Random Critical Analysis

The explanatory power of drugs, car accidents, and homicides on US life expectancy gaps

May 16, 2017November 17, 2018by rcafdm3 Comments

Although I discussed similar issues in a prior post on US health outcomes, I recently stumbled across a JAMA article authored by several CDC researchers (h/t @bswud) which points out that drug poisonings, fire-arm homicides, and motor vehicle accidents can directly explain a large part of the US life expectancy gap with several major comparison countries. By “directly” I mean that which can be mathematically estimated through the actual causes of death instead of its statistical association with life expectancy more broadly. The actual causal effect from statistical estimates are likely to be inflated by other factors that are associated with it (though I personally believe there is still a meaningful signal in the difference between the two estimates insofar as it can act as a proxy for other lifestyle differences as well and that these sorts of differences are far more important than modest differences in how health care is provisioned between different developed countries at present)

In 2012, the all-cause, age-adjusted death rate per 100 000 population was 865.1 among US men vs 772.0 among men in the comparison countries (Table 1), and 624.7 among US women and 494.3 among women in the comparison countries. Men in the comparison countries had a life expectancy advantage of 2.2 years over US men (78.6 years vs 76.4 years), as did women (83.4 years vs 81.2 years). The injury causes of death accounted for 48% (1.02 years) of the life expectancy gap among men. Firearm-related injuries accounted for 21% of the gap, drug poisonings 14%, and MVT crashes 13%. Among women, these causes accounted for 19% (0.42 years) of the gap, with 4% from firearm-related injuries, 9% from drug poisonings, and 6% from MVT crashes. The 3 injury causes accounted for 6% of deaths among US men and 3% among US women.

The US death rates from injuries exceeded those in each comparison country (Table 2). Among men, these injuries accounted for more than 50% of the life expectancy gap with Austria, Denmark, Finland, Germany, and Portugal. Among women, they accounted for more than 30% of the gap with Denmark, the Netherlands, and the United Kingdom. The country-specific comparisons depend partly on the actual size of the gap in life expectancy between the United States and each country. For example, men in Portugal have lower injury mortality than US men, but a small life expectancy advantage, which results in the 3 injury causes accounting for more than 100% of the gap.

They didn’t provide any visualizations so I thought I’d share some using their estimates.

Towards a general factor of consumption

May 9, 2017November 20, 2018by rcafdm5 Comments

Previously I demonstrated that actual individual consumption (AIC) is a superior predictor of national health expenditures (NHE) and largely explains high health spending in the United States. Towards this point it is instructive to show that not only are health expenditures generally coordinated with AIC, but that all other major categories of expenditure are too, i.e., at given level of real consumption per capita all countries will tend to allocate their consumption quite similarly.

In this post I make extensive use of Principal Components Analysis (PCA) and related dimension reduction techniques to better characterize consumption patterns across several major categories of consumption in both the spatial and temporal dimensions. I find that there is a latent factor that explains the great majority of the variance in consumption, that it is exceptionally well correlated with AIC, and that GDP has essentially zero incremental validity once we have accounted for AIC for practical purposes. I also show that this factor holds up well to price adjustment for each consumption category and correlates similarly with AIC within the OECD.

Although my interest here is (was) largely in verifying my prior analysis as it pertains to health expenditures, i.e., that AIC is real, meaningful, and the measure we probably ought to prefer when discussing the efficacy of cost containment regimes, my analysis has broader implications. For instance, it provides evidence (albeit in a roundabout fashion) that argues rather strongly against Scott Alexander’s widely cited post on cost disease, i.e., if health, education, construction, and so were truly uniquely expensive in the United States, the United States ought to stick out like a sore thumb in PCA and the like. Instead what we found is that the US consumption patterns track well with its high overall level of real consumption (AIC). Moreover, anticipating the argument that perhaps cost disease is simply well correlated with AIC, when we adjust for category specific price levels (i.e., “volumes”) we find PPP-adjusted AIC holds up very well in explaining the variance in the actual volumes consumed overall and that the US is, again, well on trend (which suggests actual apples-to-apples differences in cost are not the problem and actual increase in the quantity and quality of goods & services consumed in these categories drive most of the variance).

