These are just a few relevant differences I can think of off the top of my head:
….. (and probably more I’m forgetting)
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The United States is sicker by many measures and much of this can be attributed to behavioral differences.
Self-reported disease rates by country and gender, ages 65+
Percentage of population using anti-hypertensive drugs, ages 50+
Life Expectancy at age 50 and rate of obesity by country and gender, 2004
Age Adjusted Obesity Rates in USA, aged 20+ by gender
Obesity Trends International Comparison
Comparison of risk factors between US and UK by education and income
Comparison of self-reported health between US and UK by education and income
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The role of diet and obesity
Comparable cross-national data on dietary practices are limited for the reasons noted above, including the challenges that countries face in evaluating the diets of their populations and inconsistencies across countries in food culture, defining indicators, sampling respondents, and administering surveys. Data collected within the United States suggest that the American diet has become less nutritious over time. Between 1971 and 2000, aver- age daily caloric consumption increased from 2,450 kcals to 2,618 kcals among men and from 1,542 kcals to 1,877 kcals among women;5 similarly, carbohydrate intake increased by 67.7 grams and 62.4 grams, respectively for men and women, and total fat intake increased by 6.5 grams and 5.3 grams, respectively, for men and women (Centers for Disease Control and Prevention, 2004). Between 1950 and 2000, annual per capita food consumption in the United States increased by 20 percent for fruits and vegetables but also for grains (by 44.5 pounds, a 29 percent increase), meats (by 57 pounds, a 41 percent increase), cheese (by 22.1 pounds, a 287 percent increase), and caloric sweeteners (by 42.8 pounds, a 39 percent increase). High-fructose corn syrup consumption per capita rose from zero in 1950 to 85.3 pounds by 2000. Some of these increases may be associated with an increase in dining out, which increased from 18 percent of total food energy consumption in 1977-1978 to 32 percent in 1994-1996 (U.S. Department of Agriculture, 2012). How do these trends compare with other rich nations? Americans consumed 3,770 kcals per person per day in 2005-2007,6 more than any other country in the world: see Figure 5-4. This trend is not new: the United States also had the highest caloric consumption in 2003-2005 and ranked fourth in the world in 1999-2001 (behind Austria, Belgium, and Italy). Between 1999-2001 and 2005-2007, the U.S. ranking on fat intake rose from seventh to fourth in the world, with Americans consuming an average of 161 grams per person per day. By comparison, in 2005-2007 the average Swede consumed 17 percent fewer calories and 24 percent less fat (Food and Agriculture Organization, 2010).
Overweight/obesity rates 5-17 years old, comparison
Cardiovascular risks comparison by sex, ages 50-54
[Note: Smoking, diabetes, and obesity are all strongly associated with cardiovascular risks and the US is very much elevated in this cohort across all three]
Diabetes rates by sex and age group comparison
Where the impact of obesity (especially at modest levels of it) on life expectancy is far from settled, it surely plays a large role in influencing type II diabetes rates and surely increases healthcare costs (e.g., treatment of diabetes, heart conditions, etc).
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Smoking/tobacco consumption
The United States historically had much higher rates of smoking and intensiveness (e.g., cigarettes per day per smoker) than any other comparable country. The effects of this are surely still being felt today.
Trends in cigarette consumption per capita, USA vs selected countries
Tobacco Consumption Rates (grams per capita), 1960-2010 USA vs typical comparison countries (decadal average)
Although the United States is no longer the top smoking nation (amongst presumably comparable countries), we are still amongst the top and, more importantly, the cumulative impact of those earlier decades of smoking has had and still have a pronounced impact on health and mortality today (this was/is especially true for US women). This behavior likely plays explains much of our elevated rates of stroke, heart disease, and more. Even when current or prior smoking rates are reported to be very similar amongst presumably comparable demographics (e.g., same age group in the US and the UK) that does not mean that both groups of “smokers” started at the same age or smoked as intensely.
