriptions, overlapping opioid and benzodiazepine prescriptions, inappropriate prescriptions, prescriptions from multiple prescribers, or overdose events. At baseline, frequent PDMP users wrote fewer high-risk opioid prescriptions than infrequent users; this persisted during follow-up with few significant group differences in trend. Thus, although opioid prescribing declined statewide after implementing the PDMP, registrants did not show greater declines than nonregistrants. PERSPECTIVE: Factors other than PDMP use may have had greater influence on prescribing trends. Refinements in the PDMP program and related policies may be necessary to increase PDMP effects.

High-risk prescribing and opioid overdose: prospects for prescription drug monitoring program-based proactive alerts.

To develop a simple, valid model to identify patients at high risk of opioid overdose-related hospitalization and mortality, Oregon prescription drug monitoring program, Vital Records, and Hospital Discharge data were linked to estimate 2 logistic models; a first model that included a broad range of risk factors from the literature and a second simplified model. Receiver operating characteristic curves, sensitivity, and specificity of the models were analyzed. Variables retained in the final model were categories such as older than 35 years, number of prescribers, number of pharmacies, and prescriptions for long-acting opioids, benzodiazepines or sedatives, or carisoprodol. The ability of the model to discriminate between patients who did and did not overdose was reasonably good (area under the receiver operating characteristic curve = 0.82, Nagelkerke R = 0.11). The positive predictive value of the model was low. Computationally simple models can identify high-risk patients based on prescription history alone, but improvement of the predictive value of models may require information from outside the prescription drug monitoring program. Patient or prescription features that predict opioid overdose may differ from those that predict diversion.

Opioid Prescribing Patterns and Patient Outcomes by Prescriber Type in the Oregon Prescription Drug Monitoring Program.

Objective: Prescription drug monitoring programs (PDMPs) were created to facilitate responsible use of controlled substances. In Oregon, physicians, physician's assistants (MDs/DOs/PAs), dentists, nurse practitioners (NPs), and naturopathic physicians (NDs) may prescribe opioids, but differences in prescribing practices, patient mix, and patient outcomes among prescriber types have not been characterized. Methods: De-identified Oregon PDMP data from October 2011 through October 2014 were linked with vital records and a statewide hospital discharge registry. The disciplines of registered prescribers were identified by board affiliations. Prescription profiles associated with opioid overdose risk were tabulated for patients with at least one registered prescriber. Opioid-related hospitalizations and deaths were identified using ICD-9 and ICD-10 codes. Results: There were 5,935 prescribers registered during the study period. Patients of NPs or NDs received more high-risk opioid prescriptions than patients of MDs/DOs/PAs. For example, they received greater proportions of high-dose prescriptions (NP 12.9%, ND 15%, MD/DO/PA 11.1%), and had greater opioid-related hospitalization (NP 1.7%, ND 3.1%, MD/DO/PA 1.2%; P < 0.005 for all). However, patients of NPs or NDs were also more likely to have four or more prescribers (NP 45.3%, ND 58.5%, MD/DO/PA 27.1%), and most of their patients' high-risk opioid prescriptions came from prescribers in other disciplines. Conclusion: Our analysis suggests significant differences in opioid prescription profiles and opioid-related hospitalization and mortality among patients receiving opioid prescriptions from nurse practitioners, naturopathic physicians, or medical clinicians in Oregon. However, these differences appear largely due to differences in patient mix between provider types rather than discipline-specific prescribing practices.

Research to support optimization of prescription drug monitoring programs.

PURPOSE: Research is needed to evaluate the impact of prescription drug monitoring programs (PDMPs). This paper describes research priorities for PDMPs that were initially discussed at a 2015 meeting of PDMP administrators, researchers, public health officials, and other stakeholders. METHODS: Meeting participants defined the current landscape of PDMP research and identified research gaps. Research priorities were grouped by theme. RESULTS: Prescription drug monitoring program research priorities were identified for 3 key areas: individual patient health outcomes, prescriber use and decision making, and population-level outcomes. Research areas for individual patient outcomes include examining drug-use thresholds that best predict risk for overdose or substance use disorder and unintended consequences of PDMP use. Proposed research on prescriber PDMP use include evaluating how enhancements to the content and format of PDMP reports informs clinical decision making and optimal clinician actions in response to a concerning PDMP report. Finally, research topics related to population-level outcomes include measuring the impact of PDMP policies on the incidence of substance misuse and harms and assessing the return on investment for these databases. CONCLUSIONS: The clinical, public health, and economic impacts of PDMPs must be evaluated, using both quantitative and qualitative methods. In addition to assessing patient outcomes, qualitative research should examine how clinicians use and interpret PDMP information. Research should also examine the impact of PDMP features and policies on prescriber utilization. Comparative analyses across states with differing PDMP policies should be conducted to inform best practices.

Association Between Initial Opioid Prescribing Patterns and Subsequent Long-Term Use Among Opioid-Naïve Patients: A Statewide Retrospective Cohort Study.

BACKGROUND: Long-term efficacy of opioids for non-cancer pain is unproven, but risks argue for cautious prescribing. Few data suggest how long or how much opioid can be prescribed for opioid-naïve patients without inadvertently promoting long-term use. OBJECTIVE: To examine the association between initial opioid prescribing patterns and likelihood of long-term use among opioid-naïve patients. DESIGN: Retrospective cohort study; data from Oregon resident prescriptions linked to death certificates and hospital discharges. PARTICIPANTS: Patients filling opioid prescriptions between October 1, 2012, and September 30, 2013, with no opioid fills for the previous 365 days. Subgroup analyses examined patients under age 45 who did not die in the follow-up year, excluding most cancer or palliative care patients. MAIN MEASURES: Exposure: Numbers of prescription fills and cumulative morphine milligram equivalents (MMEs) dispensed during 30 days following opioid initiation ("initiation month"). OUTCOME: Proportion of patients with six or more opioid fills during the subsequent year ("long-term users"). KEY RESULTS: There were 536,767 opioid-naïve patients who filled an opioid prescription. Of these, 26,785 (5.0 %) became long-term users. Numbers of fills and cumulative MMEs during the initiation month were associated with long-term use. Among patients under age 45 using short-acting opioids who did not die in the follow-up year, the adjusted odds ratio (OR) for long-term use among those receiving two fills versus one was 2.25 (95 % CI: 2.17, 2.33). Compared to those who received < 120 total MMEs, those who received between 400 and 799 had an OR of 2.96 (95 % CI: 2.81, 3.11). Patients initiating with long-acting opioids had a higher risk of long-term use than those initiating with short-acting drugs. CONCLUSIONS: Early opioid prescribing patterns are associated with long-term use. While patient characteristics are important, clinicians have greater control over initial prescribing. Our findings may help minimize the risk of inadvertently initiating long-term opioid use.