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Lower Life Expectancy in a State is Correlated with a Higher Share Voting for Trump

A lower life expectancy in a state is associated with a higher share in the state voting for Trump.  The chart above shows the simple correlation, using state-wide averages, between the life expectancy in a state and Trump’s share of the vote in that state in the 2020 presidential election.  States where life expectancy is relatively low saw, on average, a higher share of their population voting for Trump.  Life expectancy was especially low in a set of mostly Southern states that also had a high share voting for Trump (the bottom right corner of the chart).

The figures on life expectancy come from a recently issued set of estimates produced by the CDC.  The CDC estimates are geographically highly detailed, providing estimates down to the census tract level, but I have only used here the overall state-wide averages.  Due to their fine level of geographic detail, the CDC estimates are averaged over several years (2010 to 2015) to smooth out year-to-year statistical noise.  But life expectancy figures generally change only slowly over time (2020 was an exception, due to Covid-19), so figures for 2010-15 will provide a good estimate of what should be considered normal for life expectancy currently (i.e. with the exception of the Covid-19 impact).  The presidential election results are from Wikipedia, where the Trump share is his share in the overall vote in each state (including third party and other minor candidates).

The correlation is a strong one.  The regression equation (shown in the chart) for the relationship has an R-squared of 0.45.  This means that if one simply knew the life expectancy in a state, one could predict 45% of the variation in the share across the states that would vote for Trump.  This is high for such a simple cross-section relationship.  The negative slope of the equation (-0.11) means that every percentage point increase in the share of the vote for Trump is associated with a 0.11 year lower life expectancy.  Or put another way, a state with a life expectancy that is one year less than in another is associated with an expected 9 percentage point higher share of those voting for Trump (where 9 is roughly equal to 1 / 0.11).

Why this correlation?  Note that it is not saying that a high or low life expectancy in itself would necessarily be driving a tendency to vote for Trump or not.  Rather, a number of factors that enter into the determination of life expectancy are quite possibly also factors in common with the views of Trump supporters.  Life expectancy depends on personal factors and decisions (smoking, diet and exercise, obesity, vaccinations, whether to wear a mask to protect oneself and others to reduce the spread of a deadly disease), as well as on decisions made by state and local governments chosen by that electorate   (such as on access to health care, e.g. whether Medicaid should be available for the poor).  Life expectancy also depends on income levels and for any given average income level on income inequality.

And it will depend on the social norms of the region, such as car driving habits (speeding) and access to guns.  Of the factors reducing life expectancy in the US between 2014 and 2017 (mostly offsetting factors that would have, by themselves, led to a higher life expectancy) unintentional injuries accounted for just over half (50.6%) while suicides and homicides accounted for a further 15% (suicide 7.8% and homicide 7.5%).  That is, these non-medical factors accounted for two-thirds of the factors that had a negative impact on life expectancy in this period.

Few would question that better health is better than poorer health.  The high correlation seen here between life expectancy and the degree of Trump support suggests that there are significant commonalities in the various states between behaviors (both personal and social) that lead to poorer health outcomes and support for Trump.

Older Americans Account for an Overwhelming Share of the Deaths from Covid-19

It is well known that older individuals are more likely than younger individuals to die from Covid-19 should they become infected with the virus.  What some might not be aware of is how much this higher vulnerability by the older population has translated into older individuals accounting for the overwhelming share of those who have died from this terrible disease.  More than 95% of all those who have died from the disease in the US were age 50 or older.

This was not due to older individuals being more likely to get the disease in the first place.  As we will see below, the distribution of those coming down with Covid-19 is broadly similar to the distribution of population shares, and for those 50 and older, the share of this age group of all those who came down with the disease is almost identical to their population share.  Rather, the cause is that when an older individual comes down with the disease they are far more likely to die from it.

This short post will review the figures, basically through a series of charts.

The chart at the top of this post shows the shares of those who died from Covid-19 in the US by each age group (where the sum across age groups will be 100%).  These are based on totals since the start of the pandemic in February 2020.  The data comes from the CDC, which now reports on this each day.  Deaths and the causes of those deaths are regularly reported to the CDC by the US health system, and while reports with details on age and similar information will lag (the totals in these CDC figures come only to 71% of the total number of deaths from Covid-19 that the CDC also reports), for these share estimates the partial figures will be fine.

The CDC numbers on confirmed cases of Covid-19 by the same age groups are:

Note the scale, where those aged 50 and older made up just 35% of all those who had a confirmed case of Covid-19.  This is far from the 95% of deaths from the disease.

