The most scientifically honest interpretation of this data might be the most uncomfortable one. Consider this alternative explanation:

Hypothesis: Vaccines themselves contribute to hypertension, and this study accidentally documented vaccine effects while attributing them to COVID.

https://www.nature.com/articles/s41598-025-14617-5

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The Problem with “Peer Review Passed”

Last week, Nature Scientific Reports published a study claiming that COVID-19 infection increases the risk of developing hypertension for up to three years. The headline findings seemed significant: hospitalized COVID patients were 57% more likely to develop high blood pressure than uninfected controls.

But here’s the thing about scientific papers: passing peer review doesn’t mean the logic holds up to scrutiny.

I recently analyzed this paper using something called the INGA314.ai – a systematic approach to detecting logical flaws, measurement errors, and scope violations in research claims. What I found was troubling: a cascade of logical failures that undermines the study’s core conclusions.

The most devastating flaw? A complete causal attribution failure that makes it impossible to determine what’s actually causing the observed hypertension.

This isn’t about attacking the researchers. It’s about understanding how even well-intentioned studies can go wrong when basic logical principles aren’t rigorously applied.

The COVID-Hypertension Study: What Went Wrong

Red Flag #1: The Causal Attribution Crisis (Study-Breaking)

Here’s the flaw that potentially invalidates everything: The researchers cannot distinguish between COVID effects and vaccination effects.

Consider this timeline:

• Study Period: January 2020 – October 2024
• Vaccine Rollout: December 2020 onwards
• Mass Vaccination: 2021-2022
• Population Vaccination Rate: 95% by May 2022
• Hypertension Findings: Primarily from 2021-2024 period

The Problem: COVID infections and vaccinations occurred in the same population during the same time period. Both involve spike protein exposure. Both could potentially cause hypertension through identical biological mechanisms.

Critical Question: When you find increased hypertension in COVID patients from 2021-2024, is it caused by:

• COVID infection itself?
• Vaccination effects?
• Both?
• Neither (some other factor)?

The Study’s Answer: “We assume it’s COVID and don’t measure vaccination status.”

This is a fundamental causal attribution failure that makes the study’s conclusions scientifically meaningless.

Red Flag #2: The Vaccination Black Hole (Study-Breaking)

The Missing Data Crisis: The researchers studied COVID’s effects on hypertension across four years without knowing who was vaccinated.

This creates an impossible analytical situation:

• Early COVID patients (2020): 0% vaccinated (vaccines didn’t exist)
• Later COVID patients (2021-2024): 95% vaccinated

The researchers essentially pooled together two completely different populations and analyzed them as if they were the same. This is like studying “the health effects of car accidents” by combining Model T crashes with modern car crashes (with airbags, seatbelts, crumple zones), then presenting unified conclusions about “car accident health effects.”

Why This Matters: Vaccination dramatically changes:

• Who gets infected (breakthrough vs. unvaccinated infections)
• How severe infections become
• What the underlying population health profile looks like
• The biological mechanisms at play

Without vaccination data, you literally cannot determine what’s causing any observed effects.

The Unthinkable Possibility: Are Vaccines the Problem?

The most scientifically honest interpretation of this data might be the most uncomfortable one. Consider this alternative explanation:

Hypothesis: Vaccines themselves contribute to hypertension, and this study accidentally documented vaccine effects while attributing them to COVID.

Supporting Evidence:

1. Temporal Correlation: Hypertension increases coincide with mass vaccination rollout (2021-2024)
2. Biological Plausibility: Vaccines produce spike protein, which affects the same ACE2 receptors implicated in COVID hypertension
3. Statistical Pattern: The “delayed effect” (1.5-3 years post-COVID) matches the timeline of chronic vaccine effects
4. Population Paradox: High vaccination areas (Suffolk County) still show effects, suggesting vaccination didn’t prevent them

The Terrifying Implication: What if the “long-term COVID effects” are partially or primarily long-term vaccination effects?

