On the popularly reported black implicit association test (IAT) results

Recently the media and various friends and family have been asserting that implicit association tests (IAT) “prove” that whites are biased against blacks and that this presumably substantially explains the racial disparities in police shootings.

Race_630_Racist2

Since I am skeptical about the racial angle in police shooting, the validity of measures like IATs, and of received wisdom in general, I thought I would take a look at “Project Implicit” to better understand it.  The raw data for these results is available in SPSS format on OSF.io (albeit at >2GB) so I downloaded the data and performed some analysis in R.

Here are a few things I can say:

1: The reported averages by group (e.g., ethnicity, gender, political views, etc) hides a lot of variance within groups and overlap amongst groups.

black_iat_us_only_by_race_ethn_all_iat_count

The typical standard deviation is ~0.4 for every group with a reasonably large N.

black_iat_us_white_only_by_religiousity

black_iat_us_only_white_only_by_politics

[Note: 1 = strongly conservative, 7 = strongly liberal.  N for 7 is fairly small]

iat_white_by_age_level

iat_white_by_sex

2: The differences between the averages are typically very small by comparison to the variance in all of these groups

The only arguably exception to this is the target group (as in, blacks in this case), but even then this presumably implies that a long of blacks are strongly biased against black people.

3: The measured differences in the United States between whites, asians, hispanics, and other major ethnic groups are pretty negligible.

black_iat_us_only_by_race_ethn_all_iat_count

If these tests are measuring something “real” and important, the reporters ought to observe that non-hispanic whites are broadly inline with every other group, save for blacks.

4: The patterns appear to hold up internationally

iat_white_both_rez_and_cit iat_white_by_country_primary_residence iat_white_by_primary_citizenship

iat_black_non_us_citizen_residents_by_race_ethnic_group

iat_black_amongst_non_us_by_raceethnic

These patterns appear internationally and do not appear to be notably stronger amongst non-hispanic whites in the US than are in generally European countries. Other ethnic groups, both inside and outside the US (various configurations), also have this “bias” (again, save mainly for blacks/africans).  Non-Hispanic whites appear to have a slightly stronger “white” bias than other ethnic groups, but I would hardly say that that is strong evidence for even marginally higher anti-black bias.  As in, it’s more likely that groups prefer their own and certainly find them more familiar, other things being roughly equal.  I would bet that acculturation of minority groups in various majority populations likely makes them more familiar with the faces they encounter more regularly, providing that experience is not a generally negative experience.

5: Said bias appears to correlate positively with “diversity”

iat_white_by_pct_county_black_binned

Non-hispanic whites reporting to live in counties (not just the state) with large black populations are more likely, not less likely, to exhibit this bias.

iat_white_by_pct_nhw_finer

Likewise, “whiter” counties are less likely to exhibit this “bias”.  I would think this would run counter to the narrative of the people reporting this stuff, i.e., presumably whites with less interaction with blacks should harbor stronger biases, not weaker biases.

iat_black_scatter_by_pct_nhw_means iat_black_scatter_by_pct_county_black_means

iat_black_scatter_faceted_pct_black_min100

iat_black_faceted_scatter_state_pct_nhw_min100

iat_black_scatter_faceted_state_pct_black_min100

iat_black_scatter_faceted_by_state_black[Note: East Asians, Hispanics, and other groups also demonstrate more “bias” the larger the black population and/or the smaller the non-hispanic white population]

See here for: IAT (W-B) amongst non-hispanic whites PDF (long) density graphs

6: Similar “bias” exists against “Asians”

iat_asian_us_only_race_ethnicity

asian_iat_white_us_by_ed_level

asian_iat_us_white_only_by_age_level

iat_asian_us_white_only_by_politics

asian_iat_white_by_country_res

Asians have higher income, higher education, significantly lower imprisonment rates, higher life expectancy, better average grades, and so on and so forth for most measurable statistics.  A certain breed of progressive even calls them a “model minority”.  They surely also have fewer interactions with the police despite this IAT “bias” that is presumably every bit as strong against them (and I would bet that they are under-represented nationally in police shootings and in crimes against the police, i.e., based on my recollection of summary statistics from my earlier review here).

7: The tests have real measurement issues

iat_white_by_number_of_iats

People that report taking more IATs have “significantly” less bias.  I suspect there is a real training issue here (which calls into question the whole pursuit)

iat_white_by_iat_order

Likewise, there is variance (which they imply is significant in other contexts) depending on the order of the test.  If all groups are observed at a high rate this might not be an issue, but when the N is small this is likely to (randomly) skew the results further, i.e., depending on which order the test takers are randomly assigned.

8: The faces chosen surely influence the results

I have not reviewed the literature, but, other issues aside, they only use a small number of faces to reach sweeping conclusions about race bias/association.  Are these faces and expressions truly representative of the broader black or white population so that we can draw sweeping conclusions about normal interactions in the real world?  I would bet that there is considerable variability depending on the facial structure, gender, expression, facial hair, etc present on the face presented (I don’t think they had this level of detail in their data files, but it’s possible I missed it), not to mention things that they do not even show like clothing, observed behaviors, etc.

black_iat_faces

asian_iat_faces

Some additional perspective on the heat maps by state

project_implicit_heat_map

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[Note: some sample sizes with some of the minority groups are small in some of these states (I should have filtered them first!), so I wouldn’t read too much into it them other than, perhaps, to note broader patterns in this data]

A brief post on racial disparities in officer involved shootings

I have recently heard it said that the reason the police shoot blacks, especially young black men, at such a disproportionate rate is because they have an irrational fear of them because they are black.   Presumably the proponents of this view believe that shootings, “justifiable” or otherwise, should happen in roughly equal proportion to their share of population.  Although I do not believe the police are incapable of excessive force, racial discrimination, negligence, or what have you, the presumption that such disparities must be explained by presumed irrational fear of blacks strikes me as terribly naive on several levels.

Robert VerBruggen of RealClearPolicy did an interesting post on “Race, Age, and Police Killings” a few weeks back that compared nation-wide homicide rates by age group and race to the police shooting statistics.


rcp_white_black_homicide_offenders rcp_whites_blacks_killed_by_age

white_black_homicide_to_shootings_ratio

I thought this was a good and fair way to better illuminate the “fairness” issues here, since groups (e.g., sex, age, race, ethnicity, education, etc) that commit more murder (and other violent crimes) nationally can be reasonably assumed to be more likely to have confrontations with police and more violent confrontations when they do.

I found some data to take this point further by looking more granularly at the demographics of offenders that have actually killed law enforcement and offenders that have assaulted and seriously injured the police (as in with guns, knives, etc).  This data gives us a much better sense for the risks posed by each groups to the police and which groups are relatively more likely to be be confrontational, disobey, or even resort to violence, i.e., it speaks much more directly to the dynamics of police encounters with particular demographics (to the extent that one can argue that, say, national homicide rates are only black-on-black, gang-on-gang, or some such).  Most police encounters do not result in death of either party or even an exchange of gun fire, but groups that kill, injure, or assault the police at (much) higher rates can be reasonably presumed to be at (much) higher risk of getting killed by the police, “justifiable” or otherwise.

Law enforcement officers feloniously killed by demographics of known assailants

leo_killed_offender_race

[Blacks killed 44% of the police officers killed in the FBI’s 2003-2012 data]

leo_killed_by_offender_age

[Those under age 35 (all races) accounted for the vast majority of police killings]

leo_killed_by_sex

[Males killed 98% of the police in this dataset]

Law enforcement officer assaulted AND injured with firearms, knives, and other cutting instruments in 2013 by demographics of known assailants

leo_assaulted_and_injured_by_race

[Blacks accounted for 54%]


leo_injuries_white_age_group

[Age distribution of white assailants]

leo_assaults_black_age_groups

[Age distribution of black assailants.  Notice that, amongst blacks, young people account for a much larger share of the assaults than amongst whites]


Google Chrome 48Source: A study of felonious assaults on our nation’s law enforcement officers

I am not involved with law enforcement in any shape, way, or form. I do not believe that the police are beyond reproach.  I do not subscribe to the notion that police tactics are necessarily optimal today. I do not believe that there is zero bias or racism in police departments nationally.

However, it does not strike me these disparities are out of line with the data that most honest people should be well aware of by now.  The police may well be more “fearful” in dealing with some groups more than others (e.g., blacks more than others, males more than women, young more than old, poor more than rich, big more than small, etc), but that does not imply that there is not a very real rational basis for their fears or that this fear is likely to explain the outcomes we find.  When we know that some groups are actually disproportionately likely to kill, injure, or assault police we should not be very surprised when these same groups are the “victims” (“justifiable” or otherwise) of police shootings at higher rates too.

vox_police_killings_age_and_race

vox_police_shootings_bar

Some of this is likely because some groups clearly have many more encounters with police, i.e., because they commit crimes at a higher rates or are otherwise involved in circumstances that require police intervention (e.g., fights, disturbing the peace, etc).  Some groups are more likely to resist arrest, to confront the police, or even to try to assault or injure the police on average.  Some cities and communities also have more different risks and relationships with police.  These and other factors are likely to notably increase each groups’ odds of getting shot and/or killed by the police.  We do not need to invoke notions like irrational fear to explain why some groups are disproportionately likely to get shot.   In this particular case, I cannot help but noticing that blacks accounted for 44% of the LEO killed and 54% of injuries of LEO (assaults with guns and knives) but “only” 31% of LEO’s victims (at least in 2012).