The shares of individual age groups varied, but this is not surprising as the share of the population of the US by each such age group also varies widely.  The interesting question is whether the shares of those coming down with the disease were different from the population shares.

And in broad terms they weren’t:

This chart shows the ratio of confirmed cases of Covid-19 of each age group to the share of the US population of that age group (using Census Bureau numbers).  By definition, that ratio will be 1.0 for the population as a whole.  Interestingly, for those age 50 and older, the ratio is very close to 1.0.  That is, those 50 and older accounted for 35% of cases in the US of Covid-19, and they similarly account for about 36% of the US population.  In practice, at least, they were only as likely to pick up the disease as their overall population share.

For the other individual age groups the ratio varies by about +/- 20 to 30% around equal shares (a ratio of 1.0), and there might be some simple explanations for that variation.  The ratio is about 0.8 for those between the ages of 65 and 84, where this might be because most of those in this age bracket are retired and can isolate themselves from much exposure to others.  In contrast, a significant share of those aged 85 and older might reside in nursing homes or may otherwise need assistance, thus exposing themselves to others.  The ratio for that age group is 1.2.

The ratios are also above 1.0 for those of working age below age 50, as many in these age groups will work and this may necessitate exposure to others.  Finally, the far lower ratios for children through to age 17 might well be anomalies due to limited testing.  Those in these age groups are far less likely to die (as we will discuss immediately below), and will often also show only limited symptoms should they get the disease (and may indeed often be totally asymptomatic).  As a result, many in those age groups may have not been tested even if they had the disease, as they did not exhibit symptoms.  The young may still get the disease, and indeed often do, and it may be a devastating disease for a significant number of them.  But the shares appear to be well less than for other age groups.

Broadly, therefore, the distribution of confirmed cases of Covid-19 by age group is similar to the population share of that age group (with the possible exception of those age 17 and below, although this may be a testing issue).  The data suggest that if one is exposed to the virus then similar shares of the population, regardless of age, get the disease.  Certain age groups, and in particular the elderly, do not appear to be more susceptible than others.

Why then do the elderly account for the overwhelming share (95% by those age 50 and above) of those who have died from Covid-19?  It is because it is far more deadly for them should they get it:

More than one in five (22%) of those aged 85 and older die from Covid-19 should they come down with the disease.  This is incredibly high for a communicable disease.  But the mortality rates then fall steadily for younger age groups.  It is still high at more than one in ten (11%) of those between ages 75 and 84, and about 5% for those between 65 and 74.  But it then falls to just 0.01% for those between 5 and 17, and 0.02% for those 0 to 4 (and with wider testing, leading to a higher number of confirmed cases, it might in fact be less than this).  Put another way, the mortality rate from Covid-19 is 2,000 times higher for those aged 85 and older than it is for the young.

These figures provide the rationale for prioritizing the elderly in the distribution of vaccinations.  Once one is able to vaccinate the 36% of the population aged 50 and above, one would have vaccinated the age groups accounting for 95% of the deaths from Covid-19.  The similar figures for those aged 65 and above are that they make up 16.5% of the US population (54 million people), but account for 81% of Covid-19 deaths.  And as I write this (on February 16), the CDC reports that 55 million doses of the vaccine have so far been administered in the US, with almost 40 million having received at least the first dose (and 15 million the second dose as well).

The prioritization of the elderly makes sense.  Covid-19 appears to spread similarly across age groups, but mortality from it is concentrated to a shockingly high degree among those who are older.

The Percentage Increase in the US Death Rate in 2020 Was Higher than in the Worst Year of the Spanish Flu

The Covid-19 pandemic this year has often been described as the worst public health crisis in the US since the Spanish Flu of 1918.  And there has certainly been nothing since then that compares.  But early estimates indicate that the increase in deaths this year may have even been worse than during the worst year of the Spanish Flu.

Population has of course grown, so the relevant figures to compare over time are death rates – the share of the population that died each year.  In addition, while it should not make much of a difference to the annual percentage changes, for longer-term trends one should look at the age-adjusted figures, which control for changes in the age structure of the population (as older people are more likely to die in any given year).

The National Center for Health Statistics, part of the CDC, provides such figures for the years 1900 to 2018.  The CDC also has separate figures for deaths and population for recent years, up to 2019, and hence the crude (not age-adjusted) death rate can be calculated for 2018 and 2019.  From this one can estimate what the percentage change in the death rate was for 2019, which should be close to the percentage change in the age-adjusted rate as the population age structure changes only slowly over time.

