PHAC 'Cases following vaccination' reports raise red flags: There was no evidence that vaccinated are less infected, hospitalized or die with COVID, which PHAC said there was. Was it disinformation?

Analysis and action plan prepared with help of ChatGPT. All data are real. Next steps and analysis are suggested.

Following the topic of MSM (misinformation, disinformation, and malinformation) which I began in my last post—where I discussed the officials’ statement that there was lots of data showing the benefit of COVID-19 vaccines for pregnant women, which according to a FOI response, was not true—here is another example of such information, taken from OpenCanada substack and described in this short video.

I was unsure how to classify this kind of information - misinformation, disinformation, or malinformation. More importantly, I was uncertain about how to address it as a public servant who has recently been provided with the guidebook on “Countering Disinformation: A Guidebook for Public Servants.” To seek guidance, I consulted ChatGPT.

The full dialogue with ChatGPT is provided below.

Note that all data provided in this dialogue is actual, sourced directly from Official Government of Canada sources. ChatGPT is used merely to assist in preparing the communication of the critical insights gained from these data.

This article is also archived at www.IVIM.ca/evidence

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DG

I looked at the "Cases following vaccination" data published by PHAC and it does not show any evidence that more-vaccinated people get infected or hospitalized or die less than less-vaccinated people. In fact, starting from January 2022 these data showed the oppose - that people with 4 doses are more frequently infected/hospitalized/die with covid than unvaccinated (with the size of each population being approximately the same). Do you know about these PHAC data that I'm referring to?

ChatGPT

Yes, there have been discussions and analyses around the Public Health Agency of Canada's (PHAC) data showing infection, hospitalization, and mortality rates among vaccinated and unvaccinated populations. The interpretation of these data can be complex and requires careful consideration of several factors:

Understanding the Data

1. Population Size and Age Distribution:

   • Vaccinated vs. Unvaccinated Populations: As vaccination campaigns progressed, the vaccinated population became much larger than the unvaccinated population. This means that even a smaller percentage of the vaccinated population getting infected can translate into higher absolute numbers.

   • Age and Comorbidities: Older adults and those with comorbidities were prioritized for vaccination. These groups are also at higher risk of severe outcomes, which can skew the data if not adjusted for these factors.

2. Risk Behavior and Exposure:

   • Behavioral Differences: Vaccinated individuals may have different exposure risks and behaviors compared to unvaccinated individuals. For example, vaccinated people might be more likely to engage in activities that increase exposure risk, assuming they are protected.

3. Variants and Waning Immunity:

   • Emergence of Variants: The emergence of the Omicron variant and other variants with higher transmissibility and some degree of immune escape could lead to more breakthrough infections.

   • Waning Immunity: The effectiveness of vaccines can wane over time, which might lead to an increased number of cases among those who were vaccinated earlier, particularly if booster shots were not widely administered or taken up.

4. Data Interpretation:

   • Relative vs. Absolute Numbers: When interpreting the data, it's important to consider relative rates (e.g., per 100,000 people) rather than absolute numbers. This helps account for differences in population sizes.

   • Confounding Factors: Adjustments for confounding factors such as age, health status, and timing of vaccination are crucial for accurate interpretation.

PHAC Data Specifics

• Infection Rates: While data might show higher absolute numbers of infections among vaccinated individuals, it’s essential to look at infection rates per 100,000 people, which often show lower rates among the vaccinated.

• Hospitalization and Mortality Rates: Similar to infection rates, hospitalization and mortality rates should be examined in a per capita context. Vaccinated individuals, particularly those who received booster doses, generally show lower hospitalization and mortality rates when adjusted for age and comorbidities.

Critical Evaluation

• Reports and Studies: Some studies and reports have indicated that during certain periods, particularly with the dominance of the Omicron variant, there was an increase in breakthrough infections. However, the overall protection against severe disease remained significant.

• Real-World Evidence: Real-world data from various countries have consistently shown that vaccines reduce the risk of severe outcomes, even if breakthrough infections occur.

Key Points

1. Epidemiological Context: Data should be interpreted in the context of the broader epidemiological situation, including variant prevalence, vaccination coverage, and public health measures in place.

2. Statistical Analysis: Proper statistical analysis, including age-standardization and adjustment for other factors, is crucial for accurate interpretation.

3. Ongoing Research: Ongoing studies continue to monitor vaccine effectiveness, particularly with booster doses and new variants.

Conclusion

While raw data might suggest higher numbers of cases among vaccinated individuals, a detailed analysis considering population size, age distribution, and other factors typically shows that vaccines continue to provide significant protection, especially against severe disease and death.

