Many revelations have come out that highlight the glaring inconsistencies in his own claims: his bragging about helping shut down schools and promoting lockdowns, and his disgust for Ron DeSantis and others who dared defy “the science.” Thanks to Senator Rand Paul, the treasure trove can be found here: https://www.paul.senate.gov/wp-content/uploads/2026/07/2026.07.24_Tonys-Diary-Package.pdf

Despite the PTSD that all of these revelations bring up, and instead of adding more deserved critical commentary to the noise around them, I have undertaken a natural language processing analysis of the text of Fauci’s diary entries.

My goal was to turn this 1,100-page trove of Anthony Fauci’s daily pandemic-era journal entries into something you can actually measure. After extracting the text and breaking it into roughly 575 individual dated entries, I used a range of natural language models and language-analysis techniques to find the patterns in all that writing. I mapped his most common themes — letting the software discover them rather than guessing in advance — and sorted every entry into plain-English categories like “media and press,” “vaccines,” and “politics.” I then added a psychological read of the writing, scoring each entry for emotional tone and for the balance between seeking validation and airing grievances. Finally, I charted it all over time, so that instead of impressions I get a quantified portrait of what was on his mind, how he felt, and how that shifted across the most scrutinized stretch of his career.

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Last year Dr. Høeg was brought on to the FDA under (now former) Commissioner Makary to continue her important work in holding Pharma accountable to better transparency and higher standards. During her year there, she was able to accomplish meaningful and hopefully lasting reforms. Sadly, the political machinery behind it — whose inner workings I make no claim to understand — was not friendly to true apolitical scientists with commitments to Americans’ health and to genuine scientific scrutiny. Because of that, her time there was cut short. It’s a loss that deserves acknowledgment. Rather than ruminate in frustration (which I have been doing plenty of), I would like my readers to hear from her directly, in her own words.

And finally, a personal thanks to Tracy for everything she’s done to provide parents and children with a rational, Team Reality medical perspective over the last few years. There’s a lot of work left to do to hold Pharma and other failed medical institutions accountable, and I know she will continue that work.

Better than Fiction

Grateful to have been part of meaningful work at the FDA

On Friday night, I read about Trump’s executive order to realign the US’s core vaccine recommendations with those of best practices from peer, developed countries and I felt proud of the work Martin Kulldorff and I did, along with many others, to present these best practices…

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3 months ago · 78 likes · 28 comments · Tracy Beth Høeg, MD, PhD

About 2½ years ago I was in the market for a used vehicle — a position most of us land in every few years. I’m a sucker for quality and I never buy brand new, so I’m unsurprisingly obsessed with reliability research. During that process, an idea hit me: what if we approached vehicle quality the same way we approach population health? Could we build a population-wide analysis that compares relative failure rates across models? How much more often does Model X break down than Model Y — and when it does, how bad is it?

For the last two years, that’s what I’ve been building. The result is a platform anyone can use:

vehiclereliability.com

If you’re data-informed (or data-obsessed), and either shopping for a used vehicle or know someone who is, take a look.

Don’t worry — I won’t be converting this newsletter into a marketing arm for the platform. But if you’re the type of person who reads my work, you might just find it useful.

Buying a used car is one of the biggest financial bets most of us make, and it’s usually made with surprisingly little hard data. This dashboard exists to fix that. Here’s how to read each section.

The Reliability Matrix

The Matrix plots our two core metrics — frequency (how often a vehicle breaks down) and severity (how bad it is when it does) — against the class average. Each vehicle is only compared to peers in its own class and model year, so you’re seeing apples to apples.

The four quadrants tell you the risk profile at a glance:

Bottom Left — Low frequency, low severity. Rarely breaks. When it does, it’s minor. The most reliable vehicles in the class for that year.
Top Left — Low frequency, high severity. Rarely breaks, but when it does, expect something serious — transmission, engine, drivetrain.
Bottom Right — High frequency, low severity. In the shop often, but the problems are manageable and cheap.
Top Right — High frequency, high severity. The worst of both worlds. These vehicles break the most and cost the most to fix.

Use this to quickly rule in or rule out a model before digging deeper.

Vehicle Rank by Class

Track how a model’s ranking has held up year over year. Consistency matters here — the less a vehicle “shuffles” up and down the rankings, the more confident you can be that its quality is structural rather than accidental. A vehicle that lands in the top 3 of its class five years running is telling you something different than one that bounces between 2nd and 12th.

Component Breakdown

This chart breaks reliability into 10 major component categories — engine, transmission, electrical, brakes, suspension, and so on — and shows whether each one fails more, less, or about the same as the class average. Bars below the dotted line are good: those components are higher quality than peers. Bars above it are where the trouble lives.

Use this to figure out what kind of problems you’d actually be inheriting.

Model Year Comparison

Even the most reliable brands have problem years — a redesign that didn’t land, a flawed supplier, a recall. And the hybrid version of a model can have a meaningfully different reliability profile than the gas version. This chart helps you tease both apart.

The score combines the frequency and severity indices. A combined score below 2 means both indices came in under the class average (under 1 each) — a higher-than-average quality year. Anything well above 2 is a signal to look closer before committing to that model year.

Use this when you’re deciding between two years or two powertrains of the same model.

For details on how we calculate these metrics, see our Methodology FAQ.

The debate about whether AI actually makes developers more productive is still unsettled. Some claim it dramatically accelerates work. Others argue it simply shifts effort to different parts of the development cycle. An article in Harvard Business Review recently examined this question and reached a surprising conclusion: AI doesn’t necessarily reduce work—it often intensifies it.

The hype surrounding AI is impossible to ignore. Some of the loudest voices predicting the imminent disruption of high-skill knowledge work are also the ones building the models meant to replace it. These reports should be taken with a few shakes of salt. The reality on the ground is proving more nuanced.

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Enjoy.

And here’s some research from and others.

Last month, a Minnesota high school softball team won the state championship behind a seven-inning shutout thrown by their pitcher, a biological male competing under the name "Marissa." The team, which had previously struggled, suddenly surged to a 20-2 record. It was the kind of Cinderella story that sports media usually celebrates—except this one hinges on the uncomfortable and increasingly politicized reality of male athletes playing on female teams.

