10 comments

1. 

   Shangwen · December 6, 2013 - 3:18 pm · Reply→

   Very interesting Livio! The $2500 marker looks familiar to me…I recall seeing other OECD-dervied analyses where the impact of healthcare spending on longevity and morbidity peaked in the 2500-3500 range, then flattens.
   Part of the challenge is that healthcare is not consumed at a consistent dosage over the life course–we use it more in periods of either high risk or high morbidity (infancy, pregnancy and childbirth, frail old age, the terminal year, and when seriously ill). Most people consume public healthcare very irregularly over the lifecourse (surgeries, major illness, pregnancy, etc) but consume private healthcare more consistently (pharmaceuticals, dentistry, etc). You are essentially asking a lifetime dosage question.
   There is some recent US evidence (sorry Stephen–mixing countries again) that morbidity in old age is becoming compressed into an extremely narrow timeframe at the end of life, contrary to the historical experience and current perception that age is a long and painful decline. I think the relationship of spending/consumption to longevity is that there are a small number for whom care significantly extends life, but for the majority most is accounted for by socioeconomic improvement, while there is very intense utilization associated with small gains in the final years.

2. 

   genauer · December 7, 2013 - 7:09 am · Reply→

   I think it would be worth to take a closer look at how these mortality tables (wiki/Life_table) are constructed, and how then a LE is derived. And how those are impacted by migration of various age and social economic background.
   Further it is worth to look at the non-universal health care states USA and Mexico, and the special situation of eastern Europe.

3. 

   Livio Di Matteo · December 7, 2013 - 8:55 am · Reply→

   Thanks for the reference to the paper Shangwen. It is a very recent NBER paper-great! Ultimately, I think the data as I used it is too coarse to get at differences in lifetime use.
   Genauer-the impact of differences in health systems is a good point. As for the life expectancy numbers-the impact of migration on those numbers is an interesting aspect also. The young tend to migrate and they are generally in better health than older people which aside from affecting life expectancy numbers should also affect health expenditures.

4. 

   genauer · December 7, 2013 - 10:10 am · Reply→

   just accounting for US/Mex (non universal health care) + Eastern Europe (EE)in a hyper simplistic black/white Zero /one fashion accounts for 60% of the variation:
   mean factor
   Variance 81.10 -4.36
   6.01 2.41 0.60 .=r^2
   country LE delta model US & EE
   Australia 82.0 0.9 81.1 0.00
   Austria 81.1 0.0 81.1 0.00
   Belgium 80.5 -0.6 81.1 0.00
   Canada 81.0 -0.1 81.1 0.00
   Chile 78.3 1.6 76.7 1.00
   Czech Republic 78.0 1.3 76.7 1.00
   Denmark 79.9 -1.2 81.1 0.00
   Estonia 76.3 -0.4 76.7 1.00
   Finland 80.6 -0.5 81.1 0.00
   France 82.2 1.1 81.1 0.00
   Germany 80.8 -0.3 81.1 0.00
   Greece 80.8 -0.3 81.1 0.00
   Hungary 75.0 -1.7 76.7 1.00
   Iceland 82.4 1.3 81.1 0.00
   Ireland 80.6 -0.5 81.1 0.00
   Israel 81.8 0.7 81.1 0.00
   Italy 82.7 1.6 81.1 0.00
   Japan 82.7 1.6 81.1 0.00
   Korea 81.1 0.0 81.1 0.00
   Luxembourg 81.1 0.0 81.1 0.00
   Mexico 74.2 -6.9 81.1 0.00
   Netherlands 81.3 0.2 81.1 0.00
   New Zealand 81.2 0.1 81.1 0.00
   Norway 81.4 0.3 81.1 0.00
   Poland 76.9 0.2 76.7 1.00
   Portugal 80.8 -0.3 81.1 0.00
   Slovak Republic 76.1 -0.6 76.7 1.00
   Slovenia 80.1 -1.0 81.1 0.00
   Spain 82.4 1.3 81.1 0.00
   Sweden 81.9 0.8 81.1 0.00
   Switzerland 82.8 1.7 81.1 0.00
   Turkey 74.6 -2.1 76.7 1.00
   United Kingdom 81.1 0.0 81.1 0.00
   United States 78.7 2.0 76.7 1.00
   OECD AVERAGE 80.1 -1.0 81.1 0.00