Moreover, I object to the false dichotomy being presented here by many progressive and libertarian-types, i.e., either the victim(s) “deserved” it or the individual police officer(s) are guilty of murder or, at least, manslaughter.  Police officers are imperfect human beings who we depend on to enforce the law and whose lives are at very real risk in these sorts of situations (usually).   That law enforcement may sometimes make mistakes, both in the shootings and in the moments leading up to them, does not mean that every shooting is a mistake or that every victim “deserved” it.  People that argue this point of view demonstrate a poverty of imagination.

Just because we learn that a particular victim was “unarmed”, for instance, does not mean that law enforcement (especially lone officers) do not have good reason to fear for their life or that they necessarily had other options (e.g., if the suspect already assaulted them, if the suspect is larger and/or stronger, if the suspect disobeys a direct order and charges them, etc).  It especially does not imply that the officer’s individual behavior was actually criminal or should be treated as such. There is room to acknowledge that these victims are not necessarily homicidal murderers that “deserve” to die or that police procedures might be able to change so as to minimize the frequency of these sorts of incidents, but we should also recognize that the so-called victims often play a very large role in their own deaths (e.g., in assaulting the police officers, ignoring their orders, drawing weapons, etc…. not to mention choosing to engage in criminal actions more often than not).

In any event, some groups clearly place themselves at much greater risk of being shot by the police than others for a variety of reasons. If we care about “fairness” and about actually minimizing these deaths (both LEO and the broader community), then we should look at all of the data and try to better understand the root causes instead of putting forth facile arguments about subconscious bias explaining everything with nary any reference to assaults on police officers or what have you!

Some visualizations of ancestry.com’s genetic data

As a quick follow up to my earlier post using ancestry.com’s “Genetic Census of America”, I thought I’d post some more heat maps using the data I aggregated by major continental group (“race”) and by the more granular “adjusted” European ethnicities (i.e., whereby I simply divide the ethnicity by the total european “ethnicities” in the state).

Note: You can click these images for an interactive view to see the actual numbers for each state if you care.

Adjusted European Ethnicities

Google Chrome (8)

Google Chrome (7)

Google Chrome (6)

Google Chrome (3)

Google Chrome (5)


Google Chrome (1) Google Chrome (2) Google Chrome
Google Chrome (4)Continental Groups (no adjustment)

Google Chrome 31

Google Chrome 32

Google Chrome 34

Google Chrome 29 Google Chrome 30

Google Chrome 33[Note: These values here were all well below 1 percent and openheatmap rounds to the nearest integer, so I multiplied these by 100 to show the small absolute variance in more detail]


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Please note that the low levels of subsaharan African ethnicities and american indian ethnicity in ancestry.com’s data implies that blacks and latinos, in particular, are very much under-represented based of their share of the population.  I would presume that this is because blacks and latinos are much less likely to use AncestryDNA (and probably genealogy services in general).

Below I very crudely estimated their share of the population based on the proportions of American Indian (~40% average for Latinos) and subsaharan african (~80% average for blacks) genetic material, assuming no admixture with other groups (which obviously is not quite right either).

Google Chrome 47

I am not claiming that these figures are exactly right or uniform in all states (I know there is real variance, especially with hispanics/latinos), but it ought to be pretty clear that they are using the service at something like 20-50% the rate of non-hispanic whites, or at least were at the time ancestry.com compiled this “census” (which is why I felt more comfortable using my crude adjustment method to calculate the European percentages!).

census_comparisons.png-1 census_comparisons.png-2 census_comparisons.png-3 census_comparisons.png-4

european_vs_nhw_census

The probable genetic explanation for interstate differences in mortality amongst non-hispanic whites

A couple months ago I stumbled across ancestry.com’s  “Genetic Census of America”.  Since I was researching the health outcomes question already I remembered that this data existed and I decided to bite the bullet and actually analyze this data systematically.  Lo and behold, I quickly discovered some very strong correlations between these genetic proportions (crudely without any particular techniques) and the life expectancy of non-hispanic whites in each state.  I refined this a bit and produced a toy model that can explain about 85% of the variance in life expectancy between states (not to mention other things)!

Before I get started, let me get some caveats out of the way:

  • correlation does not necessarily imply causation
  • these particular genetic groups may just be proxies in this country for particular ethnic or other genetic groups (at least in part)
  • this could “cultural” (people with particular frequencies of SNPs are also more likely to have had particular cultural mores, values, and the like passed onto them through their ancestors/parents).
  • most “whites” have some fraction of other continental groups, but it’s usually pretty small on average
  • the DNA testers may not necessarily be representative of the larger “white” population, but I think it’s good enough to represent the white population (probably less so other groups).
  • binning these together by states and other high levels of aggregation likely improves the “accuracy” of these methods since random accidents, stochastic variances in gene expression, or what have you get averaged out to large degree.  Likewise, to the extent these groups are just a crude proxy for actual groups, this level of aggregation likely further helps.
  • Ancestry.com does not provide details by race/ethnic group and my procedures cannot perfectly remove any potential signature introduced by others.   Blacks and latinos, in particular, surely introduce some european genetic groups into this data, although they are obviously much under-represented in ancestry’s DNA analysis and I do not think it would skew the results that much.

This is a simple model that I produced to calculate non-hispanic white (NHW) life expectancy by state using a simple genetic calculation and smoking rates (weighted equally on standard deviations from the national non-hispanic mean amongst states) .

actual_vs_pred_le_smoking_and_genes

As for how I got here….

First, you can start to see these patterns with the three groups, i.e., Great Britain, Ireland, and W. Europe,  I keyed in on without any adjustments at all.

No-Adjustment: NHW LE by % GB

unadj_gb_le

No-Adjustment: NHW LE by % Irish

noadj_le_irish

No-Adjustment: NHW LE by % Western European

noadj_le_weuro

“Great British” is pretty apparent, but the latter two are obviously noisier.  This is without any any adjustment for other major racial/ethnic groups in ancestry’s estimates (e.g., blacks, latinos, asians, american indians, etc) that took these DNA tests in those states and, obviously, some states have considerably more (broad-strokes) racial/ethnic diversity than others.  Put differently, states with more non-european groups and thus test takers (albeit seriously under-represented) will tend to skew the european fractions down without any adjustment.

However, if we merely aggregate these three groups together they tighten up a bit:

unadj_pct_euro

We can do quite a bit better though if we crudely divide these and other components all by the total european ethnic groups (as defined by ancestry.com, which is generally pretty similar to major continental categories broadly) to prevent these other genetic groups from obscuring this signal (Yes, I know that’s imperfect, but it seems to work!)

For each group I used:

Adj. British

adj_le_gb

Adj. Irish

adj_le_irish

Adj. Western European

adj_le_weuro

Adjusted Combined (Great British + Irish + W. European)

adj_neuro_le

I did similar analysis for every other european group (and other non-european groups), most of which produced less strong signals and/or positive relationships (e.g., E. Europe, Scandinavia, etc), but I found this to be the cleanest (probably, in part, because these populations are larger and thus their contribution to mean NHW life expectancy can be mostly clearly observed).

Moreover, there is a good reason to think that these particular groups (GB, Irish, and W. European) are closer to each other genetically than other groups (geographical, historical, and genetic analysis).

See this PCA analysis (amongst others):

uk_and_other_eu_pca

source: Population structure and genome-wide patterns of variation in Ireland and Britain.

Or this:

european_population_structure

As for ancestry.com’s methodology definitions of these three ethnic groups, see here:

ancestry_gb

[Note: As you can see I am about as “Great British” as the average English person, presumably. That is probably significantly more than I should be if I interpret this as actual strictly nationally-aligned ethnic groups rather one of probabilities of these particular ethnic/genetic groups being found in particular regions in higher concentrations.  Although, if you look carefully, you can see a fair amount of GB in northern parts of FR, DE, BE, NL, etc]

ancestry_weuro

[Much less than I would expect based on my knowledge of my family’s recent history, genealogy, and this map!]

ancestry_ireland

A list of ancestry.com’s European ethnic/genetic groups by the average proportions of people in those regions

ancestry_euro_structure

[Note: Both the “Great Britain” and “Europe West” regions have a lot of other genetic groups.  Ireland, by contrast, is much more “pure” or, more technically, less recently admixed or to a much lesser degree]

ancestry_all_groups

Their method is far from perfect and their ethnic labels are a bit confusing, if interpreted in a particular literal fashion, but there is still real information content there and it seems to be good enough to find evidence of population structure within the non-hispanic white population of the United States.

