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.

Family and carer smoking control programmes for reducing children's exposure to environmental tobacco smoke.

Family and carer smoking control programmes for reducing children's exposure to environmental tobacco smoke.

A major obstacle of evidence-based clinical decision making is the use of nonstandardized, partly untested outcome measurement instruments. Core Outcome Sets (COSs) are currently developed in different medical fields to standardize and improve the selection of outcomes and outcome measurement instruments in clinical trials, in order to pool results of trials or to allow indirect comparison between interventions. A COS is an agreed minimum set of outcomes that should be measured and reported in all clinical trials of a specific disease or trial population. The international, multidisciplinary Cochrane Skin Group Core Outcome Set Initiative (CSG-COUSIN) aims to develop and implement COSs in dermatology, thus making trial evidence comparable and, herewith, more useful for clinical decision making. The inaugural meeting of CSG-COUSIN was held on 17-18 March 2015 in Dresden, Germany, as the exclusive theme of the Annual Cochrane Skin Group Meeting. In total, 29 individuals representing a broad mix of different stakeholder groups, professions, skills and perspectives attended. This report provides a description of existing COS initiatives in dermatology, highlights current methodological challenges in COS development, and presents the concept, aims and structure of CSG-COUSIN.

In this work we propose a delay differential equation as a lumped parameter or compartmental infectious disease model featuring high descriptive and predictive capability, extremely high adaptability and low computational requirement. Whereas the model has been developed in the context of COVID-19, it is general enough to be applicable mutatis mutandis to other diseases as well. Our fundamental modeling philosophy consists of a decoupling of public health intervention effects, immune response effects and intrinsic infection properties into separate terms. All parameters in the model are directly related to the disease and its management; we can measure or calculate their values a priori basis our knowledge of the phenomena involved, instead of having to extrapolate them from solution curves. Our model can accurately predict the effects of applying or withdrawing interventions, individually or in combination, and can quickly accommodate any newly released information regarding, for example, the infection properties and the immune response to an emerging infectious disease. After demonstrating that the baseline model can successfully explain the COVID-19 case trajectories observed all over the world, we systematically show how the model can be expanded to account for heterogeneous transmissibility, detailed contact tracing drives, mass testing endeavours and immune responses featuring different combinations of limited-time sterilizing immunity, severity-reducing immunity and antibody dependent enhancement.