STUMP – DEATH AND TAXES
Lightning Strike Deaths
MARY PAT CAMPBELL
·
JUN 17
Lightning Strike Deaths
Listen now (28 min) | Lightning strike deaths have decreased by over 80% from 1968 to 2022! Thanks to lots of changes, but primarily because of improved weather forecasting. Alas, they still occur, as was just reported from Texas, but we can thank technological change for this benefit.
I got a lovely surprise when John Jensenius from the National Lightning Safety Council reached out to me after that episode to give me LOADS more information.
NCSU Belltower, Photo by: Kelly Stafford, Class of 1991
I got a chance to have a long conversation with John, so let me share some of the extra information he shared with me.
I noted in my podcast that lightning deaths had been trending down for years:
Annual death count based on death certificates reported to the CDC, UCD = X33, victim of lightning
I attributed it to better weather forecasting and people having smartphones to alert them to lightning storms.
However, it also requires understanding at what point one is in danger.
Just going by the front page of the National Lightning Safety Council:
NO PLACE OUTSIDE IS SAFE
when thunderstorms are in the area.WHEN THUNDER ROARS, GO INDOORS!
John mentioned to me that most people are struck before any rain begins (because, of course, most people will have sought shelter at that point).
You can be struck for up to 10 miles away from the thunderstorm itself.
Here is John Jensenius providing introductory information:
I had mentioned that soon I wouldn’t be able to get the data on lightning deaths easily from CDC WONDER, because it would drop below 10 deaths per year.
Well, they keep tabs on lightning deaths… and here is a detail that John mentioned to me:
We are aware of differences between our numbers and CDC’s numbers. We do our best to document every lightning fatality that we can through media reports, reports to the National Weather Service, OSHA reports, public reports provided directly to us, etc. That said, I’m sure we miss a few. We and the National Weather Service share our information so our databases typically match. [….. ] As I’m sure you know, the CDC relies on death certificates provided by medical examiners/coroners. While I assume most of these are coded accurately (possible coding errors), I am aware of one recent questionable case where the medical examiner decided to list the death as due to lightning, but our investigation (based on lightning data) did not show any lightning strikes in the immediate area. I’m also aware of two cases where we listed the death as lightning, but we were told the medical examiner did not (one heart condition, one drowning).
The issue is often the person who died is alone when they were struck — they were not able to get to shelter.
One of the reasons that the National Weather Service and the National Lightning Safety Council can keep track of weather-related deaths in such detail is that, thank goodness, such deaths are unusual nowadays.
And are pretty shocking. (Not sorry)
Weather-related deaths, the direct kind — like deaths from floods, hurricanes, direct heat and freezing deaths (for all that people want to make hay about them) — these are a very small percentage of deaths in the U.S. currently compared to all other causes. When they occur, they are bound to be reported on … if the person’s body is found.
In any case, here’s an updated graph, comparing the CDC numbers to the National Lightning Safety Council’s:
Because the National Lightning Safety Council gathers and reports information as it happens, I have a partial count for 2023, so far.
They also collect other data not on the death certificates, such as what activity the person was doing when they were struck.
When talking with John, I, of course, guessed that golfing would be one of the top activities when people are struck by lightning.
Nope.
It’s fishing:
From 2006 through 2022, 466 people were struck and killed by lightning in the United States. Almost two thirds of the deaths occurred to people who had been enjoying outdoor leisure activities. The common belief that golfers are responsible for the greatest number of lightning deaths is shown to be a myth. During this 17-year period fishermen accounted for more than three times as many fatalities as golfers, while beach activities, boating, and camping each accounted for about twice as many deaths as golf. From 2006 to 2022, there were 40 fishing deaths, 29 beach deaths, 25 boating deaths, and 23 camping deaths. Of the sports activities, soccer and golf saw the greatest number of deaths with 13. Around the home, yard work (including mowing the lawn) accounted for 21 fatalities. For work-related activities, ranching/farming topped the list with 21 deaths.
In the record compiled, there were 466 deaths, so 8.6% of the total deaths were fishing-related.
The above comes from the executive summary from the most recent analysis done by John Jensenius. I’m going to grab a few of the graphs from his report, which you may find interesting.
