Now Reading: Understanding the science of life expectancy #SDOH

The concept of tracking life expectancy as a measure of a society’s health hasn’t been a part of my (should I say most?) physician’s experience – it’s just been a yearly announcement on a news show. When I went to England in 2010 I was impressed that it’s much more deeply thought of (see below), and more recently in my social determinants reading, as a key metric to understand social determinants of health.

It’s done in the lead in on this paper:

In the Washington, D.C., metropolitan area, for example, there is a nine-year increase in life expectancy as one takes the subway ride on the Blue Line from downtown Washington to Fairfax County, Virginia.

I decided to dig deeper and reviewed the paper referenced in this post, and I was immediately taken aback by this piece of information:

Tracking county disparities after 2002 until 2009 was impossible, because the US government did not release county-level mortality data during this time period.

The paper doesn’t say why this is, other papers from this time period (by these same authors) confirm this lack of access, but none say why (if anyone knows, post in the comments).

What they did here is look at the life expectancy “frontier,” which is the expectation of where the life expectancy of a particular county was compared to the life expectancy trajectory of the countries with the highest life expectancy. And,

  • there’s huge variation in US counties around life expectancy
  • US counties are mostly behind the frontiers of peer nations
  • US counties have been falling behind from 2000-2007

In 2007, county-level life expectancies range from 15 years ahead of the international frontier to over 50 years behind for men and 16 years ahead to over 50 years behind for women.

There’s a nice discussion at the end of what to do about this and how the health system fits in to this issue. Data point: 1.8% of US deaths annually are attributed to lack of health insurance coverage, and there isn’t a difference in access to health care between people in the lowest and the highest life expectancy tiers. It’s worth reading this section because a person can interpret the health system’s role in multiple ways when looking at this data.

As a comparison example, I did some searching around online (okay, a lot of searching around online), to compare the England experience to ours, and the concept of watching life expectancy seems much more ingrained. See: Mortality Monitoring bulletin: Life expectancy and all-age-all-cause mortality, and mortality from selected causes, overall and inequalities . And within a Primary Care Trust (most closely equivalent to a US Health Plan), a thorough analysis of the trend, and causative factors. See page 5 of “Joint Director of Public Health Anuual Report 2011 – Warwickshire NHS” ). There’s even an animated map showing the increase in life expectancy across England.

I don’t know for sure what our health system should be doing differently. Life expectancy is tracked within the United States’ HealthyPeople 2020 under General Health status. What would it be like if we started with life expectancy (or disability free life expectancy) in our health planning and innovation, though? Would people (patients) identify better with “the health system is allowing me to live a longer healthier life” than “the health system is lowering my cholesterol level”?

Of course, I looked up the data for Washington, DC, and in the appendix of this paper, it shows that the 2007 life expectancy of a Washington, DC male and female are 31 and 28 years behind the international frontier. At least this is better than 49 years in 2000 for men, a little better than 29 years for women in 2000. But far worse than Montgomery County, Maryland, just a few miles away that’s 12 years ahead of the international frontier for men, 3 years ahead for women. If you’d like to see a more graphical version, tied to DC metro maps, check out A Short Distance to Large Disparities in Health.


Ted Eytan, MD