by Jacob Luria

The COVID-19 Economy

Across the United States communities are starting to implement necessary COVID-19 mitigation strategies focused on social distancing that necessitate the closing of schools, bars, restaurants and the cancellation of events. When looking at the risk of COVID-19, there are the clear medical risk factors; old age, cardiovascular disease, smoking, travel history. But the social distancing measures may also exacerbate worsening gaps in medical outcomes caused by social, economic, and environmental determinants of health, SDOH.

Using a combination of public data and personalized SDOH modeling, healthcare organizations can identify the patients who may be most at risk as the economy grinds to a halt, and those who will need the most support in getting back on their feet when the rest of the world begins to recover.

Clinical resources will be pushed to near breaking points in the coming weeks driving by a combination of increased Medicaid (and highly subsidized Exchange enrollment) and reduced supply of clinical services at all levels leaving the social service infrastructure as the dominant method to engage with members and patients in the recovery period. Efficient delivery of non-clinical support will be of paramount importance, particularly as the shutdown measures stretch into months and health care workers slowly cycle back into standard practice.

At Risk Groups

Based on early published data, COVID-19 outcomes for older persons are significantly worse than for the general population. As shown in figure 1, the case fatality rate increases dramatically with age.

How many patients have the means to reach you on their own? How many patients have the means to reach you on their own?

The research has been conclusive about the impact of age on disease severity and death rate, but that is primarily because it is the easiest dimension to measure for clinical outcomes. Most epidemiologic models are currently examining the rate of spread based on our social behavior, but why haven’t we thought about the effects of socio-economic factors on those behaviors or on outcomes directly? Researchers aren’t equipped to study this today, but health systems can be.

Specifically, let’s consider neighborhoods where populations are disproportionately low-income or where there are heavy concentrations of workers in the food, hospitality, and travel sectors, all of which are shutting down to limit the spread of the virus.

  • Low income neighborhoods – measured income is a double-edged sword, as rates of diabetes, COPD and other chronic diseases are dramatically higher in low-income populations. Beyond the comorbidity implications, however, is the reality that people will be unable to seek routine care, unable to take time off of work when sick, and unable to provide for their families in the event of a disaster.

  • Industry focus – families receiving their income from the service industry will be hard hit over the coming months as eating out and traveling fall to record lows. Many organizations are trying to provide support where possible, but it may not be enough. While clinical outcomes related to the virus may not look different, these families will not be able to afford prescriptions, healthy food, or medical care until the world returns to normal. The effects of reduced income, high stress, and lack of care is likely to have impact on other health outcomes for years to come.

Each of these pockets represents significant long term risk that extends beyond even the immediate medical crisis. There is the possibility that these communities could face continued health burdens from more than COVID-19.

COVID-19 Economic Risk Factors COVID-19 Economic Risk Factors

Turning Data Into Action

There is a saying in statistics that there is no real average person. So, in thinking about the COVID-19 impacts, we must consider that there are real people behind each statistic we hear. The key for health organizations that manage these patients is to connect the trend to real people, and to identify how they can be most effectively helped.

Algorex Health assigns a suite of risk scores derived from extensively validated social determinant data that have tested and verified to capture and predict an individual’s real SDOH burden. In effect, it takes population health and turns it into individual action.

Algorex Health combines neighborhood level socio-economic factors with individual data to identify specific intervention pathways that your members need the most:

How many patients have the means to reach you on their own? How many patients have the means to reach you on their own?
  • Food Access and Security – As members have a harder time traveling to distant grocery stores, the likelihood of consuming unhealthy, processed food from the nearest location increases
  • Transportation Deficit – Members who typically rely on public transportation to get to work or to see a doctor will have a harder time keeping a household running, and may need additional assistance
  • Social Isolation – as many people are intentionally isolating themselves, the risks of loneliness and its well documented health impacts increase. Populations who were already at risk of isolation, for example those in single parent households, will have an increased burden as we recover from the disease’s impacts
  • Unstable Housing – Without a stable income and already spending a high percent of it on monthly rent, many more families are likely to be evicted or otherwise required to move, and now will not have the same support from family members or community resources to smooth that transition
  • Neighborhood Stress – Living in a neighborhood with a high rate of burden from the COVID-19 disease will deplete resources that many members rely on already. These neighborhoods in particular would benefit from on the ground help as all individual risks may be compounded.

Our job is to use novel data sources and advanced analytics to further your member and patient knowledge to improve the overall health of your populations. We are committed to supporting your efforts to reach those in the most need under what are surely difficult circumstances. Fortunately, we have both the data capabilities and proven history to deploy non-clinical supports at scale to strengthen necessary member / patient attachment. This is the first post in making these analyses available to aid our client’s plans of action – please be in touch if this information is valuable to you, your teams, or your organization in the coming weeks.