UTopiAH. This is Part of a series for comparing census based life expectancy and death rate tables, ranking states by how long we live, from 1960 to 2015. Included are medical conditions rating Utah’s #1 health rankings. Since 2012 state rankings are now correlated to voting in the 2012 and 2016 presidential elections, with Blue states on top, and Red states at the bottom. Utopia is Sir Thomas More’s (1516) perfect place to live, and with a slight variation in spelling, perfectly describes Utah.

Part 21.   The fallacy of the ‘age-adjusted rates’ for number of deaths, death rates, and for major causes of death in the United States, and each state. 2015.

Doctrine and Covenants Section 89. Verse 21 And I, the Lord, give unto them a promise, that the destroying angel shall pass by them, as the children of Israel, and not slay them. Amen.

I am going to walk you through the life expectancy as reported by the United States Government.

In the National Vital Statistics Reports Table for death rates, published in late 2017, from the top line we learn the United States had 2,712,630 deaths for 2015 from all causes. The Table does not list the United States population, but the table does list the (CRUDE rate so called by the National Vital Statistics Reports), under the rate per 100,000 population as 844, meaning for every hundred thousand sets of people, there were 844 deaths, nation wide.  This means for every 1,000 people, there were about eight and a half deaths, and for every 119 people,   someone somewhere in the United States died. Working backward, to find the – otherwise unlisted – population, you multiply the number of deaths times 100,000, and divide by the rate per hundred thousand which rate was 844.

Or, 2,712,630 (number of deaths) * 100,000 (rate per 100,00) / 844 (calculated rate) equals X population. Doing the math, the underlying population X is 321,401,658, and rounding to three significant numbers, rounds to 321 million. 321 million is also the U.S. population given by the census for 2015.  This means there was a direct correlation between the total US population and the total number of deaths listed. So we know how many people died (2,712,630), and we know the population of the United States for 2015 (321 million). Let’s see what we can do with these numbers when the Reports want to break the numbers out by states and by maladies and causes.

In 2009, the National Vital Statistics Reports came up with a so-called ‘age-adjusted death rate’ published in the Table 12. As calculated for the entire United States, ‘age-adjusted death rate’ is given as 733.1 deaths per hundred thousand population for 2015. And ‘age-adjusted death rate’ is broken out by state and malady and cause.

Using the above formula, to calculate the population upon which the ‘age-adjusted death rate’ figure 733.1 would be computed, do this. Take the total deaths, 2,712,630 * 100,000 / 733.1 equals a population for the United States of 370,021,825 and rounded to three significant figures is 370 million. So how or where does ‘age-adjusted’ get another 49 million population for 2015? This is the fallacy of adjusting the national rate down about an eighth, because it overstates the US population by tens of millions of people.

Or, taking the table listed ‘age-adjusted rate’ of 733.1 per hundred thousand population and calculating against the real and true population of 321 million (as was given by the US Census for 2015), you can account for 2,353,251 deaths. Not the real and true number of 2,712,630, and rounding to three significant figures of 2.35 million, the ‘age-adjusted death rate’ is leaving 360 thousand deaths out of the calculations.

By leaving the U.S. population out of the table 12, the anomaly (too many people or missing deaths) is obscured. Meaning, the real and true living population doesn’t match, and can’t match, with the ‘age-adjusted’ death rates. There is a formula for calculating the ‘age-adjusted death rates’ which is given in the Technical Notes, giving population adjustments for age, but the Notes are irrelevant. One might suggest the formula was buried in the notes. The ‘age-adjusted’ death rate either leaves out a third of a million deaths, at a number of 30,000 per month, or a thousand deaths a day. Or, the ‘age-adjusted death rate’ generates out of thin air, the combined population of California and Georgia, to make up the difference in population to include actual deaths.  Oops!

You can’t understand nonsense, and the population formula used for the ‘age-adjusted death rate’ in the Technical notes is nonsense.  I mean you really have to reach, to come up with razzle dazzle, and think no one was watching.