I ran some comparisons against my crude genetic model (GB+IR+WEU fraction alone) to see if there were any apparent systematic patterns whereby other groups seemed to cause notable under- or over-prediction.  I did not find much evidence for this (note: positive Y values=over-prediction)

I also ran a series of cross-checks to see if perhaps other populations might be skewing my data somehow:

My calculated fraction by proportion of census takers reporting to be NHW

pctneuro_as_prop_of_euro_by_nhw_census

[There does not seem to be any systematic relationship between my calculation and the proportion of NHW on the US census]

euro_fraction_by_latino_census

[States with higher latino proportions on the US census do not report seem to skew significantly based on census proportions of latinos]

My calculated fraction by proportion of census takers reporting to be black

pcteuro_by_census_percent_black

[States with larger black populations report somewhat larger adj. GB/IR/WEU proportions, which is consistent with known migrations and much stronger patterns in this data.  If black test takers (national avg ~20% european) were skewing this a lot I think we’d see a stronger shift here.]

NHW LE by proportion of NHW on census

nhwle_by_census_nhw

[NHW life expectancy (LE) is not correlated with the census NHW percentage]

NHW LE by the proportion of census latino

nhwle_by_census_latino

[States with larger latino populations have very slightly higher NHW life expectancies, but that’s probably not significant]

NHW LE by proportion census black

nhwle_by_census_black

[States with proportionally larger black populations have lower NHW life expectancies.  I interpret this as mostly being a reflection of southern states having both more blacks and lower NHW LE for the reasons I am describing here (genetic and possibly cultural).  Although I suppose it is possible that the small amount of african admixture may explain a small fraction of this too]

Delta from my simple prediction (genes-only) by percentage American Indian

eu_le_delta_by_american_indian

[A genetic check for the latino angle, mainly.  Apparently not many latinos amongst the DNA testers (that outlier is NM) and they don’t seem to be skewing the results terribly]

eu_le_delta_by_black_ethnitiy

[States with larger fractions of sub-saharan african genetic/ethnic groups in the genetic data may have their life expectancy over-estimated slightly, based on my crude method (excluding smoking), but not by much all that much.  ]

NHW LE by total proportion of European ethnic groups in ancestry.com’s DNA results

nhwle_by_euro_census_fraction

[Note: Most states results are very european–obviously not many non-whites took these tests. Those states with higher european genetic fractions amongst ancestry’s results have higher NHW life expectancy, but that’s consistent with the south and the like]

I compared all of the other major continental groups versus my model (over-prediction) — See this PDF here (to save space/bandwidth)

Onward…

We can produce a simple model using this great british, irish, and w. european fraction like so. r**2=0.54, not bad.

actual_vs_pred_genes_alone

However, we can do much better if we add in the proportions current NHW smokers.

actual_vs_pred_le_smoking_and_genes

[This is a very simple model that just uses smoking rates and the european proportion, equally weighted, for r**2=0.85. I can do a bit better with more variables and stronger weighting, but to prove the point I thought I’d keep this as simple as possible (for now at least) lest I be accused of over-fitting this!]

It can be interesting to compare this analysis with life expectancy for other groups to see how they correlate (or not).  Yes, I am well aware that “Asian” and “Hispanic” are not homogeneous populations, i.e., some states have systematically different national/ethnic and admixture characteristics here than others, however lack of correspondence ought to tell us something.  As in, if we find no relationship amongst these other groups than state-wide explanations like in healthcare systems, policy, and the like are unlikely to explain much (this position ought to be especially difficult for genetic and/or culture deniers to argue with a straight face!)

State life expectancy by estimated GB/IR/WEU proportion amongst whites

ethnic_le_by_euro

Whites and blacks move together, although the fit is considerably tighter for whites, whereas asians and latinos do not appear to correlate at all!   I suppose one could make the argument that cultural/lifestyle differences explain this particular pattern, especially amongst whites and blacks, but one could also argue, if this is genetic, that that particular european admixture in the south has something to do with this too!  It is certainly hard to blame state policy without making any account for genes, culture, or other behavioral elements given the difference outcomes observed in different states

I, for one, find the similarity of the patterns that I observed earlier in the state of California very interesting.

calif_male_le_by_race_and_income

calif_female_le_by_race_and_income

[source for the data]

[Obviously this is at a different level of granularity, i.e., state vs SES, and it’s possible that the latinos and asians are significantly different than these “same” groups in other states (different national origin, culture, and/or admixture proportions), but I really do not believe that is mere coincidence!  Although these other groups are clearly not homogeneous generally, my theory is that they are actually less stratified genetically and culturally with respect to measures of SES because they have not been here nearly as long, on average, and because their historical regions have not had the same structure with respect to long established market systems and the like.]

ethnic_chd_by_euro

[Similar patterns… although, unfortunately, the only other ethnicity they had was “other” — presumably mostly asians and latinos]

ethnic_smoking_by_euro

ethnic_imr_by_euro

ethnic_obesity_by_euro

ethnic_naep_by_euro

[Other groups appear to be virtually uncorrelated with this measure]

ethnic_asthma_by_euro

[Curiously asthma rates seem to notably lower for blacks in these states]

ethnic_low_birth_weight_by_euro

ethnic_pre-term-birth_by_euro

It can also be useful to compare this against other measures.

le_by_gdp_per_capita

state_gdp_per_capita_by_euro

smoking_rates_by_gdp

ethnic_smoking_by_euro
[repeat of the same above for easy comparison]

state_le_by_gini

[Little to no correlation here and, if anything, it does not move in the expected direction]

inequality_by_euro

[Not much of are correlation here either!]

Comparison between ethnic group life expectancy and each group’s “own” (corresponding) 8th grade NAEP scores

state_life_expectancy_by_ethnicity_and_naep_scores

[Note: This fit is much better than just about anything else, save for smoking rates by group probably, and there is some data to support the notion that IQ predicts health outcomes quite well (or here).  Obviously NAEP scores are not a perfect proxy for IQ and likely more subject to variances in effort and conscientiousness, but they are certainly pretty well correlated…]

ethnic_naep_by_euro

[repeat of earlier graph for comparison]

Life expectancy by current smoking rates for each group (data is spotty for latinos and not available for asians)

ethnic_le_by_ethnic_smoking

Obviously smoking adds a lot to this, especially for non-hispanic whites:

gene_only_smoking_overest

However, it’s important to note that these genetic proportions predict much higher rates of smoking, lower NAEP scores, higher obesity, and more, so I wouldn’t assume that smoking per se explains as much itself (surely it’s terrible for health, but it’s also a signal of intelligence these days and preferences/lifestyle).  Why do some states have much higher smoking rates than others?  Genes and (arguably) culture explain this better than GDP or inequality (see above).

gene_only_gini_overest

[inequality adds virtually nothing and it’s probably not even statistically significant]

gene_only_gdp_overest

[likewise for GDP per capita]

gene_only_naep_overest

[NAEP scores seem to add something though]

Even without smoking we can improve out model by adding NAEP scores

nhwle_by_gene_and_naep

gene_and_naep_overpred_by_smoking

[smoking per se still adds something, but less than before]

Or obesity rates

gene_only_obesity_overest

gene_obese_smoking_overpred

[likewise for smoking with respect to obesity and genes]

Or we can combine genes (GB/IR/WEU fraction), NAEP, and obesity rates for an even better alternative

gene_naep_obese_le_est

gene_obese_naep_overpred_by_smoking

[smaller still]

 I still thinking smoking adds real incremental power and that it is certainly absolutely terrible for health, but my point here is that smoking per se does not explain everything (i.e., it’s as much a signal of other behaviors and other health issues as much as it is a cause in and of itself) and that still leaves open the question of why some groups smoke so much more than others (the variances today are not apt to be well explained by policy).

genes_naep_obesity_smoking_rate_pred

[We can account for most of the variance in smoking rates amongst NHW with a simple model using equal weights for genes, obesity, and NAEP scores!]

smoking_rates_by_gdp

[and the usual suspects in like GDP per capita don’t tell us nearly as much

ethnic_smoking_by_gini

[inequality tell us virtually nothing for non-hispanic whites]

ethnic_smoking_by_naep

[NAEP scores in and of themselves tell us something]

nhw_smoking_rate_by_euro_and_naep_equal_weight

[States that are 1 SD above average, i.e., average of lower gene fraction  and higher NAEP scores in SD from mean, smoke, on average, ~2.5 percentage points less per capita and vice versa]

gdppc_by_euro_and_naep_equal_weight

[States that are 1 SD above average, i.e., average of lower gene fraction and higher NAEP scores in SD from mean, have on average ~5.3 K more GDP per capita (remember this is without factoring other groups into the mix that surely confound this!)]

multi_pred_by_euro

[A giant mess of different measures/outcomes against the gene percentage]

*****************

actual_le_by_weighted_average_sd_units_above_mean

[States that are 1 SD units above the mean in GB/IR/W. Euro gene fraction and NHW smoking rates are, on average, ~1 SD units below the mean in life expectancy]

Below I tossed a lot of this data into a heat map (US states) to try to show the patterns in standard deviation terms.  Blue=better, white=average, red=worse.