I’m starting with the where. John categorized by whether it was leisure activities, daily routine, or work-related:
That gives you an idea of the level of choice involved. Given it’s leisure-related, primarily, people can choose to go do something else… but, let’s check where they are:
The issue is this: the problem with many of these situations is that people may be unable to find suitable shelter. (Golfing falls under “sports”, by the way.)
To be sure, perhaps more people fish than golf, but even without that disparity, if one were golfing, it is fairly easy to get in a golf cart and head back to the clubhouse. With fishing, depending on where you are, there may be no suitable shelter nearby if there is an unexpected storm.
I asked Jensenius what to do if you’re caught out where you can’t find shelter: the main point is to minimize your contact with the ground, which is usually how the current flows through people.
So stand on one leg and hop up and down.
Okay, that may be difficult to do, but the main thing is not to spread out on the ground, don’t stand with your feet wide apart, etc.
Science Daily: ER Physician Tells You How To Avoid A Lightning Strike And What To Do If One Occurs
The other type of strike – step potential – happens when a current traveling through the ground goes up your leg, travels through you and then goes down the other leg and back into the ground. “That is why Boy Scouts practice standing on one leg during a storm,” she explained. “They are attempting to decrease the likelihood that the current will go through them by having only one foot on the ground.”
Prevention begins by seeking cover at the start of a storm. “Lightning seems to be concentrated at the forefront of a storm,” according to Zinzuwadia, “so there tends to be a greater risk of being hit by lightning at the beginning of a storm.”
….
If you are outside during a storm, crouch down and try to touch as little of the ground as you can, Zinzuwadia suggests. “Even if you are hit by the current, the less contact there is between you and the ground, the less likely it is that all of your major organs will be hit,” she says. “It increases your chances of survival.”
I mentioned this last time, I believe, but it’s exactly who you think: mainly younger men.
Jensenius did note the little bump up for age 50-59, which may be women in early retirement with their husbands, as well as the drop off in age 30-39, the age at which mom is like… no. I’m staying inside… the kids are NOT going outside in a thunderstorm.
Lightning deaths, unsurprisingly, are seasonal:
However, day of the week doesn’t seem to have as strong a pattern:
Yes, a bit more on weekends, but if people are on vacation in July and get hit… might not be on a weekend.
John Jensenius used to work at the National Weather Service before retiring, when he had started communicating weather safety, specifically lightning. So he pointed me to this page of weather fatality statistics, which reaches back to 1940.
I may revisit this data set, as it also has flood, tornado, and hurricane deaths listed…
Let me drop the abstract here:
Importance There is evidence that Republican-leaning counties have had higher COVID-19 death rates than Democratic-leaning counties and similar evidence of an association between political party affiliation and attitudes regarding COVID-19 vaccination; further data on these rates may be useful.
Objective To assess political party affiliation and mortality rates for individuals during the initial 22 months of the COVID-19 pandemic.
Design, Setting, and Participants A cross-sectional comparison of excess mortality between registered Republican and Democratic voters between March 2020 and December 2021 adjusted for age and state of voter registration was conducted. Voter and mortality data from Florida and Ohio in 2017 linked to mortality records for January 1, 2018, to December 31, 2021, were used in data analysis.
Exposures Political party affiliation.
Main Outcomes and Measures Excess weekly deaths during the COVID-19 pandemic adjusted for age, county, party affiliation, and seasonality.
Results Between January 1, 2018, and December 31, 2021, there were 538 159 individuals in Ohio and Florida who died at age 25 years or older in the study sample. The median age at death was 78 years (IQR, 71-89 years). Overall, the excess death rate for Republican voters was 2.8 percentage points, or 15%, higher than the excess death rate for Democratic voters (95% prediction interval [PI], 1.6-3.7 percentage points). After May 1, 2021, when vaccines were available to all adults, the excess death rate gap between Republican and Democratic voters widened from −0.9 percentage point (95% PI, −2.5 to 0.3 percentage points) to 7.7 percentage points (95% PI, 6.0-9.3 percentage points) in the adjusted analysis; the excess death rate among Republican voters was 43% higher than the excess death rate among Democratic voters. The gap in excess death rates between Republican and Democratic voters was larger in counties with lower vaccination rates and was primarily noted in voters residing in Ohio.
