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.

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.

Opioid Overdose After Medication for Opioid Use Disorder Initiation Following Hospitalization or ED Visit.

Importance: Hospitalizations related to opioid use disorder (OUD) represent an opportunity to initiate medication for OUD (MOUD). Objective: To assess whether starting MOUD after a hospitalization or emergency department (ED) visit is associated with the odds of fatal and nonfatal opioid overdose at 6 and 12 months. Design, Setting, and Participants: This population-based cohort study used data from the Oregon Comprehensive Opioid Risk Registry, which links all payer claims data to other administrative health datasets, for individuals aged 18 years or older who had diagnosis codes related to OUD recorded at an index ED visit or hospitalization from January 2017 to December 2019. Data were analyzed between May 2023 and January 2024. Exposures: Receipt of MOUD within the 7 days after an OUD-related hospital visit. Main Outcomes and Measures: The primary outcome was fatal or nonfatal overdose at 6 and 12 months after discharge. Sample characteristics, including age, sex, insurance plan, number of comorbidities, and opioid-related overdose events, were stratified by receipt or nonreceipt of MOUD within 7 days after an OUD-related hospital visit. A logistic regression model was used to investigate the association between receipt of MOUD and having an opioid overdose event. Results: The study included 22 235 patients (53.1% female; 25.0% aged 25-39 years) who had an OUD-related hospital visit during the study period. Overall, 1184 patients (5.3%) received MOUD within 7 days of their ED visit or hospitalization. Of these patients, 683 (57.7%) received buprenorphine, 463 (39.1%) received methadone, and 46 (3.9%) received long-acting injectable naltrexone. Patients who received MOUD within 7 days after discharge had lower adjusted odds of fatal or nonfatal overdose at 6 months compared with those who did not (adjusted odds ratio [AOR], 0.63; 95% CI, 0.41-0.97). At 12 months, there was no difference in adjusted odds of fatal or nonfatal overdose between these groups (AOR, 0.79; 95% CI, 0.58-1.08). Patients had a lower risk of fatal or nonfatal overdose at 6 months associated with buprenorphine use (AOR, 0.50; 95% CI, 0.27-0.95) but not with methadone use (AOR, 0.57; 95% CI, 0.28-1.17). Conclusions and Relevance: In this cohort study of individuals with an OUD-related hospital visit, initiation of MOUD was associated with reduced odds of opioid-related overdose at 6 months. Hospitals should consider implementing programs and protocols to offer initiation of MOUD to patients with OUD who present for care.

Association of Household Opioid Availability With Opioid Overdose.

Importance: Previous studies that examined the role of household opioid prescriptions in opioid overdose risk were limited to commercial claims, did not include fatal overdoses, and had limited inclusion of household prescription characteristics. Broader research is needed to expand understanding of the risk of overdose. Objective: To assess the role of household opioid availability and other household prescription factors associated with individuals' odds of fatal or nonfatal opioid overdose. Design, Setting, and Participants: A retrospective cohort study assessing patient outcomes from January 1, 2015, through December 31, 2018, was conducted on adults in the Oregon Comprehensive Opioid Risk Registry database in households of at least 2 members. Data analysis was performed between October 16, 2020, and January 26, 2023. Exposures: Household opioid prescription availability and household prescription characteristics. Main Outcomes and Measures: Opioid overdoses were captured from insurance claims, death records, and hospital discharge data. Household opioid prescription availability and prescription characteristics for individuals and households were modeled as 6-month cumulative time-dependent measures, updated monthly. To assess the association between household prescription availability, household prescription characteristics, and overdose, multilevel logistic regression models were developed, adjusting for demographic, clinical, household, and prescription characteristics. Results: The sample included 1 691 856 individuals in 1 187 140 households, of which most were women (53.2%), White race (70.7%), living in metropolitan areas (75.8%), and having commercial insurance (51.8%), no Elixhauser comorbidities (69.5%), and no opioid prescription fills in the study period (57.0%). A total of 28 747 opioid overdose events were observed during the study period (0.0526 per 100 person-months). Relative to individuals without personal or household opioid fills, the odds of opioid-related overdose increased by 60% when another household member had an opioid fill in the past 6 months (adjusted odds ratio [aOR], 1.60; 95% CI, 1.54-1.66) and were highest when both the individual and another household member had opioid fills in the preceding 6 months (aOR, 6.25; 95% CI, 6.09-6.40). Conclusions and Relevance: In this cohort study of adult Oregon residents in households of at least 2 members, the findings suggest that household prescription availability is associated with increased odds of opioid overdose for others in the household, even if they do not have their own opioid prescription. These findings underscore the importance of educating patients about proper opioid disposal and the risks of household opioids.