I picked this color scheme to show the differences in the sharpest relief possible.  When it’s off by a bit, especially around the mean (+/- average) it looks worse than it really is.  Nevertheless, you can probably pick up some pretty clear patterns here!

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[Note: I accidentally flipped the sign here!.   positive = under-prediction = blue; negative = over-prediction = red.  Nevada is a clear over-prediction here!]



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[Texas NHW do surprisingly well on the NAEP.  Accurate or skewed by varying state standards?]

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And, of course, these state wide averages obscure tremendous country-level variation (which is all the more reason why “policy” is unlikely to explain much here!)

Male life-expectancy

male_life_expectancy_by_county

Female life-expectancy

female_life_expectancy_by_county

[Note: these maps include all racial/ethnic groups so it tends to exaggerate a bit]

Nevertheless, you can see these by white-only groups for specific major causes of death, i.e., CHD and stroke, both hospitalizations and death rates here:

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You can sort of observe the non-random distribution of ethnicity/ancestry by looking at specific (reported) ancestry in the US census:

“Scotch-Irish”

moa_scotch_irish

“Scotish”

moa_scotish

“English”

moa_english

“German”

moa_german

“Swedish”

moa_swedish

“Irish”
moa_irish

[Note: The responses are fairly fickle (trends, politics, etc) and not weighted by actual proportion amongst “whites”, so it’s not that useful as an absolute gauge… which is why I prefer the genetic approach here, even if it’s not quite as granular!]

major_reported_euro_ancestry_by_region

1990 US census data

In any event, I am not necessarily arguing that the three ethnic/genetic groups I picked out are homogenous (within or between said groups) or have a uniform distribution, so that we should necessarily expect the same (implied) outcomes in, say, England (although there’s probably some general directional effect here if we compare these groups to, say, southern european life expectancy) or with any individual that has said proportions (i.e., if you could somehow clone said individual or their immediate family 1000 times and observe their mortality rates).  However, it seems very likely to me that this is at least a good proxy for particular ethnic groups that settled or emigrated to this country so we can make some reasonable guesses about the sort of people in these state (on average) based on the average genetic proportions amongst (apparent) europeans broadly.

In other words, it’s not necessarily that those that the “Great British”, “Irish”, or “Western European” genetic groups are as unhealthy or underperforming otherwise as we (generally) find here, but rather that those states with high proportions of “Great British”, “Irish”, or “West European” (ancestry.com’s methods) ethnic groups amongst NHWs claim a higher share of particular finer-grain ethnic populations and that many of those groups have a long history of problems (although they could be sorted to some degree based on these sort of admixture levels).   Likewise, there are averages amongst states: different cities and regions have different distributions of ethnic groups and the like too.  The Scottish (including our so-called “Scotch-Irish”), Irish (especially Northern/Protestants), and people in Northern parts of England have long had worse outcomes (and still do) and I rather suspect many of our early immigrants came from those groups and brought their troubles with them even as they prospered.  Many of them settled in the south, greater Appalachia, etc and a good number of them have since interbred with other groups and moved to other parts of the US (which is where genetical/admixture analysis can possibly reveal more than, say, US census reported ethnicity which changes depending on current trends/popularity and the like!).

These sorts of patterns are not entirely unique to the United States.

There is a very well established “North-South” gradient in the UK in health outcomes.

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Scottish life expectancy by council area

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CHD mortality ratios for Wales, N. Ireland, and Scotland vs England

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CHD mortality in 1961 vs 2009

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[Note: Very little change in relative risk!]

uk_chd_diff

uk_stroke_diff

uk_bp_diff

[Notice the relative lack of spread in nothern ireland, by health authority at least.  I would not be surprised if this was the result of relatively (traditionally) greater genetic homogeneity in NI — see ancestry.com’s regional distribution, amongst others]

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Nor have they abolished differences by major ethnic/racial groups

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uk_ethnicity_stroke_rates

UK_life_expectancy_spatial

uk_ethnicity_diabetes_rates

uk_self_reported_diabetes_rates

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Study of Y-haplogroups and CHD risks in the UK

uk_y_halpogroup_chd_study

Patterns in health disparities are hardly unique to the UK or the US either

Health inequality study in various european countries (healthcare amenable vs non-amenable mortality)

amenable_mortality_inequality_in_europe

[There are significant long-standing differences in mortality rates according to class in all of Europe: both “amenable” and otherwise]

Finland CHD mortality rates by income group

finland_mortality_rates_chd

Life expectancy in Finland, amongst adults age 35, by class and sex

finland_life_expectancy_by_sex_and_class

********

The notion that all groups have the same risk factors, the same life expectancy, and so on, ergo all differences in outcomes can only be explained by differences in health care treatment are really not tenable any longer (it’s not even likely).  I suspect that these differences are mostly genetic, even many of the behavioral components are apt to be strongly influenced by genes (e.g., propensity for addiction to tobacco, over-eating, etc), but, if nothing else, we really do not have good environmental explanations, save for smoking, particular types of drug use, STDs, etc.  We barely even have good policy solutions to even address those limited parts that we do understand (never mind that which we do even understand!).   Many medical/technological improvements will help all groups, but, more often that not, these will tend to amplify pre-existing disparities in outcomes and rarely close them appreciably (unless a particular treatment is highly efficacious in treating diseases that disproportionately effect particular groups so as to have a pronounced effect on overall mortality/odds ratios) !

We clearly have more genetic diversity than do most European/Anglo countries that we are typically compared to, even amongst “whites”.

uk_ca_usa_pca_map

us_ca_pca_clusters

Many of our “white” immigrants came from different countries, regions, religious groups, and more.   If these different broad groups have (had) different genotypical life expectancies (given similar environmental conditions), risk factors, and so on this alone will create more apparent health “inequality” (especially if we look at the nation as a whole rather than more narrowly within a particular broad ethnic group and region) even if most people do not think of these groups as being (visibly) different.  (Not to mention some potential admixing with other groups and/or other groups identifying as NHW)

Furthermore, even many of these individual countries, regions, and the like are clearly not homogeneous either.

We can see this quite clearly with the averages produced by ancestry.com’s analysis for Europe (in these regions, some individuals clearly have more, some have less since this admixture is relatively recent):

ancestry_euro_structure

It is quite likely that they were and still are stratified genetically by class, by region, by historical religious groups, etc (whether they know it or not) and that this has implications for life expectancy, disease rates, and other important outcomes.  Emigration from these places to the United States was hardly random: some classes, religious groups, regions, and so on clearly settled here at very different rates for very different reasons at different times.  If we drew a disproportionate share of lower class or otherwise marginalized groups, it is quite likely that many of our groups could have somewhat worse outcomes even when we try to compare apples-to-apples (not that easy!).  There is some evidence for long-standing historical differences in different immigrant populations (and these differences still exist in many of these countries and, particularly, regions that they immigrated from).

See this table on Irish immigrants to the United States and historical health disparities (starting in 1850):

irish_heart_disease

[This is obviously long before modern medicine, fast food, etc and these patterns in CHD differences existed even then!]

Life expectancy at birth by region (both sexes) — zoom in Northern Europe

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Life expectancy at birth, both sexes, zoom out

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source: WHO

There is clearly not just one German, English, Scottish, French, Dutch, etc  life expectancy, not even at regional level (i.e., there is certainly more within regions, ethnic groups, classes, etc)!  [I would love to get similar level of detail for UK and various european countries to see how this sort of analysis plays out, crude though it may be].  Some regions have different distributions of risk factors and there is some evidence that this correlates with population structure (see this for CHD risks in the UK).  We also know that these groups are not that homogeneous either.

I suspect (but cannot prove) that interstate differences amongst non-hispanic whites, like differences within and between other first world countries, are largely genetic and probably somewhat cultural/behavioral (to the extent we can disentangle culture/behavior from genetics).  Of course lifestyle (e.g., smoking, eating habits, etc) and healthcare systems can have an impact, and they can change over time, but these variances are  usually not that pronounced in practice and they tend not to explain much within broadly similar environments (as in, amongst well established citizens that have adopted broadly western diets, that have decent healthcare systems, not excessively high rates of accidents/murder/etc).

Some other issues with comparing US healthcare costs and so-called “outcomes”

Besides my previously mentioned objections with simplistic comparisons between healthcare systems, vis-a-vis naive economic comparisons and the effect of taxation on behaviors, it is very difficult to compare the actual performance of healthcare systems, in both financial and human terms (e.g., life expectancy, mortality rates, etc), without accounting for other differences in the populations (e.g., genetics, health/risk behaviors, lifestyle, etc).   These simplistic comparisons of national health care systems based on crude mortality rates and the like are very much like comparing performance of goalies in various sports based on who wins the game alone, i.e., without making any real attempt to control for the performance of the rest of the teams’ defense, the performance of the offense, and so on, when what we really want to know, at bare minimum, is the number of saves as compared the number of shots on goal (and even then that’s an imperfect metric).  Of course some goalies are likely to be somewhat more effective than others and, other things equal, goalies can have a pronounced impact on the outcomes, but you cannot simply assume that there are not any significant systematic differences between teams in general or on game day.

