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.

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.

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.

Evaluation of Rural vs Urban Trauma Patients Served by 9-1-1 Emergency Medical Services.

Importance: Despite a large rural US population, there are potential differences between rural and urban regions in the processes and outcomes following trauma. Objectives: To describe and evaluate rural vs urban processes of care, injury severity, and mortality among injured patients served by 9-1-1 emergency medical services (EMS). Design, Setting, and Participants: This was a preplanned secondary analysis of a prospective cohort enrolled from January 1 through December 31, 2011, and followed up through hospitalization. The study included 44 EMS agencies transporting to 28 hospitals in 2 rural and 5 urban counties in Oregon and Washington. A population-based, consecutive sample of 67 047 injured children and adults served by EMS (1971 rural and 65 076 urban) was enrolled. Among the 53 487 patients transported by EMS, a stratified probability sample of 17 633 patients (1438 rural and 16 195 urban) was created to track hospital outcomes (78.9% with in-hospital follow-up). Data analysis was performed from June 12, 2015, to May 20, 2016. Exposures: Rural was defined at the county level by 60 minutes or more driving proximity to the nearest level I or II trauma center and/or rural designation in the Centers for Medicare & Medicaid Services ambulance fee schedule by zip code. Main Outcomes and Measures: Mortality (out-of-hospital and in-hospital), need for early critical resources, and transfer rates. Results: Of the 53 487 injured patients transported by EMS (17 633 patients in the probability sample), 27 535 were women (51.5%); mean (SD) age was 51.6 (26.1) years. Rural vs urban sensitivity of field triage for identifying patients requiring early critical resources was 65.2% vs 80.5%, and only 29.4% of rural patients needing critical resources were initially transported to major trauma centers vs 88.7% of urban patients. After accounting for transfers, 39.8% of rural patients requiring critical resources were cared for in major trauma centers vs 88.7% of urban patients. Overall mortality did not differ between rural and urban regions (1.44% vs 0.89%; P = .09); however, 89.6% of rural deaths occurred within 24 hours compared with 64% of urban deaths. Rural regions had higher transfer rates (3.2% vs 2.7%) and longer transfer distances (median, 97.4 km; interquartile range [IQR], 51.7-394.5 km; range, 47.8-398.6 km vs 22.5 km; IQR, 11.6-24.6 km; range, 3.5-97.4 km). Conclusions and Relevance: Most high-risk trauma patients injured in rural areas were cared for outside of major trauma centers and most rural trauma deaths occurred early, although overall mortality did not differ between regions. There are opportunities for improved timeliness and access to major trauma care among patients injured in rural regions.

Urban/Rural disparities in Oregon pediatric traumatic brain injury.

BACKGROUND: Traumatic brain injury (TBI) greatly contributes to morbidity and mortality in the pediatric population. We examined potential urban/rural disparities in mortality amongst Oregon pediatric patients with TBI treated in trauma hospitals. METHODS: We conducted a retrospective study of children ages 0-19 using the Oregon Trauma Registry for years 2009-2012. Geographic location of injury was classified using the National Center for Health Statistics Urban/Rural Classification Scheme. Incidence rates were calculated using Census data for denominators. Associations between urban/rural injury location and mortality were assessed using multivariable logistic regression, controlling for potential confounders. Generalized estimating equations were used to help account for clustering of data within hospitals. RESULTS: Of 2794 pediatric patients with TBI, 46.6 % were injured in large metropolitan locations, 24.8 % in medium/small metropolitan locations, and 28.6 % in non-metropolitan (rural) locations. Children with rural locations of injury had a greater annualized TBI incidence rate, at 107/100,000 children per year, than those from large metropolitan areas (71/100,000 per year). Compared to children injured in urban locations, those in rural locations had more than twice the crude odds of mortality (odds ratio [OR], 2.5; 95 % CI, 1.6-4.0). This association remained significant (OR, 1.8; 95 % CI, 1.04-3.3) while adjusting for age, gender, race, insurance status, injury severity, and type of TBI (blunt vs. penetrating). CONCLUSION: We observed higher rates of TBI and greater proportions of severe injury in rural compared to urban areas in Oregon. Rural children treated in the trauma system for TBI were more likely to die than urban children after controlling for demographic and injury factors associated with urban/rural residence. Further research is needed to examine treatment disparities by urban/rural location. Future work should also identify interventions that can reduce risk of TBI and TBI-related mortality among children, particularly those who live in rural areas.

Racial/ethnic and insurance status disparities in discharge to posthospitalization care for patients with traumatic brain injury.

OBJECTIVES: Professional, posthospitalization care (PHC) can improve outcomes among patients with traumatic brain injury. We examined disparities in discharge to PHC by patients' race/ethnicity and insurance type. PARTICIPANTS: A total of 6061 adults hospitalized for unintentional traumatic brain injury in Oregon, 2008 to 2011. MAIN OUTCOME MEASURE: Posthospitalization care was assessed on the basis of discharge disposition. Multivariable logistic regression was used to estimate effects of race/ethnicity and insurance on referral to PHC while controlling for potential confounders. Generalized estimating equations were used to calculate odds ratios (ORs) and 95% confidence intervals (CIs), accounting for clustering of data by hospital. RESULTS: 28% of patients were discharged to PHC. While controlling for potential confounders, Hispanics were less likely to be discharged to PHC (OR, 0.62; CI, 0.40-0.96) than non-Hispanic whites. Compared with patients with private insurance, uninsured patients were less likely to be discharged to PHC (OR, 0.19; CI, 0.11-0.32) whereas patients with public insurance (OR, 1.65; CI, 1.33-2.05) and worker's compensation (OR, 1.66; CI, 1.09-2.52) were more likely to be discharged to PHC. CONCLUSIONS: Results suggest that racial/ethnic and insurance disparities exist in discharge to postacute care after hospitalization for traumatic brain injury. Future research should examine factors that might contribute to and reduce these inequities in care.