ABSTRACT
BACKGROUND: Over the course of the 20th century, Monroe County NY, has developed into a community facing significant defacto segregation: a central crescent of the city has lower economic indicators and a predominantly minority community. We set out to analyze rates of SARS-COV2 as well as the distribution of SARS-COV2 testing sites across Monroe County during the first wave of the pandemic (March 2020-Sept 2020). Our hypothesis was that while disease rates would be higher in historical disadvantaged areas, the distribution of testing resources would be less accessible. This is a potentially novel methodology to demonstrate layers of unequal access to resources. METHODS: We extracted data on the total number of SARS-COV2 cases by zip code in Monroe County, NY from March 23 - October 21, 2020 and SARS-COV2 testing sites from the Monroe County Department of Health website. Sociodemographic factors were taken from the 2015 American Community Survey. We used geospatial analysis to assess the local spatial autocorrelation of SARS-COV2 rates. We adapted a definition based on the USDA's 4th definition for food deserts to create a measure of “SARS-COV2 testing site desert.” To overcome coordination of census tract level definitions with zip code level data, we assumed an equivalency factor where we divided the total zip code population by 4000 (average census tract size). We then tested whether SARS-COV2 testing sites were accessible using this definition. RESULTS: There were statistically significant differences in local spatial autocorrelation which allowed us to separate the county into “SARS-COV2 hot zones” and “SARS-COV2 cold zones.” The hot zones had a statistically significant lower median income and a higher percentage of Black and Hispanic residents. The cold zones along the perimeter had a higher median income and higher percentage of white residents (Mann Whitney p values < 0.05). Using the definition for SARS-COV2 testing site deserts, the hot zones had less access to testing sites than the cold zones. CONCLUSIONS: SARS-COV2 case rates were differentially distributed in the first wave of the pandemic in Monroe County. There were significantly higher positivity rates in areas with predominately black residents, lower median incomes, and limited car access. These areas with higher SARSCOV2 positivity rates also had lower initial access to SARS-COV2 testing sites, creating an example of compounded inequity. Creating specific definitions surrounding healthcare access that consider transportation and can be rapidly analyzed may allow for more effective future resource allocation. An early version of this analysis allowed healthcare systems and community organizations to create pilot SARS-COV2 testing sites in areas with higher rates of disease in real time. Using geospatial data analyses provides an exciting potential way to model and impact change in equitable healthcare delivery.