Universal Basic Vaccines?

The following is a bloggier, graphier version of a long-form piece I wrote for Merion West last month on the case for free vaccines. If you’re so inclined, give it a read there so the editors think I’m cool!


In July, the Trump administration announced it had committed to buying 100 million doses of an unfinished Covid-19 vaccine being developed by Pfizer and BioNTech. The vaccine entered late-stage human trials on July 27, with the hope that it will be approved before the year’s end. If and when it obtains Food and Drug Administration approval, the vaccine will be delivered at no cost to the consumer, regardless of insurance status.

That could be characterized as an interesting play for an administration that made it one of its first orders of business to weaken the Affordable Care Act—not to mention a political party that spent the Obama years railing against “socialized medicine!” And indeed, many were quick to point out that in this instance, the United States is acting very much in the image of a country with a national healthcare system, the implicit critique being that the Coronavirus pandemic exposes weaknesses of “market-based healthcare” that are present under normal conditions.1

There’s some merit to this, of course. But more importantly, I think we’ve actually hit on a rare opportunity for compromise in the national healthcare debate: free vaccines. Whether or not this ends up being the first step in the path to single-payer healthcare, I think it would be a good idea. (Note: not all reform proposals are technically for a “single-payer” healthcare system. I’m using the term as a catch-all because it has a more neutral connotation than anything else I can think of.)

Why not just do it all?

To explain why, I need to take a detour. As you may know, I’m not a proponent of a government takeover of the healthcare industry (though my resolve against it has really subsided lately). My case against full-on single-payer is pretty much utilitarian—costs versus benefits.

The “costs” part of the equation isn’t super interesting. To summarize, they are enormous. The 2016 Sanders campaign offered by far the rosiest projection, putting the cost at $13.8 trillion over ten years. Most other projections are near $30 trillion over the same period—so about $3 trillion a year, or 85% of 2019 federal revenue.

Meanwhile, the benefits—in terms of objective measures of physical health—are really uncertain! I think people take it for granted that healthcare makes people healthier, but that’s actually not super clear. A high-powered experiment on this—often referred to as the “Oregon Medicaid Experiment“—was conducted in 2008. The state used a lottery system to enroll low-income, uninsured adults in Medicaid. It also selected a control group from the same population that was not enrolled so their outcomes could be compared.

The study found that Medicaid enrollment “had no statistically significant effects on blood pressure, cholesterol, or cardiovascular risk.” It did, however, produce a marked drop in depression, about 30% relative to the control group. This is why some have derided Medicaid as a “7-trillion-dollar anti-depression program.”

Medicaid enrollment did increase healthcare utilization substantially. But is this good if it’s not paired with better health outcomes? There is a widespread belief among doctors that Americans are already over-consuming healthcare. A recent survey of over 2,000 physicians found two-thirds believed at least 15 to 30 percent of medical care was unnecessary. Likewise, doctors have observed that most patients seem to be doing “just fine” despite abstaining from visiting the doctor during the current pandemic.

So to summarize my view, single-payer healthcare will cost a lot and might not do too much for us health-wise—it might even have negative effects on healthcare delivery. Its good effects can be replicated in more direct, cheaper ways. (E.g., if people are suffering from depression because they’re stressed due to finances, just give them money.) And while the healthcare system is in obvious need of reform, I’d like to see us at least try a market-oriented approach (such as forcing hospitals to be transparent about pricing) before we do something we definitely won’t be able to undo.

There are other justifications for single-payer healthcare that are harder to refute, moral arguments and the like. But this is the lens through which I personally think about it.

Why are vaccines different?

Vaccines, however, are—forgive me—immune to some of these problems. For starters, vaccines are wildly effective when compared to other types of medicine. Vaccination allowed for the global eradication of smallpox and may soon lead to the elimination of polio. In the case of other diseases like rubella, measles, and diphtheria, the United States has achieved a near 100% reduction in cases (and deaths) following the development of vaccines.

