Comments to Ethical Skeptic (part 4) - sars2.net

Other parts: ethical.html, ethical2.html, ethical3.html (deaths in ages 0-4).

Contents

• Cancer diagnoses in England per population aged 65 and above
• Fake linear regression of cancer mortality since 2014
• Paper about cancer incidence by vaccination status in South Korea
• Was the median age at death from COVID 79.5 years in 2025?
• Ethical Skeptic's 66-gigabyte model with over 550 modules
• ES said nukes are a deterrant by demons to prevent Jesus from returning on a fleet of spaceships

Cancer diagnoses in England per population aged 65 and above

Ethical Skeptic made this plot of cancer incidence in England, where he employed a hacky method to approximate an age-standardized incidence, so that he divided the number of cases in all ages with the population estimate of ages 65 and above: [https://x.com/EthicalSkeptic/status/1966911244859842912]

His plot has a massive drop in the incidence between the start of 2019 and the end of 2019, but in the dataset he cited as his source, the incidence remains roughly flat throughout 2019: [https://nhsd-ndrs.shinyapps.io/rcrd/]

In this plot by NHS, the age-standardized cancer incidence also remained around the same level in 2018 and 2019: [https://researchbriefings.files.parliament.uk/documents/SN06887/SN06887.pdf]

I used WebPlotDigitizer to digitize the data in Ethical Skeptic's plot. Some points in his plot have irregular horizontal spacing, so I had to click on each point manually to extract the data correctly: [https://automeris.io/wpd/]

ES seems to have calculated the number of people aged 65 and above through a circuitious method, where he multiplied the yearly population estimates of England with the yearly proportion of people aged 65 and above:

I tried to replicate his methodology, so I took yearly population estimates from a page at Statista titled "Population of England from 1971 to 2023": https://www.statista.com/statistics/975956/population-of-england/. I used 2023 estimates for 2024 and 2025, but I'm not sure if for example ES used a linear projection of the 2022 and 2023 population sizes as the population size for 2024. I took the proportion of people aged 65 and above in England from the website of the ONS (where I used the 2024 proportion for 2025): https://www.ons.gov.uk/explore-local-statistics/indicators/percentage-of-the-population-aged-65-plus ("Line chart > Change areas > Other areas > All countries > England > Download line chart data (CSV)").

The result I got was close to the yearly population estimate of ages 65+ at Nomis, apart from the years 2023 and 2024: [https://www.nomisweb.co.uk/datasets/pestsyoala]

Then in order to calculate the yellow line in the next plot, I multiplied the monthly rates in Ethical Skeptic's plot with the yearly number of people aged 65 and above, and I divided the result by 100,000.

I downloaded the rapid cancer registration data from here: https://nhsd-ndrs.shinyapps.io/rcrd/. I used the version of the data adjusted for the number of working days per month, because the unadjusted version gave me a much poorer fit to Ethical Skeptic's plot, so he probably used the adjusted data. Then I took a backwards moving average of the data where the window extended up to 5 months backwards, but for example the window extended only 1 month backwards in February 2018.

I was able to replicate the values in Ethical Skeptic's closely in 2018. But he seems to have manually altered the y-axis values so that he shifted them downwards in 2019 to 2022, and he shifted them upwards in 2024 and 2025. His altered data has a particularly large downwards shift in 2019, so that the yearly number of diagnoses drops by about 13% between 2018 and 2019, even though in the real data it increases by about 1%:

ma=\(x,b=1,f=b){x[]=rowMeans(embed(c(rep(NA,b),x,rep(NA,f)),f+b+1),na.rm=T);x}

rapid=CJ(year=2018:2025,month=1:12)[!(year==2025&month>5)]
diagnoses=c(24265,23485,24346,25994,27661,26373,25833,25212,24953,24540,24564,25231,25169,24844,24873,26294,26401,25994,25037,25043,24770,25236,24966,25444,25128,25621,24809,16910,17246,18362,19806,21826,23169,23614,24274,24592,23870,23534,24370,26499,27741,25785,25686,25425,25493,25801,25927,25658,25752,26316,25826,27370,28176,27371,26549,26517,26984,26615,27285,26179,26944,26909,26700,28151,29276,27220,26845,26652,26579,26473,28109,27843,27733,27699,28826,28654,29560,28914,28213,27783,27948,27807,27565,28289,27863,28261,27960,28428,27754) # adjusted for number of working days
p1=rapid[,.(x=as.Date(paste(year,month,16,sep="-")),y=diagnoses)]
p1[,y:=ma(y,5,0)]
p1$z="Real data (6-month backward moving average)"

es=CJ(year=2018:2025,month=1:12)[1:87]
es$rate=c(239.6,235.7,237.5,242.7,248.6,250.4,253.2,255.5,256.8,254.2,249.6,247.5,239.3,230.8,222.3,218.2,212.1,206.9,206.1,206.4,206.4,204.6,203.1,202.3,206.1,210.5,215.1,206.4,198.9,191.5,184.5,178.6,176.3,185.1,196.1,204.9,212.1,214.6,216.2,220.8,226.2,227.0,230.3,233.1,234.4,233.7,231.3,230.6,230.6,232.1,232.6,236.0,238.5,241.1,242.4,242.4,244.5,243.4,242.1,240.3,242.1,243.9,244.5,248.1,252.4,255.3,254.5,255.0,254.2,251.7,250.1,250.9,253.5,255.5,260.7,266.1,269.4,272.7,273.5,273.5,271.7,269.1,267.3,266.8,268.4,270.9,272.2)

# nomispop=c(10082761,10243221,10332210,10469044,10630852,10787479,10981092) # exact population estimate of ages 65+
espop=c(55924500,56230100,56326000,56554900,57112500,57690300,57690300,57690300) # from Statista
espop=espop*c(.1803,.1822,.1834,.1851,.186,.1862,.1873,.1873) # proportion aged 65 and above from ONS website

p3=es[,.(x=as.Date(paste(year,month,16,sep="-")),y=rate*espop[factor(year)]/1e5)]
p3$z="Ethical Skeptic's rate converted to number of diagnoses"

p=rbind(p1,p3)[,z:=factor(z,unique(z))]

xstart=as.Date("2018-1-1");xend=as.Date("2025-7-1");xbreak=seq(xstart+182,as.Date("2025-1-1"),"year")
ybreak=pretty(c(0,p$y));ystart=0;yend=max(p$y*1.03)

yearly=p[,.(y=mean(y)),.(year=year(x),z)][year%in%c(2018,2019,2021,2024)]
yearly[,x:=as.Date(paste0(year,"-1-1"))]

color=c(hsv(22/36,1,.6),"#aaaa00")

anno1="ES altered his data to get a 13% decrease between 2018 and 2019, even though the real data has a 1% increase"
anno2="ES altered his data to get a 22% increase between 2021 and 2024, even though the real data only has a 12% increase"
anno=data.table(x=c(xstart+50,xend-50),y=14000,label=c(str_wrap(anno1,25),str_wrap(anno2,34)))

ggplot(p)+
geom_vline(xintercept=seq(xstart,xend,"year"),color="gray90",linewidth=.4)+
annotate("rect",xmin=xstart,xmax=xend,ymin=ystart,ymax=yend,linewidth=.4,lineend="square",linejoin="mitre",fill=NA,color="gray75")+
geom_line(aes(x,y,color=z),linewidth=.6)+
geom_segment(data=yearly,aes(x,y,xend=x+365,yend=y,color=z),linewidth=.6,linetype="42")+
geom_segment(data=yearly[z!=z[1]],aes(x+182+c(100,-100),14500,xend=x+182,yend=y-500),color=color[2],linewidth=.6,arrow=arrow(type="closed",length=unit(4,"pt")),lineend="butt",linejoin="mitre")+
geom_text(data=anno,aes(x,y,label=label),hjust=c(0,1),vjust=1,size=3.87,color=color[2],lineheight=.8)+
labs(x=NULL,y=NULL,title="Monthly cancer diagnoses in England rapid registration data")+
scale_x_continuous(limits=c(xstart,xend),breaks=xbreak,labels=year(xbreak))+
scale_y_continuous(limits=c(ystart,yend),breaks=ybreak,labels=\(x)ifelse(x>=1e3,paste0(x/1e3,"k"),x))+
scale_color_manual(values=color)+
coord_cartesian(clip="off",expand=F)+
guides(color=guide_legend(ncol=1,byrow=F))+
theme(axis.text=element_text(size=11,color="gray40"),
  axis.text.y=element_text(margin=margin(,1.5)),
  axis.ticks=element_line(linewidth=.4,color="gray75"),
  axis.ticks.length=unit(0,"pt"),
  axis.ticks.length.y=unit(4,"pt"),
  legend.background=element_blank(),
  legend.box.spacing=unit(0,"pt"),
  legend.direction="vertical",
  legend.key=element_blank(),
  legend.key.height=unit(12,"pt"),
  legend.key.width=unit(24,"pt"),
  legend.margin=margin(-2,,4),
  legend.position="top",
  legend.spacing.x=unit(2,"pt"),
  legend.spacing.y=unit(0,"pt"),
  legend.text=element_text(size=11),
  legend.title=element_blank(),
  panel.background=element_blank(),
  plot.title=element_text(size=11,hjust=.5,face=2,margin=margin(,,4)))
ggsave("1.png",width=5.1,height=3,dpi=300*4)

I'm fairly sure that ES manually falsified the data. He has not documented his methodology precisely enough that other people could reproduce his work exactly, which also makes it difficult for other people to bust him falsifying the data. But the 13% drop between 2018 and 2019 is completely unrealistic, and it's inconsistent with the real data but consistent with his history of falsifying data.