OECD Estimates of “healthcare amenable” mortality in 2007 as compared to cigarette consumption
[Note: These are all presented in standard deviation units from the mean of the reported countries — overall rates have fallen substantially so the same differences in recent years are less pronounced in absolute terms]
source: cigarette consumption data
[Note: The fit is pretty good in earlier decades with this limited data but it gets increasingly worse in subsequent decades…. which is not surprising given that we do not expect instantaneous consequences from recent smoking activity and the declining rates of consumption internationally in recent decades!]
Estimated gains in life expectancy by gender from eliminating smoking in 2003
Estimated life expectancy gains in the US at age 50 without smoking by gender
Estimated Fraction of All Deaths at age 50 and older attributable to smoking by country and gender
[Note: Observe how the fraction has increased in recent years despite falling rates of current consumption. Also the mortality rate was and is higher in the US, so the similar fractions still imply significantly more deaths from smoking per capita terms]
Using an innovative macrostatistical method, Preston and colleagues (2010a, 2010b) estimated the attributable fraction of deaths after age 50 from smoking2 and its effect on life expectancy at age 50 among 10 high-income countries in 1955, 1980, and 2003. The authors calculated that by 2003 smoking accounted for 41 percent of the difference in male life expectancy at age 50 between the United States and 9 comparison countries and for 78 percent of the difference in female life expectancy at age 50 (Preston et al., 2010b). Smoking appeared to have a larger impact on women because of the later uptake of smoking by U.S. women (U.S. Department of Health and Human Services, 2000, 2002): see Figure 5-3. The smoking-attributable fraction of U.S. deaths among males age 50 and older was 23 and 22 percent in 1980 and 2003, respectively, but during the same years increased from 8 to 20 percent among females of the same age (Preston et al., 2010b). Based on the researchers’ assumptions, smoking accounted for 67 percent of the shortfall in life expectancy gains that U.S. women experienced relative to 20 other countries between 1950 and 2003. These findings implicate smoking as a potential cause of the shorter life expectancy of adults age 50 and older, but they do not explain the lower life expectancy observed in younger people. The U.S. health dis- advantage before age 50 has worsened over the same time that smoking prevalence rates in this population have decreased. The reduction in smoking rates will produce benefits in years to come. Wang and Preston (2009) predicted that the future will bring a decline in deaths attributable to smoking among men but that improvements for women will occur later.
Time lag illustration between smoking rates and smoking related mortality
Age Standardized Mortality Rates by amongst men 50+ by cause
Age Standardized Mortality Rates by amongst women 50+ by cause
I am, of course, not the only one to point out the role of historical smoking. Some proponents of single-payer have tried to argue that our current smoking rates really are not all that different, but you might notice that: they don’t look at earlier rates; they don’t account for volume/intensity; or reference the peer reviewed literature of the impacts of life history of smoking! Nor do they mention that many of the countries with recently elevated rates of smoking have seen very similar patterns of disease rates and the like as the United States has (albeit in the earlier stages of this progression). Put simply, differences in tobacco consumption can, in and of itself, explain a very large share of differences in life expectancy, differences in mortality from various specific diseases, and rates and severity of various diseases (e.g., heart, stroke, etc)
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Patterns in trends
I, for one, find it hard to reconcile the argument that these other differences must primarily be the result of lack of single payer when our differences with Europe mostly predate the rollout of national healthcare systems. Both 55 year old men and women in the US had amongst the highest probabilities of death in 1950 and they still do. Our male trend has not much changed, relative to Europe, whereas it clearly has with women (see age 65 & 75), which is very consistent with the evidence on smoking.
Mortality rates comparison by age group amongst men
Mortality rates comparison by age group amongst women
The role of race/ethnicity on health outcomes
There are clearly very large differences in mortality rates by cause and by age between racial/ethnic groups in the United States.
