The So Called “Opioid Epidemic” Isn’t One Trend. It’s Several Trends.

The following chart appeared already in a previous post, but here it is once again for reference:

All data comes from the CDC's Wonder database. There are several things going on here in the most lethal drug categories.

Other Opioids

This is the most lethal category, and it’s seen a dramatic increase in recent years, from ~1 death per 100k population to ~4 deaths per 100k. This category represents most of the pill-form opiates, hydrocodone and oxycodone and some others. When you hear about the increase in opioid deaths, you are mostly hearing about this category. Notice, however, that it’s flattened out since 2011. It dips down in 2012 and 2013, then in 2014 matches its 2011 level. A similar rise, then dip, happens in the Benzodiazepines trendline. I’ll explain why below in the Benzodiazepines section (and I explain more thoroughly in the link above).

Cocaine

Cocaine was the most lethal category of drugs until about 2006, when cocaine deaths dropped dramatically as “other opioid” deaths continued rising. The drop in deaths is matched by a drop in measured usage rates. (See here,  page 10 figure 11 for a time series of "past month" usage rates.) This should be part of the “rising opioid epidemic” narrative. There’s no apparent explanation for the rise in cocaine deaths between 1999 and 2006, because usage rates were flat. One could be forgiven for thinking that the rise between 1999 and 2006 is spurious, related more to reporting and cause-of-death attribution trends than actual overdoses. At any rate, it should be reported that total drug overdose deaths supposedly increased from 1999 to 2014 despite the most lethal category of drugs staying flat. Furthermore, the number of drug overdoses increased from 2006 to 2014 despite the fact that 2006’s most lethal drug category plummeting. I think this is worthy of some commentary.

Benzodiazepines

The benzodiazepine trendline matches the other opioid trendline fairly closely. (See previous post; correlations are very high, and by-state correlations are also very close.) These drugs are sedatives that are often prescribed for anxiety. In a sense, benzodiazepines are just “along for the ride” with the opioid poisoning deaths. If you look at individual death records for 2014, 52% of benzodiazepine deaths also involved the “other opioids” and 76% involved “other opioids”, methadone, *or* “other synthetic narcotics.” Overwhelmingly these are not straight-up overdoses but are actually drug interactions. If you look at it the other way, ~30% of opioid poisoning deaths also involve benzodiazepines. This warning needs to be made more clearly to the public: IF you’re going to abuse either of these substances anyway, don’t mix them. We’d spare ourselves some 5,000 deaths a year if we got people to follow this advice (again, based on the 2014 numbers I have). (For a thorough treatment of multi-drug poisonings, see my previous post here.)

Heroin

Heroin deaths were on a slow increase from 1999 to 2010, and they’ve increased dramatically (a factor of ~3.5) since 2010. It’s now the second most lethal drug category, after other opioids. It’s not a simple matter of rates of heroin use increasing. If you divide the number of deaths by the number of users (admittedly this later figure is an approximation based on survey data), it looks like the habit of heroin use has become more lethal. I suspect that this is because what’s sold on the street as “heroin” is actually heroin mixed with fentanyl and other ultra-powerful opioids. I quite often see news reports of a rash of “heroin” overdoses hitting some city, often one particular neighborhood of one city. It’s usually a single bad batch of heroin that’s been spiked with something a hundred or a thousand times stronger. (See carfentanil, aka elephant tranquilizer.) This is definitely a problem created by prohibition, because in a legal market nobody would buy this stuff. The preference by dealers for super-powerful opioids is driven by the fact that these substances are easy to conceal. A kilogram brick of heroin can be replaced by a tiny vial of carfentanil. But then, these tiny volumes of carfentanil need to be mixed into a larger volume of heroin (or perhaps baby formula or some other inert base material). Obviously if the mixing is imperfect or if the mixer is an amateur who doesn’t know what he’s got, the results can be deadly for whoever consumes the final product.

