Effect of integration of prescription drug monitoring program data in the electronic health record on queries by primary care providers.

Objective: To evaluate the impact of PDMP integration in the EHR on provider query rates within twelve primary care clinics in one academic medical center. Methods: Using linked data from the EHR and state PDMP program, we evaluated changes in PDMP query rates using a stepped-wedge observational design where integration was implemented in three waves (four clinics per wave) over a five-month period (May, July, September 2019). Multivariable negative binomial general estimating equations (GEE) models assessed changes in PDMP query rates, overall and across several provider and clinic-level subgroups. Results: Among 206 providers in PDMP integrated clinics, the average number of queries per provider per month increased significantly from 1.43 (95% CI 1.07 - 1.91) pre-integration to 3.94 (95% CI 2.96 - 5.24) post-integration, a 2.74-fold increase (95% CI 2.11 to 3.59; p < .0001). Those in the lowest quartile of PDMP use pre-integration increased 36.8-fold (95% CI 16.91 - 79.95) after integration, significantly more than other pre-integration PDMP use quartiles. Conclusions: Integration of the PDMP in the EHR significantly increased the use of the PDMP overall and across all studied subgroups. PDMP use increased to a greater degree among providers with lower PDMP use pre-integration.

Closing Gaps in Public Services for US Residents With Intellectual and Developmental Disabilities.

This Viewpoint describes existing public health and social service systems for persons with intellectual and developmental disabilities as they transition to adult care, barriers and opportunities faced in service access, and potential actions to narrow these gaps and enhance equity.

The association between community social vulnerability and prescription opioid availability with individual opioid overdose.

BACKGROUND: This study aims to assess the association of community social vulnerability and community prescription opioid availability with individual non-fatal or fatal opioid overdose. METHODS: We identified patients 12 years of age or older from the Oregon All Payer Claims database (APCD) linked to other public health datasets. Community-level characteristics were captured in an exposure period (EP) (1/1/2018-12/31/2018) and included: census tract-level social vulnerability domains (socio-economic status, household composition, racial and ethnic minority status, and housing type and transportation), census tract-level prescriptions and community-level opioid use disorder (OUD) diagnoses per 100 capita binned into quartiles or quintiles. We employed Cox models to estimate the risk of fatal and non-fatal opioid overdoses events in the 12 months following the EP. MAIN FINDINGS: We identified 1,548,252 individuals. Patients were mostly female (54%), White (61%), commercially insured (54%), and lived in metropolitan areas (81%). Of the total sample, 2485 (0.2%) experienced a non-fatal opioid overdose and 297 died of opioid overdose. There was higher hazard for non-fatal overdose in communities with greater OUD per 100 capita. We also found higher non-fatal and fatal hazards for opioid overdose among patients in communities with higher housing type and transportation-related vulnerability compared to the lowest quintile. Conversely, patients were at less risk of opioid overdose when living in communities with greater prevalence of the young or the elderly, the disabled, single parent families or low English proficiency. CONCLUSION: These findings underscore the importance of the environmental context when considering public health policies to reduce opioid harms.

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.

Opioid-related overdose and chronic use following an initial prescription of hydrocodone versus oxycodone.

BACKGROUND: Hydrocodone and oxycodone are prescribed commonly to treat pain. However, differences in risk of opioid-related adverse outcomes after an initial prescription are unknown. This study aims to determine the risk of opioid-related adverse events, defined as either chronic use or opioid overdose, following a first prescription of hydrocodone or oxycodone to opioid naïve patients. METHODS: A retrospective analysis of multiple linked public health datasets in the state of Oregon. Adult patients ages 18 and older who a) received an initial prescription for oxycodone or hydrocodone between 2015-2017 and b) had no opioid prescriptions or opioid-related hospitalizations or emergency department visits in the year preceding the prescription were followed through the end of 2018. First-year chronic opioid use was defined as ≥6 opioid prescriptions (including index) and average ≤30 days uncovered between prescriptions. Fatal or non-fatal opioid overdose was indicated from insurance claims, hospital discharge data or vital records. RESULTS: After index prescription, 2.8% (n = 14,458) of individuals developed chronic use and 0.3% (n = 1,480) experienced overdose. After adjustment for patient and index prescription characteristics, patients receiving oxycodone had lower odds of developing chronic use relative to patients receiving hydrocodone (adjusted odds ratio = 0.95, 95% confidence interval (CI) 0.91-1.00) but a higher risk of overdose (adjusted hazard ratio (aHR) = 1.65, 95% CI 1.45-1.87). Oxycodone monotherapy appears to greatly increase the hazard of opioid overdose (aHR 2.18, 95% CI 1.86-2.57) compared with hydrocodone with acetaminophen. Oxycodone combined with acetaminophen also shows a significant increase (aHR 1.26, 95% CI 1.06-1.50), but not to the same extent. CONCLUSIONS: Among previously opioid-naïve patients, the risk of developing chronic use was slightly higher with hydrocodone, whereas the risk of overdose was higher after oxycodone, in combination with acetaminophen or monotherapy. With a goal of reducing overdose-related deaths, hydrocodone may be the favorable agent.