Estimated years of life lost by category cause, odds ratio relative to non-hispanic whites, amongst populations before age 70
[Note: Accidents, Suicide, and Homicides alone account for ~25% of the estimated life expectancy impact and non-hispanic whites actually do worse than the average here if we combined them all like this. However, when it comes to heart disease, stroke, diabetes, and other presumably preventable causes, save for cancer, the combined population and age-adjusted impact of our other major racial/ethnic groups skew our outcomes for the worst, especially the amongst the major causes and those that are most apt to be considered “amenable” to healthcare]
A more detailed version of the above graph
Age-adjusted mortality rates by category cause, odds ratio relative to non-hispanic whites
[Note: This is the age-adjusted mortality data across all age groups (not just <70). Also the age-adjusted mortality data does NOT map 1:1 with life expectancy (or the comparable measure years-of-life lost) because deaths at a younger age (e.g., car accidents) have a much greater impact than those that occur amongst predominantly older people]
Age-adjusted mortality rates by (detailed) cause, odds ratio relative to non-hispanic whites
There are major differences between racial/ethnic groups in terms of deaths from HIV, diabetes, suicide, homicide, cancer, stroke, heart disease, and many many more. Some of this may be purely cultural/behavioral and some of it very likely to be very much genetic (or see here)
There are probably also very significant differences within all of these populations, including non-hispanic whites, so I do not think that anyone can reasonably downplay the role of genes and behavioral differences without even trying to account for differences between major known groups.
Most countries do not track this data unfortunately but whenever they do you invariably find very similar patterns and large disparities between groups. Compare the following data from the UK.
UK Diabetes rates differences
UK Diabetes Rates by ethnic group and sex
UK infant mortality trends
UK Infant mortality rates differences
UK CHD mortality rates differences
UK stroke mortality rates differences
Canadian black immigrant outcomes (Quebec)
[Note: Canada publishes practically no data disaggregated by race/ethnicity, but you can clearly see that both Haitians and other black Caribbean immigrants have notably worse odds ratios]
I am not suggesting that there no differences between “whites” in the US versus the UK (see higher obesity & diabetes rates when adjusting for SES), but that ethnicity differences clearly play a very large role here, too, and that it is quite possible that different ethnic groups in the US have significantly different risk factors (e.g., people with more southern european vs northern european heritage).
So when I see analysis that claims to attribute differences in “healthcare amenable” mortality to differences in healthcare systems I am thoroughly unconvinced.
Below I have compared the OECD “healthcare amenable” mortality rates between the US and averages of leading European countries (Australia, Austria, Canada, Germany, Denmark, Finland France, United Kingdom, Ireland, Italy, Luxembourg, Netherlands, Norway, New Zealand, Sweden) to the United States.
Age-adjusted “healthcare amenable” mortality rates, odds ratio vs European mean
Age-adjusted “healthcare amenable” mortality, US compared to European mean
Age-adjusted “healthcare amenable” mortality, European US delta
Age-adjusted “healthcare amenable” mortality, stacked US delta vs *weighted* European mean
The US apparently outperforms in a few categories (cancers, diseases of nervous system, digestive system), but apparently mostly underperforms in heart disease, stroke, infectious diseases, and genitor-urinary systems. A lot of these categories map fairly closely with some pretty significant differences in US categories by ethnic group (see this repeated graph) and some pretty well established historical behavioral differences in the US more broadly (see smoking, over-eating, etc). [If these behavioral differences are the fault of the healthcare system, despite damn little empirical evidence for successful behavioral modification, then that must imply that these other systems are failing too– see rising obesity rates in Europe, higher smoking rates in the Netherlands today, etc] Moreover, given the large variances between these leading European countries it is hard to credit systematic differences in healthcare delivery with these (especially when they are unable to identify significant plausible causative explanations and there are known lifestyle differences, like smoking rates, obesity, and the like).