We have much less success with chronic diseases, which also happen to be responsible for the largest chunk of healthcare spending. There are probably lots of reasons for this. One is that patients simply aren’t great about adhering to long-term pharmacotherapy regiments. Approximately 50% of patients do not take their medications as prescribed, to which the Center for Disease Control and Prevention (CDC) attributes 125,000 annual deaths and 30 to 50 percent of chronic disease treatment failures. Additionally, risky behaviors—sedentarism, poor nutrition, and smoking, for example—contribute to the exacerbation of these illnesses, making treatment a more complicated process that can involve a big commitment on the part of the patient. Obviously, none of this is a problem with vaccines.

Second, vaccines are pretty cheap, especially considering their efficacy. The 100 million doses of Coronavirus vaccine the federal government has ordered will cost $1.95 billion, an average of about $20 per dose.

As advocates for single-payer like to point out, governments are often able to secure more favorable prices for medications than private sector buyers. This is true with vaccines as well, and I assume it could be more so the case under a truly monopsonic regime. (A more rigorous consideration would have to weigh this benefit against the potential drag on innovation, which is worth thinking about. Perhaps the libertarian-socialist idea of issuing government prizes in lieu of patents could obviate this concern!)

Public versus private health consumption

Another good rationale for free-to-consumer vaccines concerns public health. There are efforts from single-payer advocates to construe all health as a public good. Sometimes this is quite a reach, but in the case of vaccines, it makes a lot of sense.

As more people are vaccinated against a disease, it reduces the disease’s ability to spread and reproduce itself. This has the second-order effect of providing some protection for people who can’t get vaccinated: very young infants, people with severe allergies, or those with compromised immune systems. To a much greater extent than other kinds of health care, then, vaccines have public health benefits in a way that doesn’t require contrivance by moral argument.


The United States already has a national vaccine program that’s been quite successful. In 1994, the CDC established the Vaccines For Children program (VFC), which pays to vaccinate children who meet certain criteria. Currently, the program provides vaccines for about half of all Americans under 18. We should just expand it to cover everyone.

For the cohort born between 1994 and 2013, the CDC has estimated routine childhood immunization will prevent 322 million illnesses and 21 million hospitalizations over the course of their lifetimes, and avert 732,000 premature deaths. In financial terms, the returns on the program have been equally impressive. Again, from the CDC: “Vaccination will potentially avert $402 billion in direct costs and $1.5 trillion in societal costs because of illnesses prevented in these birth cohorts. After accounting for $107 billion and $121 billion in direct and societal costs of routine childhood immunization, respectively, the net present values (net savings) of routine childhood immunization from the payers’ and societal perspectives were $295 billion and $1.38 trillion, respectively.”

With the Democrats officially leaving Medicare for All off their 2020 platform, the debate over systemic health care reform may rage on for some time. In the meantime, perhaps, given their impressive track record, free-to-consumer vaccines are something we can all get behind.

  1. Scare quotes because whatever you may call the current healthcare system, it’s hardly a project of market fundamentalism.

Massachusetts Cities and Coronavirus

Last week, I put together a few graphs of Massachusetts covid-19 county cases and correlates. Unfortunately, working at the county level didn’t create many data points, so there wasn’t much insight to be gained. I ended the blog post with a wishy-washy pledge to maybe try to compile a city-level data set.

Well, I didn’t do it. But the Massachusetts Department of Public Health did! Starting April 15, the DPH began tracking coronavirus cases in Massachusetts’s 351 towns and cities. I took those case counts and paired them with demographic and economic data from the 2018 American Community Survey to see if any patterns emerged.

Due to some quirks of the Census, it would have been really tedious to get the data I wanted for every town and city. I ended up opting for a shortcut of sorts — using cities and towns that were their own Census Designated Place. (Don’t ask.) After all was said and done, I was left with 54 cities and towns that I could pair with data from the 2018 American Community Survey.

This data set is biased toward larger areas, which probably also means younger and more diverse areas too. And, of course, all cities and towns in the set are from Massachusetts, which probably introduces other biases.

Anyway, here’s what I’ve found so far:

(Note: I’m using log-scale to condense case counts and, in the case below, population. Boston, for example, has over 4,000 cases [log(4,000) = 3.602], while Springfield has about 600 [log(600) = 2.778].)