The dominant narrative behind the heroin overdoses is “Freely available prescription opioids have stoked a pent-up or latent demand for other opioids. People who once had a legal prescription are turning to heroin, because it’s cheaper and easier to acquire.” I seriously doubt this narrative has much merit. I think there are two different things happening here. The prescription opioid deaths represent a tiny risk multiplied across a very large population (~100 million prescription opioid users with ~200 million legal prescriptions in 2014); the heroin deaths represent a significant risk (1.5% to 3%  mortality per user per year!) across a small population of users (just shy of half a million in 2014). I think it’s wrong to pile these into the same narrative, as some news sources do. You’ll often see a story touting the total number of “opioid overdose deaths” in a year, or series of years. These stories are (in my view, incorrectly) adding the heroin deaths to the other opioid, methadone, and “other synthetic narcotics” deaths. By adding them together, the authors of these pieces are assuming their narrative is the correct one, when maybe it isn’t. When you hear the claim that 2014 was a record year for “opioid deaths”, they are improperly adding heroin deaths to these other three categories without explaining what they are doing. I think these authors need to be more explicit about their assumptions, and do a better job of explaining to their readers what’s going on.

Methadone

Along with “other opioids” and “other synthetic narcotics”, methadone is one of the three categories that are usually tabulated together as prescription painkillers. I believe the CDC uses this definition (citation needed). Take a look; methadone follows its own trend. It rises from 1999 to 2006, then starts to fall. The thing to keep in mind about methadone is that it has a very long half-life, so it stays in the body for a very long time after the user has taken a dose. It can thus build up to toxic levels for a naïve user who hasn’t had time to develop a tolerance, or hasn’t figured out how to titrate his own dose. Most methadone overdoses are from users who have just started methadone maintenance for heroin or have recently acquired a prescription for methadone as a painkiller (citation needed here, but I’m speaking from memory based on the books Buzzed and Karch’s Pathology of Drug Abuse.) 

 See Wikipedia’s description of what happened up to 2006:

In recent years, methadone has gained popularity among physicians for the treatment of other medical problems, such as an analgesic in chronic pain. Methadone is a very effective pain medication. Due to its activity at the NMDA receptor, it may be more effective against neuropathic pain; for the same reason, tolerance to the analgesic effects may be lesser compared to other opioids. The increased usage comes as doctors search for an opioid drug that can be dosed less frequently than shorter-acting drugs like morphine or hydrocodone. Another factor in the increased usage is the low cost of methadone.

On 29 November 2006, the U.S. Food and Drug Administration issued a Public Health Advisory about methadone titled "Methadone Use for Pain Control May Result in Death and Life-Threatening Changes in Breathing and Heart Beat". The advisory went on to say that "the FDA has received reports of death and life-threatening side effects in patients taking methadone. These deaths and life-threatening side effects have occurred in patients newly starting methadone for pain control and in patients who have switched to methadone after being treated for pain with other strong narcotic pain relievers. Methadone can cause slow or shallow breathing and dangerous changes in heart beat that may not be felt by the patient." The advisory urged that physicians use caution when prescribing methadone to patients who are not used to the drug, and that patients take the drug exactly as directed.

I don’t have prescription-count data for methadone, but I suspect physicians became more squeamish about prescribing a painkiller with such a low margin for error. But no doubt stiffer warnings for patients had an effect on the death totals, too.

I don’t know why this is never explained in any of the news stories about the “opioid epidemic.” The numbers are supposedly rising despite a big drop in one of the three categories that define “prescription opioids.” And, more to the point, a simple cautioning of physicians and patients about the dangers seems to have brought down the death rate. This is a useful lesson in public health if there ever was one.

I don’t see this point mentioned often, but methadone deaths are very likely to be multi-drug poisonings, which is true of *every* category of drug poisoning in the CDC death data.