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.

COVID-19-related adaptations to the implementation and evaluation of a clinic-based intervention designed to improve opioid safety.

The United States faces an opioid crisis with an unprecedented and increasing death rate from opioid overdose. Successfully reducing the rates of opioid use disorder (OUD) and overdose will require the engagement of frontline clinicians to prescribe opioids more safely and to build their capacity to treat patients with OUD using evidence-based approaches. The COVID-19 pandemic has created significant challenges for patients, clinicians and health systems and has been associated with increasing risks of overdoses and deaths. Herein, we review a multidisciplinary project designed to implement and evaluate clinic-based interventions in Oregon, USA, to improve pain management, opioid prescribing and treatment of OUD. The intervention, called Improving PaIn aNd OPiOId MaNagemenT in Primary Care (PINPOINT), combines practice facilitation, academic detailing and education through the Oregon ECHO Network. Implementation of PINPOINT has occurred across the Oregon Rural Practice-based Research Network and has involved 49 clinic sites to date. To evaluate the impact of the intervention, the research team created the Provider Results of Opioid Management and Prescribing Training (PROMPT), a dataset that links information from the state prescription drug monitoring program, all-payer claims database, emergency medical services, vital records and substance use disorder treatment system. The PROMPT dataset will allow evaluation of the impact of the intervention at both the clinician and clinic levels. Due to the constraints of the COVID-19 pandemic, elements of both implementation and evaluation required significant adaptations to continue to meet the original project goals.

Linkage of public health and all payer claims data for population-level opioid research.

OBJECTIVE: Our objective is to describe how we combine, at an individual level, multiple administrative datasets to create a Comprehensive Opioid Risk Registry (CORR). The CORR will characterize the role that individual characteristics, household characteristics, and community characteristics have on an individual's risk of opioid use disorder or opioid overdose. DATA SOURCES: Study data sources include the voluntary Oregon All Payer Claims Database (APCD), American Community Survey Census Data, Oregon Death Certificate data, Oregon Hospital Discharge Data (HDD), and Oregon Prescription Drug Monitoring (PDMP) Data in 2013-2018. STUDY DESIGN: To create the CORR we first prepared the APCD data set by cleaning and geocoding addresses, creating a community grouper and adding census indices, creating household grouper, and imputing patient race. Then we deployed a probabilistic linkage methodology to incorporate other data sources maintaining compliance with strict data governance regulations. DATA COLLECTION/EXTRACTION METHODS: Administrative datasets were obtained through an executed data use agreement with each data owner. The APCD served as the population universe to which all other data sources were linked. PRINCIPAL FINDINGS: There were 3 628 992 unique people in the APCD over the entire study period. We identified 968 767 unique households in 2013 and 1 209 236 in 2018, and geocoded patient addresses representing all census tracts in Oregon. Census, death certificate, HDD, and PDMP datasets were successfully linked to this population universe. CONCLUSIONS: This methodology can be replicated in other states and may also apply to a broad array of health services research topics.

The Impact of Social and Clinical Complexity on Diabetes Control Measures.