Some pairs of points in his plot have irregular horizontal spacing, which might be if he somehow manually moved the points around in a GUI, so that he accidentally ended up moving the points horizontally and not only vertically:

From the arrows in the next plot, you can see that I found anomalous features next to two pairs of months that had irregular horizontal spacing. It's also weird how there's several segments of months in 2019 and 2020 that look almost like straight lines, but there are sudden discontinuities in slope before or after the line-like segments, which is anomalous for a plot that is supposed to show a moving average. In my purple line below which shows a 6-month moving average of the real data, there are only two clear discontinuities in the slope, which are between March and April of 2020 because the incidence dropped dramatically in April, and between September and October of 2020 because April 2020 fell out of the window of the moving average, but in general my purple line looks much more smooth than Ethical Skeptic's blue line:

ma=\(x,b=1,f=b){x[]=rowMeans(embed(c(rep(NA,b),x,rep(NA,f)),f+b+1),na.rm=T);x}

rapid=CJ(year=2018:2025,month=1:12)[!(year==2025&month>5)]
rapid$diagnoses=c(24265,23485,24346,25994,27661,26373,25833,25212,24953,24540,24564,25231,25169,24844,24873,26294,26401,25994,25037,25043,24770,25236,24966,25444,25128,25621,24809,16910,17246,18362,19806,21826,23169,23614,24274,24592,23870,23534,24370,26499,27741,25785,25686,25425,25493,25801,25927,25658,25752,26316,25826,27370,28176,27371,26549,26517,26984,26615,27285,26179,26944,26909,26700,28151,29276,27220,26845,26652,26579,26473,28109,27843,27733,27699,28826,28654,29560,28914,28213,27783,27948,27807,27565,28289,27863,28261,27960,28428,27754) # adjusted for number of working days

espop=c(55924500,56230100,56326000,56554900,57112500,57690300,57690300,57690300) # from Statista
espop=espop*c(.1803,.1822,.1834,.1851,.186,.1862,.1873,.1873) # proportion aged 65 and above from ONS website

rapid[,x:=as.Date(paste(year,month,1,sep="-"))]
p=rapid[,.(x,y=ma(diagnoses/espop[year-2017]*1e5,5,0))]

xstart=as.Date("2018-1-1");xend=as.Date("2025-7-1");xbreak=seq(xstart,xend-10,"6 month")
xlab=ifelse(month(xbreak)==1,paste0(year(xbreak),"\nJan"),"")
ybreak=pretty(c(0,p$y));ystart=0;yend=max(p$y*1.03)

ystart=120;yend=300;ybreak=0:15*20

ggplot(p)+
geom_point(aes(x,y),size=1.4,stroke=0,color=hsv(5/6,1,.4))+
geom_line(aes(x,y),linewidth=.6,color=hsv(5/6,1,.4))+
labs(x=NULL,y=NULL)+
scale_x_continuous(limits=c(xstart,xend),breaks=xbreak,labels=xlab)+
scale_y_continuous(limits=c(ystart,yend),breaks=ybreak)+
guides(color=guide_legend(ncol=3,byrow=F))+
coord_cartesian(clip="off",expand=F)+
theme(axis.text=element_text(size=9,color="black",lineheight=.8),
  axis.ticks=element_line(color="black",linewidth=.3),
  axis.ticks.length=unit(3,"pt"),
  axis.ticks.x=element_line(color=alpha("black",1:0)),
  legend.background=element_blank(),
  legend.box.spacing=unit(0,"pt"),
  legend.key=element_blank(),
  legend.key.height=unit(12,"pt"),
  legend.key.width=unit(23,"pt"),
  legend.margin=margin(-2,,14),
  legend.position="top",
  legend.spacing.x=unit(2,"pt"),
  legend.spacing.y=unit(0,"pt"),
  legend.text=element_text(size=10,vjust=.5),
  legend.title=element_blank(),
  panel.background=element_blank(),
  panel.grid=element_blank(),
  plot.background=element_rect(fill="transparent",color=NA),
  plot.title=element_text(size=11,hjust=.5))
ggsave("1.png",width=6,height=3,dpi=300*4)

The next plot also shows how the difference between the pre-2020 and post-2021 slopes is greatly exaggerated by Ethical Skeptic's fake data:

ma=\(x,b=1,f=b){x[]=rowMeans(embed(c(rep(NA,b),x,rep(NA,f)),f+b+1),na.rm=T);x}

rapid=CJ(year=2018:2025,month=1:12)[!(year==2025&month>5)]
rapid$cases=c(24265,23485,24346,25994,27661,26373,25833,25212,24953,24540,24564,25231,25169,24844,24873,26294,26401,25994,25037,25043,24770,25236,24966,25444,25128,25621,24809,16910,17246,18362,19806,21826,23169,23614,24274,24592,23870,23534,24370,26499,27741,25785,25686,25425,25493,25801,25927,25658,25752,26316,25826,27370,28176,27371,26549,26517,26984,26615,27285,26179,26944,26909,26700,28151,29276,27220,26845,26652,26579,26473,28109,27843,27733,27699,28826,28654,29560,28914,28213,27783,27948,27807,27565,28289,27863,28261,27960,28428,27754)
p1=rapid[,.(x=as.Date(paste(year,month,16,sep="-")),y=ma(cases,5,0))]
p1$z="Real data (adjusted for working days, 6-month moving average)"

es=CJ(year=2018:2025,month=1:12)[1:87]
es$rate=c(239.6,235.7,237.5,242.7,248.6,250.4,253.2,255.5,256.8,254.2,249.6,247.5,239.3,230.8,222.3,218.2,212.1,206.9,206.1,206.4,206.4,204.6,203.1,202.3,206.1,210.5,215.1,206.4,198.9,191.5,184.5,178.6,176.3,185.1,196.1,204.9,212.1,214.6,216.2,220.8,226.2,227.0,230.3,233.1,234.4,233.7,231.3,230.6,230.6,232.1,232.6,236.0,238.5,241.1,242.4,242.4,244.5,243.4,242.1,240.3,242.1,243.9,244.5,248.1,252.4,255.3,254.5,255.0,254.2,251.7,250.1,250.9,253.5,255.5,260.7,266.1,269.4,272.7,273.5,273.5,271.7,269.1,267.3,266.8,268.4,270.9,272.2)
espop=c(55924500,56230100,56326000,56554900,57112500,57690300,57690300,57690300) # from Statista
espop=espop*c(.1803,.1822,.1834,.1851,.186,.1862,.1873,.1873) # proportion aged 65 and above from ONS website
p2=es[,.(x=as.Date(paste(year,month,16,sep="-")),y=rate*espop[factor(year)]/1e5)]
p2$z="Ethical Skeptic's fake data converted to number of diagnoses"

p=rbind(p1,p2)[,z:=factor(z,unique(z))]

lm=rbind(cbind(p,type=2)[,fit:=year(x)%in%2018:2019],cbind(p,type=3)[,fit:=year(x)%in%2022:2024])
lm=lm[,.(x,y=predict(lm(y~x,.SD[fit==T]),.SD),fit),.(z,type)]
p=rbind(p[,fit:=T][,type:=1],lm)

p[,type:=factor(type,,c("Actual","2018-2019 trend","2022-2024 trend"))]

xstart=as.Date("2018-1-1");xend=as.Date("2025-7-1");xbreak=seq(xstart+182,as.Date("2025-1-1"),"year")
ybreak=pretty(c(0,p$y));ystart=0;yend=max(ybreak)

ggplot(p)+
facet_wrap(~z,ncol=1,dir="v",scales="free_x")+
geom_vline(xintercept=seq(xstart,xend,"year"),color="gray90",linewidth=.4)+
annotate("rect",xmin=xstart,xmax=xend,ymin=ystart,ymax=yend,linewidth=.4,lineend="square",linejoin="mitre",fill=NA,color="gray75")+
geom_line(aes(x,y,color=type),linewidth=.6,linetype="11")+
geom_line(data=p[fit==T],aes(x,y,color=type),linewidth=.6)+
labs(x=NULL,y=NULL,title="Monthly cancer diagnoses in England rapid registration data")+
scale_x_continuous(limits=c(xstart,xend),breaks=xbreak,labels=year(xbreak))+
scale_y_continuous(limits=c(ystart,yend),breaks=ybreak,labels=\(x)ifelse(x>=1e3,paste0(x/1e3,"k"),x))+
scale_color_manual(values=c("black",hsv(1/3,1,.7),hsv(0,.7,1)))+
scale_alpha_manual(values=c(1,.4,.4))+
coord_cartesian(clip="off",expand=F)+
guides(alpha="none")+
theme(axis.text=element_text(size=11,color="gray40"),
  axis.text.y=element_text(margin=margin(,1.5)),
  axis.ticks=element_line(linewidth=.4,color="gray75"),
  axis.ticks.length=unit(0,"pt"),
  axis.ticks.length.y=unit(4,"pt"),
  legend.background=element_blank(),
  legend.box.spacing=unit(0,"pt"),
  legend.key=element_blank(),
  legend.key.height=unit(12,"pt"),
  legend.key.width=unit(24,"pt"),
  legend.margin=margin(-2,,4),
  legend.position="top",
  legend.spacing.x=unit(2,"pt"),
  legend.spacing.y=unit(0,"pt"),
  legend.text=element_text(size=11),
  legend.title=element_blank(),
  panel.background=element_blank(),
  panel.spacing.y=unit(3,"pt"),
  plot.title=element_text(size=11,hjust=.5,face=2,margin=margin(,,4)),
  strip.background=element_rect(fill="gray90",color="gray75",linewidth=.4),
  strip.text=element_text(size=11,margin=margin(3,,3)))
ggsave("1.png",width=5,height=4,dpi=300*4)

