Deaths versus Age

Data and Distribution Profiles

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An 'idealized' plot of number-of-deaths versus age.
(The 'blip' on the left represents infant deaths.)
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Introduction:

As the 'rampant morbid obesity' epidemic spreads over the USA in the 1960-to-2010-plus time frame, reports are getting more frequent about how the increase in the average age of death (which seemed to be a quite robust increase in the 1900 to 1980 time frame) seems to be leveling off --- both for men and for women.

In other words, 'life expectancy' in the USA seems to be leveling off.

In fact, in some localities (in some counties in the USA) the average age of death has shown some decreases --- in the 2010 to 2015 time frame.

    It seems that the beneficial effects in the USA of

    • screen (and glass) doors and screen (and glass) windows
    • draining swamps in human-populated areas
    • indoor plumbing and better sewage systems
    • better water systems
    • better food quality
    • better and more-readily-available medicines (anti-bacterials etc.)
    • better drainage and paved roads and sidewalks
    • less horse poop in the streets
    • etc.

    (especially in the 1900 to 1970 time frame)
    have done their best to improve life expectancy and, now, cannot overcome the effects of 'rampant morbid obesity'.

As I scan the obituaries in a newspaper ( the Daily Press ) --- which is a newspaper for 'the Peninsula area' (between the James and York Rivers --- especially covering news for the cities of Newport News and Hampton) in the Hampton Roads area of Virginia, I have wondered if a decline in the average-age-of-death will be showing up in data collected from those obituaries.

Also, I wonder what the 'distribution profile' of deaths will look like --- over the coming years --- on 'number-of-deaths versus age' plots --- say, for each year.

A sample 'life expectancy' plot is shown at the top of this page.

I would expect the plots for 'the Peninsula' area to exhibit the same general shape.


Deaths-versus-Age Data Collection:

I decided to collect data from Daily Press obituaries --- to show tables (and eventually plots) of number-of-deaths versus age --- for men and for women --- and for various years.

Most deaths occur between the ages of 50 and 100, and it seems that on the order of 4 deaths (or more) at each of the years in this range would be needed to get a fairly good idea of the 'real' 'deaths-versus-age distribution'. So it appears that about 4-times-50, or at least 200 data points (deaths), would be needed for each year to get a fairly good idea of 'the distribution' for the year --- at least 200 data points for men and another 200 for women.

Another criterion for judging when sufficient data is collected:
When the highest peak in the collected data exceeds a count of about 20 deaths (for the age/year at that peak), then we will probably be getting to the point of having enough data to generate a reasonably meaningful data plot (death-count versus age-in-years).

    It seems that about 5 to 20 deaths (daily) are being reported in the Daily Press obituaries each year --- say an average of about 10 per day. Then, given 365 days in the year, it seems that about 3,600 'data points' is the maximum one can expect from the Peninsula obituaries.

    Collecting somewhere between 200 and 1,800 data points (for men or for women) probably would be a reasonable number to collect to get some meaningful plots for a given year.

Some notes on the data collected :

The number of infant deaths are probably under-reported in the Daily Press obituaries. In fact, it seems that they are very seldom reported.

There are some weeks for which I may not collect data from the obituaries.

Data for people under the age of 40 may be under-reported in the Daily Press obituaries.

Some obituaries do not give the age at death (neither directly nor by giving the birth date of the deceased).

For these reasons (and others), the data tables (and eventual plots) are not meant to be 'comprehensive'.

These tables mainly are meant to satisfy my curiosity --- and perhaps answer questions like 'Is the obesity epidemic having a significant effect on life expectancy --- and on actuarial tables?'.


Plotting the data:

I intend to use the tables of data (in the web page links below) to plot the 'deaths-versus-age' distribution curves.

I will probably use the plot-from-columns-of-data utility ('free' = no-cost) --- which is described on a Freedom Environment (www.freedomenv.com) 'PLOTtools' page.



Enough of this 'Introduction'.

Here is a 'Table of Contents' which provides links (by year) to 'deaths-versus-age distribution' data tables --- and (eventually) to associated data plots . . . 'distribution' plots.


Table of Contents :

A few more comments on the data :   (and tri-glycerides)

Since the data are gathered rather randomly (with no bias toward a particular age range or medical condition or death type), these figures would presumably end up being reflective of the data that forms the basis of actuarial tables used by life insurance companies. In fact, this data might be considered to be a check on the validity of those tables, and vice versa.

Since the obituaries do not always make clear the

  • cause of death of the deceased
    NOR

  • the medical condition of the deceased
    (high blood sugar, high triglycerides, pre-diabetes, etc.)
    NOR

  • the weight or waist-size of the deceased
    NOR

  • the race or national origin of the deceased (which may be 'quite mixed')

I do not attempt to break down the data into any other categories than male and female.


I would love to be able to break the 'distribution' table into separate columns for those people whose triglycerides were greater than 150 mg/dL (a recommended maximum) and those with less than 150 mg/dL.

I think that such a table would show that median (and average) age at death would be much earlier for the high-triglycerides group.

I think the National Institutes of Health should be collecting this 'triglycerides-at-death' data, for patients who had a blood test within a year before their death.

Such data would probably go a long way toward making it clear to both the medical community (esp. the AMA) as well as the general public how important it is to minimize ingestion of refined sugar and refined flour products (the recommended action to take when triglycerides are high . . . that is, above about 100 to 150 mg/dL).

It would also be useful to have similar tables for various metropolitan areas in the country (and world) --- to see if there were differences in the statistics (median, average, standard-deviation) and the shape of the deaths-vs-age curves.

I would expect to see a significant difference in the curves of the U.S.A. and the curves for other areas of the world that have significantly different diets than the 'Sonic-Drive-In-based' diet of the U.S.A. Examples: Okinawa, Greece, Malta, Sicily, Mongolia, and aboriginal cultures (fast disappearing) in Indonesia, Australia, the Amazon, Africa, and elsewhere.


There are many other types of medical/health studies that could be instructive.

For example, if for each of the deaths, we knew whether the person had been a smoker or a non-smoker, we could have a column of death-versus-age data for the smokers and a column of death-versus-age data for the non-smokers.

It is quite likely that the distribution-curve for the smokers would show a 'significantly earlier death-proclivity' than the distribution-curve for the non-smokers.

Here are some types of medical/health studies that could benefit from doing 'deaths-versus-age distribution curve' analysis.

  • Smoking:
    the 'smokers curve' versus the 'non-smokers curve'

  • Alcohol drinking:
    drinkers versus non-drinkers

  • Water drinking:
    well-water drinkers in rural fracking areas versus urban water drinkers

  • Air breathing:
    people living near chemical plants (e.g. Gary, Indiana or the Houston/Galveston/Beaumont area of Texas) versus those living far from such plants

  • Food eating:
    meat eaters versus vegetarians

  • Food eating:
    sugar lovers versus sugar leavers (the latter are hard to find)

  • Medical drug taking:
    statin takers versus no-statin takers (with similar weight issues and blood pressure profiles)

  • 'Hard drug' taking:
    cocaine users versus no hard drugs takers (with similar, healthy diets --- i.e. with similar food-water-air environments)

  • Opioid drug taking:
    oxycontin users versus no hard drugs takers (with similar, healthy diets)

  • and so on

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Page was created 2018 May 19.
Page was changed 2018 May 21. (Added more examples of medical/health 'deaths-vs-age' studies.)