5. 

   genauer · December 7, 2013 - 2:31 pm · Reply→

   I knew that there was a bimodal distribution, when I saw the plots,
   and teasing Livio a little bit, there are way too many outliers from your 95% confidence intervals.
   My latest analysis, completely ignoring all input from the OECD, explains 95% of the variances, to well within a noise level of 1 year.
   Just health care coverage(for Mexico, USA, Turkey), bringing it up to 90%, and crude quarter fraction assignments to eastern Europe factors, going to 95%, commented individually, and explained in some sentences below. Please download the data, and challenge my assignments, the remainder is just noise.
   Being a neighbor to German speaking Europe (Poland, Czech, Slovenia) is very healthy and to German culture (Chile) too. Just the data talking : – )
   I have the advantage to have probability distribution plots way more complicated for years for breakfast as part of my job.
   wiki/Gompertz-Makeham_law_of_mortality shows that you can characterize mortality rates with really remarkable precision into a general risk, shown at low age, and an accumulated wear & tear showing up at older ages, curves with just 3 robust parameters
   These numbers are derived from mortalities from people with pretty different (wealth) histories
   Eastern European countries like Poland had food rationing into the 1980ties, with meat rationing ending 7/31/1989 (http://www.nytimes.com/1989/07/31/world/poland-to-end-rations-and-food-price-freeze.html)
   There was the bitter joke: What does a polish hamburger look like?: bread stamp / meat stamp / bread stamp
   East German school classes in the 1980ties were ordered to march into the lignite fields, to hammer away the ice to keep the power plants going. And before we develop any hubris here, what would you do? Chipping a little away of the life expectancy or to have frozen people lying in the streets and houses?
   They did not have hard currency to buy western German made filters for soots, sulfur oxides, etc. , producing high levels of air contamination.
   This did ate away on our buildings, forests, health in large areas in west Germany as well.
   They couldn’t buy anywhere enough oranges, citrones, were short on pharmaceuticals, hospital supplies. It still brings tears into my eyes.
   the data table
   81.27 -5.63 -17.49
   6.01 0.322 0.95 .=r^2
   country Life expectancy, Total population at birth, Years delta model EE non health care outlier comment
   1 Australia 82.0 0.7 81.3 0.00
   2 Austria 81.1 -0.2 81.3 0.00
   3 Belgium 80.5 -0.8 81.3 0.00
   4 Canada 81.0 -0.3 81.3 0.00
   5 Chile 78.3 -0.2 78.5 0.50 how would you rate the SA Pickelhauben?
   6 Czech Republic 78.0 -0.5 78.5 0.50 high GDP for EE, between Germany and Austria
   7 Denmark 79.9 -1.4 81.3 0.00 hmm, lower despite high minimum wage
   8 Estonia 76.3 0.7 75.6 1.00
   9 Finland 80.6 -0.7 81.3 0.00
   10 France 82.2 0.9 81.3 0.00
   11 Germany 80.8 0.0 80.8 0.08
   12 Greece 80.8 -0.5 81.3 0.00
   13 Hungary 75.0 -0.6 75.6 1.00
   14 Iceland 82.4 -0.3 82.7 -0.25 special rich resource place
   15 Ireland 80.6 -0.7 81.3 0.00
   16 Israel 81.8 0.5 81.3 0.00 no comment from a German guy
   17 Italy 82.7 1.4 81.3 0.00 really, with the mezzo giorno
   18 Japan 82.7 0.0 82.7 -0.25 Sushi?
   19 Korea 81.1 -0.2 81.3 0.00
   20 Luxembourg 81.1 -0.2 81.3 0.00
   21 Mexico 74.2 -0.1 74.3 0.00 40% 0
   22 Netherlands 81.3 0.0 81.3 0.00
   23 New Zealand 81.2 -0.1 81.3 0.00
   24 Norway 81.4 0.1 81.3 0.00
   25 Poland 76.9 -0.1 77.0 0.75 close to Germany
   26 Portugal 80.8 -0.5 81.3 0.00
   27 Slovak Republic 76.1 0.5 75.6 1.00
   28 Slovenia 80.1 0.2 79.9 0.25 Euro Area, separated in 1991
   29 Spain 82.4 1.1 81.3 0.00 do we believe the better number with 15% immigration 200-2007
   30 Sweden 81.9 0.6 81.3 0.00
   31 Switzerland 82.8 0.1 82.7 -0.25 rich folks
   32 Turkey 74.6 -0.2 74.8 1.00 5%
   33 United Kingdom 81.1 -0.2 81.3 0.00
   34 United States 78.7 0.2 78.5 0.00 16% 0
   35 OECD AVERAGE 80.1 -0.1 80.1 0.20