Other Synthetic Narcotics

This category includes fentanyl and other synthetic opioids, some of which are super-potent. As stated above, it is one of the three categories that are summed to make up the “prescription opioids” in overdose death tabulations. Fentanyl is something like 80 times as powerful as morphine. Notice its trendline. It grows by a factor of ~3 from 1999 to 2013, then it shoots up (bad pun) in 2014. As with the heroin story, this makes a lot of sense when you consider the recent rash of tainted heroin deaths. I actually suspect that many of the so-called heroin overdoses belong in this category, and it was only in 2014 that anyone caught on to this trend. Medical examiners don’t often know what kind of opioid was taken when there is a rash of overdoses; if they can determine that at all, it requires sending blood work to a lab that takes weeks to do a work-up (citation needed, but I'm speaking from memory based on a recent NPR story). So there could be some “heroin” deaths that are miscategorized, because the decedent actually (unwittingly) took fentanyl or something else in this category.

The musician Prince’s recent tragic death would probably be coded in this category; he died of a fentanyl overdose.

Psychostimulants with Abuse Potential

This is methamphetamine and its legal prescription analogues. That’s right, in official government tabulations, methamphetamine, Adderall, and Ritalin are recognized as being the same category of substance. I have minimal commentary on this, other than to note that the apparent recent increase in deaths is not matched by an increase in use in recent years. (See this again, page 9 figure 9 for stimulant use rates. They are basically flat for each age group.) So either the habit has become more deadly, or perhaps there is a spurious trend in the death totals.

Less Lethal Drug Categories

I spell this out in a previous post. There appears to be a growth in almost all categories of drug poisonings tracked by the CDC. Even for really obscure drugs that only kill a few people, the rise is similar in magnitude as for the opioids. Maybe some of this is other drugs "going along for the ride" with opioid deaths, as in someone took some cough syrup along with a fatal dose of prescription painkillers, so the coroner or medical examiner marked "antitussives" on the death certificate. But this can't be the whole story, because even when you filter out the seven most lethal drug categories (listed above) you still see a sharp rise in overdose deaths. As I say in my previous post, a likely explanation is that we're just testing dead bodies for more substances. It's not necessarily the case that more people are actually overdosing. So beware any news story that tells you the total number of drug overdoses and then spins a good yarn about the opioid epidemic. They are inflating their numbers to puff up their story, and papering over something that casts doubt on their narrative.

So Don't Be Fooled

So keep all this in mind when you read the next perfunctory piece on the “opioid epidemic.” The author of the piece is probably conflating several very different things and cramming them into a narrative that might or might not be true. He’s probably also omitting some important details. 

Heterogeneity in Human Responses to Medicine

John wants a Rolex. He acknowledges that it’s a silly, expensive trinket and that his desire for it is kind of frivolous. But this is his one major indulgence in an otherwise sensible and frugal life. He’s just always wanted one and he insists that a small part of him will be fulfilled if he gets one. He buys his Rolex and tells you that he’s now satisfied.

Do you doubt John? Do you feel tempted to probe him and measure his happiness over time? Would you even think of doing a clinical trial, where half the users get a Rolex and half get nothing, and measuring the relative happiness of the two groups? If you did so and found no difference in happiness, would you conclude that Rolexes make no difference to anyone’s happiness? And would you think your conclusion had the patina of “science!” on it? Or would your common sense tell you that different people enjoy different things? It’s possible that one-in-a-hundred feels a real attachment to expensive watches and the other 99 don’t really care. Perhaps two-in-a-hundred feels a sense of guilt over the frivolity of an uber-expensive watch (do some googling for the price of a Rolex to see what we’re talking about here), such that their decrease in happiness overwhelms the increase from the one who enjoys the watch. (Yes, yes, “I’d sell the watch and buy something I actually want.” Please, no arbitrage arguments, because that turns this thought experiment into something it’s not.)

Okay, see where this is going? How about this one:

Jim feels depressed. He wants his psychiatrist to put him on an SSRI. He gets put on an SSRI and insists he feels better.

