So, period life expectancy dropped about 12 – 13% in 1918 in the U.S., mainly due to the Spanish flu, because there was an outsized effect from young adults being the main group killed by the disease (also, period life expectancy was relatively short — under 60 years!). That was a drop of about 7 years.
But life expectancy dropped only about 1 year in 2020 due to COVID impacts, and that was a decrease of less than 3% compared to 2019.
So if you want to compare the effect of the Spanish flu vs. COVID-19 on the U.S. population, all of these rates —- percentage change in period life expectancy, age-adjusted death rates, or even crude death rate — are all more reasonable choices than simply number of people who died.
Through the mechanism of the Trust Fund, Congress can put off having to act on the fundamental demographic problem that they can’t do much about. They hope they can run the Magic Money Machine to cover all the goodies they want, and in 2034, the Boomers will mostly be over age 80. Maybe another pandemic will deal with them….
(and nobody cares about us Gen Xers. In 2034, I won’t even be eligible for Social Security old age benefits.)
Nobody expects the Social Security benefits to be cut in 2034, or whatever other magic date when the Trust Fund runs out. The only thing the current Trust Fund mechanism requires is cuts… only if Congress doesn’t actually pass legislation to “fix” the issue.
They have been doing ad hoc “fixes” to Medicare and other parts for years so as to avoid massive cuts.
There you have it — for this slice of time, the beginning of August 2021, Israel shows that the vaccines reduced risk 80%+, for all age groups.
Yes, if you just do an aggregation at the whole population level, it looks like a 67% reduction. That’s the “magic” of Simpson’s Paradox. For any given age group, the percentage reduction is much larger. But due to the relative risks by age, even with such high reductions, the overall population result shows a smaller improvement.
Takeaway: COVID vaccines greatly reduce risk
This is the main takeaway: the COVID vaccines greatly reduce the risk of adverse outcomes.
By the way, this is also true of the annual flu vaccines, which range in efficacy based on how well the vaccine that year matches up with the strains circulating, and which strains are circulating (some strains, even if you formulated the vaccine perfectly, still infect.) I could give you flu/pneumonia death rates by age groups, and you would see that flu/pneumonia is a big killer of the elderly. Get your flu vaccines, please.
But, we should also expect a lot of people hospitalized with COVID to be vaccinated old folks. Just because of the huge risk slope by age, which will still exist after vaccination.
As with heart disease, we see improvement at all ages, but the percentage improvement is not as high with cancer as it was with heart disease.
One of the biggest things, though, is how death rates go up by age group. I will use 2020Q1 cause of death rates to make comparisons, as these are in the SOA report, and the COVID impact didn’t come fully until 2020Q2.
Heart disease death rate for those aged 85+ was 3766 per 100K, and those aged 75-84 was 986. That’s a ratio of 3.8.
Cancer deaths for those aged 85+ was 1562 per 100K, and those aged 75-84 was 1004. That’s a ratio of 1.6.
Two things to note:
Cancer death rate for those age 75-84 was higher than the heart disease death rate for the same group
Heart disease death rates climb much more rapidly than cancer death rates by age
when you’ve got really steep differences between subpopulations and the subpopulations are of very different sizes, the overall population average will be very different from simply looking at the average of the two populations.
– The base risk rates for each group are extremely different (3.9 per 100K for young, and 91.9 per 100K for old) – The percentage each subpopulation makes up in the larger population is very different (67% young, 33% old) – The vaccination rates are very different by population (76% young, 92% old)
The long-term trend has been improvement for this cause of death, with it most obvious for the oldest age groups. This trend has been driven by improvement in medical treatment for the condition, but also due to the decrease in smoking rates… decades ago. Some causes of death have behavior that precedes the death by decades, which can get tricky to track for our top two causes of death: heart disease and cancer. Even so, smoking cigarettes has been a huge driver for both these causes, and made a large differentiator by sex and smoking status for a long time.
In the pre-computer days, people used these approximations due to having to do all calculations by hand or with the help of tables. Of course, many approximations are done by computers themselves — the way computers calculate functions such as sine() and exp() involves approaches like Taylor series expansions.
The specific approximation techniques I try (1 “exact” and 6 different approximation… including the final ones where I put approximations within approximations just because I can) are not important. But the concept that you should know how to try out and test approximation approaches in case you need them is important for those doing numerical computing.
Author(s): Mary Pat Campbell
Publication Date: 3 February 2016 (updated for links 2021)
Publication Site: LinkedIn, CompAct, Society of Actuaries
The Spanish flu pandemic gives us the demonstration of what happens when there is a short-term large increase in mortality.
Using Social Security records of period life expectancy, there was a huge drop in life expectancy in 1918…. and then a huge increase in 1919. But going from 1917 to 1919 wasn’t really that big of a difference.
The period life expectancy drop was 12% for females, 13% for males in 1918.
Then there was an increase of 15% for females, 20% for males in 1919. The Spanish flu hit the U.S. hard in 1918, and let up in 1919.
If you compare 1919 against 1917, the life expectancy from birth increase was 1% for females, and 4% increase for males — male life expectancy was down in 1917 compared to 1916, probably related to World War I.
I want you to notice something — the blue bars are the “with COVID” portion of deaths, and the chartreuse bars are the ones “without COVID”. The bars are weekly counts of deaths when they occurred. Ignore the most recent weeks because they don’t have full data reported yet.
The red pluses indicate excess mortality, defined as exceeding the 95th percentile for expected mortality for that week (so it includes seaonality). You can see the excess mortality from the 2017-2018 flu season, which was bad for a flu season.
The non-COVID mortality has been in excessive mortality range for almost all 2020 after March. But since the beginning of 2021, it has dropped off…. and COVID mortality has also dropped off.
I think we may be almost in “normal” range soon. We shall see!
Welcome to another episode of Positivity with Paul, where I find Fellow Actuaries – pun intended – for a conversational Q&A on their life. The focus is on their journey along the actuarial exam path and beyond, some of the challenges they faced, and how those challenges helped shape them to become who they are today.
To give some brief context on becoming an Actuary, there’s a number of actuarial exams that one has to go through. These exams are very rigorous and typically, only the top 40% pass at each sitting, They cover complex mathematical topics like statistics and financial modelling but also insurance, investments, regulatory and accounting. Candidates can study up to 5 months per sitting and they will take 7 to 10 years on average to earn their Fellowship degree. To that end, I launched this series of podcasts because I was curious about what drove my guests to surmount trials and tribulations to get to the end goal of becoming an Actuary.
My guest in this interview is Mary Pat Campbell. Mary Pat is an actuary working in Connecticut, investigating life insurance and annuity industry trends. She has been interested in exploring mortality trends, public finance and public pensions as an avocation. Some of these explorations can be found at her blog: stump.marypat.org. Mary Pat is a fellow of the Society of Actuaries and a member of the American Academy of Actuaries. She has been working in the life/annuity industry since 2003. She holds a master’s degree in math from New York University and undergraduate degrees in math and physics from North Carolina State University. In this podcast, Mary Pat discusses similarities in concepts between physics and actuarial science, the current low interest rate environment and lessons learnt in the insurance sector from the financial crisis in 2008-2009. Hope you enjoy this all-inclusive interview! Paul Kandola