COVID-19 and the Health Workforce

The health workforce has been greatly affected by COVID-19. In this commentary, we describe the articles included in this health workforce research supplement and how the issues raised by the authors relate to the COVID-19 pandemic and rapidly changing health care environment

Ensuring and sustaining a pandemic workforce

Current efforts to fight the Covid-19 pandemic aim to slow viral spread and increase testing, protect health care workers from infection, and obtain ventilators and other equipment to prepare for a surge of critically ill patients. But additional actions are needed to rapidly increase health workforce capacity and to replenish it when personnel are quarantined or need time off to rest or care for sick family members. It seems clear that health care delivery organizations, educators, and government leaders will all have to be willing to cut through bureaucratic barriers and adapt regulations to rapidly expand the U.S. health care workforce and sustain it for the duration of the pandemic

Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images.

Assessing the degree of disease severity in biomedical images is a task similar to standard classification but constrained by an underlying structure in the label space. Such a structure reflects the monotonic relationship between different disease grades. In this paper, we propose a straightforward approach to enforce this constraint for the task of predicting Diabetic Retinopathy (DR) severity from eye fundus images based on the well-known notion of Cost-Sensitive classification. We expand standard classification losses with an extra term that acts as a regularizer, imposing greater penalties on predicted grades when they are farther away from the true grade associated to a particular image. Furthermore, we show how to adapt our method to the modelling of label noise in each of the sub-problems associated to DR grading, an approach we refer to as Atomic Sub-Task modeling. This yields models that can implicitly take into account the inherent noise present in DR grade annotations. Our experimental analysis on several public datasets reveals that, when a standard Convolutional Neural Network is trained using this simple strategy, improvements of 3- 5% of quadratic-weighted kappa scores can be achieved at a negligible computational cost. Code to reproduce our results is released at github.com/agaldran/cost_sensitive_loss_classification

Summary and Recommendations from Working Group 1: model uncertainty representations in convection-permitting / shorter lead-time / limited-area ensembles

WG3 discussed both the pros and cons of existing schemes as Working group 1 considered the treatment of model uncertainty (MU) in high-resolution ensembles, at grid spacings of order 1-5 km. These systems are often run for regional weather forecasting, perhaps over a single country, and for lead times of up to 5 days. Looking ahead, ECMWF’s strategy seeks to deliver global medium-range ensemble forecasts with 3-4 km grid spacings by 2030. It is questionable for what grid spacing we should dispense with a deep convection parameterization, but it will be either switched off or damped in these systems, such that deep convection can be assumed to be dominated by explicit motions. One of the problems with limited-area ensemble systems at this scale is that spread depends not only on the modelling system itself but also on the variability inherited from the large-scale boundary conditions. There is often thought to be a lack of spread in our high-resolution EPS (ensemble prediction systems), but this could reflect a lack of diversity on larger scales. The relative importance of lateral-boundary diversity and the model uncertainty mechanisms is regime dependent. The lateral boundaries will generally be more important in midlatitude winter but less so for summertime convection in relatively weak synoptic flow

Climate impact on contaminant dispersion in the river basin of Göta Älv, Sweden

This study is the result of the DiPol project (Impact of climate change on the quality of urban and coastal waters) that was conducted 2009-2012 as part of the EU Interreg IVB North Sea Program. The overall aim with DiPol was to gather knowledge regarding how climate changes affect water quality. Additionally, to communicate such knowledge and increase awareness about climate changes among decision makers to be able to counteract the effects of climate changes both on the local and regional levels as well as to involve the general public in this work. Four study sites were choosen in Sweden, Norway, Denmark and Germany. This report covers the results from the Swedish study site; the river basin of Göta Älv. Based on samples from wet and dry periods as well as spring flood events, this study presents a possible relation between a short term increase in precipitation and enhanced contaminant transport in urban water courses of the Göta Älv river basin in Sweden. Elevated metal and PAH concentrations (polycyclic aromatic hydrocarbons) in the studied river system was linked to increased surface runoffs and shallow groundwater flows from urban areas, rather than re-suspension of river sediments. Based on future prediction of precipitation, also the climate change impact on ground water levels and river discharge are shown together with other consequences such as contaminant transport in rivers of this region. This study demonstrates an enhanced contaminant transport from urban areas to the river system due to increased precipitation and ground water levels

Modernizing Scope-of-Practice Regulations - Time to Prioritize Patients.

Ongoing payment reforms are pressing health systems to reorganize delivery of care to achieve greater value, improve access, integrate patient care among settings, advance population health, and address social determinants of health. Many organizations are experimenting with new ways of unleashing their workforce’s potential by using telehealth and various forms of digital technology and developing team- and community-based delivery models. Such approaches require reconfiguring of provider roles, but states and health care organizations often place restrictions on health professionals’ scope of practice that limit their flexibilit