A Death Count Is Not a Death Rate

Introduction

A list saying that 10, 20 or 25 researchers died can sound alarming, particularly when the people are associated with UFOs, unconventional propulsion, defence laboratories or other secretive work. Statistically, however, the number of deaths is only the numerator. Without the denominator — how many comparable people were alive and at risk, for how many years, and at what ages — the list cannot show that researchers were dying unusually often.

This is not a technical quibble. A mortality rate is defined by deaths occurring in a specified population during a specified period, divided by the population at risk. Epidemiological studies therefore compare observed deaths with the number expected after accounting for the amount and composition of person-time under observation.[cdc.gov]cdc.govRate - Health, United StatesAugust 12, 2024…Published: August 12, 2024

That missing denominator is one of the central evidential problems with alarming researcher-death lists. Individual deaths may still warrant investigation. But the list itself cannot establish an excess mortality rate until the population from which those deaths arose has been defined.

Why the numerator alone cannot show excess risk

Twenty deaths among 200 people followed for two years would be very different from 20 among 20,000 people followed for 20 years. Both stories have the same headline numerator, yet the underlying mortality experience differs enormously. The US Centers for Disease Control and Prevention defines a mortality rate in precisely these population-and-time terms: deaths in a defined population during a specified interval relative to the size of that population.[CDC Archive]archive.cdc.govArchive Principles of Epidemiology | Lesson 3CDC ArchivePrinciples of Epidemiology | Lesson 3 - Section 3…

Professional cohort studies handle the problem more carefully by measuring person-years at risk. One person observed for ten years contributes roughly ten person-years; 1,000 people followed for ten years contribute roughly 10,000 before adjustments for entry, exit and death. Researchers can then apply appropriate reference mortality rates to those person-years to calculate how many deaths would ordinarily be expected. Occupational epidemiology has used this approach for decades.[nih.gov]pubmed.ncbi.nlm.nih.govPub Med Methods old and new for analyzing occupational cohort dataMethods old and new for analyzing occupational cohort data - PubMed…

That distinction exposes a basic limitation of historical death compilations. A list may painstakingly document the people who died while saying almost nothing about all the eligible researchers who did not die during the same period. Those surviving researchers belong in the denominator just as much as the deceased belong in the numerator.

The famous GEC-Marconi story illustrates the difficulty. Accounts eventually associated roughly 25 deaths between the 1980s and around 1990 with British defence and electronics work; contemporary reporting initially focused on a much smaller group before the list expanded. The eventual total has remained a striking feature of the story.[The Independent]independent.co.ukAfter the storyThe IndependentBBC pay author over stolen plot lineOctober 9, 1996 — 9 Oct 1996 — Mr Collins, 41, executive editor of Computer Weekly, br…Published: October 9, 1996 Yet a count of 25 cannot, by itself, answer the statistical question. To do that, an analyst would need to know how many people satisfied the same inclusion criteria throughout the relevant years and how much observation time they collectively contributed.

This matters especially when the definition of an eligible “researcher” can expand. Does the denominator include only scientists directly employed by one company? Engineers working for related contractors? University researchers doing defence work? People on Strategic Defense Initiative projects? Employees of laboratories whose work had nothing to do with UFOs or antigravity? A numerator assembled with broad inclusion rules cannot legitimately be compared with a denominator defined narrowly afterwards.

Choosing the comparison population and time period

A defensible analysis would begin by defining the cohort independently of who later died. For example, an investigator might identify everyone employed in specified scientific and engineering occupations at particular aerospace or defence organisations on a fixed starting date, then follow all of them until death, loss to follow-up or a predetermined closing date.

The comparison population matters almost as much as the denominator’s size. Researchers and engineers are not a random sample of the public. Their age, sex, education, income, employment status and geographical distribution may differ markedly from the population as a whole. Even comparison with “scientists” generally can become misleading if the supposedly exposed group is concentrated in older, late-career personnel.

