I see many non-experts misinterpret COVID-19 hospitalization figures
Here's the thing: raw figures for the last 1-2 weeks almost always show a PERMANENT DIP even when hospitalizations are INCREASING
Case in point, my county, San Diego (charts by @SDCountyHHSA):
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The animation above shows how the hospitalization chart from the San Diego County Health & Human Services Agency changes over time (current chart: sandiegocounty.gov/content/dam/sd…)
I didn't modify the charts except by adding the red text "Peak hospitalizations"
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The animation shows the chart produced as of 5/8, 5/11, 5/12, 5/17, 5/20, & 5/23
We know that, in May, peak hospitalizations occurred on 5/5 but this day isn't the highest peak until the chart of 5/20, 15 days later!
It is because data for the last 1-2 weeks is incomplete
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See the note that @SDCountyHHSA put in the upper right corner of the chart:
"Illnesses that began during this time may not yet be reported"
Why is the data incomplete?
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When a patient is admitted, the hospital may suspect it is a probable case, but they won't count it as such until a diagnosis is issued, a test is ordered, and results are received.
The county's systems take days to process admission records from 100+ hospitals.
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And if the data is further aggregated from counties to states, there are more delays for the information to pass through. (Believe it or not, a lot of these processes are ad-hoc and only semi-automated by IT staff.)
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Bottom line, when you see news reports that hospitalizations are increasing, it's often information that journalists get directly from hospitals, BEFORE the county or state official hospitalization figures actually show an increase.
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In the case of San Diego, our peak was on 5/5, but it took 15 days, 2 full weeks, for the data to be complete and finally reflected in the county charts.
So keep that in mind. There is almost always a 1-2 week delay until hospitalization figures are complete.
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In fact, in San Diego, based on the present COVID-19 hospital admissions trend being FLAT in last 1-2 weeks, we know with near certainty that hospitalizations are actually INCREASING (because more recent days are more incomplete)
Let's point out numerous obvious flaws in his analysis, shall we?
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Firstly, Berenson chooses to ignore Asian countries. Why? Because geography is "the most important factor in how hard Covid hits a country"
In other words: "countries who did better than Sweden don't count, because, well, I sAy So"
Such well-reasoned logic. Much wow 🤣
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Secondly, he claims geography is the most important factor, but IGNORES ALL the countries geographically close to Sweden: Finland, Norway, Denmark (none of them are charted)
I modeled excess deaths per capita by age group, for each US state, since the beginning of the pandemic.
I believe this is the first time an analysis of this type has been done BY AGE GROUP for each state. This removes the need to do "age-adjustment" to compare states.
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Based on NYT's mask survey across United States counties, plot deaths/capita recorded during the survey & up to 30 days later, along with each county's mask wearing score
Result:
Linear regression (Y log-transformed) R²=0.144 in red:
There are hundreds of factors affecting the dependent variable (deaths). Ignoring ALL of these factors, looking at mask usage only, and still finding R²=0.144 is pretty cool/unexpected
Confounders abound!: people who wear masks often are likely doing more social-distancing. Etc.
Methodology behind the chart:
Two data sources:
- mask wearing survey github.com/nytimes/covid-…
- COVID deaths (and population) by US county as per JHU CSSE
His charts seem to claim that nothing works. Locking down doesn't work, masking doesn't work, vaccination doesn't work, your printer doesn't wo—wait scratch that one
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One iota of critical thinking is all you need to expose numerous errors in his charts:
Error #1 — Case ascertainment rate bias:
A country may detect 1 in 2 cases, while another 1 in 4. We say the case ascertainment rate is respectively 50%, and 25%.
This variance in case ascertainment rate alone is enough to put half of @ianmSC's charts where they belong: in the trash🗑️
Real Science™ looks at covid deaths—not cases—to compare the severity of the pandemic across different regions. This avoids case ascertainment rate bias.
I compiled a list—as exhaustive as possible—of all peer-reviewed & published research articles that evaluate the effectiveness of nonpharmaceutical interventions, specifically lockdowns on COVID-19
➡️Papers finding NPIs effective outnumber, by 8 to 1, those finding the opposite
Criteria for inclusion in the list:
1-Be a RESEARCH ARTICLE (data, methods, results). Commentaries, opinion pieces, etc, do not qualify
2-Be PEER-REVIEWED & PUBLISHED among the 26,000 titles in Scopus
3-Be EXPLICIT. No secondhand interpretation of the data
Regarding criterion #3: the authors must explicitly state in the text whether their results suggest NPIs are effective or not
Their exact words have been peer-reviewed & published. Your interpretation of figures or data tables has not.
2. President of Burundi died on 8 June 2020. The cause of death was given officially as "cardiac arrest" by the Burundian government, but is suspected to be COVID: economist.com/middle-east-an…
His wife was flown to Kenya and hospitalized for COVID a week before his death.