Association of opioid or other substance use disorders with health care use among patients with suicidal symptoms.

INTRODUCTION: Prior literature establishes noteworthy relationships between suicidal symptoms and substance use disorders (SUDs), particularly opioid use disorder (OUD). However, engagement with health care services among this vulnerable population remains underinvestigated. This study sought to examine patterns of health care use, identify risk factors in seeking treatment, and assess associations between outpatient service use and emergency department (ED) visits. METHODS: Using electronic health records (EHRs) derived from five health systems across New York City, the study selected 7881 adults with suicidal symptoms (including suicidal ideation, suicide attempt, or self-harm) and SUDs between 2010 and 2019. To examine the association between SUDs (including OUD) and all-cause service use (outpatient, inpatient, and ED), we performed quasi-Poisson regressions adjusted for age, gender, and chronic disease burden, and we estimated the relative risks (RR) of associated factors. Next, the study evaluated cause-specific utilization within each resource category (SUD-related, suicide-related, and other-psychiatric) and compared them using Mann-Whitney U tests. Finally, we used adjusted quasi-Poisson regression models to analyze the association between outpatient and ED utilization among different risk groups. RESULTS: Among patients with suicidal symptoms and SUD diagnoses, relative to other SUDs, a diagnosis of OUD was associated with higher all-cause outpatient visits (RR: 1.22), ED visits (RR: 1.54), and inpatient hospitalizations (RR: 1.67) (ps < 0.001). Men had a lower risk of having outpatient visits (RR: 0.80) and inpatient hospitalizations (RR: 0.90), and older age protected against ED visits (RR range: 0.59-0.69) (ps < 0.001). OUD was associated with increased SUD-related encounters across all settings, and increased suicide-related ED visits and inpatient hospitalizations (p < 0.001). Individuals with more mental health outpatient visits were less likely to have suicide-related ED visits (RR: 0.86, p < 0.01), however this association was not found among younger and male patients with OUD. Although few OUD patients received medications for OUD (MOUD) treatment (9.9 %), methadone composed the majority of MOUD prescriptions (77.7 %), of which over 70 % were prescribed during an ED encounter. CONCLUSIONS: This study reinforces the importance of tailoring SUD and suicide risk interventions to different age groups and types of SUDs, and highlights missed opportunities for deploying screening and prevention resources among the male and OUD populations. Redressing underutilization of MOUD remains a priority to reduce acute health outcomes among younger patients with OUD.

Social connectedness as a determinant of mental health: A scoping review.

Public health and epidemiologic research have established that social connectedness promotes overall health. Yet there have been no recent reviews of findings from research examining social connectedness as a determinant of mental health. The goal of this review was to evaluate recent longitudinal research probing the effects of social connectedness on depression and anxiety symptoms and diagnoses in the general population. A scoping review was performed of PubMed and PsychInfo databases from January 2015 to December 2021 following PRISMA-ScR guidelines using a defined search strategy. The search yielded 66 unique studies. In research with other than pregnant women, 83% (19 of 23) studies reported that social support benefited symptoms of depression with the remaining 17% (5 of 23) reporting minimal or no evidence that lower levels of social support predict depression at follow-up. In research with pregnant women, 83% (24 of 29 studies) found that low social support increased postpartum depressive symptoms. Among 8 of 9 studies that focused on loneliness, feeling lonely at baseline was related to adverse outcomes at follow-up including higher risks of major depressive disorder, depressive symptom severity, generalized anxiety disorder, and lower levels of physical activity. In 5 of 8 reports, smaller social network size predicted depressive symptoms or disorder at follow-up. In summary, most recent relevant longitudinal studies have demonstrated that social connectedness protects adults in the general population from depressive symptoms and disorders. The results, which were largely consistent across settings, exposure measures, and populations, support efforts to improve clinical detection of high-risk patients, including adults with low social support and elevated loneliness.

Recommender system of scholarly papers using public datasets.

The exponential growth of public datasets in the era of Big Data demands new solutions for making these resources findable and reusable. Therefore, a scholarly recommender system for public datasets is an important tool in the field of information filtering. It will aid scholars in identifying prior and related literature to datasets, saving their time, as well as enhance the datasets reusability. In this work, we developed a scholarly recommendation system that recommends research-papers, from PubMed, relevant to public datasets, from Gene Expression Omnibus (GEO). Different techniques for representing textual data are employed and compared in this work. Our results show that term-frequency based methods (BM25 and TF-IDF) outperformed all others including popular Natural Language Processing embedding models such as doc2vec, ELMo and BERT.

Extracting social determinants of health from electronic health records using natural language processing: a systematic review.

OBJECTIVE: Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs. MATERIALS AND METHODS: A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review. RESULTS: Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9). CONCLUSION: NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.