The common rejoinder is familiar: trans-identifying athletes comprise a tiny fraction of competitors, so why all the fuss? Aren’t these debates just veiled prejudice against trans people? Exclusion is bad! Well, no- the only way single-sex leagues exist at all is due to exclusion. Cases like this underscore what many already know intuitively: putting a biological male in a key role on a girls' or women's team fundamentally alters the competitive landscape. That this even requires debate—after a raft of executive actions, legal challenges, and the much-publicized controversy at UPenn—speaks to how disconnected our institutions have become from basic truths about athletic fairness.

Despite our culture's obsession with sports stats—from ESPN to fantasy leagues to sports betting apps—there is a striking absence of institutional effort to track the presence and impact of males competing in female categories. The NCAA doesn’t. ESPN doesn’t. Nor do the major professional leagues. This statistical void isn’t accidental; it's political.

If not for the brave people behind HeCheated.org, who dedicate their time to track as much of the occurrence of men in women’s sports, we would not be able to quantify. As a disclaimer, while the data from hecheated.org is as thorough as possible given how much of the narrative and political climate suppresses truth- it is not exhaustive and is most certainly an under-count of the occurrence. When individuals face harassment and threats and are being removed from games where male players are included- we know that the full scope of the problem is still much worse.

Let’s start with incidence: the number of known male athletes competing in female team sports has risen steadily over the past few years, particularly in high school and college. The patterns aren’t random; they follow the incentives. Males are overwhelmingly placed in high-impact roles—pitchers, forwards, sprinters—positions where strength, speed, or endurance provide the greatest marginal advantage.

Here’s a Breakdown of the Number of Male Athletes by Sport.

Here’s A Ranking of States by How many Women’s Teams include Male Players.

Deeper Analysis: Win Percentage of Teams with Male Players

When we look at something like team win percentages, there's a specific statistical pattern we often expect to see, especially when we have a good, large collection of teams. This pattern is called a truncated normal distribution.

So, when you have a good sampling of teams – meaning enough teams playing enough games in a well-structured league – you'd generally expect their win percentages to settle into this kind of truncated bell curve. What that means for sports is simple: the vast majority of teams would be clustered around an average win percentage, playing at a pretty similar level. There would be fewer teams at the very top (the dominant, almost undefeated squads) and also fewer teams at the very bottom (those winning very few games). Crucially, the number of teams at the top extreme would roughly match the number at the bottom extreme, showing a natural balance of competition where excellence and struggles are both present but less common than solid, middle-of-the-pack performance.

Here’s and example of such a distribution from The Sport Journal that reflects the distribution of win percentage in Collegiate Women’s Lacrosse. As you can see, it’s a pretty standard smattering of win percentages all around the middle with fewer towards the extremes. With a balanced distribution of teams both above and below that average win mark.

Compare the previous with the distribution of win % for Collegiate and High School Women’s teams with men.

Distribution of Win % for Women’s High School and College teams with Males

Looking closely at both graphs, the Distribution of Win % for Women’s High School and College teams with Males has a noticeably larger proportion of its "records" (teams) with win percentages above 50%. The clustering of teams on the upper end of the distribution- with 54 teams having win % over 50%, and 24 teams below 50%. The implications are obvious: inserting a male athlete into a female roster, particularly in a key position, confers a consistent competitive edge.

All of this to say- it actually should not matter what impact to overall performance a male player has on female teams, as it’s still unfair and against the entire point of female only athletics.

Though we’re seeing progress, as with the US Olympic Committee now claiming it will comply with the Law and President Trump’s executive order, there’s still much work to be done in courts, State-level associations, and local schools across the country to end the men-dominating-women-in-sports movement. When institutions abdicate the responsibility to count, to track, to assess impact, it falls to private citizens to do what entire leagues and governing bodies refuse to. The numbers may be incomplete, but the pattern is clear. Fairness isn't just being compromised—it's being erased.

Sources:

https://hecheated.org/Totals_teams.html

https://thesportjournal.org/article/decision-making-on-injury-prevention-and-rehabilitation-in-professional-football-a-coach-medical-staff-and-player-perspective/

https://www.msn.com/en-us/sports/other/hs-girls-softball-team-wins-championship-behind-arm-of-male-pitcher/ar-AA1FW2Ea

For years, the public has been told that concerns over males competing in girls’ sports are overblown—fringe talking points from politically motivated corners. But the data now speak for themselves. This isn’t rare or isolated. It’s systemic. And until now, no national institution—neither the NCAA, nor the NFHS, nor any major medical or athletic body—has bothered to count the cost. That silence has come at the expense of young women and girls who are losing scholarships, medals, and hard-earned opportunities to male athletes. This article is the first in a series drawing on data from HeCheated.org, the only public effort systematically documenting where and how this is happening. The goal is simple: to shine light on a growing and measurable injustice that institutions refuse to confront—and to ask why they won’t.

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HeCheated.org has assembled the most comprehensive public record to date of biological males competing in female sports. While this issue continues to generate public debate, few institutions have attempted to quantify its scale or impact. Even organizations that support men in women’s sports have shown little interest in tracking numbers, participation trends, or performance outcomes. Instead, the conversation is too often framed in abstraction: isolated examples, not systemic shifts.

But it’s happening—and it’s measurable.

To date, the NCAA, the NFHS, and other major athletic bodies have yet to take a definitive stand affirming the principle of sex-based competition in women’s sports. While some have released statements or issued policy documents, few have taken action that prioritizes fairness or safety for female athletes. Most have opted for ambiguity—gesturing toward inclusion and fairness, while protecting neither.

Part of the problem lies upstream. The medical establishment has increasingly embraced ideological frameworks in place of rigorous evidence. The rapid rise of “gender medicine” has captured major institutions, including the American Academy of Pediatrics and the American Medical Association, and pushed protocols that many physicians now openly question. High-profile investigations, whistleblower accounts, and internal documents have revealed just how far some systems have strayed from science and caution.

The public conversation has followed a predictable trajectory:

• It’s not happening.

• It’s happening, but it’s rare.

• It’s happening, and it’s good.