Conclusions and Relevance In this cross-sectional study, an association was observed between political party affiliation and excess deaths in Ohio and Florida after COVID-19 vaccines were available to all adults. These findings suggest that differences in vaccination attitudes and reported uptake between Republican and Democratic voters may have been factors in the severity and trajectory of the pandemic in the US.
Given people forgot, this was released in preprint form last fall.
I wrote about it back in October 2022, when it was a preprint:
On COVID (and all-cause) Mortality and Political Affiliation Studies
I had some specific comments regarding: how they identified party affiliation.
For one, there is no party affiliation in Ohio. They went by how people voted in specific years’ party primaries. So that required people to have voted in specific primary elections.
That excluded a lot of people who may have otherwise identified themselves as Republican or Democrat. It includes people who actually consider themselves unaffiliated, but felt moved to vote in particular primaries, because Ohio primaries are open.
(This contrasts drastically with New York, where you need to be registered with a particular party a full year before the given primary in order to vote in that party’s primary. The parties do not want spoilers playing at “Operation Chaos” and any such bullshit.)
Florida party affiliation was identified in the normal way: party registration. Whether or not the people voted in a primary.
So you’re already starting with very inconsistent definitions.
There are far more technical critiques I have, but I’m just getting in the weeds at that point. I’ll show you one item:
Mmmmm, big old cloud of dots.
There are issues. One of the big issues they should have checked for, though they probably don’t have this data, is obesity levels. I bet that would have shown a much stronger result than this haze.
If you want to see another’s recent technical critique, here ya go:
A recent paper in JAMA IM claims that individuals who were Republican were more likely to die during the pandemic than Democrats were after vaccines were out. This paper happily fuels a narrative that Republicans died because they are stupid and didn’t get vaccinated…
Read more
5 days ago · 421 likes · 40 comments · Vinay Prasad
A lot of these were the same critiques I had back in October 2022.
But this is the thing that I found to be total crap:
https://twitter.com/jburnmurdoch/status/1686813844469858319
You want to know why Republicans don’t trust “science”?
It’s not science that we conservatives don’t trust.
It’s scientists. Particularly the ones who work for the government and who get most of the media coverage.
Because instead of checking some driving variables (such as obesity and diabetes … you know, the co-morbidities of COVID), you go straight for supposed political party affiliation.
Some people are not being trusted because they have shown themselves not to be trustworthy.
This was the second, supposedly tragic graph.
https://twitter.com/jburnmurdoch/status/1686813850123853824
https://twitter.com/jburnmurdoch/status/1686813855874170891
In Ohio (for their weird definition of party affiliation) and Florida (using imputed data), that is. And then trying to extrapolate it to the whole country.
Ohio and Florida are hardly representative of a continental-wide country. A country that is the third-largest in area and in population in the world. Come on.
Dude, you’re a Brit. I know you’re in the journalisming biz, but have some pride.
But also, don’t pretend like you think this is some great tragedy.
As Vinay notes:
It is OK to criticize Republicans as being stupid people who deserve to die
John Mandrola wrote: Bad Science Needs to Be Called Out
I won’t repeat the many limitations of this paper. The simple fact is that it is an observational study looking back at associations. Even the highest quality non-random comparisons suffer from substantial threats of confounding factors. It borders on anti-science to make causal inferences from highly biased studies. Experts in public health who amplified this study surely know that.
The authors find an association between higher death rates based on voter registration but then strongly suggest that vaccine denial was the cause. Yes, of course, they include the usual ‘limitations’ section buried at the end of the paper. But their feelings are clear.
Such foolish leaps of causation are common in the medical literature. But this is worse because it is nakedly political.
The authors implicate vaccine denial but they did not have vaccine records. They did not control for basic things like socio-economic status, BMI, diabetes.
That leads me to the second problem: that this analysis was conceived. These are Yale scientists. Why would they spend time and effort on a project that had no chance of reliably answering a question? And. They would have been unlikely to write this manuscript if the results were different.
The researchers may have looked… and not published if it came out differently.
But the main thing is they could have looked at other variables and THEY DIDN’T.
Or… didn’t they?