This possibility alone should have prevented publication until proper vaccination-stratified analyses were performed.

Red Flag #3: The Impossible Statistics

The paper claims that non-hospitalized COVID patients were “not more likely to develop hypertension than controls” overall. But then it reports that these same patients were significantly more likely to develop hypertension in the later follow-up period.

This is mathematically impossible. If there’s a significant effect in part of the time period, the overall effect can’t be non-significant unless there’s an unexplained protective effect early on. The authors never address this contradiction.

Red Flag #4: Correlation Becomes Causation

Throughout the paper, observational associations magically transform into causal claims:

• Abstract: “COVID-19 can trigger new cardiovascular events”
• Discussion: “mechanisms by which COVID-19 causes hypertension”

But the study design – a retrospective analysis of medical records – can only show associations, not causation. The authors provide no biological measurements, no mechanistic evidence, just speculation presented as established fact.

Red Flag #5: The Wealthy White Population Problem

The researchers studied a predominantly white, wealthy, highly-vaccinated population in Suffolk County, New York (median income $141,671), then presented their findings as applying to COVID patients generally.

This matters enormously because Suffolk County represents a unique population that may not be generalizable to:

• Lower-income populations
• Different racial/ethnic groups
• Areas with different vaccination rates
• Different healthcare access patterns

Red Flag #6: The Missing Deaths (Survivorship Bias)

COVID patients who died can’t develop hypertension. Hospitalized COVID patients had higher mortality rates, but only survivors could be included in the hypertension analysis. This creates systematic bias toward healthier COVID survivors.

The authors never mention this obvious bias.

Red Flag #7: The Propensity Matching That Didn’t Match

The researchers claim they “propensity-matched” COVID patients with controls for age, sex, race, and health conditions. But their own data table shows massive, statistically significant differences between supposedly matched groups:

• Age: 50.4 vs 35.9 years (p < 0.001)
• Sex distribution: 54% vs 45% male (p < 0.001)
• Multiple health conditions: all significantly different (p < 0.001)

Either the matching failed completely, or the authors don’t understand what propensity matching means.

The Vaccination Problem Goes Even Deeper

Multiple Control Groups Needed

To properly investigate COVID’s cardiovascular effects, you need these comparison groups:

• Unvaccinated, uninfected (baseline)
• Vaccinated, uninfected (vaccine-only effects)
• Unvaccinated, infected (COVID-only effects)
• Vaccinated, infected (combined effects)

This study had: One mixed group of unknown vaccination status compared to another mixed group of unknown vaccination status.

The Biological Mechanism Problem

Both COVID infection and vaccination:

• Expose the body to spike protein
• Affect ACE2 receptors
• Can cause endothelial dysfunction
• May trigger autoimmune responses
• Could lead to chronic inflammation

If the mechanisms are identical, how can you attribute effects to one cause without measuring both?

The Timeline Evidence

From emerging research:

• Yale studies show spike protein persisting 700+ days post-vaccination
• Post-vaccination syndrome includes cardiovascular symptoms
• Immune system changes from repeated vaccination documented

The study’s “delayed COVID effects” (1.5-3 years) perfectly match the timeline for chronic vaccination effects.

The Suffolk County Paradox

If vaccines were highly protective, why did Suffolk County (95% vaccinated) still have:

• 64,000 COVID-positive patients
• Increased hypertension in those patients
• Effects extending 3 years post-infection

Possible explanations:

1. Vaccines provided minimal protection (consistent with real-world effectiveness data)
2. Vaccines themselves contributed to hypertension risk
3. Both factors operated simultaneously

Why This Matters Beyond Academic Journals

Immediate Clinical Impact

• Doctors might implement unnecessary screening based on false attributions
• Patients might worry about “COVID effects” that could be vaccination effects
• Treatment decisions might target the wrong underlying causes

Public Health Policy Disasters

• Resource allocation based on incorrect causal models
• Vaccination programs that don’t account for potential cardiovascular risks
• Long COVID clinics focusing on the wrong interventions