In the next plot I calculated age-standardized incidence rates by downloading an age-stratified version of the rapid registration data from here: https://nhsd-ndrs.shinyapps.io/rcrd/ ("Demographic factors > Downloads > Download time trend data for all cancer groups and demographic factors"). I took yearly population estimates by age from Nomis: https://www.nomisweb.co.uk/datasets/pestsyoala. The age-standardized incidence increased by only about 3% between 2022 and 2024:

t=fread("Incidence_Treatment_statistics_England.csv.gz")
t=t[`Cancer group`=="All sites combined"]
t=t[Metric=="New cancer diagnoses (working day adjusted)"]
t=t[Breakdown=="Age-group"]
t[,age:=as.integer(sub("\\D.*","",Demographic))]
t=t[,.(year=Year,month=match(Month,month.name),diagnoses=Statistic,age)]

pop=fread("https://sars2.net/f/englandpop.csv")
ages=c(0,5:8*10)
pop=pop[,.(pop=sum(pop)),.(year,age=ages[findInterval(age,ages)])]
pop=rbind(pop,pop[year==2024][,year:=2025])
t=merge(t,pop)
t=merge(pop[year==2020,.(age,std=pop/sum(pop))],t)

p=t[,.(y=sum(diagnoses/pop*std*1e5)),.(x=as.Date(paste(year,month,16,sep="-")))]

xstart=as.Date("2018-1-1");xend=as.Date("2025-7-1");xbreak=seq(xstart+182,as.Date("2025-1-1"),"year")
ybreak=pretty(c(0,p$y));ystart=0;yend=max(p$y*1.03)

ggplot(p)+
geom_vline(xintercept=seq(xstart,xend,"year"),color="gray90",linewidth=.4)+
annotate("rect",xmin=xstart,xmax=xend,ymin=ystart,ymax=yend,linewidth=.4,lineend="square",linejoin="mitre",fill=NA,color="gray75")+
geom_point(aes(x,y),size=1.3,stroke=0)+
geom_line(aes(x,y),linewidth=.6)+
labs(x=NULL,y=NULL,title="Age-adjusted rate of monthly new cancer diagnoses per\n100,000 people in England rapid registration data")+
scale_x_continuous(limits=c(xstart,xend),breaks=xbreak,labels=year(xbreak))+
scale_y_continuous(limits=c(ystart,yend),breaks=ybreak)+
scale_color_manual(values=c(hsv(0,.7,1),"black"))+
coord_cartesian(clip="off",expand=F)+
guides(color=guide_legend(ncol=1,byrow=F))+
theme(axis.text=element_text(size=11,color="gray40"),
  axis.text.y=element_text(margin=margin(,1)),
  axis.ticks=element_line(linewidth=.4,color="gray75"),
  axis.ticks.length=unit(0,"pt"),
  axis.ticks.length.y=unit(4,"pt"),
  panel.background=element_blank(),
  plot.subtitle=element_text(hjust=.5,margin=margin(,,4)),
  plot.title=element_text(size=11,hjust=.5,face=2,margin=margin(,,4)))
ggsave("1.png",width=5.2,height=3,dpi=300*4)

Yearly cancer incidence data is available from the NHS publication "Cancer Registration Statistics, England, 2022": https://digital.nhs.uk/data-and-information/publications/statistical/cancer-registration-statistics/england-2022#resources (file "Cancer Registration Statistics, updated to use 2021 census population estimates").

Without any kind of adjustment for population size or age, the yearly number of diagnoses increased by only about 3% between 2019 and 2022. In Ethical Skeptic's plot, the rate of new diagnoses per population aged 65 and above increased by about 10% between 2019 and 2022, even though his adjustment for population size should make the percentage smaller and not bigger:

t=fread("https://sars2.net/f/Table_1_machine_readable_updated_June2025.csv.gz")
s=t[stage_at_diagnosis=="All stages"&imd_quintile=="All quintiles"&gender=="Persons"]

 # `All` is for breast cancer, empty is for other cancers
s=s[hormone_receptor%in%c("","All")&hormone_receptor_status%in%c("","All")]

s=s[ndrs_detailed_group%like%"^All|ALL"]
s[,.(diagnoses=sum(count)),.(year=diagnosisyear)]
# year diagnoses
# 2013    491633
# 2014    506390
# 2015    521302
# 2016    532723
# 2017    536826
# 2018    554847
# 2019    565363
# 2020    486232
# 2021    551173
# 2022    582160 # about 3% higher than 2019

From the plot below where I overlaid the yearly data from the NHS report over the rapid registration data, you can see that the rapid registration data is missing about half of all new diagnoses, because it's missing non-melanoma skin cancer which is very common in England, and it's also missing a fraction of diagnoses for other types of cancer:

ma=\(x,b=1,f=b){x[]=rowMeans(embed(c(rep(NA,b),x,rep(NA,f)),f+b+1),na.rm=T);x}

y=fread("https://sars2.net/f/Table_1_machine_readable_updated_June2025.csv.gz")
y=y[stage_at_diagnosis=="All stages"&imd_quintile=="All quintiles"&gender=="Persons"]
y=y[hormone_receptor%in%c("","All")&hormone_receptor_status%in%c("","All")] # `All` is for breast cancer, empty is for other cancers
y=y[ndrs_detailed_group%like%"^All|ALL"]
p1=y[,.(y=sum(count)),.(x=diagnosisyear)][,.(x=as.Date(paste0(x,"-7-1")),y=y/(365+(x%%4==0)))]
p1$z="Cancer Registration Statistics, England, 2022"

rapid=CJ(year=2018:2025,month=1:12)[!(year==2025&month>5)]
rapid$cases=c(24265,23485,24346,25994,27661,26373,25833,25212,24953,24540,24564,25231,25169,24844,24873,26294,26401,25994,25037,25043,24770,25236,24966,25444,25128,25621,24809,16910,17246,18362,19806,21826,23169,23614,24274,24592,23870,23534,24370,26499,27741,25785,25686,25425,25493,25801,25927,25658,25752,26316,25826,27370,28176,27371,26549,26517,26984,26615,27285,26179,26944,26909,26700,28151,29276,27220,26845,26652,26579,26473,28109,27843,27733,27699,28826,28654,29560,28914,28213,27783,27948,27807,27565,28289,27863,28261,27960,28428,27754) # adjusted for number of working days
p2=rapid[,.(x=as.Date(paste(year,month,16,sep="-")),y=cases)][,y:=y/days_in_month(x)]
p2$z="Rapid registration dashboard"

es=CJ(year=2018:2025,month=1:12)[1:87]
es$rate=c(239.6,235.7,237.5,242.7,248.6,250.4,253.2,255.5,256.8,254.2,249.6,247.5,239.3,230.8,222.3,218.2,212.1,206.9,206.1,206.4,206.4,204.6,203.1,202.3,206.1,210.5,215.1,206.4,198.9,191.5,184.5,178.6,176.3,185.1,196.1,204.9,212.1,214.6,216.2,220.8,226.2,227.0,230.3,233.1,234.4,233.7,231.3,230.6,230.6,232.1,232.6,236.0,238.5,241.1,242.4,242.4,244.5,243.4,242.1,240.3,242.1,243.9,244.5,248.1,252.4,255.3,254.5,255.0,254.2,251.7,250.1,250.9,253.5,255.5,260.7,266.1,269.4,272.7,273.5,273.5,271.7,269.1,267.3,266.8,268.4,270.9,272.2)
espop=c(55924500,56230100,56326000,56554900,57112500,57690300,57690300,57690300) # from Statista
espop=espop*c(.1803,.1822,.1834,.1851,.186,.1862,.1873,.1873) # proportion aged 65 and above from ONS website
p3=es[,.(x=as.Date(paste(year,month,16,sep="-")),y=rate*espop[factor(year)]/1e5)]
p3[,y:=y/ma(days_in_month(x),5,0)]
p3$z="Ethical Skeptic's rate converted to number of diagnoses"

p=rbind(p1,p2,p3)[,z:=factor(z,unique(z))]