Maybe they did and saw that there were stronger links to obesity, income levels, and other similar issues.
The reason I say this is that we’ve seen those linkages in the actuarial results, even preceding the pandemic, and noted diverging results during the pandemic.
The Society of Actuaries has been doing a different kind of analysis of U.S. mortality experience.
It still has correlative issues – as I often say, we actuaries will just tell you what’s going on, but we can’t tell you why. I can give you a bunch of possibilities. But correlation isn’t causation, after all.
One method has been to categorize each county in the U.S. by socioeconomic quintile.
Check out what mortality looks like when stratified by these quintiles:
These are age-adjusted death rates. Being on top is bad.
Quintile 1 is the lowest in terms of socioeconomic status – lowest income, lowest education, etc.
Quintile 5 is the highest in terms of socioeconomic status.
Look at that graph: all quintiles got worse in 2020, but in 2021, they all got worse, except quintile 5.
That is, the top socioeconomic status counties improved in age-adjusted death rates in 2021 compared to 2020.
Isn’t that interesting?
It’s interesting to make comparisons by cause of death. Most physiological causes of death break out like this:
But drug overdoses look like this:
Isn’t that interesting.
It may be the case that these other variables may have even stronger correlations than political parties. Political parties are likely only downstream of these other issues.
And… if you want people to trust you — perhaps you should seek truth and behave in more trustworthy ways.
I highly recommend getting back to basics in terms of throwing the net wide for explanatory variables.
If you only seek the answer you desire, I’m sure you will find it… but you will persuade only those who already agree with you.
]]>In 2022, Portland experienced a spate of homicides and other violence involving homeless victims that rattled many in the community: a 42-year-old homeless woman shot in the face by two teenagers who were hunting rats with a pellet gun; a 26-year-old homeless woman stabbed in the chest outside her tent; another homeless woman, 31, fatally shot at close range by a stranger.
The search for answers points in many directions: to city and county officials who allowed tents on the streets because the government had little to offer in the way of housing, to Oregon voters who backed decriminalizing hard drugs and to the unrest that rocked Portland in 2020 and left raw scars.
But what has turbocharged the city’s troubles in recent years is fentanyl, a deadly synthetic drug, which has transformed long-standing problems into a profound test of the Portland ethos.
….
In November 2020, amid the national reckoning over policing and criminal justice, Oregon voters approved a ballot measure that lowered the penalties for possessing small amounts of drugs like meth and opioids.
When police in Oregon see someone using these drugs, they can hand out a $100 ticket and a card listing a hotline for addiction treatment.
Known as Measure 110, the law was meant to focus the government’s efforts on treating addiction, not on arresting users.
Let’s look at the results.
In 2020, the year voters approved the measure, 69 people in Multnomah County fatally overdosed from synthetic opioids, mainly fentanyl, according to the county health department.
Last year, such overdoses killed 209 people in the county, and the drug is smoked openly on Portland’s downtown streets.
Fifty times more powerful than heroin, fentanyl sets off a high that “human brains have never seen before,” said Dr. Andy Mendenhall, who runs Central City Concern, one of Portland’s largest nonprofit providers of mental health and homeless services.
Let’s look at drug overdose death statistics through 2022 for the U.S.
Let’s see how much “good intentions” have done for people.
First, here are the high-level rate results. It doesn’t matter if I do a crude rate or age-adjusted rate here – it looks essentially the same.
If that doesn’t look drastic enough for you, here you go.
That is a huge increase from 2019 to 2021 in drug overdose deaths.
This is just a snapshot, and the 2022 numbers are still provisional.
Still, this is hideous, especially for Native Americans, both males and females.
I do have the trajectories for the different demographic profiles. Before the pandemic, black males were not higher than white males for drug ODs deaths. Indeed, white males and Native American males had higher death rates — until the pandemic.
As an aside, drug overdoses are not the only cause of death for which Native Americans have far worse outcomes than other racial/ethnic groups in the U.S., both male and female.
As a Catholic, I have an intention for the cause of Nicholas Black Elk, who is currently recognized as a Servant of God.
Seeing these horrid mortality statistics make me double down — but of course, one can see all of the groups are seeing worse outcomes. But those which started out worse are seeing substantially worse outcomes.