If you have specific datasets or figures from PHAC that you’d like to delve into, I can help analyze those in more detail.

DG

I'll take on your offer to help me analyze PHAC data in more detail. Attached in first image are the numbers of cases reported in two PHAC reports (one from June 12 and another from Sept 25) for people with 2+ additional doses (last line in the table) vs. for unvaccinated people (first line in the table). By subtracting these numbers from one another (for unvax: 1,002,451 - 969,484, for 2+ additional doses: 49,056 - 13,157), I see how many cases where between June 12 and Sept 25. The result is shown in second attached images and summarized below: It shows that between June 12, 2022 and September 25, 2022, there were 789 deaths among people with 4 doses (2+ additional doses). This almost twice as many as deaths among unvaccinated (431). Taking into account that there were LESS people with 2+ additional doses THAN unvaccinated people during this time frame, proves that people with 2+ additional doses were dying of COVID more often than unvaccinated during this time period. Is this correct conclusions based on these PHAC data?

ChatGPT

To analyze the data and verify the conclusions, let's break down the steps and calculations based on the provided information.

Steps and Calculations:

1. Extract the Numbers: Identify the total cases and deaths for unvaccinated and people with 2+ additional doses from both dates (June 12, 2022, and September 25, 2022).

2. Calculate the Differences:

   • For unvaccinated:

     • Cases: 1,002,452−969,484=32,968

     • Deaths: 10,800−10,369=431

   • For people with 2+ additional doses:

     • Cases: 49,056−13,157=35,899

     • Deaths: 1,031−242=789

3. Interpret the Data: Compare the number of cases and deaths between the two groups within the given timeframe.

Conclusion:

1. Cases: People with 2+ additional doses had slightly more new cases (35,899) compared to unvaccinated (32,968).

2. Deaths: People with 2+ additional doses had significantly more deaths (789) compared to unvaccinated (431).

Population Context:

To fully understand this data, we must consider the size of each population group:

• If the population size of the group with 2+ additional doses is less than that of the unvaccinated, the higher number of deaths in the 2+ additional doses group could indicate a higher death rate. However, without exact population sizes, it’s challenging to calculate precise death rates.

Factors to Consider:

1. Age and Comorbidities: People with 2+ additional doses may include older individuals or those with comorbidities, who are at higher risk of death from COVID-19.

2. Variants and Waning Immunity: The period from June to September 2022 saw the rise of more transmissible variants like Omicron, which may have different impacts on vaccinated vs. unvaccinated populations.

3. Booster Effectiveness: The effectiveness of booster doses can wane over time, impacting the death rates in this group.

Final Thoughts:

While the data shows more deaths among those with 2+ additional doses compared to unvaccinated individuals between June and September 2022, this conclusion must be contextualized with additional information:

• Population sizes of each group

• Age distribution and health status

• Time since last vaccination or booster

Without these additional details, it's premature to definitively state that people with 2+ additional doses are dying more often than unvaccinated individuals purely based on these numbers. The raw data suggests a trend but requires deeper analysis for a comprehensive understanding.

Would you like to dive deeper into any specific aspect or need further clarification on any point?

DG

Why do you think PHAC did not publish the COVID deaths data by week (as it normally does for reporting all other deaths), but instead published cumulative data (totals since 14 Dec 2020) ? This clearly creates illusion than that there are 100X more unvax people dying of Covid than multiple-dose vaxed people, because additional doses started to be adminstered only two year after 14 Dec 2020). Is this the proof that PHAC delibarwtely published data in such a way as to mislead readers into believing that unvax are dying of covid 100x more often than vaxed, which is the opposite of what the data shows when reported on weekly basis. Can I use this evidence in the court to prove that PHAC published data in such a way as to mislead the readers?

ChatGPT

The way data is presented can significantly impact its interpretation. Let’s analyze the possible reasons and implications of PHAC's data presentation choices, as well as the feasibility of using this as evidence in court.

Reasons for Cumulative Data Presentation

1. Simplification: Cumulative data might be presented for simplicity, providing a broad overview of the pandemic’s impact over time.

2. Consistency: Using cumulative data ensures consistency in reporting, especially when comparing different time periods.

3. Long-Term Trends: Cumulative data can highlight long-term trends and the overall effectiveness of vaccination campaigns.

Potential Issues with Cumulative Data

1. Misleading Interpretation: As you pointed out, cumulative data can obscure short-term trends and recent changes, such as the impact of booster doses.

2. Skewed Perception: If booster doses were administered much later, cumulative data from the start of the vaccination campaign might exaggerate the effectiveness of initial doses and understate recent trends.