xstart=as.Date("2013-1-1");xend=as.Date("2025-7-1");xbreak=seq(xstart+182,as.Date("2025-1-1"),"year")
ybreak=pretty(c(0,p$y),7);ystart=0;yend=max(ybreak)

ggplot(p)+
geom_vline(xintercept=seq(xstart,xend,"year"),color="gray90",linewidth=.4)+
annotate("rect",xmin=xstart,xmax=xend,ymin=ystart,ymax=yend,linewidth=.4,lineend="square",linejoin="mitre",fill=NA,color="gray75")+
geom_point(aes(x,y,color=z,alpha=z),size=1.4,stroke=0)+
geom_line(aes(x,y,color=z),linewidth=.6)+
labs(x=NULL,y=NULL,title="New cancer diagnoses in England (divided by number of\ndays in year or month)")+
scale_x_date(limits=c(xstart,xend),breaks=xbreak,labels=year(xbreak))+
scale_y_continuous(limits=c(ystart,yend),breaks=ybreak)+
scale_color_manual(values=c(hsv(1/3,1,.6),hsv(22/36,1,.6),"#aaaa00"))+
scale_alpha_manual(values=c(1,0,0,0))+
coord_cartesian(clip="off",expand=F)+
guides(color=guide_legend(ncol=1,byrow=F))+
theme(axis.text=element_text(size=11,color="gray40"),
  axis.text.y=element_text(margin=margin(,1.5)),
  axis.ticks=element_line(linewidth=.4,color="gray75"),
  axis.ticks.length=unit(0,"pt"),
  axis.ticks.length.y=unit(4,"pt"),
  legend.background=element_blank(),
  legend.box.spacing=unit(0,"pt"),
  legend.direction="vertical",
  legend.key=element_blank(),
  legend.key.height=unit(12,"pt"),
  legend.key.width=unit(24,"pt"),
  legend.margin=margin(-2,,4),
  legend.position="top",
  legend.spacing.x=unit(2,"pt"),
  legend.spacing.y=unit(0,"pt"),
  legend.text=element_text(size=11,margin=margin(,,,1)),
  legend.title=element_blank(),
  panel.background=element_blank(),
  plot.subtitle=element_text(size=11,hjust=.5,margin=margin(,,4)),
  plot.title=element_text(size=11,hjust=.5,face=2,margin=margin(,,4)))
ggsave("1.png",width=5.1,height=3.5,dpi=300*4)

The website of the NHS says: "The Rapid Cancer Registration Data contains proxy tumour registrations and some associated events on the cancer patient pathway (e.g. surgery, radiotherapy and systemic anti-cancer therapy data) from January 2018 to the most recently available data on cancer diagnoses. RCRD provides a quicker, indicative source of cancer data compared to the National Cancer Registration Data (NCRD), which is the 'gold-standard' registration data set and which relies on additional data sources, enhanced follow-up with trusts and expert processing by cancer registration officers. Due to the lower quality of RCRD, the data will not match the eventual National Statistics published on the full NCRD." [https://digital.nhs.uk/ndrs/data/data-sets/rcrd]

A document titled "Rapid Cancer Registration Dataset - Data Quality - August 2025" says: [https://digital.nhs.uk/ndrs/data/data-sets/rcrd]

This document outlines the main features of the data to be aware of when interpreting the Rapid Cancer Registration Dataset:

• across all cancer types included approximately 12.1% of cases are missing and 6.0% of cases are included erroneously or with incorrect cancer type or diagnosis date (when compared to 'Gold Standard' registration data for 2018 data)
• these figures vary strongly with cancer site. Broadly, more common cancers (particularly breast and prostate cancer) perform best and less common cancers (particularly bone and soft tissue and cancers of unknown primary) perform worst
• non-melanoma skin cancer (ICD-10 C44) tumours are excluded from the majority of data shown (Figure 3 onwards). Carcinoma of the cervix in-situ (ICD-10 D06) is excluded from all data presented
• there are more missing tumours in those aged over 70 compared to younger age groups
• other factors that reduce data completeness include the patient's route to diagnosis, mortality within 30 days of diagnosis, and the presence of multiple cancers
• usable data is available approximately 4 to 5 months after diagnosis or other clinical activity occurs
• data on cancer stage group at diagnosis is available for a number of common tumour types, although completeness is lower than that for the Gold Standard registration data. Where data is available it generally agrees with the Gold Standard stage group in 80 to 90% of tumours

A presentation about the rapid registration dataset said that the dataset "Covers all malignant cancers (excl. NMSC) and selected non-malignant cancers: Breast, Bladder & Brain (ICD-10 C00-C97 excl. C44, plus D05, D09, D32, D33, D35, D41, D42-D44)". [https://www.ndrs.nhs.uk/wp-content/uploads/2020/10/NDRS-Webinar-Rapid-Registrations-Cancer-Dataset-21.10.2020.pdf]

Fake linear regression of cancer mortality since 2014

Ethical Skeptic wrote: [https://theethicalskeptic.com/2025/09/25/the-state-of-things-pandemic/]

All the trolls had to do was wait, and see if my models proved correct. They did prove correct. But when you are spinning deception, you must declare truth early, often, and loudly. This is the heart of Narrative Science. Real credibility is earned by engaging in the meticulous and intensive work entailed in describing a system, not in running stand-alone stats, deceptive linear regression graphs, or academic poseur heuristics.

For instance, making the baseless or linear-extrapolated and lagged claim 'There is no increase in cancer mortality', in a vacuum, and not even knowing what an 'MCoD' or 'Spring Lull' is, constitutes an act of appeal-to-ignorance deception. So much more than pablum is required from these claimants - and no matter how much they scream and insist, they are still wrong (see Excess Cancer Mortality in Charts 5 and 6 below). In the realm of systems analysis, adherence to such a professional standard is paramount, and I make no apologies for upholding it.

Where the third link pointed to an image file for this plot:

I immediately thought that the slope of his supposed "2014-2019 regression baseline" didn't look steep enough. ES has a habit of faking these baselines or drawing them by hand so that he doesn't do any real regression of the data. When I tried to reproduce his plot, I confirmed that a real linear regression of the data was in fact much steeper than his baseline, as you can see from the red line I added here:

old=fread("https://sars2.net/f/Weekly_Counts_of_Deaths_by_State_and_Select_Causes__2014-2019.csv.gz")
old=old[`Jurisdiction of Occurrence`=="United States"]
old=old[,.(year=`MMWR Year`,week=`MMWR Week`,dead=`Malignant neoplasms (C00-C97)`)]

t=fread("https://sars2.net/f/wondermalignantweekly.csv")[year>2019,.(year,week,dead)]
t=rbind(old,t)

p=t[,.(x=MMWRweek::MMWRweek2Date(year,week,4),y=dead,z="Actual deaths")]
p=rbind(p,p[,.(x,y=predict(lm(y~x,p[year(x)<2020]),p),z="2014-2019 linear regression")])
p[,z:=factor(z,unique(z))]

xstart=as.Date("2014-1-1");xend=as.Date("2025-7-1");xbreak=seq(xstart,xend-1,"6 month")
ystart=10e3;yend=13e3;ybreak=seq(ystart,yend,500)

ggplot(p)+
geom_line(aes(x,y,color=z,linewidth=z))+
labs(x=NULL,y=NULL)+
scale_x_date(limits=c(xstart,xend),breaks=xbreak,labels=ifelse(month(xbreak)==7,year(xbreak),""))+
scale_y_continuous(limits=c(ystart,yend),breaks=ybreak)+
scale_color_manual(values=c("black","#ff6666"))+
scale_linewidth_manual(values=c(.3,.6))+
coord_cartesian(clip="off",expand=F)+
theme(axis.text=element_text(size=9,color="black"),
  axis.text.y=element_blank(),
  axis.ticks=element_line(color="black",linewidth=.3),
  axis.ticks.length=unit(3,"pt"),
  axis.ticks.x=element_line(color=alpha("black",1:0)),
  legend.background=element_blank(),
  legend.box.spacing=unit(0,"pt"),
  legend.justification=c(.5,0),
  legend.key=element_blank(),
  legend.key.height=unit(12,"pt"),
  legend.key.width=unit(23,"pt"),
  legend.position=c(.5,.86),
  legend.spacing.x=unit(2,"pt"),
  legend.spacing.y=unit(0,"pt"),
  legend.text=element_text(size=10,vjust=.5),
  legend.title=element_blank(),
  panel.background=element_blank(),
  panel.grid=element_blank(),
  plot.background=element_rect(fill="transparent",color=NA),
  plot.title=element_text(size=11,hjust=.5))
ggsave("1.png",width=6,height=3.2,dpi=300*4)

Ethical Skeptic's plot says that "Weekly cancer death rates are up above the 2014 - 2019 regression baseline". The way he appears to have accomplished the feat was to first fake his regression line, and then to manually move points upwards when the points happened to fall below his fake regression line.

In the plot above, my black line otherwise matches Ethical Skeptic's blue line, except there are clusters of weeks in 2022 and 2023 where my line is much lower. I think it's because ES manually shifted points up that were below his dashed orange baseline. In 2022 and 2023, his blue line has only 4 points that are below his dashed orange baseline, but my black line has 12 points.

