In cities/metros across the US, the areas most affected by the pandemic are often the slowest to be vaccinated, reflecting long-standing patterns of segregation
Our story begins in Chicago, one of the most segregated cities in the US.
HT @ChiVaxBot and @WF_Parker who have been regularly tracking these disparities & whose work partly inspired us to do our own digging into other localities
Variations on this same theme of "Who is vaccinated?" vs" Who is dying?" (this is an actual meme) are playing out in other cities & surrounding areas.
In Washington, DC:
In New York City:
In Baltimore, Maryland:
In King County, Washington: (map shows Seattle and surrounding areas)
Here's the same data in Baltimore & Chicago plotted a different way: income is highly correlated with first dose vaccination rate, weeks into the expansion of eligibility past Phase 1a ...
And yet another way of visualizing the racial disparities in vaccinations, inspired by @KFF's state-by-state report kff.org/policy-watch/e…
(Note all records have race/ethnicity. Only Black vs white shown bc the quality of data across cities was less good/comparable for others)
Why the disparities? Main factors are access, eligibility & hesitancy.
ACCESS/ELIGIBILITY:
> In the US, vaccine rollout is what @helenegayle called a "patchwork quilt" — the CDC makes recommendations but final plans are left to states & localities
Part of this is rooted in a long history of medical malpractices, often by the US government (Tuskegee, Marion Sims, Henrietta Lacks are among most high-profile cases)
However, David Hodge @TuskegeeUniv says focusing too much on the past diminishes the salience of more immediate disparities in health treatments among communities of color: pubmed.ncbi.nlm.nih.gov/25032386/
"The present factors are just as vicious" in creating distrust—"perhaps more so"
What are some solutions? @helenegayle and others are calling for geographical prioritization of hardest-hit areas or areas w/ high SVI atsdr.cdc.gov/placeandhealth…
> Interestingly, in Chicago's 15 target neighborhoods, ALL who live in these areas are eligible wbez.org/stories/chicag…
Other recommendations include:
> overlapping phases
> a lottery system within phases
> more outreach/transparent communication
> geographic allocation of vaccine supply proportional to need
More on that last point, @WF_Parker puts it this way: if a zip code on the South Side of Chicago has 5x the death rate than one on the north side he says it shld have 5x the vaccine supply
"Basically, we need to pour the water where the fire is burning"
Finally, these inequalities are not unique to the US. @jburnmurdoch notes disparities in the UK vaccine rollout, according to data analysis by @bengoldacre & team
HT @jburnmurdoch for encouraging me to look into this by pointing out how much more data there is in the US given its size — which is true, although the quality/consistency of said data & how to present it is best summed up by:
🙃🙃🙃 and will be the subject of my next thread😅
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NEW: #Election2020 turnout is the highest in over a century, but who's gotten the most votes, and where? We looked at precinct & county data in key areas to find out: [THREAD]
1/ Much of the story's in the suburbs, which Biden "won back" to a certain extent. Meanwhile, a red rural wave has countered the suburban swing.
Result: an increasingly polarized US.
2/ More detail here: taking battlegrounds MI, PA, WI & GA as a whole, there's a clear & pronounced net vote increase in the suburbs for Biden in 2020 vs Clinton 2016
0/ TLDR: Here's our summary #dataviz that shows the shifts, because my time in journalism has taught me not to bury the lede 🤓
But hey, it's my twitter thread so I'm gonna answer the "How did you get these numbers?" Qs that @jburnmurdoch & I grappled with *A LOT*
1/ In order to (responsibly) make statements like "___ voters moved away from Biden" and "___ voters shifted toward Trump" since '16 where ___ is a demographic, you need to know:
(a) How this demographic voted in '16
(b) How this demographic voted in '20
POLLS EXPLAINER: it's natural to want to compare 2020 election polls to 2016's, especially in swing states, but here's why that might not be such a good idea. My story in the @FT today reviews the key differences: (THREAD)
1/ there's a lot of talk abt Trump/Biden betting odds being at 50:50, but here's a chart via @martinstabe showing in 2016, betting mkts were confident Clinton would win. in surveys more ppl including Trump voters also said they thought Clinton would win ft.com/content/3c9487…
2/ this yr it's the opposite perception; more ppl/mkts think Trump will win. despite Biden's robust national polling lead, compared to Clinton's. now let's address the "the polls were wrong" critique. it's an understandable reaction to 2016, but *national* polls did pretty well!