Dataset Information

Enhancing research data infrastructure to address the opioid epidemic: the Opioid Overdose Network (O2-Net).

ABSTRACT: Opioid Overdose Network is an effort to generalize and adapt an existing research data network, the Accrual to Clinical Trials (ACT) Network, to support design of trials for survivors of opioid overdoses presenting to emergency departments (ED). Four institutions (Medical University of South Carolina [MUSC], Dartmouth Medical School [DMS], University of Kentucky [UK], and University of California San Diego [UCSD]) worked to adapt the ACT network. The approach that was taken to enhance the ACT network focused on 4 activities: cloning and extending the ACT infrastructure, developing an e-phenotype and corresponding registry, developing portable natural language processing tools to enhance data capture, and developing automated documentation templates to enhance extended data capture. Overall, initial results suggest that tailoring of existing multipurpose federated research networks to specific tasks is feasible; however, substantial efforts are required for coordination of the subnetwork and development of new tools for extension of available data. The initial output of the project was a new approach to decision support for the prescription of naloxone for home use in the ED, which is under further study within the network.

SUBMITTER: Lenert LA

PROVIDER: S-EPMC9243402 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Enhancing research data infrastructure to address the opioid epidemic: the Opioid Overdose Network (O2-Net).

Lenert Leslie A LA Zhu Vivienne V Jennings Lindsey L McCauley Jenna L JL Obeid Jihad S JS Ward Ralph R Hassanpour Saeed S Marsch Lisa A LA Hogarth Michael M Shipman Perry P Harris Daniel R DR Talbert Jeffery C JC

JAMIA open 20220630 2

Opioid Overdose Network is an effort to generalize and adapt an existing research data network, the Accrual to Clinical Trials (ACT) Network, to support design of trials for survivors of opioid overdoses presenting to emergency departments (ED). Four institutions (Medical University of South Carolina [MUSC], Dartmouth Medical School [DMS], University of Kentucky [UK], and University of California San Diego [UCSD]) worked to adapt the ACT network. The approach that was taken to enhance the ACT ne ...[more]

PMID: 35783072

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Project description:BackgroundDrug overdose mortality continues to increase, now driven by fentanyl. Prevention tools such as naloxone and medications to treat opioid use disorder are not sufficient to control overdose rates; additional strategies are urgently needed.ObjectiveWe sought to adapt a behavioral intervention to prevent opioid overdose (repeated-dose behavioral intervention to reduce opioid overdose [REBOOT]) that had been successfully piloted in San Francisco, California, United States, to the setting of Boston, Massachusetts, United States, and the era of fentanyl for a full efficacy trial.MethodsWe used the assessment, decision, adaptation, production, topical experts, integration, training, and testing (ADAPT-ITT) framework for intervention adaptation. We first identified opioid overdose survivors who were actively using opioids as the population of interest and REBOOT as the intervention to be adapted. We then performed theater testing and elicited feedback with 2 focus groups (n=10) in Boston in 2018. All participants had used opioids that were not prescribed to them in the past year and experienced an opioid overdose during their lifetime. We incorporated focus group findings into our initial draft of the adapted REBOOT intervention. The adapted intervention was reviewed by 3 topical experts, and their feedback was integrated into a subsequent draft. We trained study staff on the intervention and made final refinements based on internal piloting. This paper describes the overall ADAPT-ITT process for intervention adaptation, as well as a qualitative analysis of the focus groups. Working independently, 2 authors (VMM and JA) reviewed the focus group transcripts and coded them for salient and common themes using the constant comparison method, meeting to discuss any discrepancies until consensus was reached. Codes and themes were then mapped onto the REBOOT counseling steps.ResultsFocus group findings contributed to substantial changes in the counseling intervention to better address fentanyl overdose risk. Participants described the widespread prevalence of fentanyl and said that, although they tried to avoid it, avoidance was becoming impossible. Using alone and lower opioid tolerance were identified as contributors to overdose risk. Slow shots or tester shots were acceptable and considered effective to reduce risk. Naloxone was considered an effective reversal strategy. Although calling emergency services was not ruled out, participants described techniques to prevent the arrival of police on the scene. Expert review and internal piloting improved the intervention manual through increased participant centeredness, clarity, and usability.ConclusionsWe successfully completed the ADAPT-ITT approach for an overdose prevention intervention, using theater testing with people who use opioids to incorporate the perspectives of people who use drugs into a substance use intervention. In the current crisis, overdose prevention strategies must be adapted to the context of fentanyl, and innovative strategies must be deployed, including behavioral interventions.Trial registrationClinicalTrials.gov NCT03838510; https://clinicaltrials.gov/ct2/show/NCT03838510.

