Canadian Gov’t Database Reveals Catastrophic…

[Jonathan]

I think you should physically re-draw those graphs with the scales starting from zero so that the physical relationships between each of the columns actually reflects the reality of the percentage differences between them. It won't look remotely as spectacular as you might like it to look but it will reflect the reality to a casual layperson who might be looking at it without going into the details. For example, your code 634 graph has the right hand column well over twice the height of the left hand column whereas it really should only be around 18% higher. You should also normalize the summaries as percentages of the either vaccinated cohort since the outright numbers you are providing are incredibly low to begin with. My point is that I think you are abusing statistics to make a point and you are making it out to be far worse than it is with those alarming looking graphs. Yet in outright terms, we are talking incredibly small numbers to begin with and you opponents would rightly argue that given such tiny cohorts, the vaccine still did more good than harm.

I am not taking sides since I am fully aware the vaccine did far more harm than good and I have two members of the immediate and extended family who suffered as a result of it. I just don't like statistics being taken advantage of in order to make an issue seem vastly more severe than it really is.

[Naomi Wolf and Amy Kelly]

Hello - There is no "abusing statistics" nor a desire for things to look "spectacular" or "worse than it is." The columns are clearly labeled with data labels showing the average number for distinct patients per year - in your example of Code 634, 18,960 vs. 22,465. DailyClout wants readers to know what the real numbers are and, thus, provided them.

[Rodewaryer]

My thoughts exactly, I don't like the way they have manipulated the graphics which amounts to a form of deceit. Not that I don't believe them but all graphics like this should be shown by year, and from zero.

[Astragale]

I just searched « Pfizer vaccines infertility » using Google.

Remember Von der Leyen saying the globalists’ highest priority is countering «misinformation»?

Remember Obama talked about « flooding the zone »?

Well Google is certainly flooding the injection-infertility zone.

There are HUNDREDS of hits turning up articles which say not only that infertility isn’t harmed by the injections but that it’s ENHANCED. There’s not a single link to any content even suggesting harm to fertility. This is clearly information that they REALLY want to suppress.

No wonder a lot of people still suspect nothing. They are BLOCKED from finding information that dissents.

[TNG Alliance]

The reality in Canada is that a) the gov't AND the judiciary (and subsequently their police goons) are part of the same crime syndicate

B) the gov't of Canada/judiciary are totally illegitimate to begin with

According to the International Covenants and treaties/the common law it is our duty to form grand juries at the local levels to indict the criminals operating a de facto system... Which we'll be doing in the Peel region in Ontario on June 3rd

[KW1962]

I just heard today 80% of judges in Canada are picked within Government! They are Corrupted before they even become a Judge by Government! It’s sickening!

[TNG Alliance]

70% 😅. Exhibit 4 of our treaty/court case/grand jury evidence is all about the corrupt and captured judiciary

https://drive.google.com/file/d/1OczJXAXVYUk0FW48-Hhpa1TSirBU281o/view?usp=drivesdk

[Anon]

Where is the link for the raw data? I can't find it. I have the excel sheet downloaded from the article, but I want to find the entire raw data set and I can't find the URL for it. The first link of the article brings me to a useless webpage.

I want to do some stats analysis, so getting the exhaustive raw data would be great.

https://www.ontario.ca/page/apply-ohip-and-get-health-card This link from the article is useless and there is no link to data anywhere. Where did you get the numbers for the exel sheet?

[Naomi Wolf and Amy Kelly]

DailyClout didn't receive the entire raw dataset. The Excel sheet provided in the article is what DailyClout was given.

[Anon]

Can you contact them and get it? I can't find anything here either: https://www150.statcan.gc.ca/n1/en/type/data?text=OHIP

[Naomi Wolf and Amy Kelly]

Hello again - The raw data is still being analyzed by the Canadians who initially obtained it by FOI requests, as well as their scientific team. Because they aren't yet done analyzing the data and reporting on their findings, they aren't ready to release the datasets publicly. If you are Canadian and would prefer not to wait for its release, you may submit a FOI request for the data to Ontario Health: https://www.ontariohealth.ca/freedom-information-requests.

