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Twitter user @LCHF_Matt has created a NSW Surveillance Report which is a dashboard that compare the rate of "events" (which can be configured to include COVID-19 deaths, COVID-19 ICU admissions, COVID-19 non-ICU hospital admissions or reported COVID-19 infections) per capita from New South Wales, Australia, against their vaccination status. He says it is based on data published by NSW Health.

The graph below (which includes hospital admissions + deaths) appears to show that people with four or more doses are at most risk, and unvaccinated people at the least risk.

Image of dashboard from 23rd July 2022

The Notes tab includes this summary:

It is well known that younger people are less likely to need hospitalisation as a result of Covid-19 than older people and that vaccination rates are lower in the very young. The hospitalisation and death observations in the unvaccinated cohort is no doubt skewed by this fact. These data still however indicate that the cohort of people currently unvaccinated are, collectively at least, not being as adversely affected by Covid-19 resulting in the need for hospitalisation as are the vaccinated cohorts. This could be the result of lower infection rates, lower hospitalisation rates when infected, both, and/or other mechanisms unknown […]

Note that apparently NSW Health doesn't publish the data needed to normalize for age. The author acknowledges that for this reason, the chart cannot demonstrate causation.

The graph has been referenced by Joel Smalley's blog, on Del Bigtree's show The Highwire, and endorsed by the Director General of the Israel Institute for Biological Research (IIBR) Shmuel Shapira.

Is this chart accurate? Does it misinterpret the NSW Health data? Is there a correlation between COVID-19 vaccine doses and hospitalizations in NSW?

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    I'm trying to understand the asterisked footnote: there is separate data on vaccination rates and hospitalization rates and somehow we make some assumptions to come up with hospitalizations by vaccination status without having the actual data?
    – richardb
    Aug 2 at 6:45
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    @richardb: I was going to point to the table on the right to show that the Unknown cohort accounts for a large percentage of the population (and therefore it seems there was no attempt to classify vaccine status when it was unknown), but the Unknown cohort seems to account for 100% of the total, so either there is an error or I am not understanding it either.
    – Oddthinking
    Aug 2 at 11:09
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    @Oddthinking the "Unknown" category is a whole other subject arkmedic.substack.com/p/nsw-health-manipulated-their-vaccine Aug 3 at 4:13
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    @Oddthinking That column in the link outputs —as you noted— a wrong number. Since we do not know how this 'presentation' works (or how do we download the 'spreadsheet'?), we don't know whether this trickles down (these cells used to make the graphs) or lingers in the periphery without consequence (independent list for illustration). 'Count cases' are 'accurate'. Now the fun thing: ignore the 'unknown', just add up the rest of the column to try & 'reverse engineer' what might fit in that category. See a problem there? Aug 3 at 13:16
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    @actual_kangaroo: You claim that causation isn't the issue but then ask "Does this call into question the effectiveness of the [vaccines]?" That is ambiguous: Is is the question "Are the vaccines effective?" - i.e. the very causation question you think is out of scope, or "Can this be used to raise a question?" because the answer is self-evidently yes, because you and Del Bigtree are doing so.
    – Oddthinking
    Aug 3 at 18:21

2 Answers 2

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Yes on correlation, but an emphatic no on causation.

Firstly, the claim being made is in fact a highly common data misinterpretation from anti-vaccination groups, and has previously been widely debunked.

To explain further, assuming the data being presented is accurate, it is impossible to draw a causative conclusion from this. This paper explains that positive test rates strongly correlates with age, whereas the data presented in the graph has not been through this basic correction. This is problematic, as the NY Department of Health explains:

Age confounding occurs when the two populations being compared have different age distributions and the risk of the disease or outcome varies across the age groups.

