Why Many Substance Use Treatment Facilities Don’t Take Insurance

According to the latest data, about one in four facilities doesn’t accept private insurance or Medicaid, and more than half don’t accept Medicare. This makes substance use treatment something of an outlier, since 91% of all US health spending is paid for through insurance. Still, there are many reasons to prefer being paid in cash: insurance might reimburse at low rates, impose administrative hassles, and generally try to tell you how to run things.

Providers generally put up with the hassles of insurance because they see the alternative as not getting paid. But if demand for their services gets high enough that they can stay busy with patients paying cash, they will often try going cash-only. Some try to generate high demand by providing excellent service. Sometimes high demand comes from a growing health crisis, as with opioids.

Demand can also be high relative to supply because supply is restricted. US health care is full of supply restrictions, but in this case I wondered if Certificate of Need laws were playing a role. As we’ve written about previously, CON laws require health care providers in 34 states to get the permission of a government board to certify their “economic necessity” before they can open or expand. But there’s a lot of variation from state to state in what types of services are covered by this requirement; acute hospital beds and long-term care beds are most common. 23 states require substance use treatment facilities to obtain a CON before opening or expanding.

States with Substance Use–Treatment CON Laws in 2020. Created using data from Mitchell, Philpot, and McBirney

How do these laws affect substance use treatment? We didn’t really know- only one academic article had studied substance use CON, finding it led to fewer facilities in CON states. But I’ve studied other types of CON, so I joined forces with Cornell substance use researcher Thanh Lu and my student Patrick Vogt to investigate. The resulting article, “Certificate-of-need laws and substance use treatment“, was just published at Substance Abuse Treatment, Prevention, and Policy. Here’s the quick summary:

We find that CON laws have no statistically significant effect on the number of facilities, beds, or clients and no significant effect on the acceptance of Medicare. However, they reduce the acceptance of private insurance by a statistically significant 6.0%.

Overall I was surprised that CON didn’t significantly affect most of the outcomes we looked at, and appears to be far from the main reason that treatment facilities don’t take insurance. Still, repealing substance use CON would be a simple way to improve access to substance use treatment, particularly since CON doesn’t appear to bring much in the way of offsetting benefits.

Going forward I aim to investigate how these laws affect health outcomes like overdose rates, and to dig more into the text of state laws and regulations to determine exactly what is covered by substance use CON in different states. As the article explains, we identified several errors in the official data sources we were using. This makes me worry there are more errors we didn’t catch, and there are certainly things the sources just don’t specify, like in which states the laws apply to outpatient facilities. So I hope we (or someone else) will have even better work to share in the future, but for now this article is as good as it gets, and we share our data here.

COVID Deaths, Excess Deaths, and the Non-Elderly (Revisited)

While we know that COVID primarily affects the elderly, the mortality and other effects on the non-elderly aren’t trivial. I have explored this in several past posts, such as this November 2021 post on Americans in their 30s and 40s. But now we have more complete (though not fully complete) mortality data for 2021, so it’s worth revisiting the question of COVID and the non-elderly again.

For this post, I will primarily focus on the 12-month period from November 2020 through October 2021. While data is available past October 2021 on mortality for most causes, data classified by “intent” (suicides, homicides, traffic accidents, and importantly drug overdoses) is only fully current in the CDC WONDER data through October 2021. This timeframe also conveniently encompasses both the Winter 2020/21 wave and the Delta wave of COVID (though not yet the Omicron wave, which was quite deadly).

First, let’s look at excess mortality using standard age groups. For this calculation, I use the period November 2018 through October 2019 as the baseline. The chart shows the increase in all-cause deaths in percentage terms. It is also adjusted for population growth, though for most age groups this was +/- 1% (the 65+ group was 3% larger than 2 years prior).

A few things jump out here. First notice the massive increase in mortality for the 35-44 age group (much more on this later). Almost 50% more deaths! To put that in raw numbers, deaths increased from about 82,000 to 122,000 for the 35-44 age group, and population growth was only about 1%. And while that is the largest increase, there were huge increases for every age group that includes adults.

Also notice that the 65+ age group certainly saw an increase, but it is the smallest increase among adults! Of course, in raw numbers the 65+ age group had the most excess deaths: about 450,000 of the 680,000 excess deaths during this time period. But since the elderly die at such high rates in every year, the increase was as large in percentage terms.

One related fact that doesn’t show up in the chart: while there were about 680,000 excess deaths during this time frame in the US in total, there were only about 480,000 deaths where COVID-19 was listed as the underlying cause of death. That means we have about 200,000 additional deaths in this 12-month time period to account for, or a 24% increase (population growth overall was only 0.4%).

