How Can We Reduce Community Impacts of COVID-19?
Tags: Data Science, Technology, Health, Vulnerability, Coronavirus

The UHVI considers ages 65+ and serious medical conditions as “high risk” factors. The UHVI does not visualize occurrences of COVID-19. It does, however, include state-level data about overall testing and confirmed cases. It also includes hospital data to show geographic proximity, and it shows nursing home locations should additional safety protections in surrounding areas be required.

How does identifying vulnerable neighborhoods support a strong response to coronavirus?

The novel coronavirus, as reported by the CDC, disproportionately affects older adults and people of all ages with chronic conditions, like diabetes, lung disease, and heart disease. The UHVI quickly communicates where high-risk populations are located to help decision-makers keep people safe in the following ways:

Helps government and health officials target public health interventions.

Offers empirical insights for urban communities at the local level.

Allows organizations to target outreach and assistance where it is most needed across cities.

Provides information about the prevalence of chronic conditions related to those the CDC has identified as at-risk conditions.

Identifies proximity to hospitals.

Keeps vulnerable communities top of mind.

How should we prioritize this information?

The Urban Health Vulnerability Index offers a view of a community’s vulnerability based on health factors. This provides a good foundation for a variety of analyses to explore how we can combine health and other types of risk to better understand where populations may be most impacted by COVID-19.

For example, we combined health vulnerability with social vulnerability to identify where populations are at particularly high risk from a health and social perspective. Social factors such as poverty, crowded housing, and language barriers can inhibit access to care, increase transmission, and predispose people to severe economic hardship as a result of measures taken to “flatten the curve”.

For this analysis, we used the CDC Social Vulnerability Index (SVI). Similar to our Urban Health Vulnerability Index, the SVI measures factors that play a part in health and wellbeing: Housing Type and Transportation, Housing Composition and Disability, Socioeconomic Status, Minority Status and Language.

Variables used to measure the CDC Social Vulnerability Index. Source: CDC

Using New York City as an example, we evaluated overall vulnerability based on health and social variables, and narrowed our focus to census tracts that fall in the top 25% of each type of vulnerability.

Close-up of legend. © RS21

Purple areas indicate high vulnerability of both health and social factors; red areas denote high health vulnerability; blue shows high social vulnerability; and gray tracts represent areas in the lower 75% of risk for both health and social factors.

We then segmented the social vulnerability factors to look at how each of the four SVI sub-themes correlated with health indicators. We found a high correlation between the socioeconomics and health indicators.

TIME Magazine has already reported on how the coronavirus could disproportionately hurt the poor. Lower income communities are often less protected, with circumstances such as reduced access to health care, being underinsured, or experiencing food insecurity.