(Ethical Skeptic's blue line is also slightly higher than my black line from 2020 onwards. I took data for 2014-2019 from the same CDC dataset that ES uses in his plots. [https://data.cdc.gov/National-Center-for-Health-Statistics/Weekly-Counts-of-Deaths-by-State-and-Select-Causes/3yf8-kanr] I took data from 2020 onwards from CDC WONDER, but I don't know why my number of deaths from CDC WONDER would be slightly lower than Ethical Skeptic's number of deaths, because I just did a simple query for weekly deaths with the underlying cause malignant neoplasms (C00-C97). So maybe ES applied a very slight upwards adjustment to all points from 2020 onwards.)

Paper about cancer incidence by vaccination status in South Korea

Ethical Skeptic posted this tweet: [https://x.com/EthicalSkeptic/status/1972021203867951336]

The paper he linked looked at cancer incidence during the first year from vaccination, where vaccinated people had a hazard ratio of about 1.27 relative to a matched cohort of unvaccinated people. [https://biomarkerres.biomedcentral.com/articles/10.1186/s40364-025-00831-w]

One reason why vaccinated people had a higher incidence of cancer might be that vaccinated people are more likely to get screened for cancer than unvaccinated people, because unvaccinated people tend to have lower health-seeking behavior and to be more hesitant to interact with medical services.

In a Japanese study unvaccinated women were much less likely than vaccinated women to get screening for breast cancer. The authors wrote that when they did a regression adjusted for age, socioeconomic variables, and health-seeking behavior traits, "Individuals who remained unvaccinated due to health concerns (incidence rate ratio (IRR) = 0.47, 95% confidence interval (CI) 0.29-0.77, p = 0.003) and for other unspecified reasons (IRR = 0.73, 95% CI 0.62-0.86, p < 0.001) were significantly less inclined to opt for screening when compared to their fully vaccinated counterparts." [https://www.mdpi.com/2072-6694/16/9/1783]

A Canadian paper from 2024 said: "Unvaccinated respondents were less likely to have received serum cholesterol (aOR 0.69; 95 % CI [0.50-0.70), serum glucose (aOR 0.65; 95 % CI [0.56-0.75]), or blood pressure measurements (aOR 0.47; 95 % CI [0.33-0.66]); and were less likely to have received breast cancer (aOR 0.35; 95 % CI [0.25-0.48]), colorectal cancer (aOR 0.52; 95 % CI [0.46-0.60]) and prostate cancer screening (aOR 0.61; 95 % CI [0.48-0.76])." [https://www.sciencedirect.com/science/article/abs/pii/S0264410X24000070]

A paper about cancer screening behavior in South Korea said: "In the city with the highest cancer screening participation rate, factors such as oral health, physical activity, breakfast habits, and past smoking history were all associated with higher participation. This finding aligns with the conclusion of a previous study that identified a correlation between healthy dietary habits, physical activity motivation, and cancer screening participation [35]. A similar trend has been observed in Lithuania, where factors such as the consumption of fresh vegetables, physical activity, and abstinence from alcohol are associated with higher participation in the national breast cancer screening program for women aged 50-69 [36]. In addition, a previous study of cancer screening activity in adults aged 50 years and older in the United States reported that healthy lifestyles, such as physical activity and smoking cessation, are associated with cancer screening behaviors [27]." [https://www.mdpi.com/2227-9032/13/6/664]

Most of the population of South Korea is vaccinated, so if vaccines would've caused a 27% increase in the incidence of cancer in vaccinated people, you'd expect the overall population of Korea to have a major increase in cancer incidence above the pre-COVID trend. However from this plot of age-standardized cancer incidence in South Korea, you can see that the incidence in 2021 and 2022 roughly fell on the pre-COVID trend: [https://e-crt.org/journal/view.php?doi=10.4143/crt.2025.264]

The Korean study was promoted by Kevin McKernan, who says there has been a massive increase in cancer caused by plasmid DNA from mRNA vaccines, and who blocked me after I showed him why Ethical Skeptic's SV40 cancer wave plot was fake: [https://x.com/Kevin_McKernan/status/1972004072439140451]

However in the Korean study, people who got mRNA vaccines had a much lower cancer HR than people who got viral vector vaccines: [https://static-content.springer.com/esm/art%3A10.1186%2Fs40364-025-00831-w/MediaObjects/40364_2025_831_MOESM1_ESM.docx]

The authors seem to have referred to AstraZeneca and other viral vector vaccines as "cDNA vaccines". Supplementary file 1 says: "Regarding vaccine types between the first and second vaccinations, 1,928,363 individuals (81.02%) were treated with mRNA vaccines only, 333,698 individuals (14.02%) were given cDNA vaccines only, and 117,967 individuals (4.96%) were administered with heterologous vaccinations in the COVID-19 vaccinated cohort (Table S2)."

Normally the term "DNA vaccine" refers to vaccines that deliver DNA in a plasmid, like the Indian ZyCoV-D vaccine, the Korean Genexine vaccine, and the Inovio vaccine. There is no approved DNA vaccine for COVID in South Korea, even though there have been clinical trials for the Inovio and Genexine DNA vaccines, but they wouldn't account for 14% of total subjects in the cancer study. AstraZeneca and J&J are normally called viral vector vaccines, where a modified adenovirus is used as a vector to deliver spike DNA to the cell. Adenoviruses are DNA viruses, so the spike gene inside the adenovirus vector is coded as DNA that is complementary to the original RNA, so the term "cDNA vaccine" was likely used in the paper as a nonstandard term for a viral vector vaccine.

A paper about COVID vaccination in South Korea said: "As of September 25, 2022, 128,710,064 doses of COVID-19 vaccines were administered: BNT162b2 (62.9%), mRNA-1273 (19.5%), ChAdOx1 (15.8%), Ad26.COV2.S (1.2%), NVX-CoV2373 (0.6%) and GBP510 (< 0.1%)." [https://jkms.kr/DOIx.php?id=10.3346%2Fjkms.2022.37.e351] So if AstraZeneca accounted for about 16% of doses in South Korea, and about 14% of subjects in the cancer study were in the cDNA-only group, then it's likely that the cDNA group included AstraZeneca.

Table S3 has a list of vaccine brands that were administered for booster doses, which includes only the three brands of Pfizer, Moderna, and AstraZeneca. There's only a few doses of AstraZeneca listed in the table, because AstraZeneca was generally only administered for primary course doses. The distribution of vaccine brands for primary course doses was not shown anywhere in the paper.

Cancers take time to develop, but Figure 1B shows that even during the first month of follow-up, vaccinated people already had a much higher incidence of cancer than unvaccinated people:

Supplementary file 2 says that the follow-up period started from the day after the primary series was completed, and not from the day of the first dose. But still, most people got the second dose within a few weeks from the first dose, so if the difference in incidence during the first month of follow-up would be explained by vaccines causing cancer, then the vaccines would have to be causing super fast-acting turbo cancer.

Uncle John Returns posted the images below and wrote: "Why should cancer registration rates among the unvaccinated approximately double in a year? Unless they were undercounted to begin with?" [https://x.com/UncleJo46902375/status/1972676088640757796]

His plot demonstrates clearly how unvaccinated people had low incidence during the first 3 months of follow-up, even though you can ignore his "South Korea average 2022" line, because it is not too meaningful. The line shows the age-standardized incidence of cancer in 2022 among the overall population of Korea, which was about 522.7 cases per 100,000 person-years, which he converted to about 4.3 cases per 10,000 people per a 30-day period. [https://www.cancerdata.re.kr/surveillance/en/data?menuId=40]

However Uncle John didn't take into account that the Korean study excluded ages below 20, and in general it doesn't make too much sense to compare the incidence among the PSM-matched cohorts against the incidence among the overall population of Korea.

The authors of the Korean study did propensity score matching to match vaccinated people to unvaccinated people 4-to-1, which ended up skewing the age distribution of the cohorts included in the study, because more than 80% of elderly people were vaccinated, so many vaccinated elderly people had to be discarded. The age distribution of the subjects before the PSM was not shown anywhere, but Table S2 shows the distribution after the PSM, which is compared here to the age distribution of the overall Korean population: [https://x.com/UncleJo46902375/status/1972945872452517945]

The PSM similarly also skewed other attributes of the matched cohorts relative to the distribution of the attributes among the overall population of Korea.

And the Korean study also excluded non-melanoma skin cancer (C44), which was not excluded in the dataset Uncle John used to calculate the "South Korea average 2022" line.

At first I didn't understand why Table S2 showed that the subjects were not matched by income level: [https://static-content.springer.com/esm/art%3A10.1186%2Fs40364-025-00831-w/MediaObjects/40364_2025_831_MOESM1_ESM.docx]

But apparently the matching was done based on the insurance level, and the income level was estimated from the insurance level. Supplementary file 2 says: "The following covariates were considered: age, sex, insurance levels, Charlson comorbidity index (CCI) scores, and prior COVID-19 infection (history of SARS-CoV-2 infection). Insurance levels (recipients of medical aid, grades 1-5, grades 6-10, grades 11-15, and grades 16-20) were defined based on the National Health Insurance premium, which was used as a proxy for income since it is proportional to monthly income and includes both earnings and capital gains." [https://static-content.springer.com/esm/art%3A10.1186%2Fs40364-025-00831-w/MediaObjects/40364_2025_831_MOESM2_ESM.docx]

But anyway, the cohorts were matched by so few variables that there likely remains major residual confounding for the level of health-seeking behavior.