We’re currently in the second phase. The existence of males in girls’ sports can no longer be denied—but the scale is still routinely downplayed, and the harms minimized. The loss of titles, scholarships, and fair competition for young women is treated as an unfortunate side effect rather than a central concern.

Recently the Department of Health and Human Services released a landmark report evaluating pediatric gender medicine in the United States. Commissioned by Executive Order, the report documents serious shortcomings in the evidence supporting “gender-affirming” care, and highlights the risks posed by puberty blockers, cross-sex hormones, and surgical interventions. It marks a notable shift in federal posture—one that signals growing institutional awareness, even if actual reform remains slow.

But clarity at the executive level does not mean the issue is resolved. Our athletic, medical, and educational institutions are captured by trans activists and remain deeply compromised and unaccountable. As J.K. Rowling aptly put it:

“it's a complete waste of time telling a gender activist that their favourite slogan is self-contradictory nonsense, because the lie is the whole point. They're not repeating it because it's true - they know full well it's not true - but because they believe they can make it true, sort of, if they force everyone else to agree.”

The mechanism is recognizable: reality is redefined by repetition, consensus manufactured by institutional power. This isn’t a grassroots movement. It’s a top-down policy agenda, often enforced without public consent or open debate.

I’ll be analyzing where, how often, and in what sports male participation is occurring. The data come from HeCheated.org, which has done the work no major agency has attempted: counting cases, tracking outcomes, and documenting impact.

We begin with high school sports—perhaps the most tragic arena for this issue. Teenage girls, who lack the institutional support or freedom to speak out, are left to watch their records fall to boys with an inherent biological advantage. The adults—coaches, school officials, athletic associations—know better. And yet, many have chosen to say nothing.

In this first report, we’ll examine:

• The states with the most documented cases.

• Placement data from competitive events—who’s winning, and by how much.

• The sports most affected: Track and Field, Cross Country, Swimming, and Volleyball.

Looking at the worst offenders: Connecticut, California, Massachusetts, Michigan, Washington State…. what’s the common theme?

As you can see from the chart, boys are predominantly stealing 1st, 2nd, and 3rd place from the girls the compete against.

Track and Field is overwhelmingly the most problematic sport for allowing boys to compete.

What the numbers show is simple and uncontroversial: when boys compete in girls’ events, they win. That this even needs to be said underscores just how detached from reality our institutions and policymakers have become.

All data sourced from HeCheated.org

President Trump has recently taken decisive action against Diversity, Equity, and Inclusion (DEI) programs within the federal government by signing executive orders that dismantle these initiatives. His actions include revoking an order by Lyndon B. Johnson on affirmative action for federal contractors and placing all federal DEI staff on paid administrative leave with plans for their eventual layoff. These moves have sparked significant controversy, with critics arguing that they undo decades of progress toward racial and gender equity in federal employment, while supporters believe they restore merit-based governance. This fulfills Trump's campaign promise to eliminate what he describes as "radical and wasteful" DEI programs, aligning with his commitment to a colorblind, merit-based society. The controversy reflects a broader debate on the role of government in promoting diversity versus ensuring equal opportunity based solely on merit. But what does the evidence show?

The Rise of Woke Indoctrination

Diversity, Equity, and Inclusion (DEI) initiatives have become increasingly prevalent in workplaces, educational institutions, and other organizations across the United States. These programs’ stated goals are to foster more inclusive environments, reduce bias, and promote equity for all individuals. A key component of many DEI programs is diversity pedagogy, which often includes lectures, trainings, and educational resources designed to educate participants about their own biases and the ‘systemic nature of oppression’. A growing body of research suggests that DEI programs, particularly those emphasizing "anti-oppressive" frameworks, have consequences that are completely opposite of their stated goals. While many might give DEI practitioners the benefit of the doubt and see these trainings as well intentioned, that is up for debate. This study, conducted by the Network Contagion Research Institute (NCRI) and Rutgers University, investigates the potential for these programs to increase intergroup hostility and even contribute to the rise of authoritarian tendencies.

Putting DEI to the Test

The study employed an experimental design to examine the impact of different types of educational materials on participants' attitudes and beliefs. Participants were randomly assigned to one of two groups:

• Control Group: Exposed to neutral control materials, such as an essay on corn production.

• Intervention Group: Exposed to DEI materials emphasizing systemic oppression, anti-racism, and narratives of victimization. These materials included excerpts from works by prominent DEI scholars like Ibram X. Kendi and Robin DiAngelo, as well as materials used in caste sensitivity training.

Participants then evaluated scenarios designed to assess their perceptions of bias, willingness to punish perceived oppressors, support for punitive measures, and overall attitudes towards different groups.

Results:

The study found that exposure to "anti-oppressive" DEI materials had several significant effects:

• Increased Perception of Bias: Participants exposed to these materials were more likely to perceive prejudice and discrimination where none existed, even in neutral scenarios. For example, in a scenario involving a college admissions decision, participants exposed to DEI materials were more likely to perceive the admissions officer as racially biased against the applicant, despite the absence of any evidence of discrimination.

• Promoted Punitive Attitudes: Participants exposed to these materials showed increased support for punitive measures against perceived oppressors. They were more likely to endorse measures such as suspension, public apologies, and mandatory DEI training, even when there was no evidence of wrongdoing.

• Heightened Authoritarian Tendencies: The study found a correlation between exposure to these materials and increased authoritarian tendencies. Participants exposed to "anti-oppressive" DEI materials were more likely to endorse demonizing statements about perceived "oppressor" groups, reflecting a shift towards a more punitive and intolerant mindset.

Key Charts:

Share

Discussion:

These findings raise significant concerns about the consequences of DEI programs. By emphasizing ‘systemic oppression’ and focusing on narratives of victimization, these programs:

• Increase intergroup hostility: The heightened perception of bias and the promotion of punitive attitudes can contribute to increased mistrust and hostility between different groups.

• Foster a climate of fear and suspicion: A constant focus on systemic oppression and the perception of pervasive bias can create a climate of fear and suspicion, where individuals are constantly on the lookout for signs of prejudice.

• Contribute to the rise of authoritarian tendencies: The emphasis on punitive measures and the demonization of perceived "oppressors" may contribute to the rise of authoritarian tendencies, such as the suppression of dissenting views and the erosion of civil liberties.

