A prioritization model for COVID-19

April 22nd, 2020 / By L. Gordon Moore, MD

How might a health ministry, insurer, or health system use existing health information to inform a data-driven prioritization model in response to COVID-19?

In the movie Contagion, countries distributed the limited supply of vaccine by means of a lottery. Health authorities across the globe are faced with gaps between supplies and the demands created by COVID-19. What framework could help prioritize resource allocation better than the “when was your birthday” model from the movie?

Let’s say you’re in charge of health policy and social security for the nearly five million people in the Valencian Community of Spain and you have about 3.6 million masks available for distribution to the public (presuming the healthcare work force all have enough). 

Published (but mostly not yet peer reviewed, so take-with-a-grain-of-salt) studies tell us that those most at risk of poor prognosis should they contract the disease are: older people and those with diabetes, chronic heart, lung, kidney disease and a few other conditions.

Because you have administrative data (e.g. health insurance claims) you might use diagnosis codes to flag everyone with diabetes, but you’re aware that some people with diabetes are relatively healthy and others are desperately ill. In addition to the wide variation in severity of diabetes, you know that a person’s total burden of illness (and risk of poor prognosis) is predicted by co-morbidity: the number of chronic conditions, the severity of those conditions, the number of organ systems involved and more. 

Another concern is that the list of conditions predicting poor prognosis for people who contract COVID-19 is relatively short and clinicians suspect that other conditions are enough alike that they should be on the list of “if you have this underlying condition, your risk of poor prognosis is high.” If chronic obstructive lung disease puts a person at high risk, what about cystic fibrosis or systemic lupus erythematosus with lung manifestations?

A potentially more elegant framework would include age, a broader list of diagnoses and an indication of hierarchically ranked co-morbidity status. 3M Clinical Risk Groups (CRGs) is an example of a classification methodology capable of providing this level of information.

The Ministry of Universal Health and Public Health of the Valencian Community resolved on April 15, 2020 to use the 3M CRG methodology to prioritize mask distribution. Using CRGs, the Region of Valencia will be identifying the most vulnerable members of the population, who, if infected, would be at the highest risk of hospitalization, admission to ICU or in need for mechanical respirator. That is, all people over 65 years old and those citizens who are under 65 but with one of the following conditions: significant chronic disease in multiple organ systems (Health status 6), dominant chronic disease in three or more organ systems (Health status 7), malignancies under active treatment (Health status 8) or catastrophic conditions (Health status 9).

This is an example of using data to achieve a more scientific prioritization framework in addressing COVID-19. This framework might be useful for individuals who choose to extend their social distancing because of their high risk of poor prognosis. It may also be useful for those who should maybe not try to “ride it out” at home if they contract the disease.

L. Gordon Moore, MD, is Senior Medical Director, Clinical Strategy and Value-based Care for 3M Health Information Systems.

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During a pandemic, healthcare information is gathered, studied, and published rapidly by scientists, epidemiologists and public health experts without the usual processes of review. Our understanding is rapidly evolving and what we understand today will change over time. Definitive studies will be published long after the fact. 3M Inside Angle bloggers share our thoughts and expertise based on currently available information.