Perhaps, there was a theory as to why ‘age-adjusted’ was attempted. The theory is not explained. Perhaps someone thought Utah, which had the most children per capita and had the lowest death rate, was because of its young population. And concluding that combination skewed the death rate favorably for Utah, increasing Utah’s longevity and life expectancy.

Or California was disadvantaged, because California had a twelfth of the population of the United States, a base range so broad, it approached the mean, median and average for the United States. This is expected, as California did approach the median (bouncing between the 2nd and 3rd quintiles for half a century) using the rates of deaths divided by sets of 100,000 people. One projection (Statistica) has the United States at 370 million population by 2035, two decades after the base year of 2015. Perhaps ‘age-adjusted’ death rate was a Back to the Future approach, at the end of two decades, of whatever. But moving deaths around the country won’t work, if you have to leave out hundreds of thousands of deaths.

The ‘age-adjusted’ death rate reminds me of an episode in a TV series called Third Rock From the Sun (shown circa 1996 to 2001). One episode has the ‘Harry’ character (actor French Stewart) hired to work in a video store, as such video stores were still popular in the late 1990s, before Netflix drove the video stores out of business. The backstory for the plot was that from the middle 1970s, Video stores rented and sold movies on individual tapes, VHS, Betamax, Blue Ray, DVD, for home viewing. A prominent chain included BlockBuster Video, and so continued until kiosks and internet replaced the video chains.  So, Harry is the manager who rents videos, from an inventory of thousands or tens of thousands of tapes and discs on shelves and tables. So, Harry has the idea to sort the tapes and disks on the shelves by putting together all the video movies Harry has seen which he likes along one wall. Then Harry has the movies which he has seen, but which he didn’t like, along a different wall. And finally Harry put the movie videos which he had not seen, together along another wall. It was a method. It was different from alphabetical titles, or genres like comedy, drama, romance, adventure, or ratings, as what we might expect.

Following Harry’s (3rd Rock) business model, ‘Age-adjusted’ death rates, were apparently assigned to states as follows. Like videos, States were divided into those the National Vital Statistics Reports liked (Blue States), and states which they did not like (Red States), and those two categories absorbed all the list, so there were no states which the Statisticians had not seen. Why say the statisticians liked Blue States? With age-adjusted, Hawaii has the longest life expectancy and that Utah had dropped out of the top quintile, plunged to 13th, citing the year 2014.   California had climbed from 3rd quintile to 1st quintile, a first time ever. Quite an accomplishment for California! It also happens, that the 11 healthiest states, as reported by MSN.com, just so happened to have all voted Blue in the 2016 election (over half of the 20 BLUE states that did so vote Blue, while 30 states voted Red). The state ranking death rate list was compiled in 2017 and 2018, after the 2016 election results were in.

Statistically, the 2/5ths of the states (20 of 50) voting Blue in 2016 should have been randomly spread thru five quintiles ranked by the life expectancy table as follows – 4 states in the top quintile (top 10), and 4 in each of the rest of the quintiles, (to wit, 4 in the ranks from 11 to 20, 4 from 21 to 30, 4 from 31 to 40, and 4 in the bottom quintile, from 41 to 50). Likewise, if random placement applied to the Red states, (30 of 50) the Red states would have had 6 Red states in each quintile. It would sort of be like putting 50 marbles in a jar, with 20 Blue and 30 Red marbles. 20 Blue marbles in a jar with 30 Red marbles, then pulling marbles out one by one, your first 11 marbles should be around 4 Blue, and 7 Red.   As you take out more Blue marbles, the Number of Blue marbles drops, one by one, and the ratio of marbles remaining in the jar changes, Red to Blue marbles increase. So if you have pulled 10 Blue marbles out, the chance of pulling an 11th blue marble out with 10 blue marbles and 30 Red marbles remaining, is about one in four (10 of 40). Or, imagine a lottery ticket where the first eleven numbers have to be from the pool of 50, selected at random. Or roulette with 30 red and 20 blue (instead of black) slots? I think roulette has only 38 slots, not 50, but the concept is the same. What are the chances of covering Blue 11 times in a row, but filling up each Blue slot as it is taken, so it cannot be used again? Or put another way, I think the odds of the top eleven States in longevity, being all Blue, are less than one in six hundred and fifty thousand (1 in 650,000). What a coincidence! Who knew? This can only be explained, either by astonishing results, or more possibly, the National Vital Statistics Reports state rankings were correlated to the 2016 Presidential Election results.