Conclusion:

This study offers valuable insights into the consequences of DEI programs. While these initiatives may be well-intentioned, they can sometimes backfire, inadvertently increasing intergroup hostility and fostering a climate of fear and suspicion. At the very least, these findings underscore the urgent need for careful consideration and rigorous evaluation of DEI efforts. More concerning, however, is the extent to which DEI culture has become toxic and counterproductive—so much so that it exacerbates the very problems it claims to resolve.

References:

https://networkcontagion.us/wp-content/uploads/Instructing-Animosity_11.13.24.pdf

Over two years ago, I analyzed the Pfizer toddler COVID-19 vaccine trial. At the time, many parents felt pressured to vaccinate their young children, especially with media headlines touting “80% Vaccine Effectiveness.” However, as I detailed in my earlier article, those claims were misleading. The actual trial data, which was available for anyone to review, told a very different story.

What the Pfizer Trial Really Showed

For children aged 2–4 years, the trial data revealed troubling findings:

• Vaccinated children experienced more than three times the rate of severe COVID-19 compared to the placebo group.

• The actual vaccine effectiveness ranged between 10% and 30%, far below the claimed 80%.

Fast forward two years, and the broader impacts of these mRNA vaccines—including outcomes beyond COVID-19, such as susceptibility to other infections and severe adverse effects—have been studied more thoroughly. The results are concerning, though not surprising for those who have been closely following the data.

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In a poetic moment of triumph for science, reason, and sanity, Jay Bhattacharya has been nominated to lead the NIH. The NIH whose former head Francis Collins called for a “quick and devastating take-down” of Jay, Martin Kulldorff, and Sunetra Gupta’s Great Barrington Declaration. In the same email, Collins smeared Dr. Bhattacharya as a “fringe epidemiologist.” We know about these emails only because a FIOA request. What unfolded in the days after the Declaration was nothing less than a coordinated attack from government, media, and academia to smear the man who had the gall to simply make a statement that summarized one of the most fundamental realities of epidemiology.

As immunity builds in the population, the risk of infection to all – including the vulnerable – falls. We know that all populations will eventually reach herd immunity – i.e. the point at which the rate of new infections is stable – and that this can be assisted by (but is not dependent upon) a vaccine. Our goal should therefore be to minimize mortality and social harm until we reach herd immunity.

The Great Barrington Declaration was authored on October 4th, 2020, and called for an end to the already-failed lockdown policies, focused protection for the elderly and disabled, and for the young and low risk to return to just making their own decisions about risk. Read the Declaration here.

Immediately after the Declaration was released to the public, a barrage of attacks from media, academia and government officials ensued. “Anti-lockdown”, “Let it rip”, “pro-infection” were phrases commonly used. Even today, the headlines describe Jay as a “Contrarian” and “Lockdown Critic” and my favorite: “Unorthodox.”

Nothing could be further from the truth. The history of evidence-based pandemic policy, epidemic mitigation and public health practices supported the approach outlined in the GBD. The views espoused by the authors were mainstream. Marc Lipsitch, a Harvard epidemiologist wrote in 2011 after the H1/N1 response-

“Ideally, data on the economic costs (including indirect costs for socially disruptive measures such as school dismissals) and the public health and economic benefits of interventions would be formally weighed within a cost-benefit or cost-effectiveness framework to inform policy decisions.”

This paper typified the analysis of experts in pandemic policy and epidemiology in the decades preceding Covid. Yet Dr. Bhattacharya calling for cost benefit analysis somehow makes him “Fringe.”

The same March Lipsitch, who signed on to the Jon Snow Memorandum (a polar opposite, maximum intervention approach with many signatories having pharmaceutical interests), was already moderating his lockdown support in a debate with Jay in November 2020. In the debate, Lipsitch concedes the realities of costs, harms and many other points that have been Bhattacharya’s recurring themes that earned him the “contrarian” moniker. To Lipsitch’s credit, he had the integrity to actually debate the issue.

Another example of this moderate, measured policy opinion is found in a report authored by Boston University bioethicists for the ACLU in 2008, (in what now seems like an alternate universe from what the ACLU has devolved into) “The Need for a Public Health - Not a Law Enforcement/National Security- Approach” states

Coercion and brute force are rarely necessary. In fact they are generally counterproductive—they gratuitously breed public distrust and encourage the people who are most in need of care to evade public health authorities. • On the other hand, effective, preventive strategies that rely on voluntary participation do work. Simply put, people do not want to contract smallpox, influenza or other dangerous diseases. They want positive government help in avoiding and treating disease. As long as public officials are working to help people rather than to punish them, people are likely to engage willingly in any and all efforts to keep their families and communities healthy.

In the long past Bush-era, it was the academic left who were advocating for Human Rights and restraint of government overreach in the face a pandemic. A mere 12 years later, suddenly the same exact ideas are “right wing.”

The above examples are simply non-controversial summaries of what the majority opinion and mainstream lefty academics believed. In the pre-Covid world, the ideas that Human Rights should be upheld even in crisis, that vulnerable communities should be prioritized, and that disease spread was inevitable, were not even in question.

This brings us to the most glaring example of intellectual dishonesty and hypocrisy of the academics who piled on the personal attacks of Dr. Bhattacharya.

This letter was dated March 2, 2020, before the Covid Lockdowns happened in the US. It was sent to Vice President Mike Pence and was signed by over 800 public health experts. Below is a selection of quotes.

Read the letter in it’s entirety here.

Clearly this was just an early draft of the Great Barrington Declaration, right? The language of balance of the government response with the importance of protection the vulnerable, along with the admission that the spread of the coronavirus is a biological inevitability, rather than a policy decision. It was not crackpot nonsense, but a somewhat boring mainstream position.

The letter was authored by none other than Yale epidemiologist Gregg Gonsalves. The same Gonsalves who dedicated a considerable portion of his time post March 2020 to attacking Jay and his colleagues.

