On popular health utilization metrics

This Commonwealth Fund report has been widely cited for explaining why US health expenditures are so high.

The analysis finds that the U.S. spends more than all other countries on health care, but this higher spending cannot be attributed to higher income, an aging population,
or greater supply or utilization of hospitals and doctors. Instead, it is more likely that higher spending is largely due to higher prices and perhaps more readily
accessible technology and greater obesity.

Since I have already spoken to the incomes argument at some length and explained why I find overall “high prices” to be unpersuasive as it pertains to NHE in general and the US specifically, I will instead focus narrowly on this utilization argument since there are a number of similar analyses with identical/similar indicators.

The report proffers this table as an explanation for why high utilization cannot explain high US health expenditures.

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Similar analyses are found elsewhere:

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My problem with these sorts of analyses is that these sorts of indicators do not themselves account for enough NHE directly or correlate with NHE well enough to claim to account meaningfully for utilization and other major non-price drivers of NHE (if you wish to remove quantities of technology, prescription medicines, etc from “utilization” for semantic reasons).  When (1) your utilization measures can only account for maybe 10-15% of the variance (2) only relates to a modest proportion of NHE in most developed countries and (3) one ought to know there are other major cost drivers to account for, it’s pretty silly to claim that your half-hearted attempt to explain the variance honestly means it cannot be utilization and that it must be (mostly) the result of some US specific prices.

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Some useful data on the dispersion characteristics of US health expenditures

Some people (1, 2, 3, 4)  have made hay out of data showing total health spending in the United States is heavily concentrated on a small fraction of the population:

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My intuition and knowledge of the health care industry has long led me believe this likely not too dissimilar from what goes on in other developed countries (the young and/or reasonably healthy simply do not need or want much in the way of health care).   If nothing else it seems unreasonable to use this data to argue the US is an outlier without at least going through the exercise of comparing it to other countries.  Unfortunately the OECD and related entities provide little in the way of public data along these lines, so I have not been able to do this analysis myself.

However, I recently stumbled across a blog post from IFS regarding a study they published that speaks directly to these and related topics, so I thought I would briefly share this and related information in a quick-and-dirty blog post (full-text copy here).

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Disposable income also explains US health expenditures quite well

few months ago I argued consumption, specifically Actual Individual Consumption, is an exceptionally strong predictor of national health expenditures (NHE) and largely explains high US health expenditures.  I found AIC to be a much more robust predictor of NHE than GDP and at least an order of magnitude stronger than other components of GDP when disaggregated (collectively and separately) in multiple regression analysis.

However, because some people are inherently suspicious of consumption per se and because others are under the impression this is primarily about financing (health) consumption out of savings/debt, I think it useful to also demonstrate these patterns as it relates to household disposable income (tl;dr they’re very well correlated and produce very similar results in the long term)

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US life expectancy is below naive expectations mostly because it economically outperforms

In my prior few posts I made a strong case that the United States’ exceptionally high health care expenditures are well explained by its unusually high material standard of living.   In response to this several people I have interacted with have fallen back to the position that something still must obviously be uniquely wrong with the US health care system because US outcomes are significantly below what one might expect given its level of spending:

rcafdm_306_life_expectancy_by_hcepc_oecd.png
Life expectancy in OECD

They believe it cannot be a coincidence that the country that spends so much more than expected (according to naive expectations) also gets worse outcomes than expected and generally gets worse outcomes than the most developed countries of predominantly European and Asian origin.

In this blog post I will address the so-called “outcomes” dimension and explain why these apparently sub-par outcomes are not only not otherwise inexplicable, but can actually be explained in a fairly straight forward and parsimonious fashion  For the moment, I will narrow my focus on the subset of factors that drive US health outcomes significantly below naive expectations (not necessarily the full residual) and that I have good reason to suspect are significantly causally related to the expenditures issue.  Later, perhaps in another lengthy blog post, I will address other factors that are mostly orthogonal to expenditures and that further affect US health outcomes.