Finally, the estimate for what deaths in 2020 will be was obtained from a December 22 Associated Press article reporting on recent CDC figures.  It reports that the US “is on track to see more than 3.2 million deaths this year”.  And this may be a conservative estimate.  It would imply an increase of 345,162 over the number of deaths in 2019.  But as I write this, the number of deaths reported by Johns Hopkins from Covid-19 alone was 345,271.  While a share of those who have died due to Covid-19 this year would have died later in the year from other causes, analyses of “excess deaths” have consistently found that the increase in deaths this year has been well in excess of the number that has been attributed specifically to Covid-19.  For example, a CDC study in October estimated that for the period from late January (when Covid-19 was first recognized in the US) to October 3, there were 299,028 more who had died than one would have expected under normal conditions, while (over that period) only 198,081 deaths were directly attributed to Covid-19.  That is, the increase in the number of deaths during the period (over what would have been expected had this been a normal year) was 51% higher than the number of recorded deaths due to Covid-19.

We will not have the final figures for 2020 for some time.  But even accepting the conservative estimate of 3.2 million deaths in the US in 2020, the percentage increase in the number of deaths in the US per 100,000 of population was about the same as (and indeed very slightly higher than) in 1918, the year of the Spanish Flu.  In both years, the increase in the death rate was close to 12%.

I was surprised that the increase in the death rate this year was anywhere close to what it was during the Spanish Flu.  It is truly astounding.  But it is especially surprising given that, as the AP article cites, the number of deaths of Americans in 1918 rose by 46%.  How can that be consistent with the 12% increase in the death rates?  There are several factors that account for this.

To start, the increase (using another CDC table) was from 981,239 deaths in 1917 to 1,430,079 in 1918, or an increase of 448,840 (or 45.7%).  But note that a footnote to that CDC table indicates that these figures include military deaths in World War I.  This differs from the practice in later years, where military deaths are excluded.   World War I military deaths totalled 116,516 (according to official Department of Veterans Affairs figures), and almost all came in 2018.  While some share of these were due to soldiers dying from the Spanish Flu, I do not have figures for what those might have been.  Leaving that aside, the increase in non-military deaths in 1918 would have been close to 34%, and adjusting for US population growth in those years the increase in the death rate would have been 32%.

That is still significantly higher than the close to 12% increase in the death rate in the CDC figures.  There are two reasons for this.  First, note, as was discussed above, these CDC figures adjust for changes in the age structure of the US population over the century, and use population weights of the year 2000.  The US age structure has shifted markedly since 1918, with a far higher share of the population now in the older age groups.  Had the age structure in 1917 and 1918 been the same as it was in 2000, the base overall death rates in those years, excluding the impact of the Spanish Flu, would have been higher.  Second, the Spanish Flu was especially lethal for those in the middle age groups – those in their 20s, 30s, and 40s.  The young and the old were less affected.  But those in the middle age groups account for a smaller share of the age structure using the weights of the year 2000 than they had in 1918.  While I do not have the data to allow me to decompose the specific numbers, simple simulations with plausible parameters suggest that at the year 2000 population shares, the increase in the age-adjusted death rate of 12% can be consistent with the 32% increase observed in 1918 when military deaths are excluded, or the 46% overall increase when military deaths and population growth are included.

Few expected when Covid-19 was first detected in the US that the death rate this year would be anywhere close to what it was during the Spanish Flu.  But it has been.  Sadly, the crisis was severely exacerbated by the singularly incompetent management of the Trump administration.  The Washington Post had a particularly good summary of the many things the Trump administration got wrong in addressing Covid-19 this year in a December 26 editorial.  Or one can compare the US record to that of other countries.  The US this year had 1,040 deaths due to Covid-19 per million of population.  Canada had 412 per million, or 40% of the US rate.  If the US had simply matched what its neighbor to the north was able to do, the US would have had 137,000 deaths instead of more than 345,000, or 208,000 fewer deaths.  And others have done even far better.  New Zealand has had only 5.0 deaths per million, and Taiwan just 0.3 per million.

Better management would have been possible.  But the Trump administration failed, and hundreds of thousands of Americans have died.  As the Post editorial noted:

“It was always going to be hard. But the worst did not have to happen. It happened because Mr. Trump failed to respect science, meet the virus head-on and be honest with the American people.”