Transit mortality comparison
Violence related mortality comparison
Mortality from injuries comparison
Deaths from communicable diseases
Deaths from non-communicable diseases
Death rates from specific causes
Years of life lost before age 50, males
Years of life lost before age 50, females
Years of life lost before age 50, major causes for males
Years of life lost before age 50, causes for females
Comparison of mortality rates by age group between US and Canada
Many of of the differences within the United States are not at all consistent with the argument that “class” or healthcare explains group differences. Actually poor Asians and Hispanics out live most whites regardless of SES (and especially blacks) and, unlike other groups, they demonstrate much less difference (“inequality”) in outcomes by SES.
Male Life Expectancy at birth in California, 1999-2001 by SES
Female Life Expectancy at birth in California, 1999-2001 by SES
source: California Life expectancy data by ethnic group
The arguments for healthcare differences would be much more persuasive if this sort of evidence did not exist and if the national healthcare systems of Europe demonstrated dramatically smaller differences in healthcare outcomes. If these differences are not genetic and if the modern healthcare can presumably make irrelevant differences in obesity, smoking, drug use, and the like, then we should not find the outcomes that we routinely find when we look closely.
UK Spatial/Racial inequality
Relative Inequality Comparison
Inequality in Europe: both “amenable” and non-amenable”
[Note: Both amenable and non-amenable causes of death demonstrate very consistent patterns within and between nations by levels of SES. National healthcare systems clearly do not eliminate differences in genetics, behaviors, or what have you.]
Age-adjusted mortality rates by class in selected European countries
Finland age-adjusted smoking rates
Finland differences in CHD mortality by income level
Age-adjusted obesity rates by class in Finland
Finland male and female life expectancy at age 35 by educational level
Finland life-expectancy by SES by sex at age 35
source for Finland health data
Mortality rate data according to education, males 30-74 (includes black and white in US)
Mortality rate data according to education, female 30-74 (includes black and white in US)
[Note: One of the problems with the two above graphs is that the US has many more “educated” people, with their particular method than others in proportional terms, so the least educated in the US are truly not comparable to the same categories in, say, the UK or France. Moreover, the United States is much more heterogeneous, even within these groups, than most of Europe, is larger, and is more distributed geographically, culturally, and otherwise so we should probably expect somewhat more distance given larger differences in behaviors at least…. compare whites in, say, Minnesota to West Virginia]
We also observe very large differences within the US, even at the level of states, when we compare the “same” groups.
State Life Expectancy by ethnicity and 8th grade math scores by ethnicity
[Note: This is 8th grade math scores and the correlation here is much better than it is with income or inequality statistics below! Yes, I know they’re not the “same” people but we can reasonably assume that the current cohort of children in each group are broadly representative of the overall population in each group]
State Life Expectancy by Ethnicity and state average TIMSS 8th grade math scores
[Note: These scores are the average for the entire state, not by race/ethnic group. The prior graph (NAEP) has test scores by ethnic group and correlates much better overall]
State Life Expectancy by Ethnicity and State GDP per capita
[Note: They do appear to be somewhat correlated, but not nearly as much as life expectancy is with test scores]
State Life Expectancy by State Income inequality (GINI)
[Note: These are basically completely uncorrelated]
source: Life expectancy data by state and race/ethnicity
source: NAEP scores by state and race/ethnicity
Smoking and obesity rates, males 50+ international comparison
Smoking and obesity rates, females aged 50+ international comparison
Amenable mortality international comparison, 1998 & 2007
Amenable mortality by state map
Some Infant Mortality Rate Comparisons and Data
Infant mortality rate comparison, international and state ranking, 2002
Odds ratio by race/ethnicity and education relative to mean infant mortality rate of non-hispanic whites
Infant Mortality Rate odds ratio in Massachusetts by ethnicity/country of origin and education level
MA IMR by ethnicity (actual rates)
MA IMR odds ratio by ethnicity
CDC: Infant mortality rate by ethnicity
CDC: Infant mortality rate by birth weight and birth cohort
CDC: Infant Mortality Rate by State
CDC: Infant mortality rate by major race/ethnicity, 1960 – 2010
[Note: Infant mortality rates have fallen across groups and, especially, if you look at survival by birth weight, prematurity, etc…. it’s only when you average it all together without regard for race/ethnicity, maternal age, and type of delivery do you find these apparent (naive) underperformance of the “system”]
Pre-term birth rates
CDC comparison of pre-term birth rates in the US and various OECD countries
Adolescent pregnancy rates comparison
US vs Canada, IMR by birth weight comparison
CDC: Various childbirth related mortality rates by race/ethnicity
Perinatal mortality rate under alternative definition
CDC compilation of infant mortality rates by gestational age between US and Europe
[Note: Even without taking into account race/ethnicity or other characteristics such as birthweight, the US is pretty comparable in IMR]
Perinatal mortality rates amongst reasonably comparable European countries
[Note: If you even compare perinatal mortality rates with the OECD’s official data, which addresses many of the vagaries of IMR calculations and differences between countries, especially with respect to premature and other presumably non-viable fetuses, the US compares much more favorably (and would likely be well above average if we account for other previously mentioned issues). They define it thusly “The ratio of deaths of children within one week of birth (early neonatal deaths) plus fetal deaths of minimum gestation period 28 weeks or minimum fetal weight of 1000g, expressed per 1000 births.”]
