More from @mlipsitch

Marc Lipsitch

@mlipsitch

May 24

@cdcgov

Thrilled to announce that our data science (modeling/analytics) jobs are now live for the @cdcgov Center for Forecasting and Outbreak Analytics:
closing June 3 so apply now!

Analyst (GS-13) usajobs.gov/job/655773800

Team lead (GS-14) usajobs.gov/job/655773600

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Marc Lipsitch

@mlipsitch

Dec 21, 2021

Hey #econtwitter is there a common explanation behind 1) the incomparability of medical data from different EMR instances; 2) the incomparability of mattresses made by different manufacturers and sold by different retailers 3) the incompatibility of different mfg camera lenses?

Feels like the common thread may be that the opportunity to prove oneself better/ better for the cost that would arise from comparability is less appealing than the risk of being proven worse; better to hold onto market share from loyalists than risk competition.

but having a hard time seeing the medical record analogy, perhaps because it's not there. If this is something Freakonomics covered in 2017 I'd be happy for a reference.

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Marc Lipsitch

@mlipsitch

Dec 18, 2021

@CCDD_HSPH

the following thread is purely my personal opinion as a @CCDD_HSPH @HarvardEpi epidemiologist. I would welcome feedback though I am trying to take some breaks so don't promise to reply. Thanks in advance to anyone who provides critique or further info

Impact of #Omicron obviously depends on severity. Number of cases is growing exceptionally fast and several countries have seen such growth continue for quite a while (weeks, which is a lot of time with doubling times of small number of days

@Imperial_IDE

AFAIK there are two publicly available assessments of severity. Kudos to @Imperial_IDE and Discovery Health for rapidly putting out reports imperial.ac.uk/mrc-global-inf… and discovery.co.za/corporate/news… (site down as I write, but previously had a link to a detailed webinar

Read 21 tweets

Marc Lipsitch

@mlipsitch

Dec 6, 2021

https://twitter.com/HelenBranswell/status/1467294212202901505

Clear explainer of a very carefully nuanced report. While the main thing to say is "small numbers," it does seem that the average severity among those in the hospital is comparatively low. Two thoughts about interpretation:

https://twitter.com/HelenBranswell/status/1467294212202901505

1/ Omicron growing very fast. Patients hospitalized for COVID are typically (not always) well into their infection. In a fast-growing epidemic, the proportion infected 10d ago, say, is lower on any given day than in a slow-growing one. This alone could cause unusually lo ...

proportion of hospitalizations with primary diagnosis. Fast-growing epidemic = most infections are new is basic demographic theory (just like in a fast-growing population of people, most people are young). The unusually high growth rate can at least partly explain observation.

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Marc Lipsitch

@mlipsitch

Aug 18, 2021

@CT_Bergstrom

@CT_Bergstrom @jsm2334 Certainly this kind of bias merits consideration. I think the particular figure cited in that table is an example of Simpson's paradox, which is a special type of confounding.

@CT_Bergstrom

@CT_Bergstrom @jsm2334 For those new to these terms, confounding is just the problem that (in this case) vaccine is not randomly distributed in the population, so the vaccinated have different risks from the unvaccinated for reasons other than their vaccine: in this case, age.

@CT_Bergstrom

@CT_Bergstrom @jsm2334 Simpson's paradox is an extreme form of confounding in which a combined analysis for two groups of people gives an unusually misleading estimate, relative to the (more) accurate estimate for each group individually.

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