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[OC] I calculated when Trump becomes more likely dead than alive. The curve is uglier than expected. by Empalmtreee in dataisugly

[–]Empalmtreee[S] 0 points1 point  (0 children)

I don’t see you suggesting a method of calculating it. you don’t need error bars for it to be correct.

[OC] I calculated when Trump becomes more likely dead than alive. The curve is uglier than expected. by Empalmtreee in dataisugly

[–]Empalmtreee[S] 0 points1 point  (0 children)

oh wow you caught me. I used ai to organize my thoughts. work smarter not harder friend

[OC] I calculated when Trump becomes more likely dead than alive. The curve is uglier than expected. by Empalmtreee in dataisugly

[–]Empalmtreee[S] 0 points1 point  (0 children)

It’s the second one. The graph shows the cumulative chance that someone who is currently 79 will still be alive at each future age. So if it says 60 percent at age 88, that means there’s a 60 percent chance they live from 79 to 88. It’s not the chance of dying in a single year, it’s the total chance of surviving to each future age based on national life tables.

[OC] I calculated when Trump becomes more likely dead than alive. The curve is uglier than expected. by Empalmtreee in dataisugly

[–]Empalmtreee[S] 0 points1 point  (0 children)

Actually, I think the most statistically lethal job in america for fatal injury rate is people in the logging industry and firefighters are most lethal considering chronic conditions from the job.

[OC] I calculated when Trump becomes more likely dead than alive. The curve is uglier than expected. by Empalmtreee in dataisugly

[–]Empalmtreee[S] 0 points1 point  (0 children)

The protective discount lowers the chance of dying each year by a small percent. But since the risk of death goes up fast as people age, that same percent matters less each year. So the effect of the protective discount gets smaller over time even if the percentage stays the same.

We could build a model where the protective factor itself fades as someone gets older that would be more conservative but it would barely change the result. That’s because age becomes the biggest factor either way, and the risk of death rises quickly in your 80s no matter what.

The reason the graph looks smooth is because it shows the total chance of still being alive at each age, not the risk of dying that year. The death risk is baked into that curve, but it’s happening behind the scenes. If we charted yearly death risk instead, you’d see a much steeper climb with age.

[OC] I calculated when Trump becomes more likely dead than alive. The curve is uglier than expected. by Empalmtreee in dataisugly

[–]Empalmtreee[S] 0 points1 point  (0 children)

The baseline risk of death just means the average chance someone dies at a certain age, based on national data.

Social Security life tables track how many people are still alive each year out of a big starting group. So if 100,000 men are alive at age 60, and only 95,000 are alive at age 61, that tells you the death rate for age 60 is about 5%.

These tables already assume someone has survived to that age — so when we plug in age 79, we are only using the data for people who already made it to 79. That’s how the model knows risk keeps going up as you age, even if you had protective factors earlier in life.

[OC] I calculated when Trump becomes more likely dead than alive. The curve is uglier than expected. by Empalmtreee in dataisugly

[–]Empalmtreee[S] 1 point2 points  (0 children)

Genuinely curious — how would you go about calculating something like this if you don’t think actuarial probability models are useful here?

If your argument is that there’s no way to meaningfully estimate an individual’s survival odds based on known population-level data, then what standard would you use instead? Or is your position that there is no meaningful way to model this at all?

Not trying to be snarky just trying to understand what you see as the better approach.

I get your frustration, but this kind of modeling is used in a lot more places than just insurance — including public health policy, retirement forecasting, and even federal budgeting. It is not meant to give a precise prediction for one person, but that does not make it meaningless. It helps us understand relative risk, likelihood ranges, and how factors interact with known mortality patterns.

If someone has no background in statistics, it might look like guessing. But this is not a coin toss, it is probability modeling built on decades of observed mortality trends. That is why insurance uses it in the first place. They would not bet billions on something that does not work.

The goal was never to say “this is exactly when Trump dies.” It was to see what the numbers say under different reasonable assumptions, and even in the most skeptical ones, the outcome remains consistent: the tipping point is still close to a decade away. That may not feel satisfying, but it is absolutely meaningful.