The scale of the broader scientific workforce also puts small death lists into perspective. The US Bureau of Labor Statistics estimated about 24,600 physicist jobs and 1,800 astronomer jobs in 2024 — and those are merely two relatively narrow occupations, not the much larger universe of engineers, aerospace workers, laboratory staff, defence contractors and other people who can become incorporated into an “advanced research” category.[Bureau of Labor Statistics]bls.govBureau of Labor StatisticsPhysicists and Astronomers: Occupational Outlook Handbook:: U.S. Bureau of Labor StatisticsAugust 28, 2025…Published: August 28, 2025 The US National Science Foundation’s National Center for Science and Engineering Statistics estimated that about 1.22 million people worldwide held US research doctorates in science, engineering or health fields in 2023.[NCSES]ncses.nsf.govRetirement Experiences of U.S.-Trained Doctoral Scientists and Engineers: Findings from the 2023 Survey of Doctorate Recipients | NC…

Those figures are not proposed as denominators for any particular UFO or antigravity death list. They demonstrate why the denominator has to be constructed rather than guessed. Depending on the inclusion rule, the relevant population could be dozens, thousands or far more.

Time creates the same problem. Ten deaths in six months and ten deaths accumulated over 15 years carry very different implications. A retrospective compilation can obscure this by displaying all names together, visually compressing years of ordinary mortality into a single apparent event. Proper cohort analysis instead assigns the observation time to the people who were actually at risk during each period.[PubMed]pubmed.ncbi.nlm.nih.govPub Med Methods old and new for analyzing occupational cohort dataMethods old and new for analyzing occupational cohort data - PubMed…

There is also a subtler danger when group membership is defined using achievements or associations acquired late in life. Epidemiologists call one version of this immortal-time bias: people may have had to survive long enough to acquire the characteristic by which researchers later classify them. Methodological work has used Nobel-winning scientists as an example of why the start of follow-up, eligibility and the timing of the supposed exposure must be aligned carefully.[arXiv]arxiv.orgOpen source on arxiv.org. The broader lesson applies here: “people who eventually became associated with unusual research” is not automatically a statistically coherent population from birth or from the beginning of their careers.

Ordinary mortality changes what a death list means

Age is particularly important because death risk changes sharply across the lifespan. A collection containing researchers in their 50s, 60s and 70s cannot sensibly be evaluated as though every person had the mortality risk of a young adult. National statistical agencies therefore publish deaths and mortality rates by age and sex rather than relying solely on an undifferentiated national death count. The Office for National Statistics, for example, publishes England and Wales mortality data broken down by age, sex and underlying cause.[Office for National Statistics]ons.gov.ukOffice for National Statistics Deaths registered in England and WalesOffice for National Statistics Deaths registered in England and Wales

This is highly relevant to research populations because scientific careers can remain active unusually late. In the US Survey of Doctorate Recipients, nearly 40% of US-resident science and engineering doctorate holders aged 71–75 were still employed in 2023.[NCSES]ncses.nsf.govRetirement Experiences of U.S.-Trained Doctoral Scientists and Engineers: Findings from the 2023 Survey of Doctorate Recipients | NC… A person can therefore still accurately be described as a working scientist or researcher at an age when ordinary background mortality is already much higher than it was earlier in their career.

A rigorous calculation would consequently split the cohort’s person-time into age, sex and calendar-period categories and apply the corresponding background mortality rates. This produces an expected number of deaths. The observed number can then be divided by the expected number to produce a standardised mortality ratio, or SMR. In occupational epidemiology, that observed-to-expected framework is a standard way of asking whether mortality in a defined workforce is above or below an appropriate reference level.[PubMed Central (PMC)]pmc.ncbi.nlm.nih.govPubMed Central (PMC)Negative Control Outcomes and the Analysis of Standardized Mortality Ratios - PMC…

Consider a purely illustrative example. If a properly defined research cohort accumulated enough age-adjusted person-time that ordinary mortality rates predicted 18 deaths, observing 20 would mean something very different from observing 20 when only two were expected. The raw headline remains “20 researchers dead” in both cases. The denominator and expected count transform its evidential meaning.

Cause of death requires the same discipline. If a claim concerns homicide, suicide, accidents or a particular disease, the appropriate comparison is not simply all-cause mortality. Cause-specific mortality uses deaths from that cause as the numerator and the corresponding population at risk as the denominator.[CDC Archive]archive.cdc.govArchive Principles of Epidemiology | Lesson 3CDC ArchivePrinciples of Epidemiology | Lesson 3 - Section 3… Otherwise, shifting between “unusual deaths”, all deaths and selected causes can make an apparent excess impossible to interpret.

Even a denominator is not enough unless it is comparable

Adding a denominator is necessary, but it does not automatically produce a good comparison. Occupational epidemiology has long recognised the healthy worker effect: employed populations often show lower overall mortality than the general population because people sufficiently healthy to enter and remain in employment differ from the population that includes people unable to work because of illness or disability. A review of occupational cohort methods describes this as a major selection problem, and a study of 270 retrospective occupational cohorts found that most displayed some form of healthy-worker effect.[PubMed Central (PMC)]pmc.ncbi.nlm.nih.govOpen source on nih.gov.

