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)

********

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).

********

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)

*******

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

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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).
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2 thoughts on “Some other issues with comparing US healthcare costs and so-called “outcomes”

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