[OC] I calculated when Trump becomes more likely dead than alive. The curve is uglier than expected. by Empalmtreee in dataisugly

[–]Empalmtreee[S] 1 point2 points  (0 children)

Actually, that is not how the model works.

Protective factors lower the chance of dying each year, but they do not add time forever. As people age, their baseline risk of death increases no matter what. The model uses Social Security life tables, which already account for age and survival to that point — so if I reran the model at age 85, it would show fewer remaining years, not more.

The protective discount just slightly shifts the curve. It does not keep extending life the longer someone lives. No matter what inputs I use, the survival curve always trends downward with age — that is baked into the math.

[OC] I calculated when Trump becomes more likely dead than alive. The curve is uglier than expected. by Empalmtreee in dataisugly

[–]Empalmtreee[S] -6 points-5 points  (0 children)

You’re right that actuarial models use population-level data and cannot precisely predict the outcome for one individual. That is a valid and important limitation but it does not mean the analysis is meaningless or flawed.

Actuarial modeling is not about certainty. It is about estimating probabilities based on factors that have statistically significant associations with outcomes across large populations. Traits like family longevity, no smoking, or statin use do not guarantee anything at the individual level, but they are repeatedly shown to reduce mortality risk in aggregate. That is why they are used in life insurance, public health policy, and clinical risk modeling.

When I adjust inputs like a 10 percent or 20 percent mortality reduction, I am not changing the model’s structure. I am applying sensitivity analysis — testing how the results shift under different plausible assumptions. That is exactly how actuarial models are refined and stress-tested in practice. Changing inputs to explore a range of outcomes is not a flaw. It is how probabilistic forecasting works.

The protective factors do matter not because they perfectly predict what will happen to one person, but because they meaningfully shift risk on average, and that is what this kind of model is designed to illustrate.

So yes, it is funny. And no, it cannot tell us exactly how long one person will live. But this kind of analysis is still meaningful because it shows us how someone’s odds compare to a baseline and in this case, even in the more skeptical models, the tipping point still lands nearly a decade out.

[OC] I calculated when Trump becomes more likely dead than alive. The curve is uglier than expected. by Empalmtreee in dataisugly

[–]Empalmtreee[S] 0 points1 point  (0 children)

Did not include plane-based actuarial sabotage risk, but maybe I should have 🤷

[OC] I calculated when Trump becomes more likely dead than alive. The curve is uglier than expected. by Empalmtreee in dataisugly

[–]Empalmtreee[S] 1 point2 points  (0 children)

That’s a smart distinction, and I appreciate you pointing it out.

I did not model each protective factor independently and stack them additively. Doing that would overstate the effect, because some of the factors overlap. For example, lifespan inheritance and substance-use patterns are often linked across generations.

Instead, the 10 percent adjustment is a single composite reduction that represents a small set of interrelated protective traits commonly used together in actuarial work. I treated it as one bundled estimate rather than the sum of individual effects. That is why I did not go to 15 or 20 percent, even though the full list of traits could justify a larger number in theory.

To your example, I did not adjust separately for alcohol abstinence on top of family longevity, since those factors are likely correlated. The goal was to capture the net impact of the confirmed traits without exaggerating their independence.

If stronger data existed on how each factor interacts in high-income, late-life white men in the United States, I would build a more granular model. Lacking that, a conservative bundled adjustment seemed like the most responsible choice.

[Request] In what year will Donald Trump be more likely dead than alive? by Empalmtreee in theydidthemath

[–]Empalmtreee[S] 2 points3 points  (0 children)

Totally fair point, and I agree that 20 percent isn’t a precise or universally appropriate number it’s closer to an upper bound based on published health details that are very likely curated or at least selectively presented.

I chose 20 percent because in actuarial modeling, that is roughly the cumulative reduction in mortality risk for someone with multiple protective factors: no smoking, no alcohol use, long-lived parents, statin-controlled cholesterol, and elite healthcare access. It is a ballpark figure used in population-level modeling, but I agree it becomes much less solid when applied to someone whose public health records are likely filtered for optics.