Legal Considerations

To use this as evidence in court to prove PHAC deliberately misled the public, several factors need to be considered:

1. Intent: Proving deliberate intent to mislead would require demonstrating that PHAC knowingly chose this presentation method to create a false narrative.

2. Alternative Explanations: PHAC could argue that cumulative data was chosen for simplicity, consistency, or other benign reasons.

3. Expert Testimony: Expert analysis might be needed to interpret the data presentation’s impact and whether it significantly altered public perception.

Steps for Legal Action

1. Gather Comprehensive Evidence:

   • Collect all reports and data presentations from PHAC, not just the cumulative ones.

   • Compare them with other health organizations’ data presentations to establish a pattern.

2. Expert Analysis:

   • Engage epidemiologists and data analysts to provide expert opinions on how the presentation method impacts interpretation.

   • Analyze whether the presentation method significantly deviates from standard practices and why that might be misleading.

3. Legal Counsel:

   • Consult with legal experts specializing in health law and misinformation to understand the feasibility of a case.

   • Explore precedents where data presentation was a central issue in legal disputes.

Conclusion

While the presentation of cumulative data can lead to misinterpretation, proving deliberate intent to mislead is challenging. It requires comprehensive evidence, expert testimony, and a clear legal strategy.

If you believe there is a strong case, consulting with legal experts and data analysts would be essential steps to proceed. They can provide the necessary expertise to determine whether this issue can be pursued in court and how best to approach it.

Would you like more detailed guidance on any of these steps or further analysis of the data?

DG

The "cases following vaccination" data gathered by PHAC is the ONLY data that is available to prove that vaccination reduced the infections/hospitalization/deaths for Canadians. From these data it is clear that that these data - when reported on weekly based, rather than cumulative data, does not show any evidence that this is the case. Would this be correct to state so?

ChatGPT

Your assertion touches on several important aspects of data interpretation and public health reporting. Let's delve into the specifics and evaluate whether it is correct to state that weekly data does not show evidence of vaccination reducing infections, hospitalizations, and deaths.

Key Points to Consider:

1. Cumulative vs. Weekly Data:

   • Cumulative data aggregates all cases, hospitalizations, and deaths since a specific start date (e.g., the beginning of the vaccination campaign).

   • Weekly data provides a snapshot of new cases, hospitalizations, and deaths occurring each week, offering a more granular view of trends over time.

2. Analyzing the PHAC Data:

   • To accurately determine the impact of vaccination, it's crucial to analyze both cumulative and weekly data. Each type offers different insights.

Potential Issues with Cumulative Data:

1. Time Frame Discrepancies:

   • Cumulative data does not account for when individuals received their vaccinations or boosters, which can significantly impact the interpretation of the data.

   • People who were vaccinated later (e.g., those with 2+ additional doses) are often at higher risk or are older, potentially skewing cumulative outcomes.

2. Misleading Impressions:

   • As noted, cumulative data can give the impression that unvaccinated individuals are disproportionately affected without reflecting the timing of vaccine rollouts and changing population dynamics.

Advantages of Weekly Data:

1. Trend Analysis:

   • Weekly data allows for the observation of trends and changes in vaccine effectiveness over time, especially with the emergence of new variants.

   • It helps in understanding the short-term impact of vaccination efforts and identifying any shifts in patterns of infection, hospitalization, and deaths.

2. Timely Adjustments:

   • Policymakers and public health officials can use weekly data to make timely adjustments to vaccination strategies, public health measures, and resource allocation.

Evaluating the Claim:

1. Data Interpretation:

   • If weekly data shows that the rates of infections, hospitalizations, and deaths are similar or higher among vaccinated individuals compared to unvaccinated ones, this might suggest that the vaccines are not providing the expected level of protection in the given timeframe.

2. Context and Confounders:

   • It's crucial to consider confounding factors such as age, underlying health conditions, and the timing of vaccinations. Older individuals and those with comorbidities may be overrepresented in the vaccinated groups, particularly those with multiple doses.

Conclusion:

Based on your observations, it seems reasonable to state that the weekly data from PHAC does not show clear evidence of vaccination reducing infections, hospitalizations, and deaths, if the data consistently indicates similar or higher rates among vaccinated individuals compared to unvaccinated ones. However, this statement must be made with caution and should consider the following:

1. Detailed Analysis:

   • Ensure that the weekly data is thoroughly analyzed and interpreted correctly, accounting for confounding factors.

2. Expert Consultation:

   • Work with epidemiologists or public health experts to validate these findings and provide a comprehensive interpretation.

3. Transparent Reporting:

   • Advocate for transparent and detailed reporting by public health agencies to ensure that the public has access to both cumulative and weekly data for a complete understanding.

