• How do we rate the quality of the US health care system – Population Statistics

    If you haven’t read the introduction, go back and read it now.  That introductory post also includes links to all the posts in this series on how we can rate the quality of the US health care system.  Each of these pieces will discuss another way to look at quality, and how the US compares to comparable countries in that domain.

    We’re going to start with what may be the most controversial measurements of quality: population statistics.  These are ways of measuring how a population of people compares to others.  Those with better numbers are often thought to have better health care systems.

    As with all metrics, they are flawed in some way.  For instance, many will argue that life expectancy is due to more than a health care system.  I addressed this last week, in fact.  That being said, they are still worthwhile as data points, as a health care system must play at least a part in their measurement.

    For each of these, I will present OECD data on the G8 countries, without the Russian Federation, which does not submit comparative data.  I present the G8 countries because of all countries, these are the ones whose relative wealth and standing in the world should make them most likely to compete with us.  And, for each of these measurements, I will present all available data from 1990 onwards, lest you accuse me of cherry picking a year.  To make it easy to read these graphs, when I am making charts from OECD data, I will always make the United States a nice red line.

    Let’s start with life expectancy, or how long a person can expect to live in a certain country when they are born:

    Pretty consistently for the last 20 years, the United States has had the lowest life expectancy of comparable countries.  Did I say that I acknowledge that there may be other reasons for this?  Good.  Then please don’t email me to tell me that.

    Another popular statistic in this category is infant mortality.  This is a measurement of how likely a baby will die in childbirth:

    Again, pretty consistently for 20 years, the United States has had the highest infant mortality when compared to comparable countries.  More than almost any other metric, this one is dismissed instantly by many people; most of those people claim that we measure it “differently” than one country or another.  But remember, we are worse than all of them.  And, since we are supplying our own data to the OECD, why would we not complain or change our own methodology to match others if we are such outliers only because of measurement methods?

    Regardless, the next metric is more difficult to explain away.  Here’s maternal mortality, or how likely a mother is to die in childbirth:

    Back in the 1990s we were in the middle of the pack, but things haven’t been as good in recent years.  More mothers die in childbirth in the United States per 100,000 births than in any other comparable country.

    Here’s one last commonly used metric: years of preventable life lost.  The OECD defines this as:

    Potential Years of Life Lost (PYLL) is a summary measure of premature mortality which provides an explicit way of weighting deaths occurring at younger ages, which are, a priori, preventable. The calculation of PYLL involves summing up deaths occurring at each age and multiplying this with the number of remaining years to live up to a selected age limit.

    So how does the US stack up?

    The United States has more preventable years of life lost than any other comparable country.

    We’re off to a bad start.  When it comes to population statistics like these, the US looks absolutely horrible.  This leads many to the conclusion that these metrics must all be fatally flawed.  I’d be more likely to agree if we weren’t dead last in all of them.

    But population statistics are only one way to measure a health care system’s quality.  We will need to consider many others before we come to any overall  conclusions.

    Here’s the first scorecard:

    And here’s the running total for the series:

    UPDATE: Fixed typos and chart.  A further explanation of these charts can be found here.

    • (First, a minor admin thingee… near the end of the article is the statement “When it comes to population statistics life these”. I think it was meant to read “statistics LIKE these”.)

      Can you explain the last two charts — the scorecards? First, given the way the first charts criss-cross how do you determine the actual scoring? For example, in the maternal deaths chart the US started out (in 1990) in the middle of the pack, but on the scorecard it comes in last. Is it just the order at the last year of data?

      Secondly, in the “running total” the US comes in worst. But it’s apparently the only one in the race, so I suppose it also comes in best. Where are the other countries, or am I misunderstanding it?

      Thanks for your efforts, by the way.

    • @Ken Hamer


      Typo fixed, and further explanation posted here.

    • The CDC data suggest that white/hispanic america are much closer to the european benchmark on infant mortality, while black america is the outlier. Likewise for life expectancy.


      The map is also informative. Suggestions that the healthcare system is failing Americans primarily in the rural South and urban ghettos?

    • These OECD studies make no attempt to control for racial composition. What is the percentage of the population in France, Germany, Italy, and the UK that DO NOT descend from regions of the Caucasus mountains? What percentage of Japanese citizens are not of Japanese descent?

      These factors are ignored and yet extremely important. Without knowing the genetic composition of the comparison countries (and controlling for the differences) these studies are virtually meaningless.

    • I challenge one to make a case than any of the differences here in life expectancy below are due to the health care system:

      Life Expectancy Japan 82.1
      Life Expectancy France 81
      Life Expectancy United Kingdom 79
      Life Expectancy United States 78.1
      Life Expectancy Costa Rica 77.6

      Life Expectancy Hawaii 81.7
      Life Expectancy Minnesota 80.5
      Life Expectancy Connecticut 80.1
      Life Expectancy North Dakota 79.8
      Life Expectancy Massachusetts 79.8

      Life Expectancy Oklahoma 75.1
      Life Expectancy Alabama 74.6
      Life Expectancy Louisiana 74.0
      Life Expectancy Mississippi 73.9
      Life Expectancy District of Columbia 73.8

      Why is the UK so much lower than France and Japan and how does Costa Rica come closer to the USA than the UK does to Japan. How do Hawaii, Minnesota, Connecticut beat the UK and come so close to France. I say that the differences have little to do with the health care system.

    • Infant mortality isn’t to do with childbirth – the definition is the number of infant deaths (1 year or younger) per 1000 live births. Reference in sig

    • Hi, liked your series very much. How does immigrants in one hand, and illegal immigrants on the other affect these totals? I am saying this because this is certainly one major difference between the US and other countries in the comparison list. Also, it might underscore the importance of early childhood in development, and also the need for better care even for those who are not legally here and therefore do not have access to a good standard of care.

    • Flocina above also seems to present interesting numbers. Is racial composition playing a role? Weather? What else? It seems economics play a major role, as states that are poorer have worse care. Maybe the quality of doctors is not even?

    • I’m not very good at writing esp. coming from a non-English speaking background (pardon my grammar) but here it goes…

      In response to Floccina’s comment,

      ” Why is the UK so much lower than France and Japan how does Costa Rica come closer to the USA than the UK does to Japan. How do Hawaii, Minnesota, Connecticut beat the UK and come so close to France. I say that the differences have little to do with the health care system.”

      – This is my personal opinion but I believe the main reasons are because of obesity, chronic diseases, many more . We need to consider all factors such as social and environmental as they do play their part on life expectancy.

      I could be wrong, but I believe we need to remind ourselves, yes, there is a limit to how much we can trust or how we actually interpret
      these statistics. Statistics are not meant to be perfect, but at least to give us an idea how we can actually solve a problem, that’s what matters.

      Thank you for reading, listening and have a good debate! 🙂 *since this will be my last*

    • I would guess that our lousy crude rates in the population statistics are primarily associated with the great disparity in income and wealth in the US, which in turn translates through education and a small constellation of related things, to variations in healthy lifestyles and access to timely, quality care.