Do you doubt Jim because of studies that show no difference between the control group and the treatment group for SSRIs? Or do you admit to my point above, about different people responding differently to the same treatment? I like the idea of running clinical trials and studying the effects of medicine in a systematic way, but I have serious doubts about measuring subjective feelings. It’s not hard to understand that different people have different preferences for consumer goods. If you switched my shopping cart with a random person at the checkout lane, we’d both be very disappointed. Everyone understands this. We have heterogeneous tastes in consumer goods. When I’m putting things into my shopping cart, I’m mixing my own tonic that will improve my well-being. It would be utter nonsense for someone take the contents of my cart and give them to a treatment group, while simultaneously monitoring a control group who gets nothing. You just sort of have to trust my subjective judgment that “I want some Old Rasputin Imperial Stout and Hanes Premium boxer-briefs.” If you see that stuff in my cart, you presume that I’m satisfying a set of preferences that you can’t possibly observe, that I know better than anyone else. It’s no big mystery that there are numerous versions of every product, that any one product is purchased by a tiny minority of shoppers, or that any shopping cart with more than a few items is completely unique. Is it hard to believe that such heterogeneity of responses holds true for medicine? Does it rankle our feathers to admit that the effectiveness of some medicines might be beyond the grasp of science, as much so as is the “effectiveness of shopping carts?” My experience, having talked to people who are on lots of psychiatric medications, is that they have optimized their “shopping cart” over time after learning about their personal response to various mixes of drugs. A blog that I frequently read (Slate Star Codex), written by a psychiatrist, suggests something similar. The author frequently talks about how one patient might respond well to a given drug while others don’t, and I’m sure his experience is typical of the profession as a whole.

Try another one:

Gary has post-traumatic stress disorder. Gary says that smoking marijuana helps his post-traumatic stress disorder.

I think the only sensible option is to believe Gary. You could do a study and find out that there’s “no difference between the control group and the treatment group.” But maybe that’s because half of the treatment group gets really paranoid and feels worse while the other half improves, such that the overall magnitudes cancel out. In reality, everyone knows goddamn well whether they feel better or not. Everyone given the option of picking and fine-tuning their own treatment can make themselves feel better. The people who are made to feel worse simply stop smoking. These questions of subjective judgment are beyond the realm of science, because they depend on things that aren’t observable. I get very annoyed with people who insist that there’s “no science behind the claim” that marijuana is medicine. For one thing, it’s shown promise in treating objectively measureable problems, such as seizures. So the claim is untrue on its face. But just as importantly, many of the problems that marijuana treats are things that can’t be measured by science. Suppose somebody says, “I smoke marijuana and it makes me feel better. I feel better-rested, less anxious, less bothered by stress.” The decent thing to do is believe them. “I do X and it makes me feel better” is more akin to “I wanted a Rolex and getting one satisfied me” than to “snake-oil cured my cancer.” People who want a scientific answer to this kind of question are barking up the wrong tree.

I don’t want to overstate my point. You really can rule out the possibility that, say, vaccines cause autism. You can show that certain cancer drugs are extremely ineffective. You can demonstrate that a back surgery does not meaningfully affect back pain. There are some questions that randomized controlled trials can answer. But even so, this problem of heterogeneous response is lurking in the background. It really is possible that different people respond differently to the same cancer treatment, such that there is no good way of knowing which treatment is most appropriate to which person.

One might hope that we can get a handle on this homogeneous response problem by identifying what kinds of people respond well/poorly to which medicines. Perhaps some genetic marker or some physical trait makes you more receptive to certain kinds of drugs. And there is certainly some value in this; some genes have been identified that correspond to rapid/slow metabolism of certain drugs, such that the drugs might be ineffective or dangerous to people with those genes. (The textbook Karch’s Pathology of Drug Abuse, which I’ve blogged about before, mentions this genetic heterogeneity problem in practically every section.) But this problem may be very prone to overfitting. There are too many conceivable correlates to specify which one is responsible for good/bad responses to a drug. Supposing even that you have a large enough sample (say, thousands of people). If you have thousands of genes and physical/mental traits to test, some of them will correlate very well with outcomes just by sheer chance. We may eventually get a better handle on the problem. Principle component analysis and various clustering methods might reduce the number of correlates to a manageable few. A solid understanding of the chemical and physiological effects of a drug might inform our ideas of who will respond well or poorly. (For a trivial example: “This drug is hard on the liver, so it won’t be effective for people with cirrhosis.”) But no doubt it is a hard problem. The properties that correlate with treatment outcomes might not be observable in any obvious way.