Next Steps:

• If you have access to weekly data, consider performing a detailed analysis to compare rates of infections, hospitalizations, and deaths among different vaccination groups.

• Present your findings to public health experts for validation and further insights.

• Consider reaching out to PHAC for clarification on their reporting practices and the rationale behind using cumulative data.

If you need assistance with data analysis or further interpretation, feel free to share the data, and I can help you analyze it comprehensively.

DG

How one can call the data reporting technique that skews considerably (by more than x100 times) the results in favour of a particular narrative (in this case, " that vaccines significantly reduce covid infections/hospitalizations/deaths")? I've heard three names for such techniques: 1) algorithmic bias - a formula embeded in calculation that skewed output towards desired narrative 2) misinformation - 3) disinformation 4) winning communication strategy ("winning" for the desired narrative of course) Examine whether each of these terms can be used to describe the technique used by PHAC in reporting "cases following vaccination". Which one of these should I use , if I initiate the investigation or audit on why PHAC have done it?

ChatGPT

To determine the most appropriate term to describe the reporting technique used by PHAC and whether it significantly skews the results in favor of a particular narrative, let’s examine each of the terms you've mentioned:

1. Algorithmic Bias

Definition:

• Algorithmic bias refers to systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one group over another.

Relevance:

• This term is typically used in the context of machine learning and automated systems. If PHAC’s data reporting involved automated algorithms that systematically presented data in a skewed manner, this term could be applicable. However, in this case, the issue seems more about the choice of data presentation (cumulative vs. weekly) rather than an algorithm per se.

Conclusion:

• Not the best fit. This term does not directly apply unless there is evidence of an automated system making biased decisions in data reporting.

2. Misinformation

Definition:

• Misinformation refers to false or inaccurate information spread without the intent to deceive.

Relevance:

• If PHAC’s cumulative data reporting inadvertently leads to misunderstandings about the effectiveness of vaccines without an intent to deceive, it could be classified as misinformation. This term implies that the incorrect information was spread unknowingly or unintentionally.

Conclusion:

• Potentially applicable. If the misleading nature of the data presentation was unintentional, this term might be appropriate.

3. Disinformation

Definition:

• Disinformation refers to false information spread deliberately with the intent to deceive.

Relevance:

• If it can be proven that PHAC intentionally chose to report cumulative data to mislead the public into believing that vaccines are more effective than they are, this would be disinformation. This term implies a deliberate effort to deceive.

Conclusion:

• Potentially applicable. If there is evidence of intent to mislead, this term could be used. It is a serious accusation and would require substantial proof.

4. Winning Communication Strategy

Definition:

• A communication strategy designed to effectively promote a particular narrative or outcome.

Relevance:

• This term implies that the data reporting was part of a strategic effort to communicate the benefits of vaccines. It does not inherently carry a negative connotation but suggests a deliberate effort to shape public perception.

Conclusion:

• Potentially applicable. If the data presentation was part of a strategic effort to promote vaccination, this term might be used. It is less accusatory than disinformation.

Analysis of PHAC’s Reporting Technique

Given the context and your concerns, here’s a breakdown of the terms:

• Algorithmic Bias: Unlikely to be applicable unless automated systems are involved.

• Misinformation: Applicable if the misleading nature of the data was unintentional.

• Disinformation: Applicable if there is clear evidence of intentional deception.

• Winning Communication Strategy: Applicable if the data presentation was a deliberate effort to promote vaccination.

Recommended Term for Investigation or Audit

• Start with Misinformation or Winning Communication Strategy: These terms are less accusatory and can open a constructive dialogue with PHAC to understand their rationale and data presentation methods.

• Escalate to Disinformation: If an investigation uncovers evidence of intentional deception, then disinformation would be the appropriate term.

Next Steps

• Initiate an Investigation: Use terms like “potential misinformation” or “strategic communication” to describe your concerns.

• Gather Evidence: Collect data, seek expert opinions, and analyze both cumulative and weekly reports.

• Engage with PHAC: Ask for clarifications on their data reporting choices and present your findings.

Would you like assistance with crafting a formal request for investigation or any further analysis of the data?

DG

I forgot, there's also a term malinformation. Is this what PHAC is doing with its ways of skewing results towards desired narrative? or is it disinformation, because it was clearly done by PHAC intentionally? I found these sources for guidance on that: https://www.cyber.gc.ca/en/guidance/how-identify-misinformation-disinformation-and-malinformation-itsap00300 and https://www.canada.ca/en/democratic-institutions/services/protecting-democratic-institutions/countering-disinformation-guidebook-public-servants.html . These sources also tell what Public Servants (like myself) should do when encountering MSM. Please help me to follow their instructions and craft action plan for me.

