Mitchell’s Musings 10-9-2017: The Meaning of Life Daniel J.B. Mitchell From time to time, you see stories in the news media and other sources about “life expectancy.” There may be comparisons of the U.S. with other countries or with particular groups within the U.S. There may also be comparisons over time. Table 1 provides an example. === Table 1: Selected Life Expectancies at Birth in the U.S. (years) 2015
1900
-----------------------------------------------Both Sexes
78.8
47.3
Males
76.3
46.3
Females
81.2
48.3
-----------------------------------------------Males (2015)
Females (2015)
-----------------------------------------------Hispanic
79.3
84.3
White Non-Hispanic
76.3
81.1
Black
71.8
78.1
-----------------------------------------------Source: https://www.cdc.gov/nchs/data/hus/hus16.pdf#015. === You may see things on the table that you already know without seeing the numbers. For example, life expectancy has grown over time or females live longer than males. You may also see things that surprise you, i.e., that Hispanics have a longer life expectancy than non-Hispanic whites. But what precisely do these numbers mean? In essence, to calculate these figures, data on survival rates are used. What is the probability within the group that a newborn will reach age 1. What is the probability that a 1-year old will reach age 2, etc., based on current probabilities? So what is really being measured is what would happen - on average - to someone born today if these survival rates were indefinitely frozen.
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Now we know (because of the large increase in life expectancy over time shown on Table 1) that those probabilities in fact have changed over time. Death rates have declined for any age group due to such important factors as the development of public health measures and to advances in medical science and practice. So someone born in 1900 who survived to 1965 and then had, say, a heart attack would likely be taken to a hospital in a fast-moving, motorized ambulance rather than a buggy and would have been treated using 1965 methods rather than 1900 methods. In short, if you really wanted to calculate the life expectancy of someone born today, you would need to know what the state of world would be in the future that the newborn will face. There is no guarantee, by the way, that there will be linear progress. For example, when the Soviet Union collapsed, it was noted that life expectancy for men fell, presumably because of disruption of medical services and the social and economic dislocation.1 More recently, much has been made of increased death rates among U.S. middle-aged whites.2 While life expectancy figures tell you something, the fact that they provide no adjustment for changes in future survival rates is a definite limitation. You can be sure, for example, that the average person born in the U.S. in 1900 turned out to have lived longer than the 47.3 years shown on Table 1. So you don’t want to push such estimates too far. I was therefore struck recently by a headline indicating that figures were being released in Los Angeles County, purporting to show life expectancy by community.3 A press release, for example, tells us that life expectancy in Walnut Grove (population around 16,000) was an astounding 90.5 years, in contrast to a meager 75.8 years in Sun Village (population under 12,000).4 Both of these places are Census areas, not independent cities. The latter, at least according to Wikipedia, is notable because “composer and musician Frank Zappa played his music and made many friends in Sun Village when he first got started. Thus, Sun Village is the setting of the Frank Zappa song ‘Village of the Sun’ from the 1974 album ‘Roxy and Elsewhere.’”5 Zappa lived only to age 53, so maybe there truly is a Sun Village jinx. (If only he had made friends in Walnut Grove and instead had composed “Village of the Walnut”…) Seriously, to come up with such statistics, you have to assume that someone born in one of these places will not only be subject in the future to the survival probabilities that characterize
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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1116380/. https://www.washingtonpost.com/national/health-science/a-group-of-middle-aged-american-whites-is-dying-ata-startling-rate/2015/11/02/47a63098-8172-11e5-8ba6-cec48b74b2a7_story.html. 3 https://patch.com/california/santamonica/s/g8w8j/la-county-life-expectancy-report-shows-big-gap-betweencommunities. 4 https://ssrc-static.s3.amazonaws.com/moa/MOA%20LA%20Life%20Expectancy%20Report%20Release_Final.pdf. 5 https://en.wikipedia.org/wiki/Sun_Village,_California. Apparently, there were (are?) turkey farms there. You can hear it at: https://www.youtube.com/watch?v=xEZwa1Funh0, or just read the lyrics at: https://genius.com/Frankzappa-village-of-the-sun-lyrics. 2
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them today, but will live out their lives entirely in these communities.6 The probability that someone born in either Walnut Grove or Sun Village will stay there for a lifetime has to be, well, low. And, of course, there is a tremendous noise factor when you try and estimate survival rates in small areas. My city, Santa Monica, has a reported poverty rate of 13.5%, a population of around 93,000, but a life expectancy of 83.2 years. Walnut Grove has a poverty rate of 19%. Is it really less healthy to live in Santa Monica than Walnut Grove? I realize that those folks putting out these numbers wanted to call attention to discrepancies in health care and other socio-economic inequities. Obviously, some places are more prosperous than others, and health conditions are going to be correlated with the variation. There is the old line, “I’ve been poor and I’ve been rich; rich is better,” that has been ascribed to many, and is surely true, whoever first said it.7 But you don’t need silly statistics to prove it to anyone.
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From the report: “Life expectancy at birth in a geographic area can be defined as an estimate of the average number of years a newborn baby would live if they experienced the particular area’s age-specific mortality rates for that time period throughout their life.” (Page 16 of the report.) Source: https://ssrcstatic.s3.amazonaws.com/moa/LIEXBrief_FINAL.pdf. 7 https://quoteinvestigator.com/2017/07/01/poor-rich/.
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