That’s a lot of other, non-COVID deaths! What were those deaths? Let’s dig into the data.

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Eat 20 Potatoes a Day…. For Science

Several people have tried eating an all-potato diet for a few weeks and reported losing lots of weight with little hunger or effort. Could this be the best diet out there? Or are we only hearing from the rare success stories, while all the people who tried it and failed stay quiet?

Right now we don’t really know, but the people behind the Slime Mold Time Mold blog are trying to find out:

Tl;dr, we’re looking for people to volunteer to eat nothing but potatoes (and a small amount of oil & seasoning) for at least four weeks, and to share their data so we can do an analysis. You can sign up below.

I was surprised to see that they are the ones running this, since they are best known for the “Chemical Hunger” series arguing that the obesity epidemic is largely driven by environmental contaminants like Lithium. The conclusion of that series noted:

Bestselling nutrition books usually have this part where they tell you what you should do differently to lose weight and stay lean. Many of you are probably looking forward to us making a recommendation like this. We hate to buck the trend, but we don’t think there’s much you can do to keep from becoming obese, and not much you can do to drop pounds if you’re already overweight. 

We gotta emphasize just how pervasive the obesity epidemic really is. Some people do lose lots of weight on occasion, it’s true, but in pretty much every group of people everywhere in the world, obesity rates just go up, up, up. We’ll return to our favorite quote from The Lancet

“Unlike other major causes of preventable death and disability, such as tobacco use, injuries, and infectious diseases, there are no exemplar populations in which the obesity epidemic has been reversed by public health measures.”

That said, they did still offer some advice based on the contaminant theory that is consistent with the potato diet:

1. — The first thing you should consider is eating more whole foods and/or avoiding highly processed foods. This is pretty standard health advice — we think it’s relevant because it seems pretty clear that food products tend to pick up more contaminants with every step of transportation, packaging, and processing, so eating local, unpackaged, and unprocessed foods should reduce your exposure to most contaminants. 

2. — The second thing you can do is try to eat fewer animal products. Vegetarians and vegans do seem to be slightly leaner than average, but the real reason we recommend this is that we expect many contaminants will bioaccumulate, and so it’s likely that whatever the contaminant, animal products will generally contain more than plants will. So this may not help, but it’s a good bet. 

Overall though I think the idea here is to ignore grand theories and take an empirical approach. The potato diet works surprisingly well anecdotally, so lets just see if it can work on a larger scale. Seems worth a try; I’m sure plenty of my ancestors in Ireland and Northern Maine did 4-week mostly-potato diets and lived to tell about it. You can read more and/or sign up here. Let us know how it goes if you actually try it!

Are the COVID Vaccines Effective at Preventing Death?

A recent analysis by the Kaiser Family Foundation of CDC data suggests that about 234,000 COVID deaths in the US could have been prevented if everyone was vaccinated. That’s about 25% of all COVID deaths throughout the pandemic, and about 60% of COVID deaths since June 2021 (roughly the time when most older adults in most states had had a chance to be vaccinated).

The first way to think of that death rate is tragic, given that so many lives could have been saved. Rather than being the high-income nation with the highest COVID death rate, the US could have been more in line with countries like Italy, the UK, and France. The US actually had a lower COVID death rate than Italy and the UK when the vaccine roll-out began, and today we could be at about France’s level with better vaccination rates.

But there’s a flipside to the KFF numbers. If 60% of COVID deaths since June 2021 were preventable, that means 40% weren’t preventable. Furthermore, the same data show that about 40% of COVID deaths in January and February 2022 were fully vaccinated or had boosters. That sounds like the vaccines might not work very well! So what does this all mean? Let’s dig into the data from the CDC a little bit.

The first, and most important thing, to recognize is that most American adults are vaccinated (about 78%), so unless vaccines are 100% effective (and they aren’t, despite some public officials overenthusiastic pronouncements early in the vaccine rollout), there are still going to be a lot of COVID deaths among the vaccinated. If 100% of the population was vaccinated, 100% of the deaths would be among the vaccinated. The key question is whether vaccines lower the chance of death.

And they do. Let’s see why.

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How to Get People Vaccinated for 93 Cents

We’ve talked a lot about vaccines on this blog, including both the benefits of vaccines and how to get people vaccinated. For example, last month I posted about Robert Barro’s estimate on the number of additional vaccines needed to save 1 life. Barro put it at about 250 vaccines. Using some reasonable assumptions, I further suggested that each person vaccinated has a social value of about $20,000. That’s a lot!