These are just a few relevant differences I can think of off the top of my head:

  • The United States population is not a mirror image of Europe: genetically, culturally, or otherwise
  • Much higher smoking rates historically
  • Relatively high rates of obesity (although other countries are starting to catch up to us now)
  • Much higher homicide rate.
  • Higher rates of sexually transmitted diseases (see the AIDS crisis)
  • More geographically distributed than most (as in, lower population density, significant populations living in rural locations, etc)
  • Higher rates of serious automobile accidents per capita

….. (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+

crimmins_diseases_by_country_and_gender_65plus

Percentage of population using anti-hypertensive drugs, ages 50+

crimmins_antihypertensive_drugs

Life Expectancy at age 50 and rate of obesity by country and gender, 2004

life_expectancy_and_obesity_50plus_comp

Age Adjusted Obesity Rates in USA, aged 20+ by gender

age_adjusted_obesity_rates_US_by_decade

Obesity Trends International Comparison

obesity_trends_comparison

Comparison of risk factors between US and UK by education and income

us_uk_risk_factors_by_education

Comparison of self-reported health between US and UK by education and income

us_uk_self_reported_health_by_education_and_income_weighted

*****

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).

caloric_intake_map

Overweight/obesity rates 5-17 years old, comparison

childhood_obesity_comparison

Cardiovascular risks comparison by sex, ages 50-54

cardiovascular_risks_comparison

[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
diabetes_rates_comparison_pt1 diabetes_rates_comparison_pt2

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

cig_trends_selected_countriesTobacco Consumption Rates (grams per capita), 1960-2010 USA vs typical comparison countries (decadal average)

oecd_tobacco_grams_per_capita_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

oecd_cig_consumption_mortality_comparison_bar_chart

[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

life_expectancy_gains_wo_smoking_50plus

Estimated life expectancy gains in the US at age 50 without smoking by gender

life_expectancy_graph_wo_smoking

Estimated Fraction of All Deaths at age 50 and older attributable to smoking by country and gender

estimated_share_of_mortality_attributable_to_smoking

[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.

smoking_current_rate_1965_on

Time lag illustration between smoking rates and smoking related mortality

smoking_delay_graph

Age Standardized Mortality Rates by amongst men 50+ by cause

mortality_by_cause_and_country_men_50plus

Age Standardized Mortality Rates by amongst women 50+ by cause

mortality_by_cause_and_country_women_50plus

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_adult_men_by_age_group

Mortality rates comparison by age group amongst women

mortality_rates_adult_women_by_age_group

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

cdc_years_lost_odds_ratio_by_major_group

[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

cdc_years_lost_odds_ratio_detail_by_ethnicity

 

Age-adjusted mortality rates by category cause, odds ratio relative to non-hispanic whites

cdc_mortality_rate_odds_ratio_category_by_ethnicity

[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
cdc_mortality_rate_odds_ratio_detail_by_ethnicity

Source: CDC

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)

diabetes_admixture_black

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_ethnicity_diabetes_rates

UK Diabetes Rates by ethnic group and sexuk_self_reported_diabetes_rates

UK infant mortality trends

uk_infant_mortality_trend

UK Infant mortality rates differences

uk_ethnicity_infant_mortality_rates

UK CHD mortality rates differences

uk_ethnicity_chd_rates

UK stroke mortality rates differences

uk_ethnicity_stroke_rates

Canadian black immigrant outcomes (Quebec)

quebec_white_vs_black_odds_ratios

[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

oecd_odds_ratio_amendable_mortality_vs_unweighted

Age-adjusted “healthcare amenable” mortality, US compared to European mean

amenable_mortality_oecd_raw_bars

Age-adjusted “healthcare amenable” mortality, European US delta

amenable_mortality_raw_delta

Age-adjusted “healthcare amenable” mortality, stacked US delta vs *weighted* European mean

oecd_amenable_mortality_weighted_stacked_comparison

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

US_driving_mortality_comparison

Violence related mortality comparison

US_youth_violent_mortality_comparison

Mortality from injuries comparison

injury_mortality_comparison

Deaths from communicable diseases

communicable_diseases_comparison

Deaths from non-communicable diseases

noncommunicable_diseases_comparison

Death rates from specific causes

death_rates_comparison

Years of life lost before age 50, males

years_of_life_lost_before_50male

Years of life lost before age 50, females

years_of_life_lost_before_50female

Years of life lost before age 50, major causes for males

years_of_life_lost_before50_causes_male

Years of life lost before age 50, causes for females
years_of_life_lost_before50_causes_female

Comparison of mortality rates by age group between US and Canada

us_canada_mortality_accidents_heartdisease_comp

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

calif_male_le_by_race_and_income

Female Life Expectancy at birth in California, 1999-2001 by SES

calif_female_le_by_race_and_income

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

UK_life_expectancy_spatial

Relative Inequality Comparison

european_usa_healthcare_inequality_comparison

Inequality in Europe: both “amenable” and non-amenable”

amenable_mortality_inequality_in_europe

[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

age_related_mortality_eu_comp

Finland age-adjusted smoking rates

finland_age_adjusted_smoking_rates

Finland differences in CHD mortality by income level

finland_mortality_rates_chd

Age-adjusted obesity rates by class in Finland

finland_obesity

Finland male and female life expectancy at age 35 by educational level

finland_life_expectancy_by_sex_and_education

Finland life-expectancy by SES by sex at age 35

finland_life_expectancy_by_sex_and_class

source for Finland health data

 Mortality rate data according to education, males 30-74 (includes black and white in US)

mortality_rate_comparison_by_education_male_european

 Mortality rate data according to education, female 30-74 (includes black and white in US)

mortality_rate_female_comparison_by_education_european
female_educational_proportions

[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

state_life_expectancy_by_ethnicity_and_naep_scores

[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

life_expectancy_by_state_ethnicity_and_timss_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

StateLifeExpectancyByEthnicityAndGDPpercapita

[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)

LifeExpectancyByStateInequality

[Note: These are basically completely uncorrelated]

source: Life expectancy data by state and race/ethnicity

source: NAEP scores by state and race/ethnicity

us_life_expectancy_map_age_50

Smoking and obesity rates, males 50+ international comparison

smoking_and_obesity_rates_intl_male_50_plusSmoking and obesity rates, females aged 50+ international comparison

smoking_and_obesity_rates_intl_female_50_plus

Amenable mortality international comparison, 1998 & 2007

common_wealth_amenable_mortality_intl

Amenable mortality by state map

amenable_mortality_by_state_2005

Some Infant Mortality Rate Comparisons and Data

Infant mortality rate comparison, international and state ranking, 2002

infant_mortality_2002_comparison

Odds ratio by race/ethnicity and education relative to mean infant mortality rate of non-hispanic whites

imr_odds_ratio_by_race_and_education

Infant Mortality Rate odds ratio in Massachusetts by ethnicity/country of origin and education level

infant_mortality_rate_odds_ratio_in_mass_by_ethnicity_and_education

MA IMR by ethnicity (actual rates)

ma_imr_by_ethnicity

MA IMR odds ratio by ethnicity

ma_imr_odds_ratio_by_ethnicity

CDC: Infant mortality rate by ethnicity

cdc_imr_by_ethnicity_bar_chart

CDC: Infant mortality rate by birth weight and birth cohort

cdc_imr_by_birth_weight_by_cohort

CDC: Infant Mortality Rate by State

cdc_imr_map

CDC: Infant mortality rate by major race/ethnicity, 1960 – 2010

cdc_imr_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”]

Underweight birth rates

lowbirthweight_comparison

Pre-term birth rates

preterm_birth_map

CDC comparison of pre-term birth rates in the US and various OECD countries

cdc_preterm_birth_rate_by_country

Adolescent pregnancy rates comparison

adolescent_pregnancy_comparison

US vs Canada, IMR by birth weight comparison

us_canada_birthweight_imr_comparison

CDC: Various childbirth related mortality rates by race/ethnicity

perinatal_mortality_rates_us_ethnicity_methodOne

Perinatal mortality rate under alternative definition

perinatal_mortality_rates_us_ethnicity_methodTwo

CDC compilation of infant mortality rates by gestational age between US and Europe

infant_mortality_rate_by_gestational_age_us_vs_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

2009_perinatal_mortality_rates

[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

us_2007_imr_by_ethnicity_and_gestational_age

us_2007_imr_by_ethnicity

us_imr_by_cause_black_vs_white

us_pre_term_birthdates_by_ethnicity

Here is a study into the reasons behind the black – white gap in IMR

Black vs white IMR by birth weight, 2001

US_imr_by_race_and_birth_weight_2001

Black vs White IMR by gestational age

US_imr_by_race_and_gestational_age

Birth Statistics by birth cohort, part 1

US_IMR_birth_cohort_statistics_part1Birth Statistics by birth cohort, part 2 (1986 | 1991 | 1996 | 2001 | 2004)