Let’s check out what the drug OD death rates have been by age groups.
I’ll break this out by the highest death rates:
Maybe you weren’t expecting the young middle-aged people to be the peak of drug OD deaths, but they are.
These are rates, not numbers of deaths, so this has nothing to do with the age structure of the population. I will note the rate has been increasing for all of these age groups, and all of these age groups are prime “working age”.
The deaths due to drug ODs really jumped up in 2020 and 2021 for these groups.
Now, let’s look at other age groups [little kids do not die from drug ODs in any significant amounts.]
This vertical axis starts much lower than the prior graph, but isn’t it interesting that seniors of age 65-74 years old have higher drug OD death rates than teenagers?
That’s not too surprising if you think about it. Seniors have more money than teenagers, and seniors are less likely to be able to survive adverse health events from anything, which isn’t drug-specific.
But I will point out there was a huge relative jump up in drug OD deaths for teenagers in 2020.
Yeah. I will have to do a deeper dive on relative increases in mortality — because there have been huge movements in mortality for teens and young adults in the pandemic, and this has horrible effects.
Finally, for the geographic footprint, I am going to look only at 2019-2022.
I have found many interesting patterns, but I will make it simple: Oregon had the worst increase in drug OD deaths from 2019 to 2022 of all the states in the U.S.
Here is my ranking table.
Oregon started out ranking relatively low: 43rd out of 50 states. It moved up to 31st, because it had the highest increase in death rate from 2019 to 2022. Its neighboring state of Washington also had a very large increase.
As you can see, all these states essentially more than doubled their drug overdose deaths from 2019 to 2022.
By the way, only one state actually improved in drug OD age-adjusted death rates between 2019 and 2022, according to preliminary statistics.
New Jersey.
So, congratulations, New Jersey.
If all you did was repel people to go to other states to die, that’s still something to celebrate.
All the other states saw increases.
I will be writing/speaking more about this.
A research paper in The Lancet seems straightforward enough.
Background
Heat and cold are established environmental risk factors for human health. However, mapping the related health burden is a difficult task due to the complexity of the associations and the differences in vulnerability and demographic distributions. In this study, we did a comprehensive mortality impact assessment due to heat and cold in European urban areas, considering geographical differences and age-specific risks.
Methods
We included urban areas across Europe between Jan 1, 2000, and Dec 12, 2019, using the Urban Audit dataset of Eurostat and adults aged 20 years and older living in these areas. Data were extracted from Eurostat, the Multi-country Multi-city Collaborative Research Network, Moderate Resolution Imaging Spectroradiometer, and Copernicus. We applied a three-stage method to estimate risks of temperature continuously across the age and space dimensions, identifying patterns of vulnerability on the basis of city-specific characteristics and demographic structures. These risks were used to derive minimum mortality temperatures and related percentiles and raw and standardised excess mortality rates for heat and cold aggregated at various geographical levels.
Findings
Across the 854 urban areas in Europe, we estimated an annual excess of 203 620 (empirical 95% CI 180 882–224 613) deaths attributed to cold and 20 173 (17 261–22 934) attributed to heat. These corresponded to age-standardised rates of 129 (empirical 95% CI 114–142) and 13 (11–14) deaths per 100 000 person-years. Results differed across Europe and age groups, with the highest effects in eastern European cities for both cold and heat.
I bolded one part. They came up with a modeled excess mortality estimate.
The number may look odd, but their modeled estimate says about 200,000 extra deaths due to cold and 20,000 due to heat for a 20-year period.
So about 10,000 per year due to cold and 1,000 per year due to heat.
In any case, my dispute isn’t even with their results, which may be reasonable.
It’s this:
There are so many things wrong with this graph.
Perhaps I will do a video critiquing this graph, because what I’m about to show you, while one of the worst things about the graph, is far from the only thing wrong with this graph. The data are available here, so perhaps I will circle back and redo in the future.
But I will use U.S. data in a moment with 50 states, which is much more than the few countries represented above, so let us move on.
Bjorn Lomberg fixed one of the worst features of the above graph in the graph below:
Did you catch that?
The two sides of the graphs had unequal horizontal scales. This was incredibly misleading, because one would assume, the way these were lined up, that you were able to make eyeball comparisons of heat and cold deaths.