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Predicting health care expenditures in OECD data with non-linear model specification

In my prior post, wherein I argued at length that US health care expenditures are reasonably well explained by Actual Individual Consumption (AIC) and that GDP is an inferior predictor, I pointed out toward the end that the linear specification I used is likely to significantly overstate US residuals because there is good evidence for non-linearity and because the US is far out on the frontier vis-a-vis consumption.

This non-linearity can be seen pretty clearly if you look at the 2011 data derived from the World Bank (for AIC) and WHO (for HCE).

rcafdm_52_who_and_worldbank_nhe_by_aic
In per capita terms
rcafdm_54_who_and_worldbank_nhe_pct_aic_by_aic
In percentage terms

Since some people may (1) doubt the accuracy of these statistics outside of the few highly developed countries (2) imagine that these poor countries are somehow qualitatively different in a way that’s not well correlated with their level of economic development or (3) are particularly reluctant to accept non-linearity as a potential partial explanation for the US here, I thought I’d approach this from a somewhat different angle.


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High US health care spending is quite well explained by its high material standard of living

About two years ago I created a long blog post arguing that the United States is not an outlier in healthcare expenditures per capita.   Following renewed interest from a link from Marginal Revolution recently and some criticism from a few people on various comment threads, I thought I’d take the time to update the evidence, address some areas of criticism, and muster yet more lines of evidence to support my argument.   This post should largely make the earlier post obsolete, but I will keep the earlier post up for posterity and to retain data/information that won’t necessarily be perfectly duplicated in this post.

There exist several popular plots like these that people use to make the argument that the United States spends vastly more than it should for its level of wealth.

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

These plots and the arguments that usually go with them give the strong impression that US spends about twice as much as it should.  However, these are misleading for several reasons, namely:

  1. GDP is a substantially weaker proxy for “wealth” and a substantially weaker predictor of health care expenditures than other available measures.
  2. The US is much wealthier than other countries in these plots in reality.
  3. The arbitrary selection of a handful of countries tends to hide the problems with GDP in this context and, oddly enough, simultaneously downplay the strength of the relationship between wealth and health care spending
  4. Comparing these two quantities with a linear scale tends to substantially overstate the apparent magnitude of the residuals from trend amongst the richer economies when what we’re implicitly concerned with is the percentage spent on healthcare.

When properly analyzed with better data and closer attention to detail, it becomes quite clear that US healthcare spending is not astronomically high for a country of its wealth.  Below I will layout these arguments in much greater detail and provide data, plots, and some statistical analysis to prove my point.

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On the relationship between negative home owner equity and racial demographics

There are a large and growing number of popular media articles alleging racial discrimination in the mortgage market.   It is simply assumed that if lenders are less willing to extend credit to blacks or make loans in “black neighborhoods” as often or with similarly favorable terms as they do whites or “white neighborhoods” that this apt to be explained by explicit racism or (subconscious) bias.  These naive arguments persist despite tremendous evidence that there are observable and unobservable differences that have profound effects on credit risk.

I will briefly describe some of this evidence before making my own modest contribution using data from zillow.com and the US census. You can click here if you are familiar with this literature already and wish to skip ahead to my analysis.

1- Blacks have much lower credit scores (e.g., FICO) 

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The difference in between the white and black means is about 1 standard deviation.

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Understanding socioeconomic mobility

Although it may not seem like it at first blush, given the apparently modest correlations, the socioeconomic figures that I blogged about earlier largely agree with the published data on economic mobility.

They are measuring income (or earnings) whereas I am measuring with the ELS SES index (which is an equally weighted average of the respondents own earnings as of 2011, educational attainment, and occupational prestige), but the systematic income differences between classes (however measured) are almost certainly virtually fully mediated by this more comprehensive SES measure.