6. 

   Livio Di Matteo · December 7, 2013 - 2:42 pm · Reply→

   I appreciate the additional material Genauer. I guess the next step would be to go beyond just one year of data as well as put in variables to capture institutional differences (eg. universal versus non universal health care systems) as well as regional variables. When combined with some of the other variables discussed in the post, it would probably up the explanatory power of the regressions substantially. I also would like a better health outcome indicator than just life expectancy.

7. 

   genauer · December 7, 2013 - 2:49 pm · Reply→

   maybe a useful comment:
   I actually started the analysis with downloading the OECD data, Livio referenced, with the intent to do a nonlinear fit, started with health expenditure as % GDP, and then just veered off to things (health care coverage, eastern europe) which seemed to be obvious to me to account for.
   I can still acount for other factors, provide intelligent excel sheets doing so, maybe via Livio, but found the total divergence really remarkable : – )

8. 

   genauer · December 7, 2013 - 4:05 pm · Reply→

   Livio,
   I am thinking similar lines of thoughts.
   I will work, based on the same excel sheet from OECD, a little longer, the multi year is inherently built it, make it useable, and then sent it to you, tomorrow, to hammer away it.
   We are both looking for the same results, how much of what, nurses etc. do we need.
   I provided some european, engineering input today.
   And If I would believe, I am good enough alone, I wouldnt post here.

9. 

   Livio Di Matteo · December 7, 2013 - 4:23 pm · Reply→

   thank you Genauer!

10. 

    genauer · December 8, 2013 - 11:53 am · Reply→

    If I look at your CI plots,
    I think it is actually much easier, you just put the health care coverage into your analysis yourself.
    US 16%, Mexico 40% from the wiki. The 5% for Turkey was my estimate for their kurd regions, but how to make that presentable? I got so carried away yesterday and forgot to copy it over here.
    I remembered a recent “marginal revolution” thread “The Myth of Americans’ Poor Life Expectancy”
    where somebody tried to explain the bad US data with car accidents and gun violence, but in a somewhat hairy way. Hmmm, how to separate?
    To be an eastern european country, as a 0 or 1, you also easily do yourself too, and my gut feelings of relative eastern europeanness (that was the last number in my unreadable table above) are not exactly academic style. But I think taking into acount past GDP per capita would do something very similar, and would also be possible to apply for earlier years.
    I am still puzzled with how I got the hunch with health care coverage and eastern europe. I have no idea, how one could get to that in a systematic approach.
    It would be nice to know, whether all the stuff, MRI, paying nurses, physicians, hospitals has some calculable payoff.
    With the LE data, I yesterday also looked up the CIA world fact book data, checking if the “outlier” denmark maybe a typo, and the CIA and OECD data differ by 0.6 – 1.13 years for the 5 countries I just picked. I thought about your comment, and I also dont have any better idea than LE.
    The British use some “quality of life years” measurements in medical papers, trying to assess the impact of various treatment options.