But how do we convince people to get vaccinated? Lotteries? Pay them? In addition to just paying them (the economist’s preferred method), another good old capitalist method is advertising (the marketer’s preferred method). And a new working paper tries just that, running pro-vaccine ads on YouTube with a very specific spokesman: Donald Trump.

Running ads on YouTube is pretty cheap. For $100,000, the researchers were able to reach 6 million unique users. And because they randomized who saw the ads across counties, they are able to make a strong claim that any increase in vaccinations was caused by the ads. They argue that this ad campaign led to about 104,000 more people getting vaccinated, or less than $1 per person (the actual budget was $96,000, which is how they get 93 cents per vaccine — other specifications suggest 99 cents or $1.01, but all of their estimates are around a buck).

Considering, again, my rough estimate that each additional vaccinated person is worth $20,000 to society (in terms of lives saved), this is a massive return on investment. Of course, we know that everything runs into diminishing returns at some point (they also targeted areas that lagged in vaccine uptake). Would spending $1,000,000 on YouTube ads featuring Trump lead to 1 million additional people getting vaccinated? Probably not quite. But it might lead to a half million. And a half million more vaccinated people could potentially save 2,000 lives (using Barro’s estimate).

I dare you to find a cheaper way to save 2,000 lives.

Highlights from EAGx Boston

Last weekend I was at Effective Altruism Global X Boston, a great conference that worked very differently from the academic ones I usually attend. The attendees were younger and the topics were different, but the big innovation was the use of Swapcard to encourage 1-on-1 meetings. At academic conferences I spend most of my time listening to formal presentations or talking to people I already know, but here I talked to 13 new people for a half hour each, and many others more briefly.

That said, the talks I did attend were excellent. Alvea is a 3-month-old company that already has a novel DNA-based Omicron-targeted Covid vaccine in Phase 1 trials. My notes on co-founder Ethan Alley’s talk:

Learning by doing is the way to go. I learned more in 3 months as a founder than 12+ months as an MIT grad student. Like that you have to pay a company $125k to randomize your clinical trial, and they take 8 weeks to do it

Richard Cash talked about the Oral Rehydration Therapy he helped develop that has saved tens of millions of lives. In short, many people who died of diarrheal diseases like Cholera were simply dying from dehydration, and he realized that this can be prevented cheaply and easily in most cases by having them drink a solution of water, glucose, and certain salts (basically Gatorade). He noted that much of the basic research behind this had been done in the US well before it was applied in the developing countries where it has helped most, so it was crucial to simply notice how important and broadly applicable the findings were. On the other hand, some things really did work differently in developing countries; here the medical conventional wisdom was that people shouldn’t eat while they had diarrhea, but if kids are already malnourished it turns out they are better off eating anyway.

Wave is a mobile payment company that is hugely successful in Senegal but has been slow to expand elsewhere. I asked their Chief Technical Officer Ben Kuhn why this was, and his answer made perfect economic sense:

Fixed costs plus local network effects. Fixed costs: need to get approval of a country’s central bank to operate, need to hire local staff, et c. Network effects: our system gets more valuable as more of the people you send money to/from use it, and these are usually within-country. Makes more sense to keep expanding within a country until its nearly totally saturated, and only then move to the next country. There’s also a limit of how much $ we have to expand, especially since we don’t want VCs to control the company.

(My notes, not a verbatim quote)

As I talked to people I was trying to narrow down my post-tenure plans. This didn’t really work, because people gave me good new ideas without convincing me to abandon any of my old ideas. Although I talked to several senior researchers at NGOs, the ideas that stuck with me most came from talking to undergrads, and were all things that sound obvious in hindsight but that I hadn’t actually been planning to do. The one I’ll mention here as a commitment device is to post my research ideas on my website. I have many more paper ideas than I have time to write about them, and I no longer care much about whether I get credit/publications for them or someone else does. This summer I’ll post a list of ideas there, and perhaps a series of posts fleshing them out here.

P.S. If you identify at all with Effective Altruism, I recommend trying to attend a conference. I’m planning to go next to the one in DC in September.

$5,000 Worth of Vaccines Saves One Life

I’ve written about the social benefits (in terms of the value of lives saved) of COVID mitigation measures, such as wearing face masks, before. But at this juncture in the pandemic (and really for the past 12 months), the key mitigation measure has been vaccines. How much does it cost to save one life through increased vaccination?