[Naomi Wolf and Amy Kelly]

I will ask about it.

[StellaMaris]

Rolling out just like they said it would....read it, listen to it, share it and do not comply...... https://expose-news.com/2023/04/14/depopulation-plans-revealed-over-50-years-ago/

[Trevor]

The height of this evil tops the tallest mountain on the land.

[BlackThunder]

evil genocide

[Love_Learning]

🙏 Father God Heavenly Father Have Mercy on Us in Jesus Name Amen..

[Mary-Ann]

Great article, thank you!

The role of the highly criminal WHO(and all the others), which already has a history in this regard, should not be forgotten here - you should always put 1 and 1 together!!!

https://www.scirp.org/journal/paperinformation?paperid=81838

https://maryann255.substack.com/p/the-truth-is-always-on-the-other-f9b

https://ia902201.us.archive.org/9/items/TheLimitsToGrowth/TheLimitsToGrowth.pdf

And in the meantime, these soulless monsters have developed a new insidious method to bring infirmity and death to mankind, as well as to promote sterilization - and to reduce people.... no one is ever told the truth and THAT has been going on for decades!

https://maryann255.substack.com/p/the-truth-is-always-on-the-other-4ab

https://maryann255.substack.com/p/the-truth-is-always-on-the-other-fba

https://maryann255.substack.com/p/the-truth-is-always-on-the-other-0b0

https://agilityrobotics.com/

https://youtu.be/q8IdbodRG14?si=K8N5BVnRA-ip4G19

etc. etc.

[RLM RLM]

Well. The gullible faint of heart MOSTLY took the shot. It's literally survival of the most fit. Period. The death jab as I've said from day 1..

[Thomas Lewis]

.

I Wouldn’t Jump To Conclusions

About The Vaccine.

I’d Wait Until They’re All Dead

Just To Be Sure.

.

[Anon]

I did some graphical representations of the female fertility data. I didn't see anything that 'bad.' Here is my code below:

---

title: "Vaccine Infertility OHIP"

author: "Anonymous"

date: "`r Sys.Date()`"

output:

word_document: default

html_document: default

pdf_document:

fig_width: 5

fig_height: 3.5

---

```{r setup, include=FALSE}

knitr::opts_chunk$set(echo = TRUE, warning = FALSE)

```

```{r message=FALSE, warning=FALSE}

# Import Libraries

library(ggplot2)

library(dplyr)

library(tidyr)

library(tibble)

library(tinytex)

```

```{r}

Female_Infert_2015_date <- rep(2015, each=8) # Number of Year

Female_Infert_2015_Dx <- c(110, 11666, 56049, 18666, 1113, 260, 94, 67) # Number of Diagnosis

Female_Infert_2015_age <- c("0-17", "18-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80+") # Age Group

Female_Infert_2015_Df <- tibble(Female_Infert_2015_date, Female_Infert_2015_age, Female_Infert_2015_Dx)

ggplot(Female_Infert_2015_Df, aes(x=Female_Infert_2015_age, y=Female_Infert_2015_Dx)) +

geom_col() +

labs(caption = "Female, 2015, Age Groups, Diagnosis Total")

```

```{r}

Female_Infert_2016_date <- rep(2016, each=8) # Number of Year

Female_Infert_2016_Dx <- c(103, 11973, 55217, 17996, 965, 177, 101, 57) # Number of Diagnosis

Female_Infert_2016_age <- c("0-17", "18-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80+") # Age Group

Female_Infert_2016_Df <- tibble(Female_Infert_2016_date,

Female_Infert_2016_age,

Female_Infert_2016_Dx)

ggplot(Female_Infert_2016_Df,

aes(x=Female_Infert_2015_age, y=Female_Infert_2016_Dx)) +

geom_col() +

labs(caption = "Female, 2016, Age Groups, Diagnosis Total")