I initially thought that unvaccinated people were assigned index dates that matched the index dates of the paired vaccinated people. But supplementary file 2 said that the index date of unvaccinated people was always set as January 1st 2022: "For the unvaccinated group, the index date was set as January 1, 2022; those who had a vaccination history within 1 year based on the index date and those who were deceased were excluded (n = 30,955). A total of 599,124 unvaccinated individuals were included. For the vaccinated group, the index date was set as the day after the vaccination completion date, and those with incomplete vaccination (n = 278,610), unspecified vaccine type (n = 77,674), deceased (n = 33,836), and a prior medical history of overall cancers within 1-year based on index date (n = 499,572) were excluded."

So the follow-up period of all unvaccinated people consisted of January 1st 2022 to December 31st 2022, but the follow-up period of most vaccinated people already started in 2021.

So the reason why unvaccinated people had low incidence during the first 3 months may have been if people avoided getting screening during COVID waves. The first two major COVID waves in South Korea were the Delta wave which peaked around December 2021, and the Omicron wave which peaked around March 2022:

kim=\(x)ifelse(x>=1e3,ifelse(x>=1e6,paste0(x/1e6,"M"),paste0(x/1e3,"k")),x)

t=fread("https://srhdpeuwpubsa.blob.core.windows.net/whdh/COVID/WHO-COVID-19-global-data.csv")

p=t[Country_code=="KR"][,.(x=Date_reported,dead=New_deaths,case=New_cases)]

xstart=as.Date("2020-1-1");xend=as.Date("2024-1-1");xbreak=seq(xstart+182,xend,"year")
ybreak=0:6*5e5;ystart=0;yend=max(ybreak);ybreak2=ybreak/1e3
secmult=1e3

color=c("black",hsv(0,.5,1))

lab=p[,.(lab=c("Cases","Deaths"),x=c(xstart+50,xend-50),y=yend*.92)]

ggplot(p)+
geom_vline(xintercept=seq(xstart,xend,"year"),color="gray90",linewidth=.4)+
geom_segment(data=CJ(x=seq(xstart,xend,"month")),aes(x,0,xend=x,yend=yend*.015),color="gray75",linewidth=.4)+
geom_hline(yintercept=ybreak,color="gray90",linewidth=.4)+
annotate("rect",xmin=xstart,xmax=xend,ymin=ystart,ymax=yend,linewidth=.4,lineend="square",linejoin="mitre",fill=NA,color="gray75")+
geom_line(aes(x,case),linewidth=.6)+
geom_line(aes(x,dead*secmult),color=color[2],linewidth=.6)+
geom_text(data=lab,aes(x,y,label=lab,hjust=0:1),color=color,size=3.87)+
labs(x=NULL,y=NULL,title="South Korea: Weekly COVID cases and deaths by date reported to WHO",subtitle="Source: data.who.int/dashboards/covid19/data")+
scale_x_date(limits=c(xstart,xend),breaks=xbreak,labels=year(xbreak))+
scale_y_continuous(limits=range(ybreak),labels=kim,breaks=ybreak,sec.axis=sec_axis(trans=~./secmult,breaks=ybreak2,labels=kim))+
coord_cartesian(clip="off",expand=F)+
theme(axis.text=element_text(size=11,color="gray40"),
  axis.text.y=element_text(margin=margin(,2,,2)),
  axis.text.y.left=element_text(color=color[1]),
  axis.text.y.right=element_text(color=color[2]),
  axis.ticks.length=unit(0,"pt"),
  legend.background=element_rect(color="gray70",linewidth=.4),
  legend.box.spacing=unit(0,"pt"),
  legend.direction="vertical",
  legend.justification=c(.5,.5),
  legend.key=element_blank(),
  legend.key.height=unit(12,"pt"),
  legend.key.width=unit(24,"pt"),
  legend.margin=margin(3,5,3,3),
  legend.position=c(.5,.5),
  legend.spacing.x=unit(2,"pt"),
  legend.spacing.y=unit(0,"pt"),
  legend.text=element_text(size=11),
  legend.title=element_blank(),
  panel.background=element_blank(),
  plot.subtitle=element_text(hjust=.5,margin=margin(,,4)),
  plot.title=element_text(size=11,hjust=.5,face=2,margin=margin(,,4)))
ggsave("1.png",width=5.5,height=3.2,dpi=300*4)

I didn't find monthly cancer incidence data for 2022, so I wasn't able verify if the cancer incidence was low in early 2022 relative to later in 2022.

But for example an article from April 2022 said in Korean: "As the spread of COVID-19 continues, there are many people who are procrastinating the check-up, so please do not delay and actively participate in the early check-up as there is a concern about congestion due to the year-end concentration." [https://blog.naver.com/hdnews9001/222715512561] In the Korean cancer screening program, people born on even years are asked to get screening during even years, and people born on odd years are asked to get screening during odd years. People have until the end of the year to get screening, but apparently due to COVID, some people delayed getting screening until the end of the year.

The deadline for screening in 2021 was extended until June 2022 due to COVID. An article from January 2022 said in Korean: "2021 flew by in the blink of an eye! Many people may have postponed their national health screenings to adhere to quarantine guidelines and then forgot about them. In December 2021, the government announced, 'To ensure access to screenings for those who have postponed or refrained from using them to comply with COVID-19 guidelines, the health screening period will be extended.'" [https://blog.ibk.co.kr/2703]

I didn't find data for overall monthly cancer screening rates, but Uncle John Returns found a paper with the monthly number of colonoscopies performed in 2019-2021, where each year there were more colonoscopies performed towards the end of the year than the beginning of the year (so even in 2019, people seem to have procrastinated until the end of the year to get screening). Data for the Omicron wave in spring 2022 is missing, but there was a big drop in colonoscopies performed during the first COVID scare around March 2020: [https://www.irjournal.org/journal/view.php?number=1091]

I now found that screening volume was higher than normal in 2021. The screening volume was much lower than normal in 2022 for breast cancer, but close to the pre-COVID trend for stomach cancer, colorectal cancer, and cervical cancer: [https://www.cancerdata.re.kr/surveillance/en/data?menuId=38]

The follow-up period of all unvaccinated people started from January 1st 2022, so 100% of unvaccinated follow-up time was in 2022, but vaccinated follow-up time was divided between 2021 and 2022, which might have skewed the results of the study.

Was the median age at death from COVID 79.5 years in 2025?

Ethical Skeptic retrieved the number of deaths with UCD COVID in 2025 by 10-year age groups, and since the median group was 75-84, he said that the median age at death was 79.5, which was the average of the minimum and maximum age within the age group: [https://x.com/EthicalSkeptic/status/1971310148619522151]

He could've calculated the exact median age if he would've used broad age groups for ages with suppressed deaths and single-year age groups for ages with no suppressed deaths.

When I did a query at CDC WONDER on September 30th 2025 UTC, there were a total of 10,470 deaths in 2025 with the underlying cause COVID. CDC WONDER suppresses the number of deaths when there are 1 to 9 deaths, but there were 10 or more deaths for each single year of age from 40 up to 99, and even for ages 100 and above. Ages 0-39 had 141 deaths. The median age of death was 82:

d=data.table(age=c(0,40:100),dead=c(141,11,13,11,10,11,16,11,12,16,23,26,26,
  28,21,49,48,40,45,59,56,78,75,77,84,124,126,160,145,151,156,165,203,245,
  207,263,240,285,354,372,278,309,372,421,365,353,374,389,383,344,370,370,
  291,285,267,228,195,164,165,123,87,154))
median(d[,rep(age,dead)])
# 82

The mean age was about 79.3 when I treated ages 0-39 as age 0, or about 79.9 when I treated ages 0-39 as age 39:

d[,weighted.mean(age,dead)]
# 79.32808
d[,weighted.mean(ifelse(age==0,39,age),dead)]
# 79.8533

The median age of COVID deaths seems to have gone up over time, because it was 72 in 2021, 77 in 2022, and 81 in 2023. The median age was particularly low in 2021, because in 2021 there were still many unvaccinated people who had not acquired natural immunity, and unvaccinated people are younger than vaccinated people:

v=fread("curl -Ls sars2.net/f/vital.csv.xz|xz -dc")
a=v[cause=="U071"&age!=999,.(dead=sum(ucd)),.(year,age)]
a=rbind(a,a[,.(dead=sum(dead),year="Total"),age])
a[,.(median=as.double(median(rep(age,dead))),mean=round(weighted.mean(age,dead),1)),year]
#  year median mean
#  2020     78 76.1
#  2021     72 70.6 # low median age because unvaccinated people were younger than vaccinated people
#  2022     77 75.0 # rebound in median age after unvaccinated people acquire natural immunity
#  2023     81 79.4
# Total     75 73.8