The marble in the bottle analogy could just as well be described using Jelly Belly Beans in a bag, with dozens of colors based on tastes, all shaped the same size. Using the marble analogy to rates instead of states, instead of 50 marbles, you would need a bag with 2,712,630 jelly beans, and need to assign them through all 50 states (and D.C.). You can’t quit with 360 thousand jelly beans left in the bag. But ‘age adjusted rates’ did so quit.

Previous parts of this series listed life expectancy ranked by state, demonstrating how random patterns occurred, until 2017, when politics influenced ranks. See Parts 17, 18, 19 and 20 for 2005 and 2015, part 2 for 1960 census, part 3 is 1970 census, part 4 for 1980 census, part 5 for 1990 census part 7 for 2012, part 9, 10, 11, 12, 13 for 2005 and 1016.

The Relevant portion, United States, of Table 12 is given below. Population Column B added. The National Vital Statistics Reports termed the column under death rate the ‘Crude’ death rates, and is compared alongside the ‘age-adjusted death rates.’ Only the United States rates are given from the Table 12. The rest of Table 12 has columns for every state and many malady or cause of death. A malady is a disease, a cause may be accident but not disease.

https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_06.pdf

Table 12. Number of deaths, death rates, and age-adjusted death rates for major causes of death: United States, each state, p. 49.

National Vital Statistics Reports, Vol. 66, No. 6, November 27, 2017 49

All causes  and Age-adjusted population Number rate1

Rates per 100,000 population; age-adjusted rates per 100,000 U.S. standard population; see Technical Notes. Codes in parentheses after causes of death are categories of the International Classification of Diseases, Tenth Revision (ICD–10). The asterisks (*) preceding cause-of-death codes indicate they are not part of ICD–10; see Technical Notes]

 

 

ALL CAUSES – disease Mortality  
State- locationB 2015, number of Deaths –

 

C-1 NVSR population US census 2015, MillionsD from Tab 12 National Vital Statistics Reports, CRUDE rate per 100,000 27 Nov 2017

 

E Age Adjusted death rates National Vital Statistics Reports,   per 100,000 27 Nov 2017
United States22,712,630321M844733.1

 

Malignant neoplasms (COO-C97  
United States2595,930321M185.4158.5

 

Diseases of the Heart (I00-I09, I11, I13 I20-I51 
United States2633,842321M197.2168.5

 

Accident (V01-X59, Y85-Y86) 
United States2146,571321M45.643.2

 

Motor vehicle accidents 
United States237,757321M11.711.4

 

Accidental poisoning and exposure to noxious substances (X40-X49) 
United States247,478321M14.814.8

 

Intentional self-harm suicide (U03,X60-X84, Y87.0) 
United States244,193321M13.713.3

 

Assault homicide (U01-U02,X85-Y09, Y87.1) 
United States217,793321M5.55.7

This is a list of the various maladies and mortal causes which are death rated.

Table 6. Number of deaths from selected causes, by age: United States, 2015

[Only selected causes of deaths are shown; therefore, subcategories do not add to totals; see Technical Notes]

Cause of death (based on International Classification of Diseases, Tenth Revision)

Enterocolitis due to Clostridium difficile. . (A04.7) Septicemia . . . (A40–A41) Viral hepatitis . (B15–B19) Human immunodeficiency virus (HIV) disease . . . . . (B20–B24) Malignant neoplasms. (C00–C97)

Malignant neoplasms of lip, oral cavity and pharynx . . Malignant neoplasm of esophagus Malignant neoplasm of stomach . .

