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.

In this work we construct a mathematical model for the transmission and spread of coronavirus disease 2019 or COVID-19. Our model features delay terms to account for (a) the time lapse or latency period between contracting the disease and displaying symptoms, and (b) the time lag in testing patients for the virus due to the limited numbers of testing facilities currently available. We find that the delay introduces a significant disparity between the actual and reported time-trajectories of cases in a particular region. Specifically, the reported case histories lag the actual histories by a few days. Hence, to minimize the spread of the disease, lockdowns and similarly drastic social isolation measures need to be imposed some time before the reported figures are approaching their peak values. We then account for the social reality that lockdowns can only be of a limited duration in view of practical considerations. We find that the most effective interval for imposing such a limited-time lockdown is one where the midpoint of the lockdown period coincides with the actual peak of the spread of the disease in the absence of the lockdown. We further show that the true effectivity of imposing a lockdown may be misrepresented and grossly underestimated by the reported case trajectories in the days following the action.

The ongoing pandemic of COVID-19 is a threat to various routine healthcare services. India’s routine immunization (RI) campaign is one of largest ever known. In this review, we discuss the magnitude of disruption of RI activities due to COVID-19 pandemic, various causes of it and recommend ways to reduce the disruptions. Prominent literature databases were searched till April 30, 2021 for articles reporting disruptions of RI due to COVID-19. One study from India and numerous from outside India reported significant declines in the vaccine coverage rates during the lockdown period, which ranged from March 2020 till August 2020 in different regions of the world. Some reported disruptions for all vaccines, while a few reported sparing of birth doses. Shortage of healthcare workers due for them being diverted to patient care services and their reduced movement due to lockdowns and non-availability of public transport were prominent causes. Parents avoided RI sessions as they feared them or their children getting infected. They also faced travel restrictions, just like the healthcare workers. Children of school entry age and those from poorer socio-demographic profile appeared to miss the doses more frequently. Ministry of Health and Family Welfare, India has issued guidelines for conducting fixed and outreach RI sessions while following COVID-appropriate behavior. Promptly identifying missed out children and scheduling catch-up sessions is required to sustain the gains made over the decades by the immunization program of India.

In this work we propose the retarded logistic equation as a dynamic model for the spread of COVID-19 all over the world. This equation accounts for asymptomatic transmission, pre-symptomatic or latent transmission as well as contact tracing and isolation, and leads to a transparent definition of the instantaneous reproduction number R. For different parameter values, the model equation admits different classes of solutions. These solution classes correspond to, inter alia, containment of the outbreak via public health measures, exponential growth despite public health measures, containment despite reopening and second wave following reopening. We believe that the spread of COVID in every localized area such as a city, district or county can be accounted for by one of our solution classes. In regions where R > 1 initially despite aggressive epidemic management efforts, we find that if the mitigation measures are sustained, then it is still possible for R to dip below unity when far less than the region's entire population is affected, and from that point onwards the outbreak can be driven to extinction in time. We call this phenomenon partial herd immunity. Our analysis indicates that COVID-19 is an extremely vicious and unpredictable disease which poses unique challenges for public health authorities, on account of which case races among various countries and states do not serve any purpose and present delusive appearances while ignoring significant determinants.