1) From county to county, urban/suburban/rural, across all age groups -- the race/ethnic disparity with coronavirus is pervasive. New story with @richoppel, @kkrebeccalai, @MitchKSmith and @NYT_Wright https://www.nytimes.com/interactive/2020/07/05/us/coronavirus-latinos-african-americans-cdc-data.html
The Fullest Look Yet at the Racial Inequity of Coronavirus
New federal data provides the most comprehensive view to date of how Black and Latino people have been likelier than their white peers to contract the virus and die from it.https://www.nytimes.com/interactive/2020/07/05/us/coronavirus-latinos-african-americans-cdc-data.html
2) So as you may know, the NYT collects and disseminates county level summary data on coronavirus cases and deaths. https://github.com/nytimes/covid-19-data
12) It was also important for us to examine the breadth of the disparity. One of the big demographic stories of the past 20 years has been the diversification of the suburbs.
11) Epidemiologists suspect that crowded housing, certain types of occupations, and use of public transportation all increase risk factors, and demographic data shows Black and Latino people as a group are more at risk by these factors.
6) But for this story, we were able to obtain from the CDC a database of nearly 1.5 million person-level case records, which allowed us to look at the pandemic in a more granular manner.
10) By looking at infections, we were shifting the focus more to: Who is at risk of being infected in the first place and why?
3) While this is really useful for tracking where the virus has hit now and in the past, it doesn't tell us much about who within these counties is most impacted.
4) Yes, the NY Times and others have kept up with state and local health departments that post infection and death rates by race and ethnicity, and those stories began to paint a picture of race/ethnic disparity.
5) There is also some CDC reports and reports from researchers that have begun to take this on in a more robust manner.
7) We filtered dataset down to only cases for which the data had a county name and the race/ethnicity of the infected person was known.
8) This was 640K cases over 974 counties -- see below for why so much was missing, but it was still far more information that was available previously.