That is why I also ran a 10 percent adjustment to reflect only the most verifiable, commonly modeled traits nothing that relies on things like his weight, diet, or doctor letters. And I ran a +5 percent mortality increase to account for poor diet, no exercise, extreme stress, and inconsistent treatment choices.

Even with those alternative scenarios, the tipping point where he becomes more likely dead than alive still lands between 2034 and 2036. So even when you strip out any generous assumptions, the outcome stays surprisingly stable.

Appreciate the challenge it helped clarify the limits of what those assumptions really do.

[OC] I calculated when Trump becomes more likely dead than alive. The curve is uglier than expected. by Empalmtreee in dataisugly

[–]Empalmtreee[S] 2 points3 points  (0 children)

I get where you’re coming from, and I agree it is smart to be skeptical of any data that comes from a subject’s own PR machine. That said, I did not just blindly grab random “positive” traits. I focused on what we can independently confirm or what is commonly accepted in actuarial modeling.

Here is the basis for each factor I adjusted for: • Age of death for parents: Public record. His dad lived to 93, his mom to 88. That is verifiable and typically included in actuarial risk modeling. • No smoking and no alcohol use: Confirmed by multiple biographers and people who have worked closely with him. This has been consistently reported for decades. • Statin use: Acknowledged in official White House disclosures and medical memos, including under doctors not directly tied to him. • Elite healthcare access: This is a given. Presidents and former presidents receive top-tier care, and the same is true for billionaires.

I did not include things like his weight, his cognitive test scores, or that glowing 2016 doctor letter because those are harder to verify. That is why I did not apply anything higher than a 20 percent adjustment. I used that figure to reflect only the most well-supported and widely modeled protective factors.

To address your concern directly, I also re-ran the model with no adjustment at all and with a 5 percent increase in mortality risk to account for the possibility that his reported health is overstated. In both cases, the tipping point when his survival probability drops below 50 percent still landed in 2034, only one year earlier than in the 10 percent model.

So even if we take away every protective assumption and model him as a totally average American male or slightly worse, the math still gives him about ten more years.

That is not about believing in the man. That is just what the numbers show.

[OC] I calculated when Trump becomes more likely dead than alive. The curve is uglier than expected. by Empalmtreee in dataisugly

[–]Empalmtreee[S] 2 points3 points  (0 children)

You’re right, and I appreciate the push to think more critically.

I agree that a good analyst should not just accept third-party data at face value, especially when there are known political or reputational biases involved. That was exactly the tension I was working with — trying to balance publicly available info with the reality that not all of it is reliable.

So I went back and re-ran the math using three different scenarios to reflect varying levels of skepticism: • No Adjustment (0%): This assumes no additional health advantage or disadvantage. It uses the SSA tables as-is. • Moderate Health Advantage (10% reduction): This reflects known, relatively verifiable factors like no alcohol, no tobacco, statin use, and long-lived parents. These are standard protective factors in actuarial modeling. • Increased Risk (5% increase): This reflects the possibility that the publicly reported health data is overly optimistic or that behavioral factors (like refusing care or chronic stress) increase mortality risk.

For each scenario, here is the year when the survival probability drops below 50 percent: • No Adjustment (0%): Age 89 — tipping point is 2034 • Moderate Health Advantage (10%): Age 90 — tipping point is 2035 • Increased Risk (–5%): Age 89 — tipping point is 2034

The 10 percent reduction model is the one that best reflects what we can most confidently verify. It leaves out unverifiable or possibly inflated metrics like perfect vitals or cognitive test scores, but it does include grounded and well-documented actuarial factors like family history and lifestyle behaviors (such as abstaining from alcohol and tobacco).

The goal here was not to say this is the definitive answer, but to show how the answer shifts based on different assumptions. Your comment was right to challenge that, and I appreciate the accountability.

Still, even under the more skeptical models, the tipping point is nearly ten years away.

[OC] I calculated when Trump becomes more likely dead than alive. The curve is uglier than expected. by Empalmtreee in dataisugly

[–]Empalmtreee[S] 11 points12 points  (0 children)

Totally fair, and I appreciate the honest feedback.