Researchers can therefore obtain a misleading result even after calculating an apparently sophisticated observed-to-expected ratio. A general-population benchmark may differ from a specialised technical workforce in ways unrelated to UFOs, classified information or unconventional technology. Conversely, comparing a highly selected group of deceased researchers with a denominator consisting only of currently employed scientists could bias matters in the other direction.

A stronger design would, where possible, use a comparison group resembling the allegedly affected researchers in ordinary characteristics but lacking the exposure under investigation. The National Academies’ discussion of occupational mortality notes that internal comparison groups can sometimes be preferable because they may resemble the cohort more closely than the general population.[NCBI]ncbi.nlm.nih.govOpen source on nih.gov.

For an alleged UFO or antigravity-related excess, that could mean comparing clearly defined researchers in the supposedly relevant work with researchers of similar age, era, occupation and institutional setting who worked on conventional projects. The exact design would depend on the claim. What matters is that the comparison be specified independently of the deaths one is trying to explain.

The current “missing scientists” story has the same denominator problem

The issue is not confined to historical Marconi accounts. In 2026, a widely circulated US narrative grouped deaths and disappearances involving people associated in varying degrees with aerospace, nuclear, laboratory and other sensitive work, sometimes suggesting a connection to UFO knowledge or advanced technology. Reporting described roughly 11 cases and noted that federal attention had been drawn to the claims.[The Guardian]theguardian.comThe Guardian The missing scientists at the centre of a UFO conspiracyThe Guardian The missing scientists at the centre of a UFO conspiracy

For the statistical question addressed here, however, the conspicuous number is not enough. The cases span several years and encompass people with different occupations and circumstances. Some were scientists; others had different roles at scientific or national-security institutions. Later reporting also documented known or apparently unrelated circumstances surrounding several deaths, while authorities had not established a common mechanism linking the set.[The Wall Street Journal]wsj.comTheories escalated after the disappearance of William McCasland, a retired general with a history in classified programs. As the narrativ…

That makes the denominator problem unusually acute. If the claimed population is “people connected to sensitive American scientific work”, its size is potentially very large. If it is “scientists with direct UFO knowledge”, membership would have to be demonstrated rather than inferred from employment at NASA, a national laboratory, an aerospace organisation or a defence facility. Without a stable definition, there is no reliable way to count either the population at risk or its accumulated observation time.

This is why even official interest in individual cases should not be confused with statistical confirmation of a death cluster. An investigation can appropriately ask whether particular deaths or disappearances are connected. A mortality analysis asks a different question: whether a predefined population experienced more deaths than would ordinarily be expected. One question can remain open while the evidence for the other is still absent.

What evidence would actually establish an unusual death rate?

A persuasive claim of excess mortality among UFO, antigravity or unconventional-propulsion researchers would require considerably more than a catalogue of names. At minimum, the analysis would need a reproducible definition of who belonged to the population, records identifying both deceased and surviving members, explicit start and end dates for follow-up, and enough demographic information to calculate expected mortality.

The resulting analysis should distinguish all-cause mortality from the particular outcome being alleged, such as homicide or suicide. It should also report uncertainty. An observed-to-expected ratio above one does not automatically establish a meaningful excess, particularly when the number of events is small. Standard mortality studies use statistical inference precisely because random variation can produce observed counts above or below expectation.[ScienceDirect]sciencedirect.comOpen source on sciencedirect.com.

Just as importantly, the inclusion rules should be fixed before inspecting which definition produces the most dramatic result. If investigators can repeatedly alter the years, occupations, employers, research themes and causes of death until a striking cluster emerges, the apparent signal becomes partly a product of those analytical choices.

None of this establishes that every suspicious researcher death is ordinary, accidental or unrelated. A single homicide can be real regardless of whether an occupational mortality rate is elevated. The narrower conclusion is stronger and more useful: a death count cannot demonstrate an unusual death rate when the population and time at risk are unknown.

For claims about UFO and antigravity researchers, the missing denominator therefore changes the evidential burden. “Many researchers died” is a statement about selected events. “Researchers in this field died unusually often” is a statistical claim requiring surviving researchers, person-time and an appropriate comparison population as well. Until those quantities are supplied, an alarming list may justify examining its individual cases, but it cannot establish the excess mortality that the list itself appears to imply.

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