In fact, this is precisely what is happening. The NSW vaccination eligibility website states that:

From July 2022, you are recommended to have an additional winter COVID-19 vaccine (second booster / fourth dose) if you are: aged 50 years or over, a resident of an aged care or disability care facility, aged 16 years and over and severely immunocompromised (this will be a fifth dose), aged 16 years and over years and have complex, chronic or severe conditions

Additionally, the same page also states that:

People aged 12 to 15 years with complex health needs are also recommended to get a booster vaccination. This includes those who:are severely immunocompromised, have a disability with significant or complex health needs, have complex and/or multiple health conditions that increase the risk of severe COVID-19

Due to the government's vaccination efforts, only people who are highly susceptible to the virus are encouraged to obtain the fourth vaccination, as well as people aged 12-15 who are encouraged to obtain the third vaccination, where they would not be recommended otherwise.

Due to the large difference in populations between these two groups, unless all reasonable confounding factors (including but not limited to: age, travel habits, location, etc.) are accounted for, this data is not useful in determining the effectiveness of vaccination.

In fact, age based data is clearly available in the first link provided in the source, making it reasonable to conclude that the authors of the page are either incompetent in basic statistical analysis, or maliciously interpreting the data to support their beliefs.

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  • I would be interested to see how it would pan out if adjusted by age. Aug 3 at 3:52
  • NSW health apparently doesn't share hospitalization rates stratisfied by age and vaccination status. LCHF_Matt makes the point that it is only corellation but *37 increase in hospitalization is a huge effect, especially if the vaccines & boosters should be actively reducing hospitalization. twitter.com/LCHF_Matt/status/… Aug 3 at 4:00
  • causation wasn't really the question, It had been edited by someone else to ask for that, but it wasn't my original question, I understand that it's not normalized for age (and other variables like social activity & past infections), but was just asking about the accuracy of his analysis and if it matches the data published by NSW Health. Aug 3 at 4:11
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    @actual_kangaroo It's your question. Disagree with an edit: roll it back (or edit further). Here, I'd agree that 'Causation' is a sabotaging straw man set up by the edit. All want to know whether sth's causative, (while we all know such data cannot prove that out of basic principle, correlation is a prereq for causation, not a proof) and the authorities intentionally obfuscate the published data, openly abuse that data (claiming cause when it suits!) , hide the raw data, and worst: do a pig's breakfast mess with collecting it in the first place. Insight from that is always limited. Aug 3 at 12:38
  • Note that the boosters are not only associated with age but medical condition. More boosters will of course correlate with a higher risk of a bad outcome. Aug 6 at 4:44
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Without getting into age stratification (which I happen to think any such analysis should do) etc:

To me the bar charts are all over the place, so I don't think the dashboard shows a correlation between doses and hospitalisations or deaths.

The impression given by the chart changes week to week. For "Hospital (not ICU)", in some weeks two doses appear better than one dose; for "Deaths", in some weeks one and two doses seem far better than no doses. What is the reasonable conclusion to draw if, solely going by the chart, in eight out of nine weeks there is a higher rate of death for "no doses" than "two doses?

Indeed the Doses Summary table bears this out: "two doses" are half as likely to die as "no doses".

Is the risk U-shaped?

Also the vaccination status "Unknown" comprises 25% of the "Hospital (not ICU)" and "Hospital in ICU" events respectively, which seem large proportions to disregard.


You can drill down into age groups in these charts purporting to represent Covid-19 mortality by vaccination status

https://ourworldindata.org/covid-deaths-by-vaccination#data-on-covid-19-mortality-by-vaccination-status

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  • I think that the chart you linked needs a dedicated question on its own.
    – FluidCode
    Aug 2 at 16:36
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    This isn't an answer to the question. It is an observation that the data is complex, and needs analysis, but it doesn't link to such an analysis.
    – Oddthinking
    Aug 2 at 17:58
  • @Oddthinking It was an answer to the pre-edits question that asked if this dashboard shows what it is purported to show. Post-edits there is a different question.
    – Lag
    Aug 3 at 17:52
  • @Lag: It doesn't answer whether the dashboard shows what it is purported to show.
    – Oddthinking
    Aug 3 at 18:22

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