COVID-19 Insight: Where is food insecurity increasing?
In places where workers have been hardest hit by the coronavirus.
Understanding the secondary effects of COVID-19—from job loss to evictions to an increased need for social services—is critical for employers, policymakers and community-based organizations. This analysis examines food insecurity.
To start, we need modeled data to predict food insecurity based on projected changes to unemployment and poverty. Feeding America, the nations’ largest domestic hunger relief organization, has done just that with the 2020 Map the Meal Gap study. Read more about Feeding America’s research on this topic.
The Feeding America data examined against the Bureau of Labor Statistics’ industry sectors shows workers impacted by business closures or in industries that could not perform work from home during the pandemic are more at risk for food insecurity FAI. The most significant correlations are workers in –
- Retail NAF
- Accommodation and food services NAR
- Real estate, rental and leasing NAK
- Utilities, construction and mining NAH
- Government EMH
Click on any of these links to explore the relationship shown in a scatterplot. You can hover over any bubble to see the corresponding county on the map or use the map to find your geography.
Each scatterplot shows that an increase in the percentage of workers in the industry closely correlates with an increase in food insecurity when holding some variables constant like income, education, and demographics. Metopio provides explanations for you in the What this plot shows or the green Highly Significant drop down.
Metopio then allows you to dive deeper and examine other social factors that impact these workers. For example, we find workers in the accommodation and food services sector also are more likely to be renters NAR, not speak English as their primary language LEP, and are employed but still below the poverty threshold PEV.
Each step in this analysis creates a clearer and clearer picture of who is at risk and where. The next step is to put the insight to work. For example, you might be a —
Employer—How are you understanding burdens your associates may be facing at home, like food insecurity? Action - Consider developing an anchor mission strategy and using data like these as a baseline.
Policymaker—The food insecurity burden in the US is expected to increase 30% because of COVID-19. Action - Consider supplemental funding, perhaps through the CARES Act, for SNAP and other food access programs.
Patient Navigator—Because of COVID-19, many patients are delaying or missing routine visits, which means you are missing a contact point to collect important data. Action - Consider outreach to your patients in high-risk locations and make sure they have the resources they need.
Insurer —If you have a risk-based contract with a provider, are you modeling what social determinant burdens might increase because of the pandemic? Action - Consider matching this data with your own to see if you should be planning for increased need in certain areas.
We are constantly updating Metopio’s curated data sets to offer opportunities to understand how the places and populations you care about are impacted. Check back as we access more data and dig deeper to provide you with these valuable insights.
Metopio aggregates high-quality, verified data that has some geographic component - think address or any part thereof - to develop insights that inform your business and policy decisions. Do you have data that would enhance this analysis? Want to know more? Contact us