By Dr. Victor Collymore and David Hom

One of the keys to mitigating the effects and stopping the spread of a devastating disease such as COVID-19 is understanding which populations are most vulnerable and at-risk from exposure. Once those populations have been identified, healthcare professionals, governments and other organizations can direct their limited resources to where they are most needed – and will be most effective.

In the case of the COVID-19 pandemic, we understood almost from the beginning that the highest-risk populations were those with one or more pre-existing conditions – especially conditions that affected the lungs, such as chronic obstructive pulmonary disease (COPD), or the heart such as heart disease and chronic heart failure (CHF). Yet there is another, almost hidden population that is also at tremendous risk: those with social determinants of health (SDOH) challenges.

Their risk factors can take many forms: using the emergency department (ED) as their only access to the healthcare system, food scarcity (including lack of access to healthy foods), housing instability, lack of reliable transportation, undiagnosed depression or other behavioral health challenges that coincide with the foundational layer of Maslow’s Hierarchy of Needs.

It's easy to see why these immediate survival needs might override any abstract concerns over a global pandemic, which means that strategies that work for those who are not facing SDOH issues may not be as effective with those who experience these challenges. For example, it is difficult to “shelter-in-place” when you don’t have a permanent home. It is difficult to practice good hand hygiene when you don’t have easy access to soap and clean running water.

One of the outcomes of understanding how SDOH affects the way a virus such as COVID-19 spreads is the ability to identify, on a county-by-county basis, where the next potential hot spots may erupt. The analytics for this type of inquiry require two types of data.

The first is publicly available data from the Centers for Disease Control and Prevention (CDC) as well as other sources showing where COVID-19 is already present. While this data changes rapidly day-by-day, and sometimes hour-by-hour, it is effective in delivering an overall picture.

The second type of data combines information about risk factors for pre-existing conditions from proprietary databases with state-reported County Health Rankings and publicly available demographic and sociographic data.

By feeding the data into clinical analytics, and making the results available to experts who have actuarial expertise and deep experience with driving insights based on rich data assets, health plans and providers gain the means to speculate on how these combined factors may result in severe outcomes of a COVID-19 outbreak. They can then develop a heat map like this one that highlights the U.S. counties that are most prone to becoming the next hot spots.

With this information in-hand, health plans and providers in high-risk counties can work with community resources to prepare for a potential surge of patients in the near future. They can begin sourcing equipment and supplies, and develop multi-tiered strategies for deploying physicians, nurses, physician assistants and other clinicians needed to address increased patient loads. They will also be empowered to make better decisions that will help them manage the additional load more effectively.

With SDOH populations, however, it isn’t just about medical personnel and supplies. By understanding where the hot spots may emerge, health plans and providers can work with local resources to help mitigate social factors among high-risk populations before they hit critical mass, relieving pressure on the entire system.

The benefits go beyond the immediate crisis, however. The release of more data, such as mortality data from the CDC, will enable the data models to be further refined. Health plans and providers will be able to develop more precise personas and profiles to ensure that resources are directed where they are most needed.

The new data will also help demonstrate which levers that have already been pulled, such as closing businesses, social distancing, delivery of care in alternate settings and other options, had the greatest effect on slowing the spread. These insights will inform future decisions should a second surge take place. It may also have a significant impact on how healthcare overall is delivered (and paid for) in the future, such as increasing the use of telehealth.

Another consideration is what happens once the COVID-19 pandemic is behind us. With room to breathe, the resilient U.S. healthcare system will start focusing on its own recovery. Hopefully, more emphasis will be placed on gaining a greater understanding of how SDOH risk factors contribute to emerging clinical risk and affect health generally within the U.S.

If we can take the tough lessons we are learning about how SDOH factors affect health in the COVID-19 crucible, including studying outcomes so predictive analytics can be applied more precisely going forward, we will be able to improve how we treat patients with chronic conditions and/or SDOH issues. This will allow us to address the factors that made the current pandemic so devastating while helping us create a healthier America overall.

About the authors: Victor A. Collymore, MD, FACP is the vice president and chief medical officer at EXL Service, a multi-national company, where he oversees utilization management, coordinates disease and care management, and liaisons with pharmacy, sales and marketing, data and predictive modeling departments, and life sciences.

David Hom is the chief evangelist at EXL, and an internationally-recognized expert in the field of consumer engagement through programs such as Value Based Benefits and Employee Wellness.