Also, I have been reading the NSW Surveillance Reports for a while now and have noticed the subtle shifting in the way the tables and comments are reported to make “excuses” for declining vaccine effectiveness. Have a look at the “cleansing” that took place between Delta & Omicron, this report was very slow to be released publicly. Maybe they were short staffed, it was released by NSW Health close to Christmas during the school holidays, so they may have had staff on leave or isolating due to Omicron. Note: the report date is not the date they upload it to their web site.
Hi Andrew, great analysis. Your analysis and methods are pretty much in line with what I’ve found. The “negative” effectiveness has me baffled, being negative would imply the vaccinated are more susceptible to Omicron than the unvaccinated, this I find hard to believe. I think if you use the same technique you’ll probably find the effectiveness is overstated for the vaccinated in previous periods due to issues in the denominator “moving” in time. If you look at the U.K data video posted by Gaz in another thread you can see it go from +ve to -ve then moves back up towards 0, my guess is both 2 and 3 dose will go positive again as booster dose “kicks in”. Why would the number of boosters effect the 2 dose effectiveness? It doesn’t make sense. My conjecture is that it makes sense if you don’t properly account for people entering and leaving the group over time. I think Dr Fenton refers to a “so called deadly first dose” in one of his videos and uses an analogy where he refers to it as something like moving from a fox hole to a safe bunker and having to cross a mine field to get to the bunker and misclassifying the mine field deaths as foxhole deaths or something like that. I think it's the video where he discusses the paper below: https://www.researchgate.net/publication/356756711_Latest_statistics_on_England_mortality_data_suggest_systematic_mis-categorisation_of_vaccine_status_and_uncertain_effectiveness_of_Covid-19_vaccination
I think he’s onto something, he suggests the effects are due to misclassifications more than anything else. The “under investigation” in NSW data could also be a clue.
Also, I have been reading the NSW Surveillance Reports for a while now and have noticed the subtle shifting in the way the tables and comments are reported to make “excuses” for declining vaccine effectiveness. Have a look at the “cleansing” that took place between Delta & Omicron, this report was very slow to be released publicly. Maybe they were short staffed, it was released by NSW Health close to Christmas during the school holidays, so they may have had staff on leave or isolating due to Omicron. Note: the report date is not the date they upload it to their web site.
Hi Andrew, great analysis. Your analysis and methods are pretty much in line with what I’ve found. The “negative” effectiveness has me baffled, being negative would imply the vaccinated are more susceptible to Omicron than the unvaccinated, this I find hard to believe. I think if you use the same technique you’ll probably find the effectiveness is overstated for the vaccinated in previous periods due to issues in the denominator “moving” in time. If you look at the U.K data video posted by Gaz in another thread you can see it go from +ve to -ve then moves back up towards 0, my guess is both 2 and 3 dose will go positive again as booster dose “kicks in”. Why would the number of boosters effect the 2 dose effectiveness? It doesn’t make sense. My conjecture is that it makes sense if you don’t properly account for people entering and leaving the group over time. I think Dr Fenton refers to a “so called deadly first dose” in one of his videos and uses an analogy where he refers to it as something like moving from a fox hole to a safe bunker and having to cross a mine field to get to the bunker and misclassifying the mine field deaths as foxhole deaths or something like that. I think it's the video where he discusses the paper below: https://www.researchgate.net/publication/356756711_Latest_statistics_on_England_mortality_data_suggest_systematic_mis-categorisation_of_vaccine_status_and_uncertain_effectiveness_of_Covid-19_vaccination
I think he’s onto something, he suggests the effects are due to misclassifications more than anything else. The “under investigation” in NSW data could also be a clue.