```

```{r}

Female_Infert_2017_date <- rep(2017, each=8) # Number of Year

Female_Infert_2017_Dx <- c(69, 11179, 48013, 15596, 918, 167, 80, 46) # Number of Diagnosis

Female_Infert_2017_age <- c("0-17", "18-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80+") # Age Group

Female_Infert_2017_Df <- tibble(Female_Infert_2017_date, Female_Infert_2017_age, Female_Infert_2017_Dx)

ggplot(Female_Infert_2017_Df, aes(x=Female_Infert_2017_age, y=Female_Infert_2017_Dx)) +

geom_col() +

labs(caption = "Female, 2017, Age Groups, Diagnosis Total")

```

```{r}

Female_Infert_2018_date <- rep(2018, each=8) # Number of Year

Female_Infert_2018_Dx <- c(94, 11158, 47431, 16637, 1220, 219, 100, 52) # Number of Diagnosis

Female_Infert_2018_age <- c("0-17", "18-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80+") # Age Group

Female_Infert_2018_Df <- tibble(Female_Infert_2018_date, Female_Infert_2018_age, Female_Infert_2018_Dx)

ggplot(Female_Infert_2018_Df, aes(x=Female_Infert_2018_age, y=Female_Infert_2018_Dx)) +

geom_col() +

labs(caption = "Female, 2018, Age Groups, Diagnosis Total")

```

```{r}

Female_Infert_2019_date <- rep(2019, each=8) # Number of Year

Female_Infert_2019_Dx <- c(91, 11390, 46752, 16384, 1302, 249, 127, 50) # Number of Diagnosis

Female_Infert_2019_age <- c("0-17", "18-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80+") # Age Group

Female_Infert_2019_Df <- tibble(Female_Infert_2019_date, Female_Infert_2019_age, Female_Infert_2019_Dx)

ggplot(Female_Infert_2019_Df, aes(x=Female_Infert_2019_age, y=Female_Infert_2019_Dx)) +

geom_col() +

labs(caption = "Female, 2019, Age Groups, Diagnosis Total")

```

[Anon]

```{r}

Female_Infert_2020_date <- rep(2020, each=8) # Number of Year

Female_Infert_2020_Dx <- c(81, 11060, 44017, 15488, 1112, 278, 100, 62) # Number of Diagnosis

Female_Infert_2020_age <- c("0-17", "18-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80+") # Age Group

Female_Infert_2020_Df <- tibble(Female_Infert_2020_date, Female_Infert_2020_age, Female_Infert_2015_Dx)

ggplot(Female_Infert_2020_Df, aes(x=Female_Infert_2020_age, y=Female_Infert_2020_Dx)) +

geom_col() +

labs(caption = "Female, 2020, Age Groups, Diagnosis Total")

```

```{r}

Female_Infert_2021_date <- rep(2021, each=8) # Number of Year

Female_Infert_2021_Dx <- c(81, 10987, 42903, 15269, 1089, 254, 130, 72) # Number of Diagnosis

Female_Infert_2021_age <- c("0-17", "18-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80+") # Age Group

Female_Infert_2021_Df <- tibble(Female_Infert_2021_date, Female_Infert_2021_age, Female_Infert_2021_Dx)

ggplot(Female_Infert_2021_Df, aes(x=Female_Infert_2021_age, y=Female_Infert_2021_Dx)) +

geom_col() +

labs(caption = "Female, 2021, Age Groups, Diagnosis Total")

```

```{r}

Female_Infert_2022_date <- rep(2022, each=8) # Number of Year

Female_Infert_2022_Dx <- c(89, 10607, 39407, 13933, 1053, 273, 139, 65) # Number of Diagnosis

Female_Infert_2022_age <- c("0-17", "18-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80+") # Age Group

Female_Infert_2022_Df <- tibble(Female_Infert_2022_date, Female_Infert_2022_age, Female_Infert_2022_Dx)

ggplot(Female_Infert_2022_Df, aes(x=Female_Infert_2022_age, y=Female_Infert_2022_Dx)) +

geom_col() +

labs(caption = "Female, 2022, Age Groups, Diagnosis Total")

