Abstract
The COVID-19 pandemic of 2019 and 2020 has brought to a standstill normal life and commerce in many parts of the world. This has left policymakers challenged to determine when and how to return workers to the workforce while also balancing critical public health issues and minimizing additional harm. A number of recent proposals have suggested using risk stratification strategies to classify members of the public into “higher” and “lower” risk tiers. This article describes practical methods for creating risk stratifications using currently available information which can be updated over time. In this context, we discuss three key observations: (1) Any policy to risk stratify individuals involves implicit or explicit trade-offs (e.g.,harm from morbidity/mortality vs. harm from economic impact on health due to selfisolation), regardless of whether risk is measured using diagnostic tests, predictive models, or expert judgement. (2) Policies can be designed to minimize the harm caused by such trade-offs and failing to do so will directly result in unnecessary harm for the population. (3) Policies can be designed to take advantage of whatever information is available at a given time, and can incorporate different testing protocols within a single framework (e.g., virology tests where they are available, and age-based criteria when they are not). We demonstrate each of these points. We provide a set of simple calculation templates that policymakers can use to discuss risk stratification policies with the objective of developing Total Harm Minimization policies. We have also provided a web-based tool-set that implements these methods, and which is available publicly. Using these approaches, for example, a policymaker can input assumptions about risk stratification needs and community priorities, and get as output a risk stratification policy that minimized health and economic harm, given the community priorities. This allows policymakers to assess the advisability of a range of important options for bringing communities back on-line, as well as for making decisions, such as whether to invest in more accurate tests or gather more detailed data.
Full papaer available for download here: Drawing the line for risk stratifications: Designing return-to-work policies that consider diagnostic error, costs, benefits and COVID-19