These quotes come immediately before and after the cited graph in the report, I’ve shortened them for clarity and brevity:
These data should be considered in the context of vaccination status of the population groups … The vaccination status of cases … is not the
most appropriate method to assess vaccine effectiveness and there is a high risk of
misinterpretation.
it is expected that a large proportion of cases, hospitalisations and deaths would occur in vaccinated individuals, simply because a larger proportion of the population are vaccinated than unvaccinated … This is especially true because vaccination has been prioritised in individuals who are more susceptible or more at risk of severe disease. Individuals in risk groups … thus may be hospitalised or die with COVID-19 rather than because of COVID-19.
Interpretation of the case rates in vaccinated and unvaccinated population is particularly susceptible to changes in denominators
and should be interpreted with extra caution.
Because the elderly and the chronically sick are the most likely to get vaccinated, and the young and healthy are the least likely to get vaccinated, it distorts the numbers to look like getting the vaccine increases your chance of contracting covid.
The report you cited is overflowing with warnings not to misinterpret the data, but you missed all the warnings. Ironically, your own argument is an excellent example why we need authorities to interpret findings for the general public, and why those authorities are deserving of more trust than any stranger on social media.
Of course it is not the most appropriate way to assess vaccine effectiveness. The most appropriate way is isolate the vector (which hasn’t been done till date), then administer it to test subjects (good luck finding willing test subjects), both vaccinated and unvaccinated, and then check the covid positive rates for these test subjects. And then there have to be replication studies to verify results, etc. You’ll need no less than a dictator to get this done.
This is a generalised conjecture which can be refuted by one single appropriate example. As it happens, an appropriate example exists. Waterford, Ireland which has nearly 100% vaccination rate, became a covid positive hotbed soon after vaccination reached high percentage ( check here ).
As it happens, the denominators in the tables have been uniquely specified and rates given in proportion to them. If the PHE have not made any mistakes in making the table, it is highly unlikely that my interpretations have erred on this count.
As for your own interpretation about difference in vaccination rate by age and illness distorting rates of covid contraction, that cannot be addressed without more comprehensive data. But there are distortions from other side as well. For instance, those more likely to contract covid may also be taking more social distancing precautions and maintaining stricter personal hygeine, etc.
These quotes come immediately before and after the cited graph in the report, I’ve shortened them for clarity and brevity:
Because the elderly and the chronically sick are the most likely to get vaccinated, and the young and healthy are the least likely to get vaccinated, it distorts the numbers to look like getting the vaccine increases your chance of contracting covid.
The report you cited is overflowing with warnings not to misinterpret the data, but you missed all the warnings. Ironically, your own argument is an excellent example why we need authorities to interpret findings for the general public, and why those authorities are deserving of more trust than any stranger on social media.
source: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1025358/Vaccine-surveillance-report-week-41.pdf
All three of your quotes can be addressed:
Of course it is not the most appropriate way to assess vaccine effectiveness. The most appropriate way is isolate the vector (which hasn’t been done till date), then administer it to test subjects (good luck finding willing test subjects), both vaccinated and unvaccinated, and then check the covid positive rates for these test subjects. And then there have to be replication studies to verify results, etc. You’ll need no less than a dictator to get this done.
This is a generalised conjecture which can be refuted by one single appropriate example. As it happens, an appropriate example exists. Waterford, Ireland which has nearly 100% vaccination rate, became a covid positive hotbed soon after vaccination reached high percentage ( check here ).
As it happens, the denominators in the tables have been uniquely specified and rates given in proportion to them. If the PHE have not made any mistakes in making the table, it is highly unlikely that my interpretations have erred on this count.
As for your own interpretation about difference in vaccination rate by age and illness distorting rates of covid contraction, that cannot be addressed without more comprehensive data. But there are distortions from other side as well. For instance, those more likely to contract covid may also be taking more social distancing precautions and maintaining stricter personal hygeine, etc.