If you read the words in the summary results above, you would notice that the modeled results were 10-to-1 in ratio.
But the point of a visualization is that you can SEE to make visual comparisons. If you were going to make unequal scales (which I will be doing below), you do it in two separate figures. Not one.
What I’m going to show below is very different from what they were estimating above.
I hope.
I am updating a prior post, Cold Kills: Some Comparisons of Heat and Cold Deaths, 1999-2020, and improving some of the comparisons. This is looking at direct deaths due to excessive natural heat or natural cold. (ICD-10 codes are X30 (heat) and X31 (cold)).
What they were doing in the above research project was trying to estimate the mortality impact of hot/cold weather. For example, an extra cold winter may lead to additional respiratory diseases and deaths among the elderly. That’s different from what I’m about to show, which would be something like somebody freezing to death in their home due to insufficient heat during a winter storm.
Let’s start simple, with total death counts by year.
There are more deaths due to cold each year in the U.S. (except in 2006), but not at a 10-to-1 ratio, obviously.
Let’s switch to the death rate for a moment:
These are very low rates – death due to natural heat/cold exposure is not common.
The increasing rate for both the heat and cold deaths could potentially come from many things. Let me show you something below and I’ll give you some potential interpretations.
Let us see who dies by these causes by age.
Obviously, there is some influence based on how many people are in each age group, so let us switch to death rate by age group.
Perhaps I should’ve switched to a logarithmic vertical scale, but I’m getting fancy enough here.
I will note that the elderly are the most vulnerable to both heat and cold deaths… except that infants and toddlers are also vulnerable to deaths by heat. One can think for a moment about stories of small children who die due to heat from being left in vehicles, and that’s enough thinking about that.
But let us think about the elderly being the most vulnerable to both cold and heat deaths.
One of the reasons the rate of both types of death were increasing could be an increasing number of very old people… especially old people who don’t have anybody to care for them. They may be helpless to care for themselves, and then die in the cold or the heat.
I can imagine that many elderly people lost their caretakers during the pandemic.
Note: I am using different color scales – not just red versus blue, but different cut points.
Because the heat deaths are lower in rate in general, I used lower cut points:
Unsurprisingly, Arizona and Nevada had the highest rates.
THEY’VE GOT DESERTS, Y’ALL.
(and I understand some people have been doing stupid stuff in U.S. deserts recently)
The states that are missing from the map above simply did not have enough heat deaths recorded to show up in the statistics.
Cold deaths:
The only state with insufficient cold deaths (that is, fewer than 10) was Hawaii. What a surprise.
Look, maybe the people just sucked at making graphs.
Given that they did the scale with the ages, and I can barely tell the different age groups apart in those stacked bars, maybe they thought the different scale and then a broken scale would have been just fine.
People assumed that the researchers were trying to be deliberately deceptive about their results, linking them to global warming. In the introduction they wrote: “The associated health burden is expected to increase with climate change, especially under the most extreme scenarios of global warming.”
But a large part of their argument was that extreme cold was also a burden. They wanted to link climate change results to both sides of extreme weather.
I think they got enamored of their “cool-looking” graphs, and they didn’t worry about how the graphs might be interpreted by others. That others might consider it intentionally deceptive.
I’m willing to bet somebody wrote some code in R that they were really proud of, that made cool graphs, and they are currently annoyed other people thought they were being deceptive. THEY SAID THE NUMBERS IN THE PAPER UP FRONT IN THE SUMMARY! CAN’T PEOPLE READ?!
But let us assume for a moment that they were deliberately deceptive. What’s the point? How would it help “the cause”?
You would think people would have learned by now.
I don’t know these researchers.
But I do know people get too enamored of overly complicated graphs.
How about this other graph for size? It’s a different one from Bjorn Lomborg.
Just a simple column graph.
Pretty impactful, don’t you think?
Also…. put on a sweater.
Well, maybe not in July. In North America, at least.
Spreadsheet
]]>The rates are definitely very different, but the rank orders also differ.
But first…
Glenn Cooke, aka The Term Guy, has been scanning in some of his collection of old insurance-related books.
