Jason Block Profile picture
Sep 12 14 tweets 9 min read
Since 2018, #calorie labeling of prepared foods has been required in food retail establishments (became law with 2010 ACA). Our team has been evaluating the effect of this law in restaurants & supermarkets, funded by @niddk. What have we found so far? A thread, 1/14
Most complied: 71 of 90 (79%) of the largest restaurant chains & 9 of 10 of the largest supermarket chains by the end of 2017 & 186 of 197 (94%) of the largest restaurant chains by the end of 2018. Work led by @parksasaurus. 2/14 pubmed.ncbi.nlm.nih.gov/29927646/ & pubmed.ncbi.nlm.nih.gov/32313679/
With transaction data from 104 stores within a large national fast-food chain, we found a 23 kcal reduction in mean calories of meals purchased after labeling (an approximate 4% reduction), with some attenuation over time. 3/14 bmj.com/content/367/bm…
But, when expanded to 2 years post-labeling, we found a 4.7% reduction, suggesting some stability. Most change bc of fewer items purchased. Work led by Joshua Petimar. 4/14 journals.plos.org/plosmedicine/a…
In a large supermarket chain (173 sites), we found similar findings for 2 years pre - 7 months post labeling: a 5% reduction in kcal purchased from prepared bakery items; 11% reduction in kcal from deli items after labeling. Also by Joshua Petimar. 5/14 pubmed.ncbi.nlm.nih.gov/35913728/
Are retailers decreasing calorie content of offerings? After labeling, chain restaurants introduced new items that were approx 25% lower in calories compared to new items introduced before the law. Work led by @AnnaGrummon & Joshua Petimar. 6/14 jamanetwork.com/journals/jaman…
Calorie content of continuously available items in restaurants didn’t change.

In supermarkets, continuously available bakery items became very slightly less caloric after labeling – a reduction of 8 kcal (0.5%) per item. 7/14
New prepared bakery items introduced after labeling were much lower in calories (about 27%) after labeling compared to before, but this finding was based on a relatively small number of items. Work led by @AnnaGrummon. 8/14 pubmed.ncbi.nlm.nih.gov/34103209/
What about public discussions of calories on @Twitter and searches on @Google ? After calorie labeling, there was a slight uptick in tweets & searches regarding calories, but calories aren't discussed/searched for much to begin with. 9/14 mdpi.com/1660-4601/18/2…
Overall, using large datasets of restaurant & supermarket purchases, we found small reductions in calorie purchases after labeling. Are there risks? Some have suggested that labels could worsen eating disorders, but no evidence has emerged yet regarding this question. 10/14
Cost-effectiveness analyses are pending. Because of the widespread exposure to labels & their low implementation costs, it is likely that calorie labels will be cost-effective if they produce even very small improvements in health outcomes. 11/14
For @marionnestle’s take on calorie labeling and our supermarket paper, jamanetwork.com/journals/jamai…. No one single policy could be expected to have enough effect to alter population health but labeling is a start. 12/14
Thanks to team that has given time & energy to work: @EricRimm @AnnaGrummon @RobertoCA @parksasaurus @dsimon326 @yswen @johnbrownstein @DrBleich Joshua Petimar, Alyssa Moran, Michele Polacsek, Steve Gortmaker, Julie Greene, Sue Till, Fang Zhang, Mark Soto, Anjali Rao. 13/14

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More from @jasonpblock

Mar 22, 2020
People who are asymptomatic SHOULD NOT be tested for #COVID19. Diagnostic testing properties are very poor when pretest probability is low. People who are asymptomatic with + tests are much more likely to have a false + than a true one. Negative test may give false reassurance.
Politicians and celebrities being tested while asymptomatic is not helpful and will lead to a higher unnecessary demand for testing.
If pretest probability of having #Covid19 is 1% (1 in 100 chance you have it), then a + test only increases your probability of having it to 14.2%. That’s assuming test is nearly perfect (99% sensitive - detects nearly all cases, 95% specific - rules out most who don’t have).
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