Neil and Fenton speculated that in the ONS dataset, people may have been classified as unvaccinated for about two or three weeks after the first jab, but the ONS have answered many times that they count people as vaccinated on the day of vaccination: ons.gov.uk/peoplepopula… (search "From the day of vaccination"), ons.gov.uk/aboutus/tran…, x.com/SarahCaul_ONS/sta…, osr.statisticsauthority….

There is one dataset published by the UKHSA where people were classified as single-jabbed until two weeks after their second jab: whatdotheyknow.com/requ…. And there is another dataset published by the ONS where people were classified as single-jabbed until three weeks after the second jab: ons.gov.uk/peoplepopula…. But those are different from the ONS dataset for mortality by vaccination status.

The 2-to-3-week cheap trick is not used in the New Zealand data that was released by Barry Young, but there is still a big increase in mortality among single-jabbed people during the same time when the second dose is rolled out. From August 2021 to November 2021 when the monthly person-years of the second dose climbed up from about 6,000 to about 62,000, the excess ASMR of the first dose increases from about -62% to about 102%. And also from November 2021 to March 2022 when the monthly person-years of the third dose climb up from about 500 to about 78,000, the excess ASMR of the second dose increases from about -25% to about 80%: sars2.net/moar.html.

This spreadsheet shows that if you count the time from last vaccination to death in Barry's dataset, there are 360 deaths in the first week after vaccination, 571 the second week, 643 the third week, and so on: docs.google.com/spreads…. And the mortality rate is about -63% below the baseline the first week, -41% the second week, -33% the third week and so on, and it takes about 15 weeks until the mortality rate gets back near the baseline.

I have also tried comparing crude mortality rate within age groups in the ONS data against the mortality rate among the general English population: sars2.net/stat.html. In the ONS data in ages 70 and above, April 2022 was the first month when a large number of people were included under the 4th dose, but in April 2022 people under the fourth dose had about -66% excess mortality in ages 70-79, -73% in ages 80-89, and -59% in ages 90+. After that it took about 4 months for the excess mortality of the fourth dose to reach near zero. However at the same time the excess mortality of people who remained under the third dose increased to over 100% in ages 80-89, because the "unhealthy stragglers" who had positive excess mortality remained under the third dose but the healthy vaccinees with negative excess mortality moved under the fourth dose.

Neil and Fenton's cheap trick is not used in the Medicare "all states subset" sheet that was published by Kirsch a year ago, but there are 573 deaths during the first week from vaccination, 872 deaths the second week, 1063 deaths the third week, and so on, so the cheap trick would work in favor of unvaccinated people (kirschsubstack.com/p/ga…):

t=read.csv("sars2.net/f/kirsch_medi…")

table(as.numeric(as.date(t$date_of_death)-as.date(t$date_of_vaccination))%/%7+1)

Three months ago Kirsch also published a complete or nearly complete dataset of people who died in the Maldives in 2021-2022 along with the dates of their vaccination. So you can calculate the time from the last vaccination of each person to death manually so that you know that the cheap trick is not being used. However there's 14 deaths on the first week from vaccination, 21 deaths the second week, 32 deaths the third week, and so on, so again the cheap trick would work in favor of unvaccinated people:

t=read.csv("sars2.net/f/kirsch_mald…")

table(as.numeric(as.date(t$date_of_death)-as.date(apply(t[,7:10],1,max)))%/%7+1)

I showed these datasets to Martin Neil, but his response was to just block me and delete all of my Substack comments: x.com/mongol_fi/status/….