Note: I bought some old insurance books from Glenn via eBay years ago… so I have a collection that differs from what he’s scanned in thus far. There’s one book in particular I want to scan and republish in an annotated/extended version…. when I get to it. I’ll put it on my very long list of projects to do.
Glenn has his old books here: The Term Guy – Antique and Historical Insurance & Actuarial Books
Here is an example: Mortality Statistics of Insured Wage-Earners and Their Families
Excerpts from the Table of Contents:
I elided over some other items, but I’m going to note that I believe that tuberculosis was listed first as a cause of death as it was a very prominent cause at the time.
The particular cause of death experience study we’re looking at here is similar to a group life insurance study currently.
This is the period immediately preceding both the entry of the U.S. into World War I and the Spanish Flu pandemic, so this will be interesting to look at the numbers.
For my fellow historical mortality nerds, there are several books Glenn has scanned thus far, from a variety of nations. Glenn is situated in Canada himself, specializing in Canadian term life insurance, but the books come from Scotland, the U.S., England, … even the University of Copenhagen!
There are not just the nerdy actuarial tables, but also books intended for insurance salesmen…. (and I do mean men.) The book I have I want to republish is for saleswomen in 19th century America, and more on that at a different time.
But that’s a good time to move into the ranking tables by sex.
Let’s start with the males.
First the count:
Second, the rate:
What may help is a comparison with the rate table from 2019:
There are some slight differences in death rates that made some causes switch places in ranking between 2019 and 2022 (even ignoring COVID).
However, there is one large change that is hard to ignore: the increase in death rates due to “accidents” for younger adults.
A lot of that was drug overdoses.
I will be getting into the sex differences of that, as well as other causes — the heart disease & cancer trends for men differ a lot from that of women as you will see below.
This is the part where I will be making a comparison between the sexes.
First, the ranking table by count:
Rate table:
And 2019 for comparison:
This post is mostly focused on sex differences, so it’s more on the ratios between death rates.
That is the row above the rate tables, and the biggest disparity is in the age 15-24.
In 2019, the ratio between the male and female death rates for the ages 15-24 was 2.59. That means the death rate for males was over 150% that of females for that age band.
In 2022, the ratio was 2.56, so the gap was about the same — note that the mortality in 2022 is higher for both females and males, but the mortality ratio persisted.
If we look at the age group just higher (age 25-34 years old), the mortality gap widened for those, but I have a reason to look at the age 15-24 years old in particular: this is where the “fatal stupidity period” for males is centered.
I have long called this the fatal stupidity period. Here is a post I made on Livejournal (of all places) back in 2013:
So people were all up in my grill about my characterization of the “stupid period” and yadda yadda. I happen to have noticed this phenomenon a while back, and it’s not unique to the U.S. So I decided to demonstrate it.
So the first graph you’re going to see here are ratios of death rates (by age) of males to females for some selected countries, for observation periods 2000-2009. I picked a decadal smoothing as the mortality rates generally don’t move that fast, and one year’s worth of death stats are all over the place for ages where probability is low for dying in the first place (look at that SSA table again – the peak is at less than 0.15% probability of death in one year – that’s pretty low).
The fatal stupidity period is centered around the age of about 20. And about Russia… well. That’s the really bad line, in case it’s not clear.
Mind you, mortality increases with age for males in adulthood, just as with females. The point rate of increase, for males – is fairly fast at a young age.
That was 2000-2009, and it’s several years later. But the shape of mortality in terms of sex gaps hasn’t changed much. The difference at the young age is usually due to external causes of death: homicide, suicide, and accidents.
Accidental causes of death encompass all sorts of types of deaths: motor vehicle accidents, drowning, drug overdoses, and the results of doing all sorts of risky (and sometimes stupid) things that young men often get up to… more often than young women do.
That’s one of the biggest reasons for the difference at that age.
But I do want to point out: at every age, the male death rate is higher in the U.S., and this has been true for a very long time. But even without getting into the historical aspects of the issue, for many physiological causes of death males have much higher death rates than females. Not just the external causes of death.
In any case, we’ll see some of this as I do deeper dives into particular causes of death.
Video: U.S. Mortality Trends 2020-2022 part 3: Major Categories of Death – includes ranking tables for prior years by sex