Tag: Death rates

A Science for Everyone, Demography

Death Rates by Country

michaelmccurryphd

One of the more useful publications to compare nations is the CIA World Factbook.  While we tend to think of the CIA as secret agents, a lot of them are data geeks crunching numbers.  The data they develop about each country is impressive, and like the US Census, the CIA sets the standard for the most accessible and reliable information.  When I started using it, I needed a land-grant college library.  Now, I click World Factbook.

National death rates in 2018 ranged from 19.3 per 1000 in South Sudan down to 1.6 per 1000 in Quatar.   The reasons vary – a higher median age (Japan is 48.36) combined with healthy living and good health care can still have relatively low death rates (Japan was 9.9 in 2018).  The explanation is Demographic Transition theory – in the old days we had high birth rates and high infant/youth mortality.  The second stage occurred with health care improvements – birth rates remained high, but death rates dropped.  Stage 3 showed lower birth rates and death rates continuing to drop, but more slowly.  The fourth stage maintains the lower birth rates, but in an aging population the diseases change – in the US, the big killers are heart disease and cancer.

Lesotho, in Southern Africa, has the second highest death rate – high infant mortality (44.6 deaths per 1000 births), the world’s second highest HIV rate.  A dozen years ago, I first encountered https://www.worldlifeexpectancy.com/ and the website gets increasingly useful.  It isn’t that the covid is so insignificant in Lesotho, it’s that Diarrhea is so much more prevalent.  Click the link – and check out the demographic factors for your own country.  In the US, it shows life expectancy changes since 1960:

US life expectancy from World Life Expectancy

The personal computer has taken demography from being a science that need a major university’s library facilities in my undergraduate days into being a science with the data available to a Fortine resident who has insomnia at 3:00 am. 

Community, Demography

The Useable data on Covid

michaelmccurryphd

Figuring out the data to use is important.  On May 9, 1864, Union General John Sedgwick said “They couldn’t hit an elephant at this distance” three separate times.  After the third statement, he became the highest ranking Union officer to die in battle.  He was missing a couple relevant pieces of information – first, the Confederates had snipers with Whitworth rifles, and second, at 800 yards, the Whitworths were making 12 inch groups – far smaller than an elephant.

It’s similar with Covid – each of us is like General Sedgwick, not knowing which piece of data is relevant, or even exists.  Some data ties in blood types.  I’m not certain it is data we can use.  Death and age combine to give us the most solid data that we have – and it may be of some use.  Statista provides this information, as of January 9. 2021, for the US.

AgeNumber of Deaths
Under 134
1-421
5-1455
15-24510
25-342,196
35-445,742
45-5415,558
55-6438,830
65-7470,230
75-8490,744
85+105,673

As you look at these numbers, it’s probably worth remembering that over 50 million Americans are over 65 years old – where most of the casualties are.  Soon we’ll have the 2020 census, but until then the 2010 census data is usable – a bit low, but usable.  The tragic loss of 55 kids under 4 comes from over 20 million population in the 0 – 4 age cohort.  The 105,673 deaths of people 85 and over comes from a cohort of 5½ million.

If you want to calculate the years of life lost to covid, the social security life tables are available at: https://www.ssa.gov/oact/STATS/table4c6.html  Still, we’re dealing with big data – and, as I realized when I was being treated for colon cancer, the most important thing about your life expectancy isn’t the number for your age, it’s which side of the median you’re on. 

The data shows that Covid’s deadliness increases with the age of the person it infects.  The data isn’t adequate to show either the probability of being infected or missing the virus.  It’s easy to describe the statistics of the Spanish Flu – but most of the data was compiled and available by 1920.  This article describes how Gunnison, Colorado isolated their way out of the Spanish Flu: Isolation worked well for Gunnison. 

The folks at APM Research Lab have calculated out death rates by race – and their study is worth a look, despite the problem that race is more a social construct than genetically identifiable.  Here’s some of their data (as of January 5).

“These are the documented, nationwide actual mortality impacts from COVID-19 data (aggregated from all available U.S. states and the District of Columbia) for all race groups since the start of the pandemic.

  • 1 in 595 Indigenous Americans has died (or 168.4 deaths per 100,000)
  • 1 in 735 Black Americans has died (or 136.5 deaths per 100,000)
  • 1 in 895 Pacific Islander Americans has died (or 112.0 deaths per 100,000)
  • 1 in 1,000 Latino Americans has died (or 99.7 deaths per 100,000)
  • 1 in 1,030 White Americans has died (or 97.2 deaths per 100,000)
  • 1 in 1,670 Asian Americans has died (or 59.9 deaths per 100,000)
  • Indigenous Americans have the highest actual COVID-19 mortality rates nationwide—about 2.8 times as high as the rate for Asians, who have the lowest actual rates.

Data is data.  There’s not a lot of difference between Latino and White rates.