PURPOSE: In an age of value-based payment, primary care providers are increasingly scrutinized on performance metrics that assess quality of care, including the outcomes of their patient population in key areas such as diabetes control. Although such measures often adjust for patient clinical risk factors or clinical complexity, most do not account for the social complexity of patient populations, despite research demonstrating the strong association between social factors and health. METHODS: Using patient electronic health record data from 2 large community health center networks serving safety net patients, we assessed the effect of both clinical and social risk factors on poor glucose control among diabetics. Logistic regression results were used to estimate the impact of adjusting for both clinical and social complexity on provider performance metrics. Clinical complexity was measured at the patient-level using the Charlson Comorbidity Index. Social complexity was measured at the community-level using the Social Deprivation Index. RESULTS: Clinical complexity alone was not consistently associated with poor diabetes control (ie, HbA1c > 9%) in diabetic patients with HbA1c testing during the study period. However, increasing social complexity was significantly associated with higher rates of poor diabetic control in both cohorts. After adding adjustment for social complexity down to the national median score, our models suggest that approximately 25% of providers would have 1 to 2% improvement in the assessment of their diabetes control measures, with 45% showing a 2 to 5% improvement, and 5% showing more than a 5% improvement. CONCLUSIONS: Providers caring for patients with greater social risk factors may benefit from having their performance metrics adjusted for the social complexity of their patient populations.

Individual differences in cognitive control processes and their relationship to emotion regulation.

Cognitive control and emotional control share many similarities, but the specific relationship between these processes is not well understood. This study explored the relationship between three types of cognitive control (working memory updating, response inhibition and set-shifting) and two emotional regulation strategies (expressive suppression and cognitive reappraisal). Corrugator electromyography, behaviour and self-reports of affect were measured as indices of emotion regulation. Results indicate that working memory updating predicted negative affect reduction during reappraisal and during expressive suppression. This study specifically shows that the working memory component of cognitive control is associated with negative affect reduction. Response inhibition and set-shifting were not specifically related to negative affect reduction, but these variables did predict aspects of emotional behaviour and regulation. These results suggest a general role for cognitive control in some aspects of emotion regulation as well as a specific modulatory role for working memory updating in the regulation of negative affect.

Exploring the neurodevelopment of visual statistical learning using event-related brain potentials.

Implicit statistical learning (ISL) allows for the learning of environmental patterns and is thought to be important for many aspects of perception, cognition, and language development. However, very little is known about the development of the underlying neural mechanisms that support ISL. To explore the neurodevelopment of ISL, we investigated the event-related potential (ERP) correlates of learning in adults, older children (aged 9-12), and younger children (aged 6-9) using a novel predictor-target paradigm. In this task, which was a modification of the standard oddball paradigm, participants were instructed to view a serial input stream of visual stimuli and to respond with a button press when a particular target appeared. Unbeknownst to the participants, covert statistical probabilities were embedded in the task such that the target was predicted to varying degrees by different predictor stimuli. The results were similar across all three age groups: a P300 component that was elicited by the high predictor stimulus after sufficient exposure to the statistical probabilities. These neurophysiological findings provide evidence for developmental invariance in ISL, with adult-like competence reached by at least age 6.

Using dual-task methodology to dissociate automatic from nonautomatic processes involved in artificial grammar learning.

Previous studies have suggested that both automatic and intentional processes contribute to the learning of grammar and fragment knowledge in artificial grammar learning (AGL) tasks. To explore the relative contribution of automatic and intentional processes to knowledge gained in AGL, we utilized dual-task methodology to dissociate automatic and intentional grammar- and fragment-based knowledge in AGL at both acquisition and at test. Both experiments used a balanced chunk strength grammar to assure an equal proportion of fragment cues (i.e., chunks) in grammatical and nongrammatical test items. In Experiment 1, participants engaged in a working memory dual-task either during acquisition, test, or both acquisition and test. The results showed that participants performing the dual-task during acquisition learned the artificial grammar as well as the single-task group, presumably by relying on automatic learning mechanisms. A working memory dual-task at test resulted in attenuated grammar performance, suggesting a role for intentional processes for the expression of grammatical learning at test. Experiment 2 explored the importance of perceptual cues by changing letters between the acquisition and test phase; unlike Experiment 1, there was no significant learning of grammatical information for participants under dual-task conditions in Experiment 2, suggesting that intentional processing is necessary for successful acquisition and expression of grammar-based knowledge under transfer conditions. In sum, it appears that some aspects of learning in AGL are indeed relatively automatic, although the expression of grammatical information and the learning of grammatical patterns when perceptual similarity is eliminated both appear to require explicit resources.