Log cases are best predicted by log population. This is pretty much common sense: more people means more vectors for disease, and usually denser population. A regression summary is in the caption below the graph.

log(Cases) = 1.3472 * log(population) – 4.0471
R^2 = .7526
p-value < 2.2e-16

After population, race is the next-best indicator of case counts. I’d expected — based on media reports and the word of local officials — to find a relationship between the percentage of black or Hispanic residents and log cases. But that didn’t really show up. Instead, the proportion of residents that are non-Hispanic whites has the best linear relationship to log cases — and the only one with a negative slope. The regression summary statistics in the caption are only for the white-log case relationship.

log(cases) = -1.8623 * white + 3.5474
R^2 = .545
p-value: 1.887e-10

This does actually fit the narrative pretty nicely if we lump all non-white ethnic groups together mentally. But as I noted in the last post, race is collinear with so many other variables that it’s hard to know what we’re seeing.

There is, however, at least some indication that what we’re observing might really be about race. Other variables we would imagine to correlate to race and population have much weaker relationships to case counts. (“Public transport” is the percentage of people who take public transport to work, and “Poverty line” is the percentage of people below the poverty line.) I grabbed a bunch of variables like these, but so far, none of them are very helpful in explaining what’s going on.

Combining log population and the proportion of residents who are white gives us an adjusted R^2 of .8209, which is nice. But when I tried to use that model to predict case counts for four municipalities that weren’t among the 54 I’d defaulted into working with, it only did okay.

The problem, I think, is that the selection of cities in the data set I’m working with is biased toward larger areas. It’s also possible (in fact, likely) that there’s both more to the story and that an element of randomness is at play, too. Aside from age, it’s been hard for professionals to isolate significant risk factors.

A few MA covid graphs

This is a low-stakes post.

Massachusetts has been releasing county-level coronavirus case counts, which I paired with data from the US Census to look for patterns. I actually didn’t end up finding anything particularly interesting, but some of the graphs are nice, so I thought I’d share.

On case growth

A few days ago, it looked like the growth of covid cases in Massachusetts might be flattening. But as of yesterday, it seems like that’s not quite the case across the board. Here are the total case counts per 1,000 residents of each county county since March 15:

Dukes and Nantucket counties omitted.

And here are cases per 1,000 residents on April 7, with the geometric growth rate of cases over the last week indicated by color:

Growth rate calculated as (x_1/x_0)^(1/7)-1

Berkshire, Barnstable, and Franklin counties have the lowest case growth rates, ranging from 6.2% to 7.3% on average per day over the last week. These counties have some common characteristics:

  • They’re geographically remote;
  • They are the only MA counties to have experienced population decline over the last decade;
  • They have the highest non-Hispanic white populations per capita and the least foreign residents;
  • They have the greatest proportion of residents over 65 (at least 22% in each case!);
  • Franklin and Berkshire counties have the lowest population densities, at 102 and 141 people per square mile, respectively.

To me, the above is consistent with the idea that economic activity is a vector for the spread of coronavirus (not literally, but it gets people in contact which causes person-person transmission).

Plymouth, Hampden, and Bristol are the counties with the fastest-growing case counts, each of them averaging an increase of over 11% daily over the last week. These counties don’t have much in common, so I’m having trouble putting together a potential unifying narrative.

Race and population density as correlates

It’s starting to look like black Americans might be more susceptible to coronavirus than other racial and ethnic groups. At first glance, that appears to show up in county-level data. But upon closer examination, that doesn’t appear to be the case — first because population density and the percentage of black residents are collinear, and population per square mile has a higher correlation coefficient; and second because Suffolk county (Boston) is influencing the linear relationship in both cases. Adjusted R-squared drops heavily if we exclude Suffolk county from the data set. (Race and population density were the best predictors I found of cases per 1,000 residents.)

This isn’t to say race and its many correlates aren’t good predictors. I think it speaks more to the (severe) limits of the data set I’m working with. If I have time, I may try to build a city-level data set. If anyone knows of one (or something better), link me!