ELS data, students 2011 SES by 2002 SES of parents

ses_mobility_all_people

The correlation between parent SES and child SES is 0.35.  This may not sound like much, but if you bin child SES and parent SES into quintiles the mobility estimates are very similar to the widely publicized economic mobility estimates.

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Microsoft Excel

The average person born at the top of the SES distribution has little chance of ending up at the bottom and vice versa, but there is clearly a great deal of mobility that happens amongst less extreme groupings.

[Note: I didn’t correct these figures for oversampling, so I won’t claim they’re a perfect representation of reality, but they are generally pretty close in practice and they still are good for illustrative purposes.]

Mobility delta (child SES – parent SES) by parent SES

ses_mobilty_all_people

On average, as compared to their parents, high SES people are downward mobile and low SES people are upward mobile.  This may seem counter-intuitive to some, but this makes sense because r < 1, i.e., there is non-trivial mobility, and this must be true for there to be meaningful relative mobility.  Of course, just because there is mobility doesn’t imply that all people have an equal chance of ending up in any place in the SES distribution.

As I mentioned in my last post, there are other differences between groups besides just propensity to end up in particular SES bins and many of these differences are highly predictive of mobility — see test scores, HS GPA, etc.  Indeed, they almost fully mediate outcomes, so talking in terms of “mobility” here can be a bit misleading because relative starting position (parent SES) tells you relatively little about what is likely once you have better information (e.g., test scores, HS GPA, etc).

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Predicting economic mobility from 10th grade test scores

In my last post I briefly touched upon economic mobility vis-a-vis the link between test scores and subsequent adult incomes.  Because these individuals were still pretty young, just a few years out of college (if they graduated), the earnings correlations were weaker than one might have expected.  Since then I discovered an interesting continuous SES variable (F3SES) in the ELS:2002 data set that is probably a better measure of future earnings or mobility.

F3SES is the average of 3 inputs (2011 earnings from employment, the prestige score associated with the respondents current/most recent job, and educational attainment), each of which is standardized to a mean of 0 and a standard deviation of 1 prior to averaging.

Data users should note that, as of the third follow-up, socioeconomic status may be less-than fully stable for some third follow-up respondents, e.g., respondents with graduate-level education who are just beginning or have yet to begin their careers.  Users should also note that F3SES does not account for the income, occupation, or education of the respondents spouse/partner, and therefore may not be fully indicative of household socioeconomic status as of the third follow-up.

NOTE: While the two versions of the BY family SES composites (BYSES1 and BYSES2) were created by differential assignment of prestige scores based on the 16-category BY occupation variables, F3SES is created by assigning prestige scores based on the 2-digit ONET code associated with the respondents current/most recent job as of the third follow-up.

While I am sure I could derive my own formula to produce a similar composite score, I’ll just use theirs for the time being.


ses_by_test_white_males

ses_by_test_white_and_black_comparison

There is no statistically significance difference between blacks and whites here.

ses_by_test_wba

Asian SES is higher than white SES for most of the distribution, but that’s not statistically significant either.

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Exploring ELS 2002 data

The NYTimes turned me onto a new data source in a recent article on college graduation rates by SES.  They implied that college graduation rates are better predicted by “wealth” than by the students test scores (10th grade ELS scores taken in 2002).

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Being both curious about the underlying data and somewhat skeptical of the particular claims (or, at least, its interpretation) I decided to investigate it for myself.  Having done so now, I can tell you that it’s a pretty rich data set.  Unfortunately, a few key data points (e.g., SAT scores, HS GPA, etc) are censored or rounded/binned to protect anonymity, but there are still a lot of interesting data there to analyze.


 

Update (6/6/15):

As in my follow-up post on economic mobility, I realized that they actually provided 9th-12th grade high school GPA as a non-continuous variable in the publicly accessible file.  I have updated my post to reflect this new information in a few places!


First point

The parent’s educational attainment is a much better predictor of both test scores and subsequent child educational attainment than economic measures…..

 (Bachelor degree) Attainment rate by test scores, grouped by parent income levels

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