Ethical Skeptic's image says: "Current U.S. life expectancy for the average 80-year old = 78.4 years". I don't know if he made a typo, because the life expectancy at age 80 should be more than 80 (or in case he meant remaining life expectancy, it should be much lower than 78.4). In the 2023 US life table, the remaining life expectancy for age 80 is about 9.3 years. [https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Publications/NVSR/74-06/Table01.xlsx]

When I took the number of deaths in 2023 from CDC WONDER and I took mid-year resident population estimates for 2023 from the vintage 2024 release by the Census Bureau, I also got a remaining life expectancy of about 9.3 years at age 80:

pop=fread("https://sars2.net/f/uspopdead.csv")[year==2023]

age=0:100;n=length(age)
mx=pop[,dead/pop] # cmr
qx=pmin(mx/(1+mx/2),1) # probability of dying (cmr adjusted for exposure time)
px=1-qx # probability of surviving
lx=cumprod(c(1e5,px[-n])) # survivors in a 100,000-person population
dx=lx*qx # deaths
Lx=lx-dx/2 # person-years lived
Tx=rev(cumsum(rev(Lx))) # cumulative person-years remaining
ex=Tx/lx # life expectancy

options(digits=2)
data.table(age,mx,qx,px,lx,dx,Lx,Tx,ex)[age==80]
# age    mx    qx   px    lx   dx    Lx     Tx  ex
#  80 0.048 0.047 0.95 57918 2694 56571 541332 9.3

Ethical Skeptic's 66-gigabyte model with over 550 modules

In March 2026 people on Twitter found estimates of cancer mortality from the Global Burden of Disease dataset by IMHE, where many countries had a sharp turn upwards in cancer mortality after 2020 or 2021. [https://ourworldindata.org/grapher/death-rate-from-cancer-for-15-to-49-year-olds, https://ourworldindata.org/grapher/death-rate-from-cancer-gbd?tab=line&country=GBR%7eFRA%7eNZL%7eJPN%7eESP#all-charts] The estimates were calculated using opaque methodology, and there's no code you can run to reproduce the estimates. [https://www.healthdata.org/research-analysis/library/global-regional-and-national-burden-cancer-1990-2023-forecasts-2050]

The estimates look different from actual cancer ASMR on a national level, where generally cancer mortality has continued to decline in the 2020s. For example this plot shows that IMHE's estimates don't match the actual reported cancer mortality data in England: [https://x.com/Jean%5f%5fFisch/status/2033853216127943105]

IMHE's estimates don't match the real data for France either: [https://x.com/Jean%5f%5fFisch/status/2034205643200974902]

Ethical Skeptic declared that he had now been vindicated, because IMHE's estimates matched his plots where he gets massive inflections in cancer mortality by either falsifying data or by applying undocumented adjustments like "excess MCOD normalization": [https://x.com/EthicalSkeptic/status/2033994979157127639]

Then ES wrote: "Instead of arrogantly demanding the publisher of this chart post their 'code on GitHub' - I went and pulled the data and re-created the inflections myself." But he didn't reproduce the methodology for calculating IMHE's estimates, but he just plotted the precalculated data. So the methodology is still opaque.

Then ironically a user on Twitter commented: "To be fair, we need reproducible science. For too long so-called scientists have been publishing articles without details to reproduce their experiments. They hide behind IP. That's how we got into this mess and all this fake climate change science. Every paper should produce software to regenerate their results, period IMHO". Maybe the user didn't realize that ES himself refuses to document his methodology, so ES interpreted the comment as an attack against himself, and he said "My model is 66 Gigs, over 550 modules and 4,760 files", so he couldn't post his model on GitHub, because even though it would be possible for him to upload all of his files to GitHub, "it is the synthesis of the overall model which will not import": [https://x.com/EthicalSkeptic/status/2034355879282196800]

He probably wouldn't need 550 modules to document how he calculated the ASMR in his SV40 cancer wave plot, or to explain why the line for cancer mortality since 2018 looks completely different between the new and old versions of his SV40 plot, or to document how precisely he calculates the "excess MCOD normalization", or to document why his plot for cancer incidence in England has a massive drop between 2018 and 2019 that doesn't match the real English data.

But then again, Ethical Skeptic's IP is impossible to deploy to GitHub, because GitHub is a 2-dimensional plane, but his IP is cubic and neurally linked: [https://x.com/EthicalSkeptic/status/1585156542663229443]

ES said nukes are a deterrant by demons to prevent Jesus from returning on a fleet of spaceships

In February 2026 when Ethical Skeptic was interviewed on YouTube by a Brazilian lady called Paula Schmitt, he said: [https://www.youtube.com/watch?v=mXE9etwlAe4&t=58m51s]

However, with regard to spirituality, I have direct experience in many aspects of spirituality, and if you will, encountering dark entities, and understanding the hierarchy that's entailed there. And there is a reality to that. It's absolutely a pecking order between species, and broader species that used to inhabit the earth, and they do exist. So you have to reconcile that reality with that intellect. So spiritually, I believe in the the role of the Saōš́iiaṇt, or Son of Man, absolutely, as a rescuing redeemer for the planet, absolutely.

When he was asked to describe the dark entities, he said:

They're a one trick pony. They're very habitual. They are - they do not have free will in the sense that we have. They are subsets of this universe. They are enslaved to their addiction, and that addiction is to dark energy that we exude. They love to suck energy off of us, but that's the only way they exist. It's the only way they survive, is through the grace that of the energy that comes through us. And that circumstance can't sustain forever. It has to end at some point.

[...]

They're physically with us, but they reside in some other medium or dimension, depending on the term you use - I don't have a good Wittgenstein definition again of those terms. But they exist coincident with us. They hate us with a passion. And if we were destined to bad things, they wouldn't give a flip about us. But it's our privilege that we've been granted as humans that causes them to hate us as siblings, if you will, sibling rivalry if you will. And they're - it's clear, the experiences I've had - these entities hate us with a passion. But they are extremely terrified of an individual who knows and understands the grace that is offered humankind. Unfairly, probably, because we don't deserve it. But we are given grace and power that these beings are terrified of.

[...]

I do know that they are enslaved to their addiction. They can't rebel from it. They have to serve their master, which is the hierarchy. And they hate us with a passion. Those things are givens. And they know that they're about to be displaced from the planet. They're keenly aware of that. Almost - that's one of our that's one of our indigo indicators, is the panic on the part of these dark forces and those who serve them on the earth.

[...]

They don't have free will. They I - don't know if they can manifest in bodies. I suspect they can but, I don't know the answer to that. All I do know is that they're caught here. They're embargoed here. They're they're trapped here on earth with us And they are destined for their headed to a destination. They don't want to go to, and that's coming either sooner or later, but I think it's it's sooner. That's one of those indigo indicators.

Then he presented speculation "partially derived from an epistemology" that the true purpose of the nuclear arms race was that demons tricked humans into building up nukes to serve as leverage against Jesus, because the demons hold humans as hostage and threaten to nuke humans so Jesus won't return to earth on a fleet of spaceships: [1:05:48]

After being a custodian of nuclear warheads and holder of the nuclear codes, I began to get the suspicion that these weapons are not for a conflict between China, Russia, and the US. Just, I don't think they are. I think they're a fail-safe. I think that whatever it is, is so terrified of this intervention - and this is this is speculation, this is not something I've derived from an epistemology - although part of it is the derivation, it's an inference from epistemology, but it's speculation for the most part. I think that our chemical, our biological, radiological, nuclear weapons - that whole genre of exotic weapons - is not for a conflict between Russia and the United States, or China and the United States. It's a fail-safe option. The reach of these missiles is global, and then some, and enable a destroy us 15 times over. The purpose of those weapons, I think, is a statement on behalf of these dark forces, saying, 'If you intervene to remove us, we're at our wits end. We're at the end of our rope' - the bitter end, as they call it in the Navy - 'we're at the bitter end of our rope. And we're willing to fight to the death. And if you intervene, we'll destroy these humans that you consider so precious, and so valuable over us. We'll destroy every single one of them if you try and intervene.' And that's what I think is possibly the use of these nuclear weapons, is a deterrent for outside the planet intervention.

[...]

If the Saōš́iiaṇt, or the Son of Man, came in the sky with a rather large military force, and intervened on the behalf of mankind. Which is basically, that's what he says in the Lord's Prayer. The Lord's Prayer is a prayer for the overthrow of the kingdom that rules this earth. We just don't get to see it that way. They interpreted the language in a watered-down format, or version. If that have been happens, I'm contending that it's possible that are nuclear and exotic weapons are a fail-safe for that. Not that they can win the war against these entities. They can't. They don't have the ability. But that they would destroy us and others. In other words, they're holding mankind hostage to protect themselves. They would destroy us before they would allow themselves to be placed off the planet. So hence the reason why there's this incredible interest between anomalous craft and our nuclear weapons storage bunkers and launch silos. Hundreds of incidents where, you know, UFOs, UAP, have come and and tampered with shut down, and put into alert delta launch status, alert launch status.

