COVID-19 pandemic and reasons to prioritize the needs of the health care system to ensure its sustainability: A scoping review from January to October 2020 (Review)

Nutrition policy critical to optimize response to climate, public health crises

The effects of unanticipated crises on health care and first-responder systems are reflected in climate-fueled environmental emergencies, to which human resilience is diminished by our chronic disease epidemic. For example, people who depend on specialized medications, like refrigerated insulin for diabetes, will likely face additional challenges in receiving treatment and care during extreme heat, floods, disasters, and other adverse events. These circumstances may be compounded by staff and equipment shortages, lack of access to fresh food, and inadequate healthcare infrastructure in the wake of a disaster. Simply put, our health care and first-response systems struggle to meet the demands of chronic disease without such crises and may be fundamentally unable to adequately function with such crises present. However, nutrition's primacy in preventing and controlling chronic disease directly enhances individual and public resilience in the face of existential threats. Highlighting the shared diet-related etiology clearly demonstrates the need for a national policy response to reduce the disease burden and potentiate mitigation of the sequelae of climate risks and capacity limits in our food and health care systems. Accordingly, this article proposes four criteria for nutrition policy in the Anthropocene: objective government nutrition recommendations, healthy dietary patterns, adequate nutrition security, and effective nutrition education. Application of such criteria shows strong potential to improve our resiliency despite the climate and public health crises.

Ocular trauma, visual acuity related to time of referral and psychosocial determinants, during COVID‑19 pandemic: A prospective study

The aim of the present study was to explore the associations between visual outcomes of ocular injury patients in a tertiary hospital unit with clinical and demographic variables and to evaluate the psychosocial impact of the injury on the patients. An 18-month prospective study of 30 eye-injured adult patients was conducted in the General University Hospital of Heraklion, Crete, a tertiary referral hospital. All severe eye injury case information was prospectively collected between February 1, 2020 and August 31, 2021. Best corrected visual acuity (BCVA) was labelled not poor (>0.5/10 or >20/400 on the Snellen scale, <1.3 in LogMAR scale) and poor (≤0.5/10 or ≤20/400 on the Snellen scale, ≥1.3 on the LogMAR equivalent). Data regarding participants' perceived stress levels, by using Perceived Stress Scale 14 (PSS-14), were collected prospectively, one year after study end. Out of 30 ocular injury patients selected, 76.7% were men and most of them were self-employed and private or public sector workers (36.7%). Not poor final BCVA was related to not poor initial BCVA [odds ratio (OR) 1.714; P=0.006]. No statistical associations were found between visual outcome and demographic or clinical factors, but not poor final BCVA was associated with improved self-reported psychological condition of the sufferers, as examined by a questionnaire sheet developed to collect information for study purpose (8.36/10 vs. 6.40/10; P=0.011). No patient reported job loss or changed work status following the injury. Not poor initial BCVA was a significant predictor for not poor final visual outcomes (OR 1.714; P=0.006). Patients with not poor final BCVA expressed higher levels of positive psychology (8.36/10 vs. 6.40/10; P=0.011) and less fear of eye injury repetition (64.0 vs. 100.0%; P=0.286). Not poor final BCVA was associated with low PSS-14 scores one year after study end (77.3 vs. 0.0%, P=0.003). Collaboration between ophthalmologists, mental health professionals and primary care team may be important in order to help patients to cope with the psychosocial burden sequel to eye trauma.

Smart Healthcare System for Severity Prediction and Critical Tasks Management of COVID-19 Patients in IoT-Fog Computing Environments

COVID-19 has depleted healthcare systems around the world. Extreme conditions must be defined as soon as possible so that services and treatment can be deployed and intensified. Many biomarkers are being investigated in order to track the patient's condition. Unfortunately, this may interfere with the symptoms of other diseases, making it more difficult for a specialist to diagnose or predict the severity level of the case. This research develops a Smart Healthcare System for Severity Prediction and Critical Tasks Management (SHSSP-CTM) for COVID-19 patients. On the one hand, a machine learning (ML) model is projected to predict the severity of COVID-19 disease. On the other hand, a multi-agent system is proposed to prioritize patients according to the seriousness of the COVID-19 condition and then provide complete network management from the edge to the cloud. Clinical data, including Internet of Medical Things (IoMT) sensors and Electronic Health Record (EHR) data of 78 patients from one hospital in the Wasit Governorate, Iraq, were used in this study. Different data sources are fused to generate new feature pattern. Also, data mining techniques such as normalization and feature selection are applied. Two models, specifically logistic regression (LR) and random forest (RF), are used as baseline severity predictive models. A multi-agent algorithm (MAA), consisting of a personal agent (PA) and fog node agent (FNA), is used to control the prioritization process of COVID-19 patients. The highest prediction result is achieved based on data fusion and selected features, where all examined classifiers observe a significant increase in accuracy. Furthermore, compared with state-of-the-art methods, the RF model showed a high and balanced prediction performance with 86% accuracy, 85.7% F-score, 87.2% precision, and 86% recall. In addition, as compared to the cloud, the MAA showed very significant performance where the resource usage was 66% in the proposed model and 34% in the traditional cloud, the delay was 19% in the proposed model and 81% in the cloud, and the consumed energy was 31% in proposed model and 69% in the cloud. The findings of this study will allow for the early detection of three severity cases, lowering mortality rates.