Health, consumption, and household disposable income outside of the OECD

April 28, 2017November 17, 2018by rcafdm1 Comment

Previously I have shown that household gross adjusted disposable income and actual individual consumption (AIC) are superior predictors of national health expenditures (NHE) and that they largely explain why US national health expenditures (NHE) are so high. However, my analyses have been restricted to the handful of mostly highly developed countries affiliated with the OECD for time series and the World Bank’s International Comparison Program (ICP) for cross section for ~all countries in 2011. I know of no simple ways to retrieve AIC or adjusted household disposable income outside of OECD in readily comparable formats, so I decided to spend a little time constructing these estimates for a much broader array of countries using the official system of national accounts tables available from the UN statistics division, which mostly covers between 1990 and 2014.

This analysis is largely a reproduction of prior work, but I felt I would write this up because:

the data themselves are useful (sharing data & code this time around)
it provides additional support for my general position vis-a-vis the utility of these measures in this context
time series nature of the data helps demonstrate the non-linear relationships between these measures and NHE

On popular health utilization metrics

April 17, 2017November 17, 2018by rcafdm4 Comments

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.

Similar analyses are found elsewhere:

source

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.

Some useful data on the dispersion characteristics of US health expenditures

April 15, 2017November 17, 2018by rcafdm2 Comments

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:

source

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).

Disposable income also explains US health expenditures quite well

April 13, 2017November 20, 2018by rcafdm9 Comments

A 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)

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

November 6, 2016November 20, 2018by rcafdm12 Comments

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:

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.

Predicting health care expenditures in OECD panel data as a non-linear relationship

October 5, 2016November 17, 2018by rcafdm7 Comments

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.

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

September 25, 2016December 12, 2018by rcafdm52 Comments

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.

Edit (12/12/18): I recommend reading this newer post instead if you’re haven’t heard from me before on this topic.

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.

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:

GDP is a substantially weaker proxy for “wealth” and a substantially weaker predictor of health care expenditures than other available measures.
The US is much wealthier than other countries in these plots in reality.
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
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.

My response to the NYTimes article on school districts, test scores, and income.

May 9, 2016November 17, 2018by rcafdm13 Comments

On April 29th the New York Times posted a nominally data driven article on school districts, test scores, and socioeconomic status. Though it contained some useful data, the analysis was terribly misleading and it excluded a tremendous amount of pertinent information. Many progressives took the article as proof that the system is “rigged”.

The NYTimes did not help matters by conflating the measures of socioeconomic status (SES) with income. Although every one of their plots used a composite SES measure on the x-axis, the article itself and various annotations give a strong impression that money/income/wealth are the primary drivers of this:

The words income, economic, wealth, money, rich, poor, and other related words were littered liberally throughout the article. Not a single mention was made of other predictors or even of the composition of the SES index they used, save for an easy to miss footnote at the end of the article.

The SES measure they used was defined in the SEDA archive as:

the first principal component factor score of the following measures: median income, percent with a bachelor’s degree or higher, poverty rate, SNAP rate, single mother headed household rate, and unemployment rate

[emphasis mine]

These non-economic dimensions actually exert significant influence on the correlations, are not directly tied to income/wealth/etc, and show marked racial/ethnic differences even at the same income level (e.g., single motherhood rates are much higher in the black community at any given level of income)

Rather than focus too much on what is wrong with this specific article (this sort of article is practically a genre unto itself these days), I will instead systematically address misperceptions here and attempt to shed more light on the nature and underlying causes of these patterns. I will argue that (1) these gaps are mostly genetic (2) they generally have little to do with systematic differences in parental economics (3) they have even less to do with the school systems themselves (4) these patterns are not unique to the US.

I will use some data from the Stanford Education Data Archive (SEDA), the same used by the NYTimes article, to help make some of my points but, unlike some of my other blog posts, I will try to cover each point in just enough depth to convey the gist of it (linking out for more in-depth analysis for those that are interested in the particular point). I will also bring together good deal of supporting evidence that is buried in different academic articles, government databases, think-tank research, and so on.