Various IMR -related 2007 statistics from the CDC relating to race/ethnicity
Here is a study into the reasons behind the black – white gap in IMR
Black vs white IMR by birth weight, 2001
Black vs White IMR by gestational age
Birth Statistics by birth cohort, part 1
Birth Statistics by birth cohort, part 2 (1986 | 1991 | 1996 | 2001 | 2004)
Some more data and analysis on US state level mortality as compared to various predictors
CDC: white obesity rate by state
CDC: black obesity rate by state
CDC: latino obesity rate by state
CDC: obesity rate by state (all races/ethnicity combined)
CDC: Diabetes rate by state amongst adults with more than HS education
CDC: Diabetes rate by state amongst those with only HS education
CDC: Diabetes rate by state amongst those with less than HS education
[Note: the color coding with the diabetes maps varies for each level of education. Diabetes rates have increased across the board, but the rates are much higher amongst the less well educated]
Life expectancy by county, male
Life expectancy by county, female
Shorter version: There is tremendous variance amongst states and even counties and very little of it has to do with policy. People that smoke heavily and overeat are at much greater risk and there is only so much the healthcare system can do to counteract these effects (not to mention different risk factors due to genetics and other unmeasured differences)
If you compare state smoking rates to the variances in these outcomes you can actually account for the vast majority of the differences.
Below I analyzed available data from the CDC and KFF to compare white non-hispanic health outcomes to white non-hispanic smoking rates (amongst others) and vice versa. I have converted all of them to standard deviation units from the mean of states to make it easier to compare relative differences and the like….
Assorted health outcomes by smoking rates (complex)
State life expectancy and infant mortality rates (IMR) by smoking rates
A 1 SD increase in smoking rates amongst non-hispanic whites (by state) is associated with a ~0.85 SD decrease in life expectancy and a ~0.78 SD increase in infant mortality (note: life expectancy is inverted to make viewing here). I suppose it is possible that smoking rates may act as something of a (health) IQ test or life-style preferences test amongst states and that this is not entirely a product of actual smoking, but it’s worth nothing that is is not nearly as well associated with state GDP per capita or income inequality.
State Life expectancy and IMR by GDP per capita (inverted)
State life expectancy and IMR by income inequality
However, both smoking rates and obesity rates as quite well associated with state test scores (NAEP 8th grade test scores amongst non-hispanic whites).
They are poorly associated with “inequality”
And not nearly as well associated with state GDP per capita
So, for these reasons, amongst others, it’s not very surprising when we find that a 1 SD gain in test scores corresponds to an average gain of about ~0.7 SD in life expectancy (albeit with more variance in outcomes, possibly due to differences in smoking and other behaviors).