I’m not preaching nihilism here. I’m not saying “…therefore we can’t know anything about anything.” I think this is actually another case in which radical uncertainty leads to libertarian conclusions. We should allow lots of experimentation with lots of different analytical methods. We shouldn’t try to shoehorn everything into the FDA’s “randomized controlled study” paradigm, because it simply isn’t appropriate for many kinds of medicine. Forget the idea that we’ll know the truth if only we have a big enough sample size. To answer the question of “which medicines are effective, and to whom,” we’re going to need to marshal different kinds of evidence from different kinds of sources. We need to consider our Bayesian priors and be open to the possibility that different priors will lead to different conclusions. When these conclusions differ, it means reasonable people can disagree about whether a given drug is effective, or whether the side-effects are worth the costs. Identifying good medicine is an iterative process. The current paradigm of banning everything that doesn’t pass some official review process is wrong-headed. 

Excellent Paper on the Economics of Drug Prohibition by Gary Becker

So I wrote a few pieces a few months ago about the economics of deterring drug use. I found that essentially all of my arguments are covered in this paper by Gary Becker, Kevin Murphy, and Paul Grossman. The title is "The Economic Theory of Illegal Goods: The Case of Drugs." It's a basic microeconomic treatment of illegal drugs by the best micro guys in the world. I nearly titled this post "Gary Becker Got There First" but I thought that was a little obscure for some of my readers. It is very readable, in my opinion, even if you don't have much of an economics background. So please read for yourself if you find any of the claims in this post implausible or surprising. I was happy to see that a full, ungated pdf of the paper is freely available in the above link.

From the abstract:

Optimal public expenditures on apprehension and conviction of illegal suppliers obviously depend on the extent of the difference between the social and private value of consumption of illegal goods, but they also depend crucially on the elasticity of demand for these goods. In particular, when demand is inelastic, it does not pay to enforce any prohibition unless the social value is negative and not merely less than the private value.

He’s saying that it makes no sense to use criminal penalties to deter drug use, especially when demand is inelastic. “Inelastic demand” means that users aren’t very responsive to price changes, which is probably a pretty accurate description of most drug users. The condition at the end, the social value needing to be negative for prohibition to make sense, is essentially saying that external costs have to be so bad that they more than cancel the benefits to the user. Imagine that every time someone snorts cocaine, for every “unit of joy” enjoyed by the user some random victim feels two units of agony. This is a grossly unrealistic estimate (if it is anyone’s estimate) for the social harms caused by drug use, so it probably doesn’t make sense to ban any class of drugs. He’s not exactly saying the same thing as me, but I point out here that trying to ban or even deter a substance with inelastic demand is a losing game, at least from the viewpoint of the drug users. We aren’t helping them. For every one we deter, we make the remaining ones so much worse off that it’s not worth it.

More from the abstract:

We show that a monetary tax on a legal good could cause a greater reduction in output and increase in price than would optimal enforcement, even recognizing that producers may want to go underground to try to avoid a monetary tax. This means that fighting a war on drugs by legalizing drug use and taxing consumption may be more effective than continuing to prohibit the legal use of drugs.

I argue as much in previous posts (see here and here). The basic intuition here is that it’s better to deter drug users with a tax, which transfers value from one person to another, than with a penalty, which destroys value. These are two ways to hurt drug users, and only in the “tax” case does society at large collect the value of the harm done to users and dealers. A penalty is akin to taxing the drugs and then throwing away the revenue.

…when demand for drugs is inelastic, total resources spent by drug traffickers will increase as the war increases in severity, and consumption falls. With inelastic demand, resources are actually drawn into the drug business as enforcement reduces drug consumption.

It’s worth pondering this for a moment. Basic economics explains why drug cartels are so powerful and well-funded. Hammering the suppliers harder will mildly increase the market price of drugs, but will draw more resources into drug production and redistribution. (For a very clear explanation of this point, see this great post by Benjamin Powell on Econlib. See the graph and accompanying explanation.) In human terms, this means more young people getting recruited into drug dealing and smuggling, with all the associated violence and all the drug mules dying with exploding balloons of cocaine in their bodies.  Also, perhaps less compellingly but still costly, resources are devoted to building clandestine labs for producing drugs along with ingenious contraptions, vehicles and even tunnels for smuggling. (This story came across my news feed just today.) We could just manufacture the drugs in a single, legal, auditable, regulateable factory and ship them in bulk legally on trucks. The added cost of doing these things clandestinely is a social loss that shouldn’t be overlooked.

