May 19, 2020 by Emsi Burning Glass
Emsi has developed a Health Risk Index in order to better understand where and why COVID-19 has spread, and to help policymakers create localized responses to this (or any future) virus.
Why are some cities hit hard while others escape relatively unscathed? How can leaders help protect their communities, based on unique, local risks? These are questions the Health Risk Index helps answer.
Using the social determinants of health, the Health Risk Index gives us the unique, nuanced perspective we need to fight COVID-19 and get the economy back on its feet.
Consider the massive differences between East and West in the US. If we view the number of COVID cases per 100K people, the East Coast is clearly feeling greater impact. Incidence rates diminish in the western states.
Numbers are approximate, based on COVID-19 cases that are updated daily. Numbers are current as of May 13.
Now, East and West have responded with similar policies, but with very different results. Why?
This puzzling disparity convinced us that we must consider other factors. We found that the social determinants of health offer good clues and explanations.
The Health Risk Index features four key risk factors. These factors help explain why COVID-19 has had such a deadly impact in New York City and why other major cities (such as San Jose) have seen comparatively few cases.
In recent weeks, other researchers have explored such factors. Given that NYC became the American epicenter and that most of the cases across the nation are in more dense metro areas, many concluded that the driving factor was density.
But as Justin Fox recently argued, based on the difference between NYC and cities like San Francisco, density can’t be the only factor that determines where (and how hard) the virus will strike. Likewise, Harvard Political Review is urging people to “Stop Blaming Urban Density for Coronavirus.” Richard Florida agrees. Population density is certainly a player, he observes, but it isn’t the only player.
Along these same lines, PolicyMap recently created an index (covered in the New York Times) that identifies counties with high rates of underlying health conditions. This provides a clue on how to understand (and anticipate) COVID’s seemingly erratic geographical pattern.
So Emsi combined density, health preconditions, workplace interaction, and overall population health (largely driven by age) to see what the data would reveal. We found that the combination of all four risk factors creates a highly accurate tool for predicting impact in terms of both cases and deaths.
Using these four risk factors, our Health Risk Index correctly predicts that NYC would have the most significant trouble with COVID-19, whereas the impact in cities like San Jose and even Orlando would be much less significant.
Once we can differentiate between likely hotspots for the virus (like New York City) and lower-risk areas (like San Jose), we can create corresponding strategies. Because the truth is, multiple factors have shaped the spread of the virus, and these factors vary from place to place. Not every region shares the same risks.
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