I hate recording talks. I rely so much on audience cues to see if they are lost, confused, interested, engaged, annoyed, and I adjust accordingly. I love interaction. Live talks make me energized and I have so much fun giving them. But. I recorded a talk for #MLHC2020 /1
And instead of maybe 25 or 50 people seeing it live, now over 300 have watched it on YouTube, including my mother in law (thanks, Mom!) and apparently people in Ghana (thanks @aowusuda!) and Ireland (thanks @EvaDoherty!). @HardeepSinghMD even summarized it beautifully. /2
Does this make up for the loss of engagement and interaction that #COVID19 is forcing on us? I admit it is a real benefit I had not appreciated. Being able to reach people around the world who would otherwise be unable to access the conference is rewarding in its own way. /3
Obviously YouTube reach and democratization of knowledge access is well recognized already, but it is making me rethink my dread of my course on healthcare delivery science this fall, which has to be remote. Maybe I should make lemonade and post those lectures publicly too. /end
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#NIHRECOVER Adult is a cohort study of ~15k adults with/without #COVID, followed prospectively. They answer symptom surveys every 3 months and do additional tests yearly. 93% of cohort has been enrolled; this paper includes 9,764 participants. recovercovid.org
Main goal of this paper is to establish an expanded, working symptom-based definition of #LongCOVID for research purposes. Please note we do not propose this as a clinical definition right now pending further validation & refinement.
Have been too busy enjoying seeing people in person and seeing so much great research at #SGIM22 to have been tweeting, but what a great meeting. @nyugrossman was out in force /1
Med student Kyle Smith had a wonderful oral presentation on how we have developed a method of finding people on oral anti psychotics who haven’t had a1c testing (no pics cause I was so busy watching!) with @SaulBlecker /2
T32 trainee Rachel Engelberg had a great poster on incarceration and health outcomes /3
Fascinating article about how research into types and efficacy of traffic stops in multiple CT communities led to changes that both reduced disparities in stops and better targeted actual public safety issues. Some examples follow:
In Newington, 40% (1,608) traffic stops were for defective lights but found only 1 DUI. Dept switched focus to moving violations (defective lights ⬇️67%, moving violations ⬆️60%). Stops with DUI arrest ⬆️250%, from 18 to 63, and disparities substantially reduced: safer & fairer!
Hamden tried increasing stops for admin issues (lights, registration) to reduce crime in Black neighborhood but rarely found contraband (7%), no effect on crime, caused huge disparities. Switched to stops for hazardous driving: crime ⬇️5%, accidents ⬇️10%, found more contraband.
A lot of chatter about hospitalization "with" versus "for" COVID, implying current hospitalization wave isn't "real." NY state is going to start trying to report the distinction; UK already does. Some thoughts, with exemplar data. /1
1st, not so easy to tell. Our health system calls "for" COVID: patients with problem list or clinical impression of respiratory failure with hypoxia (various codes), or x "due to COVID" or COVID positive is the only problem. Specific, but likely not very sensitive. /2
That is, people who meet those criteria are very likely being admitted for COVID, but others will be missed (e.g. diagnosis pneumonia, sepsis, COVID-related stroke/heart attack/PE). So, likely an underestimate. Still, if used consistently, may be useful approximation. /3
Phenomenal preprint from South Africa on #omicron severity. Insanely fast analysis with multiple linked national datasets. Kudos to the authors. Results? You'll see headlines about reduced severity, but full story more complicated. My thoughts. medrxiv.org/content/10.110…
First off, methods. They link lab tests, case data, genome data and hospital data from across all of South Africa. (Wow!) They use a proxy for omicron (SFTF) and require Ct <=30 ("real" infection).
Then they run two comparisons: omicron vs not omicron Oct-Nov, and omicron Oct-Nov vs delta Apr-Nov, and compre frequency of hospitalization and of severe disease (=hospitalised + any of ICU/O2/ventilated/ECMO/ARDS/death). Outcomes assessed on 21 Dec (day preprint posted?!).
Incomplete article in the @nytimes today re: vaccine effectiveness made me finally read last week's NEJM letter suggesting more waning of Moderna than NYT suggests. Will walk you through it. /1 nejm.org/doi/full/10.10…
This reports data from a Moderna randomized trial, in which 14,746 people got vaccine Jul-Dec 2020 and 11,431 got placebo. The placebo group then got the real vaccine later, between Dec 2020-Apr 2021. /2
In Jul-Dec 2020 the vaxxed group had way lower infections than the placebo group (11.8 per 1000 person-years vs. 148.8 cases per 1000 person-years); that's of course why the vaccine got approved. May-June '21, once the placebo group vaccinated, rates were equal. But... /3