US_IMR_birth_cohort_statistics_part2

Some more data and analysis on US state level mortality as compared to various predictors

CDC: white obesity rate by state

cdc_white_obesity

CDC: black obesity rate by state

cdc_black_obesity

CDC: latino obesity rate by state

cdc_hispanic_obesity

CDC: obesity rate by state (all races/ethnicity combined)

cdc_population_obesity

CDC: Diabetes rate by state amongst adults with more than HS education
cdc_diabetes_rate_greater_than_high_school

CDC: Diabetes rate by state amongst those with only HS education

cdc_diabetes_rate_hs_grads

CDC: Diabetes rate by state amongst those with less than HS education

cdc_diabetes_rate_less_than_high_school

[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

male_life_expectancy_by_county

Life expectancy by county, female

female_life_expectancy_by_county

source: for maps and data

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)

health_outcomes_by_smoking_rate_complex

State life expectancy and infant mortality rates (IMR) by smoking rates

health_outcomes_by_smoking_reduced

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)

health_outcomes_by_gdp_reduced

State life expectancy and IMR by income inequality

health_outcomes_by_gini_reduced

However, both smoking rates and obesity rates as quite well associated with state test scores (NAEP 8th grade test scores amongst non-hispanic whites).

heatlh_behaviors_by_test_scores

They are poorly associated with “inequality”

health_behaviors_by_inequality

And not nearly as well associated with state GDP per capita


health_behaviors_by_gdp

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).

health_outcomes_by_test_scores_complex

health_outcomes_by_test_scores_reduced

[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

health_outcomes_by_weighted_predictor_complex

Life expectancy and IMR by the same

health_outcomes_by_weighted_predictor_reduced

Comparing the above specified weighted predictor to the various components used

health_predictors_by_weighted_predictor

[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

life_expectancy_predictors

CHD mortality amongst non-hispanic whites

chd_mortality_predictors

Diabetes mortality rates amongst non-hispanic whites

diabetes_mortality_predictors

Infant mortality rates amongst non-hispanic whites


imr_predictors_complex

Pre-term birth rate amongst non-hispanic whites

preterm_birth_rate_predictors

Low-birth weight rate amongst non-hispanic whites

low_birth_weight_predictors

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.

heat_nhw_predictor

heat_nhw_smoking_rate

heat_nhw_naep_scores

heat_nhw_obesity_rate

heat_nhw_life_expectancy

heat_nhw_imr



heat_nhw_chd_deaths

heat_nhw_diabetes_deaths

heat_nhw_preterm_births

heat_nhw_birthweight

heat_income_inequality

[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]

heat_gdp_per_capita

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…..

Google Chrome 7

Google Chrome 4

Google Chrome 5

Google Chrome 3

Google Chrome 6

Google Chrome 8

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.

oecd_life_expectancy_comparison

Population structure in Europe (genetic PCA analysis)

european_population_structure

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:

  • Large interstate differences amongst white non-hispanics that cannot plausibly be explained by healthcare and which appear to be strongly correlated with test scores, smoking, and obesity (especially combined)
  • Large differences within essentially all of the countries by education, class, and the like, i.e., their healthcare systems empirically do not make these behavioral and/or genetic differences between their own (relatively homogeneous) groups insignificant.
  • Large differences in life expectancy and, especially, specific causes of mortality amongst different (large grain) racial/ethnic groups in the United States, which are not well explained by SES, and which also appear to quite consistent with all the available international data (where available, e.g., UK).
  • Large differences in historical smoking rates (!!!)
  • Large differences in historical and current obesity rates (especially with respect to diabetes and complications thereof) — this also goes to costs independent of life expectancy.
  • Large observed differences in population health measures (e.g., diabetes, heart disease, etc) which cannot plausibly be blamed on healthcare system differences
  • The very real possibility that different European or “white” populations differ significantly at a genetic level with respect to health outcomes, i.e., both between US regions/states and different historically European countries (including Canada, New Zealand, etc).
  • Significant cultural and lifestyle differences and preferences driving both observed (e.g., over-eating, smoking, drug use, teen pregnancy, etc) and unobserved differences.
  • Changing population makeup and its effects on different health measures (e.g., falling proportion of “white” births in the US–many of these groups have worse statistics with respect to IMR and early childhood mortality)
  • The paucity of data published on racial/ethnic group differences within almost all European countries.
  • The existence of very large unexplained differences between these presumably comparable European countries.
  • Lack of good empirical data on the impact of nutrition (e.g., fat, carbs, nutrients, etc) on life expectancy and health more broadly.
  • Rising diabetes and obesity rates in much of Europe (if our obesity rates are the fault of our healthcare system, then theirs are too; we’ve been richer & fatter much longer than them, broadly speaking).
  • High smoking rates in several European countries (which will likely start to see the health outcomes associated with this in ensuing decades as the damage accumulates and those cohorts approach middle ages and beyond)
  • The fact that most of these differences pre-existed the rollout of national healthcare systems in Europe and that even the divergence appears to be largely a US inflection point (i.e., we stopped gaining ground, especially women, but that’s pretty well explained by smoking and other lifestyle differences).
  • Different immigrant population (see particular infectious diseases found much more amongst 3rd world countries).

United States taxation compared to various European countries

It is well established that the United States has a much lower average tax burden than Europe (broadly).

Tax Revenue as Percentage of GDP OECD comparison

source: OECD Tax Database

However, some people seem to believe that ordinary people in Europe do not actually have to pay much higher taxes and that somehow (illogically) these countries with presumably lower income inequality are able to generate all of this tax revenue to pay for all this “free” stuff by just taxing the top 5% or some such.  This is complete nonsense!  These European countries generate this revenue, in the main, with a much broader tax base, both income and social security taxes (and consumption taxes to lesser extent).

Below are a bunch of graphs and figures produced from the OECD’s estimates from the statutory tax code (note: these are particularly sensitive to assumptions made)

Income tax plus employees and employer’s social security contributions as percentage of labor costs

oecd_tax_wedge_details

If you look at the OECD’s 2014 “Taxing Wages” report these and many other estimates can be found.

The first two charts below compile the OECD’s estimates by the average effective tax rate (“tax wedge”) by percentage of each countries average wage rate by, first, single childless households and, second, married couple with two children (one earner).

oecd_tax_wedge_single_person_zero_children oecd_estimated_tax_wedge_married_couple_two_kids_one_earner

The third chart is sort of similar, but it computes the “net” tax after cash-transfers, excluding “employer” social security contributions (!!!), divided by the labor cost. [Note: Most economists believe that the economic incidence of the “employers” contribution falls almost entirely on the worker so this method dramatically understates the AETR in Europe given their high social security contributions].

oecd_net_personal_average_tax_married_two_child_one_earner3

Here are a bunch of individual OECD charts

Note: Pay particular attention to the top line (solid black), i.e., average tax wedge.

United States Average Tax Wedge

oecd_wd_US

France Average Tax Wedgeoecd_wd_FR

Germany Average Tax Wedge

oecd_wd_DE

Finland Average Tax Wedgeoecd_wd_FI

Sweden Average Tax Wedge

oecd_wd_SE

Netherlands Average Tax Wedgeoecd_wd_NL

Canada Average Tax Wedge

oecd_wd_CA


Norway Average Tax Wedge


oecd_wd_NOSwitzerland Average Tax Wedge
oecd_wd_CH

United Kingdom Average Tax Wedge

oecd_wd_UKHere is a handy table to explain their terminology and household/tax payer categories.

oecd_terminology

As you can see 50-250% of the average wage group in the United States face a much lower tax burden than does most of Europe (the only real exception, according to this data, is Switzerland), especially the more generous welfare states, even if you factor in the value of cash benefits.  Canada and other other anglo countries come fairly close to ours at times, but ours is still lower throughout most of the distribution (not even top 5% territory).

This also does not include the effects of various consumption related taxes (e.g., sales, VAT, and excise taxes on lower income groups especially).

It also probably significantly overestimates the real US tax burden, especially in broad middle income ranges, due to the fact that healthcare benefits are not taxed and are not part of the “gross labor” estimates, not to mention that we generally have many more deductions, reliefs, and the like that narrow our tax base (more than most).

OECD Non-Standard Reliefs in USA
The basic non-standard relief is the deduction of certain expenses to the extent that, when itemised, they exceed in aggregate the standard deduction. For the purposes of this Report, it is assumed that workers claim the standard deduction. The principal itemised deductions claimed by individuals where the standard deduction is not being claimed are:

- medical and dental expenses that exceed 7.5 per cent of income;

- state and local income taxes, real property taxes, and personal property taxes.
– Home mortgage interest;
– Investment interest expense up to investment income with an indefinite carry forward of disallowed investment interest expense;
– contributions to qualified charitable organisations (including religious and educational institutions);
– casualty and theft losses to the extent that each loss exceeds USD 100 and that all such losses combined exceed 10 per cent of income; and miscellaneous expenses such as non-reimbursed employee business expenses (union dues, work shoes, etc.), investment expenses, tax return preparation fees and educational expenses required by employment, to the extent that, in aggregate; they exceed 2 per cent of income.