At some point after the US locked down in March 15th, Gonsalves, along with many other academics and scientists, fell completely silent in the face of the worst example of Human Rights violations in our lifetime. The cancelling of cancer screenings, surgeries, closing of schools, closure of government services, and shutdown of the livelihoods of working class Americans somehow did not trigger a reproof or objection. Despite his letter from March 2nd warning about how harmful just such measures would be, he fell in line with Fauci, Birx and everyone else who went along with the lockdown narrative.

But Gonsalves did not stay silent for long. He became the most vocal and vehement supporter of every harsh measure imaginable, and went further by attacking critics of the response, specifically the authors of the GBD. In October 2020, Jenin Younes thoroughly documented the constant barrage of overtly political attacks against Bhattacharya, Kulldorff, and Gupta. Her article is replete with examples of Gonsalves unhinged against any objection or criticism of harsh pandemic response measures. Surveying the bulk of negative, sometimes defamatory media articles about Dr. Bhattacharya, they often cite Gonsalves as a sources or quote him directly. The sheer amount of time and effort that Gonsalves dedicated to this pursuit was impressive.

As of this writing, Gonsalves has either deactivated or deleted his Twitter account. Is it a sign of the turning of the tables, or perhaps simply a retreat to the leftist progressive echo chamber of Bluesky? Time will tell.

As we celebrate the nomination and eventual confirmation of Dr. Bhattacharya, we will continue to hear the terms “contrarian” “critic” “anti-lockdown,” and yes, even “Fringe" describe him. For posterities’ sake, let us admit that this is nonsense. Lockdowns were a security state driven pandemic response rooted in fear, panic, and authoritarianism. They were never mainstream.

The goal of this modeling project was to predict the outcome of the 2024 Election using public health, demographic, and historical data. The unique approach relies on predictors that are a proxy for public support for the Democratic party within a population. In the U.S., we are faced with a binary choice, Democrat or Republican, and our election is decided by Electoral votes from each state. Therefore, the response metric predicted was simply the margin of victory within a state. Because of the electoral college, predicting the election is essentially a matter of predicting a handful of states. Most states have a reliable history of a wide margin of victory for one party or the other, while a few do not. The data and model will be accurate to the extent that they accurately predict these states. Due to the smaller sample size of recent national elections and the importance of recent data points in the model, it will not be capable of producing highly precise predictions for states with slim margins of victory. Therefore, the success of this model will hinge on its ability to detect which swing states might have more support for Democrats (or Republicans) than what is currently being detected in the polls.

Background and Assumptions:

Over the last two presidential election cycles, we have seen public polling fail in major ways. In 2016, almost every major polling and media outlet failed to detect the degree of public support among Democrats and Independents that led to Trump's victory in key swing states and the Rust Belt. In 2020, polling outlets again underestimated Trump's support in key states. Since then, trust in the media's ability to investigate and get at the truth has further eroded.

This analysis seeks to find predictors that reflect a more accurate state of the public's political preference that are not subject to the weaknesses of the polling industry's biases. Due to the hyper-polarized nature of the Covid-19 pandemic, and the explicitly clear lines where support for the Covid-19 shot fell, public uptake for each year's "new" version of a Covid-19 shot is highly correlated with support for the Democratic party. Because there is a new Covid-19 shot every year, continued uptake is assumed to indicate Democratic vote allegiance. Other indicators, such as domestic migration rate and mail-in ballot requests, are strongly correlated with Democrat support over the last four years. In addition, population data from public health sources have been used as controlling or predictive variables, including mortality rate, birth rate, and mental health. Some demographic and population dynamics are associated with more Republican-leaning states and others with Democratic-leaning states, and these relationships have held over time in recent history. Other measures, like net migration rate, have strong associations, but those are more recent and were affected by the Covid-19 pandemic, during which many locked-down blue states saw a net loss, and red open states saw a net gain. The popularity of the now annual Covid-19 shot is waning year over year, and the data has been adjusted to measure relative popularity, with states with higher overall uptake than average reflecting higher Democratic party support.

Overall, this analysis seeks to combine both longer-term trends and more recent trends in order to estimate the current level of support for the Democratic party. As the model must be trained on data only made available in the months (Covid-Vax) and weeks (absentee ballot requests) leading up to the election, it will be unable to detect any 11th-hour shifts.

As George Box said, "All models are wrong, but some are useful." My hope with this analysis is that it might be useful to detect signals that might not be present in traditional election polling. In addition to prediction (which is mostly for fun), I have included some swing state analysis that I think might shed some light on key shifts that have been happening over the last four years.

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For the past few election cycles, we've been bombarded with outrageous claims from all sides about election misconduct. From the shock in 2016 when Trump won the election (after the polling industry and media failed to correctly gauge the public sentiment) to 2020, where Trump outperformed most polls and the race turned out closer than predicted—this topic has become a flashpoint for controversy.

With accusations of fraud, manipulation, and interference, a closely contested race only adds more fuel to partisan fights. So, why bother weighing in?

Over the past four years, as I've watched how COVID-19 data was intentionally obfuscated, misconstrued, misinterpreted, manipulated, and sometimes suppressed, I’ve developed a different perspective on how data is presented by the media. I’ve had some success in cutting through the noise, offering objective and transparent data in a way that connects and informs. Wading into such a polarizing topic now feels like less of a challenge compared to the COVID era—so, why not?

Background on My Approach:

One factor that pollster Robert Cahaly understood in 2016 was the concept of social acceptability bias. There's often a large gap between what people say publicly when they perceive the "consensus" is against them and what they actually believe. Traditional polling methods—such as surveys—are prone to bias, and that’s just the nature of the data. Nate Silver has developed a creative way to adjust for these biases by building models that weight and average polls across the country to present a more accurate picture. However, the quality of your results is only as good as the data you’re working with. Predictors and indicators are only as reliable as what a person chooses to share or wants you to hear. This issue is similar to what I’ve observed in low-quality medical papers based on surveys or other unreliable data that can’t be trusted.

But what if there were a way to forecast an election not based on polls or surveys, but on data that reflects what people actually do, rather than what they say?

While I recognize I’m wading into risky territory, I’m hopeful that the model and data I’ve found will offer a fresh approach.

Stay tuned for later this month, as I put my model to the test and let the data guide us.

