Better Metrics to Guide Public Health Policy: Lessons Learned From COVID-19 for Data Systems Improvement

Abstract

As communities adjust COVID-19 policies, objective ‘metrics’ are needed to guide decisions. But metrics are only as good as their inputs. Most COVID-19 data are based on case surveillance. Individuals’ decisions to be tested depend on concerns about exposure and symptoms, as well as the availability of testing. These factors cause drop-offs at every step in the reporting process, so the numbers of actual cases, as well as deaths and hospitalizations, are far higher than reported, varying over time and among population subgroups.Metrics are used to compare among population groups and over time, so consistency in data systems is important. The Centers for Disease Control and Prevention (CDC) must provide leadership to standardize public health, hospital, and other data systems, not just case definitions. Public health emergencies are complex phenomena that cannot be summarized in a single indicator, so CDC should develop a balanced portfolio of metrics that together describe the epidemiologic situation and provide information to guide decision-making. Metrics are intended to inform—not decide—policy decisions that balance epidemiologic benefits and social and economic costs, taking into account the current state of the pandemic. Policymakers should avoid hard triggers and consider trends over weeks to months rather than daily numbers.Going forward, we must complement case counts with population estimation methods such as sampling, excess mortality, and wastewater surveillance. While ‘estimation’ sounds less precise than ‘counting,’ these methods can provide a more comprehensive and accurate assessment of the pandemic’s impact on different populations and as it changes over time.