Negative Urgency Linked to Craving and Substance Use Among Adults on Buprenorphine or Methadone.

Despite the effectiveness of medication-assisted treatment (MAT), adults receiving MAT experience opioid cravings and engage in non-opioid illicit substance use that increases the risk of relapse and overdose. The current study examines whether negative urgency, defined as the tendency to act impulsively in response to intense negative emotion, is a risk factor for opioid cravings and non-opioid illicit substance use. Fifty-eight adults (predominately White cis-gender females) receiving MAT (with buprenorphine or methadone) were recruited from online substance use forums and asked to complete self-report questionnaires on negative urgency (UPPS-P Impulsive Behavior Scale), past 3-month opioid cravings (ASSIST-Alcohol, Smoking, and Substance Involvement Screening Test), and non-opioid illicit substance use (e.g., amphetamines, cocaine, benzodiazepines). Results revealed that negative urgency was associated with past 3-month opioid cravings, as well as past month illicit stimulant use (not benzodiazepine use). These results may indicate that individuals high in negative urgency would benefit from receiving extra intervention during MAT.

Clinical risk prediction using language models: benefits and considerations.

OBJECTIVE: The use of electronic health records (EHRs) for clinical risk prediction is on the rise. However, in many practical settings, the limited availability of task-specific EHR data can restrict the application of standard machine learning pipelines. In this study, we investigate the potential of leveraging language models (LMs) as a means to incorporate supplementary domain knowledge for improving the performance of various EHR-based risk prediction tasks. METHODS: We propose two novel LM-based methods, namely "LLaMA2-EHR" and "Sent-e-Med." Our focus is on utilizing the textual descriptions within structured EHRs to make risk predictions about future diagnoses. We conduct a comprehensive comparison with previous approaches across various data types and sizes. RESULTS: Experiments across 6 different methods and 3 separate risk prediction tasks reveal that employing LMs to represent structured EHRs, such as diagnostic histories, results in significant performance improvements when evaluated using standard metrics such as area under the receiver operating characteristic (ROC) curve and precision-recall (PR) curve. Additionally, they offer benefits such as few-shot learning, the ability to handle previously unseen medical concepts, and adaptability to various medical vocabularies. However, it is noteworthy that outcomes may exhibit sensitivity to a specific prompt. CONCLUSION: LMs encompass extensive embedded knowledge, making them valuable for the analysis of EHRs in the context of risk prediction. Nevertheless, it is important to exercise caution in their application, as ongoing safety concerns related to LMs persist and require continuous consideration.

Exploring county-level spatio-temporal patterns in opioid overdose related emergency department visits.

Opioid overdoses within the United States continue to rise and have been negatively impacting the social and economic status of the country. In order to effectively allocate resources and identify policy solutions to reduce the number of overdoses, it is important to understand the geographical differences in opioid overdose rates and their causes. In this study, we utilized data on emergency department opioid overdose (EDOOD) visits to explore the county-level spatio-temporal distribution of opioid overdose rates within the state of Virginia and their association with aggregate socio-ecological factors. The analyses were performed using a combination of techniques including Moran's I and multilevel modeling. Using data from 2016-2021, we found that Virginia counties had notable differences in their EDOOD visit rates with significant neighborhood-level associations: many counties in the southwestern region were consistently identified as the hotspots (areas with a higher concentration of EDOOD visits) whereas many counties in the northern region were consistently identified as the coldspots (areas with a lower concentration of EDOOD visits). In most Virginia counties, EDOOD visit rates declined from 2017 to 2018. In more recent years (since 2019), the visit rates showed an increasing trend. The multilevel modeling revealed that the change in clinical care factors (i.e., access to care and quality of care) and socio-economic factors (i.e., levels of education, employment, income, family and social support, and community safety) were significantly associated with the change in the EDOOD visit rates. The findings from this study have the potential to assist policymakers in proper resource planning thereby improving health outcomes.