Robert Barro has a new rough estimate: about $5,000. In other words, he finds that it takes about 250 additionally vaccinated people in a state to save one life, and the vaccines cost about $20 to produce (marginal cost). So, about $5,000.

Barro gets this number (specifically, that 250 new vaccinated people saves one life) by using cross-state regressions on COVID vaccination rates and COVID death rates. Of course, there are plenty of potential issues with cross-state regressions. It’s not a randomized control trial! But Barro does a reasonable job of trying to control for most of these problems.

Another way to restate these numbers: if we assume that the VSL of an elderly life is somewhere around $5 million, then the social benefit from each person getting vaccinated is around $20,000. In other words from a public policy perspective, it would have made sense to pay each person up to $20,000 to get vaccinated!

Or thought of one more way: each $20 vaccine is worth about $20,000 to society. That’s an astonishing rate of return. And we’re not even including the value of opening up the economy earlier (from both a political and behavioral perspective) than an alternative world without the vaccines.

Lessons from a Failed Merger

The two largest hospital systems in Rhode Island, Lifespan and Care New England, wanted to merge. I wrote previously that:

Basic economics tells us that if a company with 50% market share buys a company with 25% market share in the same industry, they have strong market power and are likely to use this monopoly position to raise prices…. I think the Federal Trade Commission will almost certainly challenge the merger, and that they will likely succeed in doing so

It turns out I was right about the FTC challenge, but wrong that it would be necessary. The same day that the FTC challenged the merger, Rhode Island Attorney General Neronha blocked it. The law in Rhode Island is such that he doesn’t need to convince a judge like the FTC would; the merger was done unless the parties tried to appeal. But today they gave up and officially terminated the merger.

I was surprised by the AG’s move because the merging parties have so much political clout in the state, and many politicians like Senator (and former RI AG) Whitehouse had expressed support for the merger. I expected that even if state leaders didn’t like the merger, they would approve it with the expectation that the FTC would step in and be the bad guy for them. So AG Neronha blocking the merger was a pleasant surprise.

I also said previously that the FTC might challenge the merger for creating a monopsony (predominant employer of health care workers) as well as a monopoly (predominant provider of hospital services). This turned out to be one vote short of true. The FTC voted 4-0 to challenge the merger, but released two concurring statements explaining why. The two Democratic commissioners wanted to challenge the merger on both monopoly and monopsony grounds, while the two Republican commissioners thought it would only be a monopoly. This didn’t matter for this case, since they all thought it would be a monopoly, and since the AG blocked it. It was also odd that the Democratic FTC commissioners were more worried about labor than the actual unions involved. But it may be a sign of more monopsony challenges to come, particularly once the vacant spot gets filled and a 3rd Democrat is breaking the ties.

This was the first big political / economic issue I’ve got involved in since moving to Rhode Island, and I have to admit I was worried about making enemies. But despite speaking against the merger at the same forum as its most powerful proponents, speaking to several journalists, and at the AG’s public forum, I didn’t hear a single angry response; if anything I made friends.

One final surprise in all this is that the two hospitals systems are reported to have spent $28 million pursuing the merger. Apparently money can’t buy everything. But what a lot to spend on something that so many of us thought was clearly destined to fail.

Health Insurance Benefit Mandates and Health Care Affordability

My article on benefit mandates was published today at the Journal of Risk and Financial Management. It begins:

Every US state requires private health insurers to cover certain conditions, treatments, and providers. These benefit mandates were rare as recently as the 1960s, but the average state now has more than forty. These mandates are intended to promote the affordability of necessary health care. This study aims to determine the extent to which benefit mandates succeed at this goal

I began my research career by writing about these mandates, and my goal with this article was to tie up that whole chapter. I realized that all my articles on benefit mandates, as well as most of what other economists write about them, simply try to measure their costs- how much they raise health insurance premiums, raise employee contributions to premiums, lower wages, lower employment, or harm smaller businesses. Its good to know their costs, but to really evaluate a policy we should learn about its benefits too so that we can compare costs and benefits.

One key benefit that had yet to be measured was how much a typical mandate lowers out-of-pocket health care costs. In this article, I estimate that the average benefit mandate lowers costs by 0.8%-1%. I argue that combining this with a measure of how mandates affect total health spending by households could provide a sufficient statistic for the net benefits of mandates for households. I’m not totally confident this works in theory though, and it has a big challenge in practice- one of my empirical strategies finds that mandates reduce total spending, but the other finds they don’t. So I think the main contribution of the article ends up being the first estimate of how the average state health insurance benefit mandate affects out-of-pocket costs.