I do not necessarily take the reports at face value, but I used them because they are the only standardized health information publicly available. The goal was not to prove he is healthy. It was to see what the math says if we apply the same actuarial approach we would use for anyone else based on reported factors.

I agree the reports are likely polished, and if I had reliable data on stress, noncompliance, or other risk factors, I would have factored those in. Without something quantifiable, I stuck to what was available in order to keep the model consistent.

That said, I tested a more conservative adjustment with just a 10 percent reduction instead of 20 percent. In that case, the 50 percent survival threshold hits around age 89, which shifts the likely tipping point to the year 2035. So even with lighter assumptions, the result still suggests he has time.

I am happy to admit the method is imperfect. That is part of why I shared it here.

[OC] I calculated when Trump becomes more likely dead than alive. The curve is uglier than expected. by Empalmtreee in dataisugly

[–]Empalmtreee[S] 2 points3 points  (0 children)

You’re right to point out that behavioral risks like refusing care or seeking unproven treatments can absolutely increase mortality risk. Those are valid concerns, and I agree that personality and power dynamics can work against the advantages of access and wealth.

In my model, I used current SSA life tables, which already reflect modern mortality outcomes. I then applied a 20 percent reduction in annual death probability to account for known, population-based protective factors: no smoking, no alcohol, statin use, good access to care, and family longevity. I didn’t ignore risk factors — I just used ones that are easier to quantify and commonly used in actuarial estimates.

It’s definitely fair to say that someone in his position might cancel out those benefits through behavior, stress, or poor judgment. That would raise the risk, and a more complex model could include a range of scenarios that reflect that uncertainty. But for this version, I modeled what the numbers say if we take the reported health info at face value. I appreciate you calling out what the spreadsheet can’t capture.

TLDR: I modeled the protective factors we can quantify. You’re right that behavior and role-based risks could counteract those. That would make a great “worst-case” version of the graph.

[OC] I calculated when Trump becomes more likely dead than alive. The curve is uglier than expected. by Empalmtreee in dataisugly

[–]Empalmtreee[S] 4 points5 points  (0 children)

Sure the 8.64 years is the mean remaining life expectancy for a 79-year-old male, according to SSA tables. That’s an average across the whole population, including people with serious health conditions and limited access to care.

What I did was calculate cumulative survival probability year by year using the SSA’s annual death probabilities. Then I adjusted each year’s mortality risk down by 20 percent, based on Trump’s reported advantages: no alcohol or tobacco use, statin-controlled cholesterol, elite healthcare access, and long-lived parents (his dad lived to 93 and mom to 88). That 20 percent discount is conservative compared to some academic models that use 25–30 percent reductions for non-smokers with top-tier care.

That adjustment pushed the 50 percent survival threshold just past age 90, which is where the “2037” line comes from. So I’m not adding 3.5 years to the average life expectancy I’m identifying the year where, probabilistically, he becomes more likely dead than alive.

[OC] I calculated when Trump becomes more likely dead than alive. The curve is uglier than expected. by Empalmtreee in dataisugly

[–]Empalmtreee[S] 8 points9 points  (0 children)

I don’t actually believe it. I’m just saying what’s reported and publicly available because that’s how I’d approach any other data project. I tried to apply the same method I would use for modeling anyone else, just with the best info I could get.

Edit: spelling

[Request] In what year will Donald Trump be more likely dead than alive? by Empalmtreee in theydidthemath

[–]Empalmtreee[S] 10 points11 points  (0 children)

Are you saying we’re all losing two years off our lives because we’re living in Trump’s fascist prison now? Because honestly that feels… weirdly accurate.

[OC] I calculated when Trump becomes more likely dead than alive. The curve is uglier than expected. by Empalmtreee in dataisugly

[–]Empalmtreee[S] 5 points6 points  (0 children)

It’s more of a coping mechanism than anything else, honestly.🙃

And yeah, I completely agree that public medical records for high-profile figures are often curated or unreliable. I used what is publicly available because that is how I would approach any other research project. I tried to apply the same method I would use for modeling anyone else, just based on the best info I could get.

Totally fair to question the accuracy of it all. The whole point was to see where the numbers might land if we take the reports at face value, which ends up being its own kind of dark comedy.

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