Figure 2: Association Between Individual Sociodemographic Features and Trust in Physicians and Hospitals in Ordinal Regression Models in Spring and Summer 2023

One incredibly interesting part of this study was the revealing of the open-text responses that survey respondents gave for their lack of trust. From the supplement, here are the top 4 themes why patients have lost trust.

1. Financial Motives Over Patient Care: This theme includes perceptions of healthcare as primarily profit-driven, where financial incentives outweigh patient welfare. Respondents believe that decisions are made based on profitability rather than the best interests of patients.

2. Poor Quality of Care and Negligence: Responses that mention experiences of neglect, inadequate care, misdiagnosis, or dismissive attitudes from healthcare providers fall under this category. This also includes perceptions of healthcare professionals not listening or taking patient concerns seriously.

3. Influence of External Entities and Agendas: Here, the focus is on the belief that decisions in healthcare are unduly influenced by pharmaceutical companies, government entities, or other external powers. This includes suspicions of dishonesty or withholding information for nonmedical reasons.

4. Discrimination and Bias: Responses indicating experiences or beliefs that healthcare providers exhibit bias, discrimination, or lack of cultural competency. This can include racial discrimination, gender bias, or insensitivity to patient backgrounds.

Another interesting analysis in the supplement was the inclusion of political affiliation. The tendency for Republicans and Independents to have lower trust overall than Democrats should not surprise anyone, as the polarization of vaccines, masks, and lockdowns made it clear that the left was in favor of doing anything at all in the name of combating Covid, no matter the cost.

As we witnessed firsthand in 2020 and 2021, and even today, the condescension, overt political motivations, and outright derision directed at those who were rationally skeptical of a brand-new vaccine, masks, and the extreme and harmful lockdown policies by medical practitioners and hospital systems, have finally led to an inevitable consequence: the public simply does not trust them anymore. And not by a small margin—there has been a massive swing from majority trust to majority distrust. For anyone who was paying attention, this is not shocking.

For my part, I hope that the practitioners we truly need to rely on when we require medical care see this as a wake-up call and understand just how much damage they have done to their long-term doctor-patient relationships. Now, instead of starting from a place of trust, they are starting from a deficit. This is not just bad for their careers; it’s bad for the patients.

In Part II, let’s break this overall spending down into categories, starting with Non-teaching staff, and see how it relates to outcomes. The chart below from the Mississippi Center for Public Policy nicely combines comparative growth between spending, enrollment, teachers, and Admin.

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A new report from the Reason Foundation that comprehensively collected and analyzed public education spending, enrollment, teacher/staff salary, and student outcomes over the past 2 decades has found some incredible insights. They compared all of these data for all 50 states, and were able to asses trends, relationships, and outcomes. The report can be found here, and I recommend saving this one as it is a treasure trove of data. The insights and analysis for this post will come from the raw data in the report, and I expect to conduct many more over the coming weeks.

Overall Spending vs. Reading & Math Scores:

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Social Emotional Learning is all the rage right now within the circles of education policy, research, and funding. Looking at Google Search Trends, we can see that the term reached it’s peak in September 2020. Which is an ironic time to have occurred. We see a slow build-up to August 2020, and then an immediate decline only a month afterwards. What could explain this? During the summer of 2020, parents, teachers, professional educators, politicians, and anyone else concerned with kids in school were frantic trying to figure out how and whether or not to open schools. I find this pattern intriguing. Does the popularity of the search term at that time indicate a push to use SEL to justify bad policy (closing schools), or perhaps the opposite? Perhaps it is indicative of the frenzy of activity around planning for school closures aka remote learning.

Whatever the reason, it’s clear that for better or worse, SEL is here to stay. It shows up in everything from STEM1 to school athletics2. As parents, we want our kids to be emotionally well adjusted, empathetic, and kind towards their piers. These are near-universal values that underpin our society. It seems as if the Education Industry is set on shouldering the burden of not just teaching reading, writing, history, math, etc.- but also rearing our kids to be good people. What could possibly go wrong?

What’s Not To Like?

The vocabulary that comprise the Social Emotional Learning framework and methodology is such that it makes it almost impossible to argue against. Wait, you’re against compassion? You don’t agree with empathy? Wow. You’re a bad person. These concepts are universally positive and altruistic. The promotion of emotional acumen and social skills can’t possibly be negative, can it?

Of course, this is the wrong question. When we’re talking about comprehensive frameworks3 all encompassing ideas, we should be asking ourselves if this is the best way to educate our children. Should this framework be so pervasive that every other framework or methodology is only secondary? Should our schools be spending energy teaching these values over and above the fundamentals?

The Evidence

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Check Your Work

CDC Downplayed News of Vax Myocarditis

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3 years ago · 27 likes · 6 comments · Kelley K

From "Check Your Work” by Kelley Kroehnert:

With the recent discovery that the CDC drafted — but never sent — a Health Alert in May 2021 about myocarditis after mRNA vaccination, I put together this timeline about vaccine myocarditis news and updates from government officials. I include a combination of documents from CDC and FDA, as well as what was covered in the mainstream media.

I think this timeline shows a pattern in which CDC & FDA failed to adequately investigate and inform the public about the risks of myocarditis early in the vaccine rollout. However, there was public acknowledgement by the CDC, as early as May 20, 2021, about a potential pattern of myocarditis after the 2nd dose of mRNA vaccines, particularly in young men.

On June 1, 2021, the CDC confirmed that they had identified a higher than expected signal of myocarditis for young men after mRNA vaccination, but that they still recommended Covid vaccination for everyone in this age group. Despite a lot more analysis and discussion of myocarditis after that, and a changing landscape with widespread natural immunity, the CDC & FDA position has changed very little since that time.

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February - March 2021

As Zachary Stieber and Lia Onely reported last fall in The Epoch Times, we now know that Israel first warned the CDC about a potential myocarditis signal in 16-30 year-old recipients as early as February 28, 2021, and that US government officials completed an internal review was completed by March 9, 2021. However, it took nearly two more months before there were any public reports in the US about the potential for myocarditis after Covid vaccination.

NOTE: Zachary Stieber has done more to investigate and report on the government’s knowledge of myocarditis from Covid vaccines than anyone I know. He maintains an excellent and extremely thorough timeline of his findings here.