In 2010, the most recent year for which such statistics are available, the 36 per cent of taxpayers with income between USD 40 000 and USD 50 000 (the AW range) who itemised their deductions claimed average deductions as follows: medical expenses, USD 3 359; taxes paid, USD 1 831; charitable contributions, USD 2 310; interest expense, USD 7 951.
Contributions to pension and life insurance plans. No relief is provided for employee contributions to employer sponsored pension plans or for life insurance premiums. However, tax relief is provided for certain retirement savings.
The national currency is the dollar (USD). In 2013, the average worker earned USD 48463 (Secretariat estimate).

The United States also relies much less on consumption taxes, both as a proportion of GDP and as a proportion of taxes raised

oecd_consumption_taxes_pct_of_gdp

consumption_as_pct_of_total_taxation

Clearly most of this burden falls on lower to middle income households that consume nearly all of their incomes (not so much the “rich”).

It is also not generally true that Europe taxes capital and corporations more heavily (in fact, many of them have significantly lower effective rates on capital than the United States does!).

More on this subject later…

P.S., Here is an interesting statistic from OECD’s Social Expenditures Database (SOCX).

OECD_social_expenditures_stacked_bar_chart

 

Make of it what you will :-)

National Healthcare Expenditure: United States versus Other Countries: The US is not really an outlier.

Numerous people have asserted that the United States spends dramatically more on healthcare than other countries, presumably even more than countries of our level of wealth and affluence, and that this can only be explained by the fact that we do not have single-payer or some such.

Here are some examples graphs used to make this point

Above-expected-500x406 (1)

health-care-spending-in-the-united-states-selected-oecd-countries_chart02

These appear to be very convincing at first blush, but i never found these arguments particularly convincing due primarily to:

  1. Imperfect comparability between the selected countries
  2. Issues relating to comparing countries of the “same” GDP
  3. cherry-picking of countries

I knew the proponents of single-payer were, at best, making an incomplete argument and that it invited an exaggerated impression of what we should likely expect from a country like ours, but, up until now, I lacked the data and the time to present these argument comprehensively.  I recently got in an argument with someone over this subject and found a treasure trove of data all in one place (mostly) to thoroughly debunk this overly simplistic argument.

To make my points, vis-a-vis fundamental issues with naive treatment of GDP per capita and sensitivity to comparison countries, here is a quick chart showing National Healthcare Expenditure (NHE) as a percent of GDP by GDP per capita

NHE_as_Pct_GDP_by_GDP_per_capita

And here is another using all countries in the WorldBanks dataset with GDP per capita > 10K.  Here I am displaying both the WorldBank’s NHE (GDP component) and the WHO’s NHE data (note: this data is not complete and the numbers are slightly different).

10KPlusNHEPerCapitaAndPctByGDP

Note: Regardless of the NHE figures used (i.e., World Bank vs WHO), neither is that well correlated with GDP per capita and these correlations are certainly not a simple linear relationship (as in the two charts at top).  This situation is even worse if you look at this in NHE at the percentage of GDP terms.  I excluded a lot of poor countries (<10K/yr GDP per capita) and I honestly did not make any attempt to manipulate this data. You might get a slightly different result with better matching of WHO figures (string matching/vlookup on different country naming conventions) but the essential conclusion is no different if you pick a reasonably broad array of countries and do not systematically exclude outliers.

The point here should be very clear: this sort of analysis is highly sensitive to which countries are selected for comparison.  A few petro states, financial centers, offshoring banking, tourism-driven economies, and other atypical countries will quickly blow this analysis up.  Moreover, it already looks as if there is not a simple linear relationship between NHE and GDP in per capita terms.

*****

When KFF et al. say stuff like “While an increasing GDP per capita is associated with increased health spending, the United States is an outlier, spending more than similarly wealthy countries” they imply that GDP is a good measure of “wealth” here and that there is a linear relationship.  GDP dramatically and systematically understates US “wealth” (perhaps better phrased as material standard of living) relative to these and other countries (some not shown in their simple analysis).   Amongst other issues here, GDP per capita includes net exports (exports minus imports), tourism, banking, and other activity that does not directly contribute to the actual standard of living of the country.  There are significant differences between countries of the “same” GDP.   A country that derives, say, half of its GDP from exports or from tourism certainly gets economic benefit from that activity in the form of jobs and income, but that activity is not directly producing goods domestically, i.e., a country of the same GDP but that is not reliant of net exports, tourism (as in, tourists spending money on hotels, restaurants, etc), or the like is apt to enjoy a much higher standard of living and higher effective personal incomes than GDP alone would lead you to believe.

So, with that in mind, here are three graphs to illustrate some differences between the United States and the OECD countries that we are frequently compared to (including some other very high GDP countries to make this point very clear).

Per Capita Components of GDP (OECD)

oecd_gdp_per_capita_categories

Per Capita Individual Consumption Expenditures (OECD)

oecd_per_capita_consumption_details

Core Components of GDP amongst top 16 countries by GDP per capita (PPP-adjusted)

marked_GDPSharesTop16

Long story short, GDP ought to be interpreted with a huge grain of salt if we are claiming to predict material living conditions and/or prevailing wages (note: healthcare is man-power intensive and does not scale like, say, manufacturing can in rich countries).  To reinforce this point, and to highlight the double-standard of the nationalized healthcare proponents, vis-a-vis “it can only be because our system is (or is not) X”, here is a graph showing tertiary education expenditures in US versus other countries.   Seeing a pattern here yet?

tertiary_education

Note: They did not use a linear projection here (unlike with healthcare) and still we are way above trend.

There are much better measures of “wealth” for our purposes here and this significantly tighten up these relationships.  No measure is perfect, but some of them are much less broken than others for our purposes here.   Because this data is readily available from the World Bank, let’s go with several measures used by the World Bank:

GDP and Actual Individual Consumption

GDP and GDP per capita are the measures most commonly used to compare the economic size of countries and the economic welfare of their populations (when economic welfare is measured by the volume of individual goods and services consumed). But they are summary measures. GDP, for example, says nothing about the distribution of income within a country, while GDP per head has limitations as a measure of economic welfare. Not only does it cover the goods and services that resident households consume to satisfy their individual needs, it also includes services, such as defense, police and fire protection, that governments produce to meet the collective needs of the community, as well as gross fixed capital formation and net exports neither of which constitute final consumption.

A possible alternative to GDP would be individual consumption expenditure of households, but for the different ways health and education services are financed in countries.

In some countries, governments or non-profit institutions serving households (NPISHs) provide the greater part of health and education services and these expenditures are included in the individual consumption expenditure of government or NPISHs. In other countries, households purchase nearly all health and education services from market producers and these expenditures are included in individual consumption expenditure of households. In these circumstances, individual consumption expenditure of households is not the correct measure with which to compare the volumes of individual goods and services actually consumed in different countries. Household in countries where government or NPISHs are the main providers of health and education services will appear to consume a smaller volume of goods and services than households in countries where households pay directly for the bulk of these services.

To overcome this problem, the ICP has, from the beginning, added the individual consumption expenditures of government and NPISHs to the individual consumption expenditure of households to derive the consumption expenditure of the population. On a per capita basis, this is a better measure of the economic welfare of households as it comprises only the individual goods and services that households actually consume. It covers all such goods irrespective of whether they are purchased by households themselves or are provided as social transfers in kind by government or NPISHs. Two decades later the concept was adopted by national accountants. It is referred to as actual individual consumption in the System of National Accounts 1993.

ICP comparisons are organized so that both GDP and actual individual consumption of countries can be compared.

 

Shorter version

  • Actual Individual Consumption (AIC) = Expenditures consumed by individuals (including private, government, and non-profit/NGO transfers, subsidies and the like).  This includes food, education, healthcare, housing, entertainment, transportation, and so on.
  • Individual consumption expenditure of households = the subset of the AIC paid for directly by individual households (as in after-tax and after-transfers)
  • Individual consumption expenditure of government = the subset of AIC paid for government (e.g., public education, public healthcare expenditures, etc)

These components (all adjusted for purchasing power parity, i.e., PPP) allow us to focus on that subset of GDP that most closely resembles the material standard of living of the country in question without injecting net exports, capital investment (as the sorts you’ll see in petro states to pay for future extraction), and so on.

Ok, with that out of the way…..

National Healthcare Expenditure (NHE) per capita by Actual Individual Consumption per capita

NHE_per_capita_by_AIC_per_capita

Observe how it accommodates a much broader array of countries here (e.g., Luxembourg, Kuwait, UAE, etc), generates a much tighter fit (r^2=~0.9), and actually does not increase linearly with income (that’s a 2nd order polynomial).