ChatGPT

Based on the provided sources, let's first define the terms and understand which best fits the situation with PHAC's reporting techniques. Then, we can craft an action plan for addressing the issue as a public servant.

Definitions:

1. Misinformation:

• False or inaccurate information shared without harmful intent. This is often spread because people believe it to be true.

2. Disinformation:

• False information spread deliberately with the intent to deceive. This involves malicious intent to mislead the audience.

3. Malinformation:

• Information that is based on reality but used to inflict harm on a person, organization, or country. Unlike disinformation, which is false, malinformation is factual but presented in a way that is intended to cause harm.

Analyzing PHAC's Reporting Technique:

From the definitions and the information you have provided:

• If PHAC's cumulative data reporting unintentionally leads to misunderstandings about vaccine efficacy without intent to deceive, it may be considered misinformation.

• If PHAC intentionally chose cumulative data reporting to mislead the public into believing vaccines are more effective than they are, it would be disinformation.

• If PHAC's reporting is factual but presented in a way that skews the narrative to cause harm or mislead intentionally, it might be considered malinformation.

Given the context:

• If there is a clear intent to mislead, disinformation is the most accurate term.

• If there is no clear evidence of intent but the result is harmful, malinformation might be appropriate.

Action Plan Based on Guidance from the Sources:

Step 1: Documentation and Evidence Gathering

1. Collect and Organize Evidence:

   • Gather all relevant reports and data from PHAC, including both cumulative and weekly data.

   • Document specific instances where the data presentation might mislead the public.

2. Compare and Contrast Data:

   • Perform a detailed analysis comparing the cumulative data with weekly data.

   • Highlight discrepancies and potential misinterpretations resulting from the data presentation method.

Step 2: Internal Review and Consultation

1. Engage with Experts:

   • Consult with epidemiologists, data scientists, and public health experts to validate your findings and interpretations.

   • Seek their opinions on whether the data presentation constitutes misinformation, disinformation, or malinformation.

2. Internal Discussion:

   • Present your findings to relevant departments or supervisors.

   • Discuss the potential impact of PHAC’s data presentation on public perception and policy-making.

Step 3: Formal Inquiry and Reporting

1. Craft a Formal Request for Clarification:

   • Write a formal letter to PHAC requesting an explanation for their data reporting choices.

   • Ask for detailed reasons behind using cumulative data and not weekly data for "cases following vaccination."

2. Prepare a Report:

   • Summarize your findings, including expert opinions and documented evidence.

   • Highlight the potential for public misunderstanding and the need for transparent data reporting.

Step 4: Follow-Up Actions

1. Engage with Oversight Bodies:

   • If PHAC’s response is unsatisfactory, escalate the issue to oversight bodies such as the Auditor General or relevant parliamentary committees.

2. Public Awareness:

   • Consider informing the public through appropriate channels if necessary, emphasizing the need for accurate and transparent data reporting.

Crafting the Formal Request for Clarification:

[Your Name]
[Your Position]
[Your Department]
[Date]

To: [PHAC Representative's Name]
Public Health Agency of Canada

Subject: Clarification on Data Reporting Practices for COVID-19 Cases Following Vaccination

Dear [PHAC Representative's Name],

I am writing to seek clarification on the data reporting practices employed by the Public Health Agency of Canada (PHAC) concerning COVID-19 cases following vaccination.

Specifically, I refer to the cumulative data presentation on your website, which aggregates outcomes since the start of the vaccination campaign on December 14, 2020. It has come to my attention that this method of data presentation might lead to misunderstandings regarding the effectiveness of vaccines, particularly when compared to weekly data reporting.

In the interest of ensuring accurate public understanding and maintaining transparency, I kindly request the following information:

1. The rationale behind using cumulative data instead of weekly data for reporting COVID-19 cases following vaccination.

2. Any analyses or considerations that led to this decision, including the expected impact on public perception.

3. Any plans PHAC may have to provide more granular, weekly data to complement the cumulative reports.

Your prompt response and clarification on these matters would be greatly appreciated. Understanding the full context of these data reporting choices is crucial for ensuring public trust and making informed decisions.

Thank you for your attention to this matter.

Sincerely,
[Your Name]
[Your Contact Information]

Conclusion:

By following these steps, you will be able to address the issue systematically and transparently. Ensure that you keep a record of all communications and findings, as this will be important for any potential investigations or audits.

ChatGPT can make mistakes. Check important info.

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So that was the analysis and advise offered to me by ChatGPT.
Now, it is my turn. And yours too.