April 2021

April 25, 2021: Israel’s Health Ministry announced they were investigating a link between myocarditis and the Pfizer Covid-19 vaccine, particularly in young men.

April 26, 2021: Military.com reported that the Department of Defense is tracking cases of myocarditis after Covid vaccination among troops.

April 27, 2021: Walensky said that she was in touch with the US DoD about the cases found in the military, she claimed that the CDC had not seen a signal for vaccine myocarditis. “We have not seen a signal and we've actually looked intentionally for the signal in the over 200 million doses we've given.”

May 2021

May 11, 2021: Matt Oster, a pediatric cardiologist and a member of the CDC COVID-19 Vaccine Task Force, emailed the CDC to share increasing concerns regarding vaccine myocarditis, stating: “I'm starting to get more and more concerned about myocarditis in adolescents and young adults following COVID-19 vaccination.”

Dr. Oster added in a follow-up email on May 12, 2021: “I keep hearing about more and more cases, and the story is almost always the same: <30 (usually teen), usually male but not always, onset typically 2-4 days after 2nd dose, high troponins, admitted but usually with quick resolution of symptoms/troponin/echo findings…”

May 12, 2021: ACIP recommended Covid-19 vaccines to children ages 12-15, following FDA authorization (EUA) on May 10, 2021. CDC Director signed off on the ACIP recommendation.

May 20, 2021: A statement from the May 17 VaST Work Group meeting was posted on the CDC website: “VaST concluded that there are relatively few reports of myocarditis to date.” The statement claimed — illogically — that while the reports typically occurred within 4 days after dose 2 of mRNA vaccines, they did not differ from expected baseline rates. The fact that they more often occurred within 4 days after dose 2 of mRNA vaccines would seem to indicate that was not part of the expected baseline of myocarditis. This announcement led to a story in the New York Times.

May 20, 2021: Oregon Health Authority issued an email alert to doctors about vaccine myocarditis. (Emails obtained by Zachary Stieber of Epoch Times show that the CDC alerted state vaccine safety liaisons about reports of myocarditis/pericarditis on May 19th.)

May 21, 2021: Email sent from Dr. Demetre Daskalakis to others at CDC, regarding the draft of a HAN (Health Alert Network) advisory on vaccine myocarditis. The email specifically mentioned calls with Oregon and California. This HAN advisory was never sent.

NOTE: We do not have a copy of the draft HAN at this time, but based on other CDC emails and documents from around this period, I suspect that the alert would have stated that cases of myocarditis and pericarditis have been reported shortly after vaccination with Covid mRNA vaccines, but that a link had not been confirmed. It would have likely also provided information about potential symptoms and diagnostic tests to perform, and encouraged doctors to be on alert for this condition and report cases to VAERS promptly, while reiterating the CDC recommendation for Covid-19 vaccination for ages 12 and older.

HAN advisories are frequently covered by the US media, so it is possible that the decision not to release a HAN was due to the CDC opting to notify medical professionals about the concerns via methods that would be less likely to trigger widespread reporting by the US media.

May 21, 2021: The same day as the draft HAN was being discussed, the Seattle / King County Public Health issued their own Health Advisory about vaccine myocarditis. A portion of the advisory is shown below. A later portion of the advisory states, “At this time it is not clear if there is a causal association with vaccination.” The advisory also referenced the statement from the VaST meeting on May 17.

May 27-28, 2021: CDC published public guidance about vaccine myocarditis and clinical considerations on their website, acknowledging that increased cases of myocarditis and pericarditis have been reported in the US after mRNA vaccination, but that “CDC continues to recommend COVID-19 vaccination for everyone 12 years of age and older….” These pages did NOT indicate that the rates after vaccination were outside expected rates or that any causal link had been established.

June 2021

June 1, 2021: A statement from the May 24 VaST Work Group meeting was posted on the CDC website finally acknowledging a potential safety signal: “there was a higher number of observed than expected myocarditis/pericarditis cases in 16–24-year-olds.”

June 1, 2021: An official report by the Israeli Ministry of Health determined that rates of vaccine myocarditis among young men, ages 16 to 24 were between one in 3,000-6,000, as reported in Science and New York Magazine.

June 3, 2021: The first children aged 12-15 to be vaccinated with the Pfizer vaccine became eligible to receive their second doses of the vaccine (3 weeks after first shots began).

June 10, 2021: FDA VRBPAC meeting discussed vaccine myocarditis as part of a larger vaccine safety presentation. This was covered briefly in the New York Times.

June 23, 2021: CDC’s ACIP meeting discussed vaccine myocarditis at length, including a VaST assessment, and a benefit-risk discussion. The VaST presentation acknowledged that there was a likely association of myocarditis with mRNA vaccination. However, the benefit-risk analysis concluded that the benefits of vaccination outweigh the risks. The ACIP did recommend that the FDA place a warning about myocarditis on the EUA fact sheets for Covid vaccinations. The ACIP myocarditis discussion was covered by CNBC and The New York Times.

June 23, 2021: Immediately following the ACIP meeting, a joint statement was released by HHS, CDC, AAP, and others in support of the CDC’s decisions on vaccination and myocarditis. For those paying attention, it was very clear that the outcome of this meeting was pre-determined.

June 25, 2021: The FDA released updated EUA fact sheets for both the Pfizer and Moderna vaccines that warned about myocarditis, but did not include any details about the groups at higher risk. CNN had a brief story on the updated fact sheets, but the myocarditis concerns still did not receive much media attention. Below is a screenshot from the Moderna fact sheet. (The Pfizer fact sheet used the same language, except with Pfizer-BioNTech in place of Moderna.)

June 29, 2021: MedPage Today published an op-ed by Dr. Vinay Prasad and others, including analysis done by mathematician Wes Pegden, critiquing the benefit-risk numbers presented by the CDC at the recent ACIP meeting.

July 6, 2021: MMWR released with findings from June 23 ACIP discussion of vaccine myocarditis risks.

