There has been a lot of chatter on the internet recently about the cost of universal health care. Will it really save money for individuals as well as for the nation’s economy? Will we have to pay more or less for health care insurance under a single-payer system? Who will pay for this, all of us or just billionaires?
I think the topic has gained more attention in response to a recent study coming out of the Yale School of Public Health purporting UHC will reduce national expenditures by $1.04 trillion while preventing 114,000 deaths a year.
According to the Google AI summary:
Cost reductions: The model estimates total yearly health expenditures would drop from a baseline of $5.28 trillion to $4.24 trillion (nearly a 20 percent reduction).
Lives saved: An estimated 114,174 annual deaths could be avoided by expanding care access and removing financial barriers for uninsured and underinsured populations.
Sources of financial savings:
- Aligning U.S. prescription drug prices with other major nations saves $377.5 billion.
- Reimbursing medical providers fairly accounts for $295.6 billion.
- Cutting general bureaucracy and waste saves $286.3 billion.
- Curbing fraudulent billing saves $285.7 billion.
- Preventing severe illness via early care reduces costly emergency room visits by $100 billion.
Source: (yale study on medicare for all – Google Search)
There are, however, some problems with this study, most importantly, it is still being peer-reviewed and has not been published in a peer-reviewed journal. For some reason, Yale decided to publicize the study before publication, which is generally unheard of. I would not like to be one of the study’s authors because of the undue pressure the prepublication announcement has created.
Additionally, in using the study politically, it is being inaccurately characterized. For example, U.S. Sen. Bernie Sanders issued the following announcement:
“New Study Confirms Medicare for All Would Save 114,000 Lives and more than $1 Trillion a Year.”
The study is an estimate, not a confirmation. I can certainly understand the senator’s enthusiasm, but as a social scientist, I have to reserve my praise until after the results have been verified.
When considering what UHC will cost and how much it will save compared to the current situation, it is important to realize that we are working with data that is in part subjective in nature. Therefore, it can be difficult to determine if these are valid and reliable estimates that hold up over time or just good approximations subject to significant fluctuation.
Case in point, the Yale prepublication announcement itself notes that there has been a major change in the projections in a period of just four to five years:
“The study builds on findings Galvani, co-author Meagan Fitzpatrick, and other colleagues published in The Lancet in 2020, which projected that universal health care would save $450 billion and more than 68,000 lives annually. The larger estimates in the new study reflect ballooning health expenditures, a widening gap between commercial and Medicare payment rates, and new estimates on recent policy changes and the underinsured.”
The projected savings in the updated study have more than doubled and the lives saved have increased by 68 percent in just five years. This raises questions about the data, the possibility that the results are flawed, or that there has been a massive change in the healthcare environment. Unfortunately, until the study is peer-reviewed and released, there is no way to examine the researcher’s methodology or attempt to replicate their findings.
For example, we know that one source of data for the Yale study comes from the American Community Survey, which is conducted by the U.S. Census Bureau. As a social scientist, I respect the work the Census Bureau produces, but I also realize they work with data that can be fluid and difficult to obtain and verify. So, I Googled “How reliable is American Community Survey data?” and got the following response.
“The American Community Survey (ACS) is considered a highly reliable source of social, economic, housing, and demographic data, but its accuracy depends on the geographic scope, time period, and statistical methods used, and it is subject to both sampling and nonsampling errors.”
Source: (how reliable is american community survey data – Search)
In other words, while the Yale study is based on good data sources, the data is still subject to some error. This doesn’t mean we should reject the Yale study, but it does mean that we should be cautious, recognizing the limitations of the data the study is based on. Something I doubt the news media will do when reporting on the study.
In The Case for Universal Health Care, I devote two chapters describing factors that influence the cost of health care. Gun violence is an example of a factor that is outside the control of the health care system; it can only respond.
Determining the costs associated with treating the victims of gun violence is difficult to do because:
- nonfatal gun injury data is not readily accessible.
- the cost of treatment varies depending on the part of the body injured and the extent and severity of the wound.
For example, being shot in the arm by a 22-caliber bullet will probably not be as life threatening nor costly to treat as being shot in the abdomen by an AR-15.
According to a RAND study (Firearm Injury Hospitalizations in America | RAND):
“There is no single resource that provides reliable estimates of state-level nonfatal firearm injuries. The data that exist are sparse, and in some cases, these data are too costly for most researchers to access.”
Nonetheless, based on available data, RAND researchers estimate that “between 2000 and 2022, there were 728,617 inpatient hospitalizations for nonfatal firearm injuries across the country: This results in an annual average of about 31,600 nonfatal firearm injuries.”
As noted, the cost for treating these injuries vary based on type, extent, and severity. For the sake of argument, let’s say that the average cost to treat a gunshot victim is $50,000. That results in $1.58 billion annual medical costs. Unfortunately, there is no way to obtain actual costs, so this estimate may be way too high or too low. Additionally, all it takes is several mass casualty events or an uptick in suicide attempts and this projection goes out the window. Consequently, you can understand why it is so difficult to estimate how much universal health care will actually cost if legislated.
Any projection of cost, whether coming from the Yale study, the 2018 Mercatus Center study (which projects increased costs rather than savings), or figures put out by Sen. Sanders will vary considerably.
So, what are we to make of all this? There is only one way to know for sure how much UHC will cost, how much it might save from current expenditures, and most importantly, how much it will cost us to pay for it. And that is to enact and create a single-payer system that everyone pays into and where everyone has access to quality health care. That is what I advocate.
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