NHE as percent of AIC by AIC per capita

NHE_per_capita_by_AIC_per_capita_10K

[Note: This isn’t a perfect fit and it’s not in GDP terms, but it captures the relative magnitude of NHE in consumptive terms than does the GDP per capita projection with a similarly large dataset.  The US certainly does not look like an outlier here!]

Here is another way to approach the “outlier” question….

NHE and Individual Consumption Expenditure by *households* by GDP per capita

NHE_and_HHConsumption_by_GDP

NHE_and_HHCons_by_GDP

[Note: I intentionally picked a very similar set of countries as the KFF analysis above.  The pattern of NHE and Household consumption are striking.  The US is a clear outlier in Individual Consumption Expenditure by Households per capita and in NHE, but the size of households consumption clearly swamps NHE.]

US disposable income is significantly higher in part because we have much lower taxes than most of Europe does (especially amongst the broad middle class), so let’s look at this by that component of AIC that’s actually spent by households directly (not government spending)!

Total NHE expenditures per capita by Individual Consumption Expenditure by Households

TotalNHE_by_HHCons_Euro

and on a broader array of countries….

totalNHE_by_HHCons_wide

[Note: This fit is pretty tight and it is linear formula]

Here is another chart whereby I remove non-government NHE (WHO numbers) from household consumption figures to exaggerate the effect of US NHE here.

marked_totalNHE_by_HHCons_less_PvtNHE

Comparing predictive skill amongst the same limited set of “comparable” countries

Even if we limit this analysis to the typical top ~40 OECD countries, thereby hiding the effects of large petro economies and the like with respect to very large systematic inaccuracies with GDP (for these purposes), we can still see that AIC significantly outperforms.  For starters, when they use NHE per capita that significantly masks the extent of proportional inaccuracies for less affluent countries (i.e., the “same” variance in Y is much greater for countries lower down in the X axis than it is for presumably high income countries on the right side of said distribution).

Even so, we can outperform GDP per capita:

  • GDP per capita: r**2=0.88 polynomial (0.87 linear) [part 1]
  • AIC per capita: r**2=0.96 polynomial (0.91linear) [part 2]

Part 1: NHE per capita by GDP per capita

marked_clean_NHEPC_by_GDPPC

Part 2: NHE per capita by AIC per capita

marked_clean_NHEPC_by_AICPC

This effect is even more apparent when you actually look at this in proportional terms, i.e., NHE as percent of GDP by GDP per capita vs NHE as percent of AIC by AIC per capita! (see part 3 and 4 below)….

  • GDP per capita: r**2=0.45 2nd order polynomial (0.45 linear) [part 3]
  • AIC per capita: r**2=0.75 2nd order polynomial (0.71 linear) [part 4]

This difference is even more impressive when you consider that the latter, NHE / AIC, is against a smaller denominator, i.e., if exports, fixed capital formation, and the like we actually driving this incrementally, then we’d expect this to significantly underperform, not outperform.  Of course there is still significant unexplained variance, but that variance is much less than what GDP predicts….

Part 3: NHE as Percent of GDP by GDP per capita

marked_clean_NHE_as_pct_of_GDP_by_GDPPC
Part 4: NHE as Percent of AIC by AIC per capita

marked_clean_NHE_as_Pct_of_AIC_by_AICPC

Now let’s try the same thing on the entire dataset (all OECD NHE numbers I could match), excluding and illustrating a few exceptional outliers…..

[Excluding these outliers — my quick one-time pass at this]

exclusion_grid

NHE / GDP by GDP per capita

NHE_div_GDP_by_GDPPC

NHE / AIC by AIC per capita

NHE_div_AIC_by_AICPC_all

And here, just for fun, I excluded the USA to demonstrate the trend w/o US NHE skewing it.  The polynomial trend still projects the USA at around 20% of AIC (which is surely repressed somewhat by Switzerland and Norway’s atypical behavior here). It’s still significantly non-linear and there is still significant variance from the trend (see the Netherlands at about 18% of AIC).

marked_NHE_div_AIC_by_AICPC_trend_wo_USA

Here is another way to show how broken GDP per capita is here for this sort of NHE analysis

[Note: X-axis is GDP per capita]

college_grads_gross_income college_grads_net_income

[Note: Like household consumption and NHE, the US is way above trend, Norway is below, and so on (especially in net income terms).  Long story short: This is NOT just a by-product of GDP, NIPA accounting, or what have you.]

Another way….

Average Wage (OECD figures & formulation) by Average Individual Consumption by AIC.  That income concept is somewhat different, but nevertheless the data points in the same direction.

AIC_by_AvgWage

Projecting NHE versus other categories of consumption

marked_ConsumptionCompsOECD_all

marked_ConsumptionCompsOECD_without_NL-CH

marked_ConsumptionCompsOECD_WithoutUS-NL-CH

[Note: Even when you exclude the US, a huge economy, from the calculation the trend is pretty clearly for the food, shelter, entertainment, and the like to plateau while healthcare and ‘misc’ spending grow.  Even if the NHE projection isn’t deemed sufficiently reliable here, the trends on the other major items implies growth of NHE.

Along a similar line here, we can look at compensation in healthcare.

nursing_salaries

physician_income_vs_college_labor (1) physician_income_vs_net_income (1)

Yes, US healthcare salaries are higher, but our salaries are higher across the board (the average, college grads, etc) because we truly are richer country and our people have higher real consumption expectations.  Again, see education and numerous other statistics.

Likewise, you can look at other measures of consumption….

clean_AIC_NHE_and_nonNHE_stackedbar

Google Chrome 2

Google Chrome

 

 

We also have much lower tax burdens than Europe, especially the Scandinavian/Nordic Countries

 

TaxRevenueAsPctOfGDPlabeled

 

Note: This includes all federal, city, state, and local taxes (averaged together nationally).

Source: OECD tax revenue database

 

Contrary to popular opinion, the bulk of this “extra” tax revenue in these European countries is raised on the broad middle class, not just the top few percent (which should be intuitively obvious if you think there is notably less pre-tax/pre-transfer income inequality there and understand these facts).

 

marked_oecd_tax_wedge_100aw_single_childless

 

 

oecd_tax_wedge_details

 

See here for more extensive discussion of effective tax rates in the United States versus other OECD countries.

 

 

 

Long story short:

  1. Richer countries tend to spend more real money (volume) on lots of different things, albeit in somewhat different proportions.
  2. Richer countries tend to have higher labor costs (which is very important in labor-intensive industries like healthcare)
  3. Lower taxed people tend to have more money to spend and, especially in rich countries, they choose to spend more of it on healthcare than some people might like.

 

 

 

The above three clearly apply to the United States and to an appreciably larger degree than the countries we are typically compared to.  We do not need to invoke bizarre explanations about lack of single-payer healthcare (or what have you) to explain this situation because we should pretty well expect this and there are lots of other industries and sectors where we see this sort of international disparity besides healthcare and education.

Single payer systems might theoretically have some ability to cram even lower reimbursement (and other payments) rates down the throats of the healthcare industry (e.g.,physicians, nursing, hospital administration, medical devices, pharma, etc), but there are very real limits on what you can expect in a high income country like ours given prevailing wages in other industries.  Comparing our healthcare salaries to that of, say, France without taking into account other wages is just silly.

Likewise, single-payer might be theoretically be able to force patients to consume less, but those standards (in the real world) are going to be set according to the ability of people to pay and their already sated desires.  Why should we expect US consumers to make the same spending choices that they do in significantly poorer European countries?  I certainly believe that much of our NHE is subject to rapidly diminishing marginal returns (especially with respect to crude measures like life expectancy), but just where is the political will going to come from to support draconian restrictions, further reimbursement cuts, and the like?

It seems to me that that people have this entirely backwards: cost containment (if this is really our #1 priority) must start with budgeting.  So long as we are willing to pay (or borrow) for it, why should we expect the same choices here?

This argument is especially bizarre when you look the falling growth rates in actual NHE spending in the US:

Microsoft PowerPoint 2

Microsoft PowerPoint

Likewise, the data tends to suggest that, over the past ~20 years, Europe has experienced very real “inflation” of healthcare , too, no less, and sometimes more, than the United States.

The growth rate of healthcare inflation

 

NHE_as_Pct_of_GDP_1990_index

If these single-payer systems have it all figured out, if real increases in healthcare demands have nothing to do with it, and if actual salary demands have nothing to do with it, then why have they not succeeded in keeping costs contained?  Obviously that’s just nonsense.  Moreover, given the vast differences between the actual per capita spending and NHE as percent of GDP between various regimes in Europe, why is there so little apparent curiosity for what explains these differences (specifics, please)?

I am not suggesting that there is no room for improvement here (obviously healthcare is very expensive) or that we cannot learn anything from other countries, but the analysis that has been presented thus far starts with the wrong premises (imho) and they present a very misleading picture.

Main data source:

World Bank International Comparison Program (ICP) 2011