The Government's official COVID data are on the website https://coronavirus.data.gov.uk. These are the data I have been using for my regular updates of cases per 1000 people tested (because this crucial plot is not on the government website).
I discovered today that the data on hospital admissions are not only flawed but also reveal a systematic problem with any data based on "suspected" COVID cases. The website says:
“Wales include suspected COVID-19 patients while the other nations include only confirmed cases”
By looking at the raw data (such as that above which includes the most recent complete data for all 4 nations) it is clear that Wales is massively overestimating the number of COVID hospital admissions. Note that Scotland consistently has about twice the number of new
confirmed case as Wales, yet Wales typically has about TWENTY times the
number of admissions than Scotland. In fact based on the above data, whereas in England, Scotland and NI on average around 4% of COVID cases are admitted to hospital, for Wales the figure is 61%.
Indeed, in July (not shown above but you can find it all on the website) when there were almost zero hospital admissions in the rest of the UK (and very few new cases) Wales was typically reporting 50-100 new COVID hospital admissions every day.
Unless Wales is routinely admitting people who should not be hospitalized, we can conclude that at least 90% of the Wales 'COVID' admissions were not COVID at all and that 'suspected' COVID cases are generally not COVID. With UK admissions currently being recorded as around 250 per day including around 60 in Wales this means the real UK admissions number should be reduced to less than 200.
There are two lessons to be drawn from this:
- Any graphs/analysis of hospital admissions should exclude Wales
- Any data about suspected COVID cases (whether with respect to hospital admissions, deaths, or anything else) should be completely ignored or treated as massively exaggerated
See also:
- Impact of false positives in Covid testing
- Don't panic: limits to what we know about Covid-19 PC testing, inferred infection rates and alse positive rates
- A privacy-preserving Bayesian network model for personalised COVID19 risk assessment and contact tracing
Covid-19: Infection rates are higher, fatality rates lower than widely reported - Coronavirus: country comparisons are pointless unless we account for these biases in testing
- Why most studies into COVID19 risk factors may be producing flawed conclusions - and how to fix the problem
- Causal explanations, error rates, and human judgment biases missing from the COVID-19 narrative and statistics
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