```

---

```{r}

Age_0_to_17_Dx_2015_to_2022 <- tibble(c(

Female_Infert_2015_Df[1,3],

Female_Infert_2016_Df[1,3],

Female_Infert_2017_Df[1,3],

Female_Infert_2018_Df[1,3],

Female_Infert_2019_Df[1,3],

Female_Infert_2020_Df[1,3],

Female_Infert_2021_Df[1,3],

Female_Infert_2022_Df[1,3]

))

year <- c(2015:2022)

count <- as.numeric(Age_0_to_17_Dx_2015_to_2022$`c(...)`)

table_0to17_2015_2022 <- tibble(count, year)

ggplot(table_0to17_2015_2022, aes(x = year, y = count)) +

geom_line()

```

```{r}

Age_18_to_29_Dx_2015_to_2022 <- tibble(c(

Female_Infert_2015_Df[2,3],

Female_Infert_2016_Df[2,3],

Female_Infert_2017_Df[2,3],

Female_Infert_2018_Df[2,3],

Female_Infert_2019_Df[2,3],

Female_Infert_2020_Df[2,3],

Female_Infert_2021_Df[2,3],

Female_Infert_2022_Df[2,3]

))

year <- c(2015:2022)

count <- as.numeric(Age_18_to_29_Dx_2015_to_2022$`c(...)`)

table_18to29_2015_2022 <- tibble(count, year)

ggplot(table_18to29_2015_2022, aes(x = year, y = count)) +

geom_line()

```

```{r}

Age_30_to_39_Dx_2015_to_2022 <- tibble(c(

Female_Infert_2015_Df[3,3],

Female_Infert_2016_Df[3,3],

Female_Infert_2017_Df[3,3],

Female_Infert_2018_Df[3,3],

Female_Infert_2019_Df[3,3],

Female_Infert_2020_Df[3,3],

Female_Infert_2021_Df[3,3],

Female_Infert_2022_Df[3,3]

))

year <- c(2015:2022)

count <- as.numeric(Age_30_to_39_Dx_2015_to_2022$`c(...)`)

table_30to39_2015_2022 <- tibble(count, year)

ggplot(table_30to39_2015_2022, aes(x = year, y = count)) +

geom_line()

```

```{r}

Age_40_to_49_Dx_2015_to_2022 <- tibble(c(

Female_Infert_2015_Df[4,3],

Female_Infert_2016_Df[4,3],

Female_Infert_2017_Df[4,3],

Female_Infert_2018_Df[4,3],

Female_Infert_2019_Df[4,3],

Female_Infert_2020_Df[4,3],

Female_Infert_2021_Df[4,3],

Female_Infert_2022_Df[4,3]

))

year <- c(2015:2022)

count <- as.numeric(Age_40_to_49_Dx_2015_to_2022$`c(...)`)

table_40to49_2015_2022 <- tibble(count, year)

ggplot(table_40to49_2015_2022, aes(x = year, y = count)) +

geom_line()

```

```{r}

Age_50_to_59_Dx_2015_to_2022 <- tibble(c(

Female_Infert_2015_Df[5,3],

Female_Infert_2016_Df[5,3],

Female_Infert_2017_Df[5,3],

Female_Infert_2018_Df[5,3],

Female_Infert_2019_Df[5,3],

Female_Infert_2020_Df[5,3],

Female_Infert_2021_Df[5,3],

Female_Infert_2022_Df[5,3]

))

year <- c(2015:2022)

count <- as.numeric(Age_50_to_59_Dx_2015_to_2022$`c(...)`)

table_50to59_2015_2022 <- tibble(count, year)

ggplot(table_50to59_2015_2022, aes(x = year, y = count)) +

geom_line()