Accuracy of behavioral health variables in Oregon national violent death reporting system data: a linked cohort study.

BACKGROUND: The National Violent Death Reporting System (NVDRS) collects data on the circumstances of violent deaths, and all firearm-related deaths, across states and territories in the USA. This surveillance system is critical to understanding patterns and risk factors for these fatalities, thereby informing targets for prevention. NVDRS variables include behavioral health conditions among decedents, but the validity of the reported behavioral health data is unknown. Using Department of Veterans Affairs (VA) healthcare records as a criterion standard, we examined the accuracy of NVDRS-reported behavioral health variables for veteran decedents in a sample state (Oregon) between 2003 and 2017. METHODS: We linked Oregon NVDRS data to VA healthcare data to identify veteran decedents who used VA services within two years of death. Veterans' VA diagnoses within this time frame, including depression, post-traumatic stress disorder (PTSD), anxiety, and substance use disorders, were compared to behavioral health variables identified in the Oregon NVDRS. Concordance, sensitivity, and correlates of sensitivity were examined over time and by decedent characteristics. RESULTS: We identified 791 VA-using veterans with violent and/or firearm-related fatal injuries documented in the Oregon NVDRS between 2003 and 2017. In this cohort, the Oregon NVDRS accurately identified only 49% of decedents who were diagnosed with depression, 45% of those diagnosed with PTSD, and 17% of those diagnosed with anxiety by the VA. Among 211 veterans diagnosed by the VA with a substance use disorder, the Oregon NVDRS coded only 56% as having a substance use problem. In general, the sensitivity of behavioral health variables in the Oregon NVDRS remained the same or decreased over the study period; however, the sensitivity of PTSD diagnoses increased from 21% in 2003-2005 to 54% in 2015-2017. Sensitivity varied by some decedent characteristics, but not consistently across behavioral health variables. CONCLUSIONS: NVDRS data from one state missed more than half of behavioral health diagnoses among VA-using veterans who died from violence or from firearm injuries. This suggests that reports of behavioral health conditions among decedents nationally may be severely undercounted. Efforts to improve validity of these variables in state NVDRS data are needed.

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.

Use of prescription opioids before and after an operation for chronic pain (lumbar fusion surgery).

Lumbar fusion surgery is usually prompted by chronic back pain, and many patients receive long-term preoperative opioid analgesics. Many expect surgery to eliminate the need for opioids. We sought to determine what fraction of long-term preoperative opioid users discontinue or reduce dosage postoperatively; what fraction of patients with little preoperative use initiate long-term use; and what predicts long-term postoperative use. This retrospective cohort study included 2491 adults undergoing lumbar fusion surgery for degenerative conditions, using Oregon's prescription drug monitoring program to quantify opioid use before and after hospitalization. We defined long-term postoperative use as ≥4 prescriptions filled in the 7 months after hospitalization, with at least 3 occurring >30 days after hospitalization. Overall, 1045 patients received long-term opioids preoperatively, and 1094 postoperatively. Among long-term preoperative users, 77.1% continued long-term postoperative use, and 13.8% had episodic use. Only 9.1% discontinued or had short-term postoperative use. Among preoperative users, 34.4% received a lower dose postoperatively, but 44.8% received a higher long-term dose. Among patients with no preoperative opioids, 12.8% became long-term users. In multivariable models, the strongest predictor of long-term postoperative use was cumulative preoperative opioid dose (odds ratio of 15.47 [95% confidence interval 8.53-28.06] in the highest quartile). Cumulative dose and number of opioid prescribers in the 30-day postoperative period were also associated with long-term use. Thus, lumbar fusion surgery infrequently eliminated long-term opioid use. Opioid-naive patients had a substantial risk of initiating long-term use. Patients should have realistic expectations regarding opioid use after lumbar fusion surgery.

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.

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.

Measures such as interstate cooperation would improve the efficacy of programs to track controlled drug prescriptions.

In response to increasing abuse of prescription drugs, forty-four states have implemented--and five more states will soon adopt--monitoring programs to track prescriptions of controlled medications. Although these programs were originally designed to help law enforcement officials and regulatory agencies spot possible illegal activity, health care providers have begun to use data from them to help improve patient safety and quality of care. For this article we reviewed government documents, expert white papers, articles from the peer-reviewed medical literature, and reports of the experiences of local health officials. We found some evidence that prescription drug monitoring programs are a benefit to both law enforcement and health care delivery. However, the programs have strengths and weaknesses, and their overall impact on drug abuse and illegal activity remains unclear. We believe that improving the efficacy of prescription drug monitoring programs will require such changes as more standardization and interstate cooperation, better training of providers, more secure funding, and further evaluation.