Malignant neoplasms of colon, rectum and anus . . . . Malignant neoplasms of liver and intrahepatic bile ducts . . . . Malignant neoplasm of pancreas. Malignant neoplasms of trachea, bronchus and lung . . . . Malignant melanoma of skin . . . . Malignant neoplasm of breast. . . Malignant neoplasm of cervix uteri . . . . . Malignant neoplasm of ovary . . . Malignant neoplasm of prostate . Malignant neoplasms of kidney and renal pelvis. . . Malignant neoplasm of bladder . . Malignant neoplasms of meninges, brain

parts of central nervous system . Non-Hodgkin’s lymphoma. .(C82–C85) Multiple myeloma and immunoproliferative

neoplasms. (C88,C90) Leukemia. . .(C91–C95)

In situ neoplasms, benign neoplasms and neoplasms of uncertain or unknown behavior . . (D00–D48)

Anemias. . . . . (D50–D64) Diabetes mellitus . . . . (E10–E14) Nutritional deficiencies(E40–E64) Obesity. . . . (E66) Parkinson’s disease . . (G20–G21)

Alzheimer’s disease (G30) Major cardiovascular diseases. (I00–I78) Diseases of heart. Essential hypertension and hypertensive renal disease. Cerebrovascular diseases . . . (I60–I69) Atherosclerosis . . (I70) Aortic aneurysm and dissection . (I71)

Influenza and pneumonia. . . . .(J09–J18) Chronic lower respiratory diseases . . .(J40–J47) Pneumonitis due to solids and liquids . . . . .(J69) Chronic liver disease and cirrhosis . . . . .(K70,K73–K74) Alcoholic liver disease . . . (K70) Cholelithiasis and other disorders of gallbladder . . (K80–K82) Nephritis, nephrotic syndrome and nephrosis. . . . . (N00–N07,N17–N19,N25–N27) Pregnancy, childbirth and the puerperium . . . (O00–O99) Certain conditions originating in the perinatal period(P00–P96) Congenital malformations, deformations and chromosomal abnormalities (Q00–Q99) Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified . . (R00–R99) Accidents (unintentional injuries). . .(V01–X59,Y85–Y86) Motor vehicle accidents. . . . (V02–V04,V09.0,V09.2,V12–V14, V19.0–V19.2,V19.4–V19.6,V20–V79,V80.3–V80.5, V81.0–V81.1,V82.0–V82.1,V83–V86,V87.0–V87.8, V88.0–V88.8,V89.0,V89.2) Falls. . . . . .(W00–W19) Accidental discharge of firearms . .(W32–W34) Accidental drowning and submersion . . . .(W65–W74)

Accidental hanging, strangulation, and suffocation,  ..Accidental exposure to smoke, fire and flames

Accidental poisoning and exposure to noxious substances . . . . . .(X40–X49)

and suffocation . (X70) Intentional self-harm (suicide) by discharge of firearms.

Assault (homicide). . . . . .(*U01–*U02,X85–Y09,Y87.1)1 Assault (homicide) by discharge of firearms . . (*U01.4,X93–X95)1 Legal intervention . . (Y35,Y89.0) Complications of medical and surgical care . . . .(Y40–Y84,Y88)

Drug-induced deaths2 . . . Alcohol-induced deaths2 . Injury by firearms2

Disclaimer: The author of each article published on this web site owns his or her own words. The opinions, beliefs and viewpoints expressed by the various authors and forum participants on this site do not necessarily reflect the opinions, beliefs and viewpoints of Utah Standard News or official policies of the USN and may actually reflect positions that USN actively opposes. No claim in public domain. Fair use and commentary claimed. UTopiAH is a trade mark of the author. Utopia was written in 1516 by Sir Thomas More, Chancellor of England. © Edmunds Tucker.