```

```{r}

Age_60_to_69_Dx_2015_to_2022 <- tibble(c(

Female_Infert_2015_Df[6,3],

Female_Infert_2016_Df[6,3],

Female_Infert_2017_Df[6,3],

Female_Infert_2018_Df[6,3],

Female_Infert_2019_Df[6,3],

Female_Infert_2020_Df[6,3],

Female_Infert_2021_Df[6,3],

Female_Infert_2022_Df[6,3]

))

year <- c(2015:2022)

count <- as.numeric(Age_60_to_69_Dx_2015_to_2022$`c(...)`)

table_60to69_2015_2022 <- tibble(count, year)

ggplot(table_60to69_2015_2022, aes(x = year, y = count)) +

geom_line()

```

```{r}

Age_70_to_79_Dx_2015_to_2022 <- tibble(c(

Female_Infert_2015_Df[7,3],

Female_Infert_2016_Df[7,3],

Female_Infert_2017_Df[7,3],

Female_Infert_2018_Df[7,3],

Female_Infert_2019_Df[7,3],

Female_Infert_2020_Df[7,3],

Female_Infert_2021_Df[7,3],

Female_Infert_2022_Df[7,3]

))

year <- c(2015:2022)

count <- as.numeric(Age_70_to_79_Dx_2015_to_2022$`c(...)`)

table_70to79_2015_2022 <- tibble(count, year)

ggplot(table_70to79_2015_2022, aes(x = year, y = count)) +

geom_line()

```

```{r}

Age_80_to_plus_Dx_2015_to_2022 <- tibble(c(

Female_Infert_2015_Df[1,3],

Female_Infert_2016_Df[1,3],

Female_Infert_2017_Df[1,3],

Female_Infert_2018_Df[1,3],

Female_Infert_2019_Df[1,3],

Female_Infert_2020_Df[1,3],

Female_Infert_2021_Df[1,3],

Female_Infert_2022_Df[1,3]

))

year <- c(2015:2022)

count <- as.numeric(Age_80_to_plus_Dx_2015_to_2022$`c(...)`)

table_80toPlus_2015_2022 <- tibble(count, year)

ggplot(table_80toPlus_2015_2022, aes(x = year, y = count)) +

geom_line()

```

---

```{r}

# All age group lines together

# Get all ages and Diagnosis Count

age_comparison <- data_frame(

"0-17"=Age_0_to_17_Dx_2015_to_2022$`c(...)`,

"18-29"=Age_18_to_29_Dx_2015_to_2022$`c(...)`,

"30-39"=Age_30_to_39_Dx_2015_to_2022$`c(...)`,

"40-49"=Age_40_to_49_Dx_2015_to_2022$`c(...)`,

"50-59"=Age_50_to_59_Dx_2015_to_2022$`c(...)`,

"60-69"=Age_60_to_69_Dx_2015_to_2022$`c(...)`,

"70-79"=Age_70_to_79_Dx_2015_to_2022$`c(...)`,

"80+"=Age_80_to_plus_Dx_2015_to_2022$`c(...)`)

year <- c(2015:2022)

age_comparison <- age_comparison %>% add_column(Year=year, .before = "0-17")

age_comparison_longer <- age_comparison %>% pivot_longer(cols=c('0-17', '18-29', '30-39', '40-49', '50-59', '60-69', '70-79', '80+'),

names_to='Age',

values_to='Diagnosis')

age_comparison_longer <- age_comparison_longer %>% mutate(across(Diagnosis, as.integer))

age_comparison_longer %>% ggplot(aes(x=Year, y=Diagnosis)) +

geom_line(aes(color=Age))

```

[Anita Söderman]

The way those injections have been hitting fertility domains is just part of the depopulation goal, so clearly stated by the Trilateral Commission, the WEF, the Rockefellers et al. No wonder these data, complete or not, show one intriguing plan, is there?