[Note: I inverted the SD units for test scores here, i.e., higher X axis values = worse test scores, to keep the directional impact the same across all measures]
We can also do a bit better with a simple weighted predictor (smoking=2, NAEP = 2, obesity = 1).
Various health outcomes by predictor variable
Life expectancy and IMR by the same
Comparing the above specified weighted predictor to the various components used
[Note: it ought to be pretty obvious that these measures are pretty well correlates with each other. High scoring states tend to be low smoking and low obesity rate states and vice versa. All 3 are pretty well correlated with each other.]
Obviously life expectancy is a product of multiple causes of death, each with different impacts (depending on mean age of death) and with different contributions, so it can be helpful to decompose various elements of it. Obesity, for example, may not be well correlated with life expectancy independent of these other predictors, but for some things it is likely much better (e.g., obesity -> diabetes -> poor control -> death from diabetes).
Here is another way to approach this problem: swap the axises so that that the outcome we are trying to predict (e.g., life expectancy) is on the X axis and the various predictors (or other presumed correlates) are on the Y (e.g., smoking).
This allows us to quickly compare multiple predictors in the same view.
Life expectancy amongst non-hispanic whites
CHD mortality amongst non-hispanic whites
Diabetes mortality rates amongst non-hispanic whites
Infant mortality rates amongst non-hispanic whites
Pre-term birth rate amongst non-hispanic whites
Low-birth weight rate amongst non-hispanic whites
There are real geographic patterns to this data too (and the few outliers usually have known systematic differences)
The heat maps below are all presented in SD units, inverted where necessary, from the mean amongst states in each category.
[Note: inequality is much more random than the others — many of the rich/progressive states have high inequality whereas others are poor and relatively poorly educated]
Below are some of the same data as heat maps with a sharper, more contrasy, color scheme to draw these differences into sharper relief. Blue = “good”, white=”average”, Red = “bad”
You should also be able to click through to them for an interactive map too…..
The National Academy of Sciences report on US health difference identified the follow key differences:
1. Adverse birth outcomes: For decades, the United States has experienced the highest infant mortality rate of high-income countries and also ranks poorly on other birth outcomes, such as low birth weight. American children are less likely to live to age 5 than children in other high-income countries.
2. Injuries and homicides: Deaths from motor vehicle crashes, non- transportation-related injuries, and violence occur at much higher rates in the United States than in other countries and are a leading cause of death in children, adolescents, and young adults. Since the 1950s, U.S. adolescents and young adults have died at higher rates from traffic accidents and homicide than their counterparts in other countries.
3. Adolescent pregnancy and sexually transmitted infections: Since the 1990s, among high-income countries, U.S. adolescents have had the highest rate of pregnancies and are more likely to acquire sexually transmitted infections.
4. HIV and AIDS: The United States has the second highest prevalence of HIV infection among the 17 peer countries and the highest incidence of AIDS.
5. Drug-related mortality: Americans lose more years of life to alcohol and other drugs than people in peer countries, even when deaths from drunk driving are excluded.
6. Obesity and diabetes: For decades, the United States has had the highest obesity rate among high-income countries. High prevalence rates for obesity are seen in U.S. children and in every age group thereafter. From age 20 onward, U.S. adults have among the high- est prevalence rates of diabetes (and high plasma glucose levels) among peer countries.
7. Heart disease: The U.S. death rate from ischemic heart disease is the second highest among the 17 peer countries. Americans reach age 50 with a less favorable cardiovascular risk profile than their peers in Europe, and adults over age 50 are more likely to develop and die from cardiovascular disease than are older adults in other high-income countries.
8. Chronic lung disease: Lung disease is more prevalent and associated with higher mortality in the United States than in the United Kingdom and other European countries.
9. Disability: Older U.S. adults report a higher prevalence of arthritis and activity limitations than their counterparts in the United Kingdom, other European countries, and Japan.
Population structure in Europe (genetic PCA analysis)
To wrap this up, I do not think anyone can plausibly argue that these differences must be, or are even likely to be, explained by differences in our healthcare system given:
