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Josh Goldstein Dec 1, 2022

CPIPR Nov 30, 2022

Slower life expectancy gains at the U.S. county level since 2000 are linked to increasing levels of poverty, unemployment, and single-parent families, plus declines in manufacturing jobs--especially in rural areas.
Read more: https://www.ncbi.nlm.nih.gov/pubmed/36061950

#NICHDimpact #Mortality #LifeExpectancy #Demography #Research #Sociology #Rural #Health #PublicHealth #SDoH #Poverty #Economics @sociology

Decomposing County-Level Working-Age Mortality Trends in the United States Between 1999-2001 and 2015-2017 - PubMed

Studies have documented significant geographic divergence in U.S. mortality in recent decades. However, few studies have examined the extent to which county-level trends in mortality can be explained by national, state, and metropolitan-level trends, and which county-specific factors contribute to r …

PubMed

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Josh Goldstein Nov 11, 2022

The 150/sqrt(PYL) formula can be obtained from the 15/sqrt(D) formula by assuming that life expectancy is on the order of 100 years, which would make PYL ~ 100*D. n/n

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Josh Goldstein Nov 11, 2022

Hanley's rule of thumb turn out to be a bit on the high side of the more exact calculations. I wonder why the above cohort approach would tend to be bigger than a more exact period approach? Any ideas? 3/n

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Josh Goldstein Nov 11, 2022

For a cohort, the SE of life expectancy would be something like SD(individual age at death)/sqrt(N). The standard deviation of age at death is about 15-17, so that gives us one of the rules of thumb formulas. 2/n

Josh Goldstein Nov 11, 2022

A first #demography post: Just read an interesting article called "The (Im)precision of Life Expectancy Numbers" by James Hanley in AJPH. In it, he gives a "rough empirical rule of thumb" that SE(e0) is about 150/sqrt(PYL) and about 15/sqrt(number of deaths). Here's a naive way to explain those numbers. 1/n