I’m currently planning to move on from writing about mandates- other topics are catching my eye, state policymakers don’t seem to particularly care what the research says about mandates, and changes in how economists use difference-in-difference methods are making it harder to publish articles like this that study continuous treatments. But I think there are still big opportunities here for anyone who wants to take up the torch. First, the ACA Essential Health Benefits provision changed the game for state mandates in a way that I have yet to see the empirical literature grapple with. Second, there are more than a hundred separate types of state benefit mandates; in most of my articles I aggregate them but they should really be studied separately. A handful have been, such as mandates for autism treatments, infertility treatments, and telemedicine. But the vast majority appear to be completely unstudied.

P.S. Writing this article gave me two wildly varying opinions of our federal bureaucracy. I tried to get both data and funding from the Agency for Healthcare Research and Quality for this article. The data side worked well- they were surprisingly fast, efficient and reasonable about the process of accessing restricted data. On the other hand, I applied for funding from AHRQ in March 2019 and still have yet to officially hear back about it (it is “pending council review” in NIH Commons). This sort of thing is why nimble organizations like Fast Grants can do so much good despite having much smaller budgets.

P.P.S. This article is part of a special issue on Health Economics and Insurance that is still accepting submissions. I’m the guest editor and would handle your submission, though my own got handled by other editors and put though multiple rounds of revisions.

Half of Deliberately Exposed Unvaccinated Volunteers in UK Study Did Not Get COVID; Why?

A British study by Ben Killingley and 31 co-authors recently appeared in pre-print form, where 36 (heroic) healthy young adult volunteers were deliberately exposed to the Covid virus by nasal drops. These volunteers then went into quarantine for 14 days, and logged their symptoms and were subjected to various tests for a total of 28 days.

Of the 36 subjects, only 18 (53%) became infected with the virus, as determined by PCR testing (the gold standard for Covid tests) and by direct counting of viral loads in mucus cells by FFA.

The study found that viral shedding (as estimated by mucus viral loads) begins within two days of exposure and rapidly reaches high levels, then declines. Viable virus is still detectible up to 12 days post-inoculation. This result supports the practice of people quarantining for at least 10 days after they first exhibit symptoms of infection. There were significant higher viral loads in the nose than in the throat,  which supports the practice of wearing masks that cover the nose as well as the mouth.

The cheap, fast, LFA rapid antigen test method (used in home tests) performed fairly well. Because it is less sensitive, it did not it did not yield positive results for infected individuals until an average of four days after infection, or about two days after viral shedding may have begun. But from four days onward, the LFA method was sensitive and reasonably accurate which supports the ongoing use of these quick, cheap tests.

These direct inclusions from the paper are helpful, but not earthshaking. The elephant in the room, which the paper did not seem to directly address, is why nearly half of the people who were exposed did NOT become infected. This raises all kinds of issues about what mechanisms the human body may have to naturally fight off COVID or similar viral infections. Gaining insight on this could lead to breakthroughs in preventing or mitigating this pernicious virus.

An article by Eileen O’Reilly at Axios probes these questions. There is nothing conclusive out there, but four ideas that are under investigation are:

1. Cross-immunity from the four endemic human coronaviruses is one hypothesis. Those other coronaviruses cause many of the colds people catch and could prime B-cell and T-cell response to this new coronavirus in some people.

2. Multiple genetic variations may make someone’s immune system more or less susceptible to the virus.  Some 20 different genes have been identified which affect the likelihood of severe infection, and a genetic predisposition to not getting infected is seen in other diseases where people have one or multiple factors that interfere with the virus binding to cells or being transported within.

3. Mucosal immunity may play an underrecognized role in mounting a defense.

This suggests nasal vaccines might have a chance at stopping a virus before it invades the whole body.

4. Where the virus settled on the human body, how large the particle was, the amount and length of exposure, how good the ventilation was and other environmental circumstances may also play a role.

These considerations support continuing with the usual recommendations of social distancing, wearing facemasks, and ventilating buildings, especially when caseloads are peaking. Also, the doses administered to the volunteers in the study were considered quite small by clinical standards. It was surprising that such a low dose was effective as it was in causing full-blown infections; and the particular strain used in the experiment was not necessarily one of the more recent highly virulent variants. After reading these results,  it is more understandable to me why so many reasonably careful friends and family members of mine (nearly all vaccinated, fortunately) have come down with (presumably) omicron COVID in the past two months. Just a little dab will do ya.