NOTE: The summer of 2021 was a pivotal time for the rollout of Covid vaccines in young people. Many colleges and universities began mandating Covid vaccination for students, even though college-aged men were in the group most at risk for vaccine myocarditis, and second doses were being rolled out to those in the 12-15 age range. Despite recognition of the link between the vaccine and myocarditis, the official position was that vaccination was less of a risk than Covid.

July 16, 2021: WIRED Magazine published an article by David Zweig, “The CDC Owes Parents Better Messaging on the Vaccine for Kids,” detailing much of the controversy behind the CDC’s nuance-free messaging on Covid vaccination for young people:

Fall 2021 & Later…

October 6-7, 2021: Scandinavian countries made headlines for limiting Moderna vaccines for young men. This news received some coverage in the US, including stories by Reuters, AP, and Wall Street Journal. The CDC never officially acknowledged that myocarditis rates were often found to be higher after Moderna vaccines, as compared to Pfizer vaccines. (The higher rate with Moderna was brought up as a question as early as the June 23 ACIP meeting, but the response was that the numbers were too small to draw a conclusion at that time.)

November 19, 2021: When the FDA granted EUA authorization for boosters for ages 18+, they released updated fact sheets for both the Pfizer and Moderna vaccines with revised warnings about myocarditis, adding that it was more common in males under the age of 40. Below is a screenshot from the Moderna fact sheet. (The Pfizer fact sheet used the same language, except with Pfizer-BioNTech in place of Moderna.)

February 22, 2022: In an update to the Clinical Considerations for Use of Covid-19 Vaccines, CDC finally suggested an 8-week interval between mRNA doses may lower the risk of myocarditis after the second dose. This is nearly a year after some experts started calling for extending second doses to reduce myocarditis risks.

It also included a section about myocarditis under “Patient Counseling,” explicitly stating that patients — “especially males ages 12-29” — should be made aware of the risk of myocarditis and/or pericarditis, and about the need for medical attention if symptoms develop shortly after vaccination.

April 1, 2022: The CDC released a controversial MMWR analysis of cardiac complications after Covid versus vaccination, claiming that in teen boys and young men, cardiac complications from Covid were higher than after mRNA vaccination. This analysis was based on electronic health records, so it only included Covid cases that were severe enough to seek medical attention, skewing the myocarditis after Covid rates higher. NBC News reported on this, explicitly stating that it “may be reassuring to those reluctant to get their teenage boys vaccinated.”

Timing Unknown: At some point in 2022 or 2023, vaccine fact sheets added additional information about specific age groups most affected by vaccine myocarditis.

• Moderna: males 18 through 24 years of age

• Pfizer: males 12 through 17 years of age

I haven’t been able to determine when these age ranges became part of the official communications about the vaccines. As far as I can tell, the 18-24 age group first appears on the Moderna website in a January 31, 2022 press release, and the 12-17 age group first appears on the Pfizer website in an July 8, 2022 press release. However, I could not confirm these age groups were consistently listed in vaccine fact sheets until the release of the 2023/2024 version of the vaccines (based on the XBB variant) in September 2023. It is difficult to locate all the old vaccine fact sheets, so I’m not certain when this language was consistently included, but here is a Pfizer EUA Fact Sheet from December 2022 listing the age range.

The FDA also added additional myocarditis symptoms that may be seen in children to some vaccine fact sheets. I believe, but am not certain, that these additional symptoms were only listed beginning with the release of the 2023/2024 version of the vaccines (based on the XBB variant) in September 2023:

In 2024 I will continue to focus on data about children & families, and how adults and expert’s decisions affect their lives. Please comment or write with feedback, suggestions, or thoughts on what you’d like to see this upcoming year.

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Here are the most popular posts from 2023:

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Australia imposed one of the harshest lockdowns in the world. While not as stringent as China's, it was certainly among the longest and most severe measures for a Westernized democratic nation. In 2020, 2021, and 2022, I observed this unfolding in real-time on Twitter. What always baffled me was the distinct level of public support that the restrictions garnered in Australia. While in the US, and to some extent, the UK, there was broad debate about what measures were excessively severe and what proved effective. The unique nature of the United States—a federation of states governing their own affairs in the majority of practical policies—presented opportunities for natural policy experiments.

A new study in the American Academy of Pediatrics’ Pediatrics, analyzes just how much increased harm was caused to children in Australia thanks to their pandemic restrictions.

A year before the publication of this study, I conducted an analysis of hospital records from the State of Rhode Island, utilizing data provided by my friend, Dr. Andrew Bostom. We discovered that we sent four times more children to the hospital for self-harm than for Covid.

Below are the key findings of the study.

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A recent paper in the ‘Behaviour Research and Therapy’ Journal reveals the results of an experiment where adolescents were given psychological therapeutic interventions. Not because they were diagnosed with mental health issues or had any specific identified mental health needs. The study’s primary purpose was to evaluate whether this 8-week program that used a therapy called Dialectical Behavioral Therapy (DBT) could improve emotional regulation, reduce mental health difficulties, and enhance overall well-being among adolescents compared to the standard school curriculum. The entire purpose of the study could be summed up as “Let’s try this and see what it does.”

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Contrary to expectations, immediately post-intervention, participants in the 'WISE Teens' intervention reported:

• Greater difficulties in various outcomes: total difficulties, poorer parent-child relationships, increased depression, anxiety, and emotion dysregulation.

• Clinically significant deteriorations in depressive symptoms compared to the control group.

• Control group participants showed more spontaneous improvement in certain outcomes like emotion regulation and parent-child relationship quality compared to 'WISE Teens' participants.

• The 'WISE Teens' intervention did not yield better outcomes compared to the usual curriculum, even showing poorer parent-child relationships.

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Below are the figures from the Study.

Figure 3: The Quality of Parent Relationships Declined in the Intervention Group

Figure 2: Anxiety and Depression Before, During, and After

Luckily, there was a return to baseline for these measures at 6 months. It’s also important to mention the limitation that this study was not randomized as originally conceived, and that these difference could potentially represent innate differences between the groups, rather than being the effect of the intervention.

While acknowledging that, it’s worth mentioning that another paper, with a similar intent but using a different intervention (mindfulness), was fully randomized, and “found no such evidence of superiority of universal SBMT.”

Here are the main lessons I think are important to take away from this study:

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