Project description:BackgroundGaps in electronic health record (EHR) data collection and the paucity of standardized clinical data elements (CDEs) captured from electronic and digital data sources have impeded research efforts aimed at understanding the epidemiology and quality of care for opioid use disorder (OUD). We identified existing CDEs and evaluated their validity and usability, which is required prior to infrastructure implementation within EHRs.MethodsWe conducted (a) a systematic literature review of publications in Medline, Embase and the Web of Science using a combination of at least one term related to OUD and EHR and (b) an environmental scan of publicly available data systems and dictionaries used in national informatics and quality measurement of policy initiatives. Opioid-related data elements identified within the environmental scan were compared with related data elements contained within nine common health data code systems and each element was graded for alignment with match results categorized as "exact", "partial", or "none."ResultsThe literature review identified 5186 articles for title search, of which 75 abstracts were included for review and 38 articles were selected for full-text review. Full-text articles yielded 237 CDEs, only 12 (5.06%) of which were opioid-specific. The environmental scan identified 379 potential data elements and value sets across 9 data systems and libraries, among which only 84 (22%) were opioid-specific. We found substantial variability in the types of clinical data elements with limited overlap and no single data system included CDEs across all major data element types such as substance use disorder, OUD, medication and mental health. Relative to common health data code systems, few data elements had an exact match (< 1%), while 61% had a partial match and 38% had no matches.ConclusionsDespite the increasing ubiquity of EHR data standards and national attention placed on the opioid epidemic, we found substantial fragmentation in the design and construction of OUD related CDEs and little OUD specific CDEs in existing data dictionaries, systems and literature. Given the significant gaps in data collection and reporting, future work should leverage existing structured data elements to create standard workflow processes to improve OUD data capture in EHR systems.

Project description:The US opioid overdose epidemic has been a major public health concern in recent decades. There has been increasing recognition that its etiology is rooted in part in the social contexts that mediate substance use and access; however, reliable statistical measures of social influence are lacking in the literature. We use Facebook's social connectedness index (SCI) as a proxy for real-life social networks across diverse spatial regions that help quantify social connectivity across different spatial units. This is a measure of the relative probability of connections between localities that offers a unique lens to understand the effects of social networks on health outcomes. We use SCI to develop a variable, called "deaths in social proximity", to measure the influence of social networks on opioid overdose deaths (OODs) in US counties. Our results show a statistically significant effect size for deaths in social proximity on OODs in counties in the United States, controlling for spatial proximity, as well as demographic and clinical covariates. The effect size of standardized deaths in social proximity in our cluster-robust linear regression model indicates that a one-standard-deviation increase, equal to 11.70 more deaths per 100,000 population in the social proximity of ego counties in the contiguous United States, is associated with thirteen more deaths per 100, 000 population in ego counties. To further validate our findings, we performed a series of robustness checks using a network autocorrelation model to account for social network effects, a spatial autocorrelation model to capture spatial dependencies, and a two-way fixed-effect model to control for unobserved spatial and time-invariant characteristics. These checks consistently provide statistically robust evidence of positive social influence on OODs in US counties. Our analysis provides a pathway for public health interventions informed by social network structures. The statistical robustness of our primary variable of interest, deaths in social proximity, supports the hypothesis of a social network effect on OODs. Using agent-based modeling (ABM) to simulate social networks can offer an effective method to design interventions that incorporate the dynamics of social networks for maximum impact.

Dataset Information

Enhancing research data infrastructure to address the opioid epidemic: the Opioid Overdose Network (O2-Net).

Publications

Enhancing research data infrastructure to address the opioid epidemic: the Opioid Overdose Network (O2-Net).

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