At time 6:03 in the interview, ES said that when he served as a nuclear weapons officer in the Navy, he held nuclear launch codes, and he had to go through an extensive background check in order to be entrusted with the launch codes. But it seems like nuclear launch codes should not be given to someone who says that nukes are a deterrant against Jesus returning on a fleet of spaceships (even though on the other hand it is highly likely that ES does not believe what he says).

His theory that demons tricked humans into developing advanced technology is similar to the teachings of Falun Gong's leader Li Hongzhi, who says that aliens taught humans to develop science so humans will build clones, and then eventually the aliens will trick humans into outlawing natural reproduction, because only alien souls but not human souls can reincarnate into clone bodies, and then the clones with alien souls will take over the earth. [https://time.com/archive/6954898/interview-with-li-hongzhi-2/, https://www.falundafa.org/book/eng/us_west1999.htm] Li Hongzhi teaches that one type of aliens are disembodied ghosts, and another type looks like humans but they have a nose made of bone, but Ethical Skeptic similarly seems to say that his "dark entities" are intermediate between physical and spiritual beings, because he says they are part of a "broader species that used to inhabit the earth", even though they "reside in some other medium or dimension".

Ethical Skeptic's pole shift theory is reminiscent of the theories presented by various Theosophical cults, which like ES have mixed elements of Christianity with New Age elements.

For example in the late 80s and 90s in the town of Tehachapi in Arizona, there was a cult led by Doris Ekker, who claimed that she was channeling a Pleiadian ascended master called Commander Gyorgos Ceres Hatonn, who commanded a fleet of spaceships above the border of Nevada and Arizona, and who lived on one of the spaceships together with Jesus Sananda, who was Jesus in the form of an Ascended Master. [https://books.google.com/books?id=-0wFZRWKdfoC&pg=PA150] Supposedly Doris Ekker received a joint transmission from Hatonn and Jesus Sananda that there would be a cataclysmic pole shift in May 2000. [http://www.phoenixsourcedistributors.com/PJ_11.pdf]

In Japan there was a cult called Pana Wave, which similarly mixed elements of Christianity with Theosophy. They predicted that in May 2003, Planet X would pass by the Earth which would result in a cataclysmic pole shift that would kill most of humanity. Members of Pana Wave believed that the communists were targeting Japan with scalar waves, so they dressed in all-white clothes in order to protect themselves from scalar waves. [https://ja.wikipedia.org/wiki/パナウェーブ研究所] Pana Wave's old website was mostly focused on the topics of scalar waves and Planet X, but the cult also published several books about Christian subjects. [https://web.archive.org/web/20050409004718/http://www.pana-wave.com/, https://web.archive.org/web/20050409013002/http://www.lr-p.com/1_tengoku.html]

A modern-day pole shift cult is led by the YouTuber Ben Davidson, who says there will be a pole shift in the year 2046. His followers live on a trailer park compound called the Observer Ranch. In 2026 Ethical Skeptic spoke at the compound about his ECDO theory.

Ethical Skeptic said he postulated there was a 12,000-year cycle and a 6,000-year cycle, and that we were currently right at the end of both cycles. [https://www.youtube.com/watch?v=mXE9etwlAe4&start=16m9s]

The concept of a 12,000-year pole shift cycle was popularized around the years 2017 to 2018 by Ben Davidson and other YouTubers like Douglas Vogt, BPEarthWatch, and Leak Project. People like Graham Hancock and Paul LaViolette had earlier connected the pole shift cycle to the precession of the equinox, but half of a precessional cycle is much closer to 13,000 years than to 12,000 years.

When I searched Twitter for 12,000 year cycle until:2017-7-1, the earliest tweets about a 12,000-year disaster cycle I found were tweets from 2017 that referred to videos by BPEarthWatch and Leak Project. [https://x.com/search?q=12%2C000+year+cycle+until%3A2017-7-1&f=live] The video by Leak Project was an interview of someone called Steve Olson, who also discussed Anunnaki during the interview, and whose bio said he was a "cyber security expert, retired USAF and held a Top Security SBI/SCI Clearance". [https://leakproject.com/leakproject-guest/steve-olson/10950b-c-massive-asteroid-impact-12000-year-reset-cycle-gobekli-tepe-ooparts/, https://leakproject.com/leakproject-guest/steve-olson/] Ethical Skeptic also spoke of Anunnaki during his interview with the Brazilian YouTube channel, and he says he held "NSA SCI black top secret clearance - and held four compartments - so it was it was above and beyond what the typical person even with a black top secret clearance is going to hold". [https://fountain.fm/episode/0yCzhxIWNx73cacq3O70, 20:37]

Ben Davidson copied the concept of a 12,000-year cycle from Douglas Vogt, who had already said there was a 12,068-year pole shift cycle in his 2007 book God's Day of Judgment, even though he remained little known until he started doing YouTube videos in 2018 and his theory was picked up by Davidson. Both Vogt and Davidson say that the next pole shift will occur in the year 2046.

In 2025 Richard Fleming coauthored a paper about DNA contamination in COVID vaccines with the Czech biologist Soňa Peková, who is a member of a neo-Theosophical cult called ALLATRA, which is basically a version of Falun Gong that is targeted against Eastern European countries instead of China. [fleming.html#Sona_Pekova_and_ALLATRA] In a video in 2024, Soňa Peková talked about the Pleiadians and how contact with aliens is a win-win situation, and she also said that Gaia has the capacity to reach up to 9D reality and not only 5D reality, even though it's possible that vaccinated people cannot move to a higher dimension. [https://www.podtatransky-kurier.sk/galaxia/sona-pekova-gaia-ma-byt-az-9d] She presented genetic evidence that Slavs are the oldest ethnic group after the fall of Atlantis. [https://cz24.news/video-molekularni-geneticka-mudr-pekova-o-genetice-nasich-predku-slovanu-jako-nejstarsim-etniku-po-padu-atlantidy-sile-a-vyjimecnosti-slovanu/] A member of ALLATRA called Zhanna is allegedly an Anunnaki who makes TikTok videos from a space station. The primary book of ALLATRA is presented as a dialogue between the author of the book and the ascended master Rigden Djappo, who is a mythical character invented by the Russian Theosophist Nikolas Roerich.

ALLATRA teaches that there is a 12,000-year pole shift cycle that is triggered by changes inside the earth's core, like in the ECDO theory (and not by cosmic events, like in the theories of Douglas Vogt, Paul LaViolette, Walter Cruttenden, and Ben Davidson). [https://allatra.org/storage/app/media/reports/en/Shortened_Climate_Report.pdf] The "DO" in ECDO stands for Dzhanibekov oscillations, but ALLATRA specifically attributes the pole shift cycle to Dzhanibekov oscillations. The earliest sources I found where ALLATRA promoted the pole shift cycle was an article from 2016 titled in Russian "'When the Sun Rises in the West...' or the Dzhanibekov Effect and the Earth's Climate". [https://allatra.livejournal.com/42182.html]

There is supposedly an international group of volunteer scientists who work for ALLATRA to monitor climate anomalies, and who have developed "a comprehensive mathematical and tectonophysical model of contemporary changes occurring on earth". [https://creativesociety.com/climate-model] The term "earth changes" was coined by the Theosophical guru Edgar Cacey. Ben Davidson supposedly also works with an international group of volunteer scientists who monitor earth changes, who he calls the "observers".

In 2020 ALLATRA released a video about the 12,000-year pole shift cycle, where they presented Graham Hancock's nonsensical theories that an underwater rock formation in Japan was man-made, and that the Great Pyramid of Giza was built about 12,500 years ago because at the time it was aligned to the constellation of Orion. Edgar Cayce also claimed that the Great Pyramid was built about 12,500 years ago. ALLATRA's video said that someone called Vladimir Vasilievich Bubnenkov had found that there was a "Great Cycle of the change of epochs lasting 11,911 years", and Alexander Mikhailovich Baturin had found there were "global catastrophes with a frequency of 12,166 years" (so the average of the two cycle lengths was close to 12,000 years, even though half of a precessional cycle is about 12,886 years). [https://www.youtube.com/watch?v=-IIoMfBK10g&t=4m5s] Vladimir Bubnenkov also says that about 25,000 years ago there was a nuclear war, but the survivors of the war developed genetic mutations due to radiation, so that one group of people became Cyclops and another group became Mongoloids, but in the ensuing war between the Cyclops and the Mongoloids, the Mongoloids won. [http://kxk.ru/awakening1/v9_200617%5f%5f.php]

The concept of a 12,000-year cycle appears to have been originated by Swami Sri Yukteswar, who wrote a book published in 1894 that later influenced Theosophical cosmology. In traditional Hindu cosmology, each Yuga cycle lasts 4,320,000 years, but Yukteswar reinterpreted each Yuga cycle to last only 24,000 years, which he divided into a 12,000-year ascending cycle and a 12,000-year descending cycle. [https://en.wikipedia.org/wiki/Swami_Sri_Yukteswar_Giri] The pole shift guru Walter Cruttenden said that he came up with his theory of a pole shift cycle after he read Yukteswar's book. [https://humanoriginproject.com/hoppodcast/hop-podcast-23-walter-cruttended-on-precessionbinary-star-and-yuga-cycles/]