Research Misdirection

• Future studies building on flawed causal assumptions
• Meta-analyses compounding attribution errors
• Clinical trials testing interventions for the wrong causes

Trust Erosion

When the true causes are eventually identified, it damages public trust in:

• Medical research methodology
• Causal claims in observational studies
• COVID-related health guidance
• Scientific institutions generally

What Good Science Looks Like

A scientifically valid approach to this question would:

Separate the Causes

• Time-stratified analysis: Compare pre-vaccine era (2020) COVID effects with post-vaccine era effects
• Vaccination status documentation: Individual-level vaccination data for all participants
• Multiple control groups: Test each potential cause separately

Use Honest Language

• “Associated with” instead of “causes”
• “In this specific population” instead of universal claims
• “Cannot distinguish from vaccination effects” when that’s true

Match Claims to Evidence

• Acknowledge when causal attribution is impossible
• Present alternative explanations
• Limit conclusions to what the data actually shows

Address Obvious Limitations

• Vaccination confounding
• Survivorship bias
• Population generalizability
• Measurement validity

The Path Forward: Honest Medical Research

For Pandemic Research

• Mandatory vaccination documentation in all COVID outcome studies
• Temporal stratification by vaccination era
• Multiple causation hypotheses tested simultaneously
• Biological mechanism measurement rather than speculation

For Peer Review

• Causal attribution audits: Can competing causes be distinguished?
• Population validity checks: Do conclusions match study populations?
• Temporal confounding assessment: Are time-varying factors accounted for?

For Science Communication

• Uncertainty acknowledgment: When causes cannot be determined
• Alternative explanation presentation: What else could explain the data?
• Population specificity: Who do these findings actually apply to?

Your Role as a Science Consumer

When evaluating medical research, ask:

• Causation Question: Could something else explain these findings?
• Population Question: Do I match the study population?
• Timeline Question: What else happened during the study period?
• Control Question: Are the comparison groups actually comparable?
• Honesty Question: Are limitations and uncertainties acknowledged?

The vaccination attribution problem in this study should be obvious to any careful reader who thinks about timing and biological mechanisms.

The Bigger Picture

This study illustrates a systemic problem in pandemic research: the assumption that correlation equals causation when the preferred cause is being studied.

The uncomfortable truth: We may have been attributing vaccine effects to COVID, COVID effects to vaccines, or both effects to neither. Without proper study designs that can distinguish causes, we’re essentially guessing.

The scientific imperative: When two potential causes have:

• Overlapping timelines
• Identical biological mechanisms
• Mixed populations
• Missing measurement data

…honest science requires acknowledging that causal attribution is impossible with the available evidence.

The Most Important Question

This paper forces us to confront an uncomfortable possibility: What if our causal attributions for post-2020 cardiovascular effects are fundamentally wrong?

If vaccines contribute to hypertension (even partially), then:

• Millions might have vaccine-induced cardiovascular effects
• Long COVID research might be studying vaccination effects
• Public health policy might be based on misattributed causation
• The interventions we’re pursuing might target the wrong causes

This isn’t anti-vaccine rhetoric – it’s basic scientific logic. If you’re going to claim COVID causes long-term cardiovascular effects, you must be able to distinguish those effects from vaccination effects. This study cannot.

The path forward isn’t choosing sides – it’s demanding better science. Science that can actually distinguish causes. Science that acknowledges uncertainty when it exists. Science that follows evidence wherever it leads, even when it’s uncomfortable.

Because the alternative – building medical policy on misattributed causation – is far more dangerous than acknowledging what we don’t know.

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The COVID-hypertension study discussed represents data through October 2024 and involves real public health implications. This analysis focuses on logical methodology and causal attribution validity rather than questioning researchers’ intentions. The goal is improving scientific rigor when multiple potential causes cannot be distinguished.

Key Takeaway: In medical research, the most important question isn’t “What do we want the cause to be?” but “What can we actually prove about causation?” When that answer is “We don’t know,” honest science requires saying so.