After a thorough mathematical treatment, one section of the paper concludes:

The conclusion that with positive marginal social willingness to pay-no matter how small-inelastic demand, and punishment to traffickers, the optimal social decision would be to leave the free market output unchanged does not assume the government is inefficient, or that enforcement of these taxes is costly. Indeed, the conclusion holds in the case we just discussed where governments are assumed to catch violators easily and with no cost to themselves, but costs to traffickers…The optimal social decision is clearly then to do nothing, even if consumption imposes significant external costs on others.

This is kind of stunning. Without even stacking the deck in favor of legalization by making some “weird” libertarian assumption about government inefficiency, their argument implies that the optimal amount of drug enforcement is zero. We should leave drug dealers alone. The argument they are making specifically applies to enforcing legal penalties; in the next paragraph they contrast this with the case of setting an optimal tax.

The authors then show that their conclusion basically holds even if they relax their assumptions. (This is a very important step to any argument, by the way. There is always a thoughtless critic ready to point out that your assumptions don’t hold 100% (duh!), but they never then bother to show you what changes when you relax those assumptions. Best to preempt them.)

Even if demand is elastic, it may not be socially optimal to reduced output if consumption of the good has positive marginal social value…[some technical details, elasticity conditions, etc.]… It takes very low social values of consumption, or very high demand elasticities, to justify intervention, even with negligible enforcement costs.

Even if you significantly relax the assumptions, the conclusion that you should not enforce drug laws at all *still holds.*

The section concludes by reiterating:

…when demand is inelastic, total production costs rise as consumption falls, and enforcement costs rise more rapidly. With inelastic demand, a war to reduce consumption would be justified only when marginal social value is very negative. Even then, such a war will absorb a lot of resources.

The next section gives a treatment of trying to deter drug use (production actually, although deterring use is the ultimate goal) with taxes rather than penalties.

Our analysis shows, moreover, that using a monetary tax to discourage legal drug production could reduce drug consumption by more than even an efficient war on drugs. The market price of legal drugs with a monetary excise tax could be greater than the price induced by an optimal war on drugs, even when producers could ignore the monetary tax and consider producing in the underground economy…With these assumptions, the level of consumption that maximizes social welfare would be smaller if drugs were legalized and taxed optimally instead of the present policy of trying to enforce a ban on drugs.

In other words, a tax is far more efficient than a ban for achieving the goal of drug deterrence. You get less overall drug use, and society pays a lower price to achieve this goal.

The paper has much more to it. There  is some discussion of what types of firms are likely to exist in a legal vs illegal market; the authors note that under alcohol prohibition the firms that produced alcohol were very different from the ones that existed before and after. It wasn’t just the same companies operating in different kinds of markets. There is a somewhat throw-away section on peer pressure, which attempts to illuminate the reasons why young people might want to try drugs and why parents might want to stop them. This is Gary Becker, after all, who became a heretic in his field by applying economic analysis to the sacred realm of the family. Another section discusses the desirability of ad campaigns aimed at dissuading drug use. And a final section gives something of a public choice explanation of why we have sub-optimal drug policy. Read the whole thing. It’s very much worth pondering. Reading this reinforces my belief that legalization is a real no-brainer. I’m also pleased to see that some of my arguments were made more convincingly by someone else. It tells me I must have been on the right track.

The all-caveats-aside takeaway: A tax on drugs is strictly superior to a ban on drugs. The attempt to deter drugs with legal penalties is like taxing drugs at some high rate, then throwing away the tax revenue. The inelastic demand for drugs makes prohibition a strictly losing game. You can make these results go away by appealing to extreme, implausible assumptions, but that's grasping at straws. 

Another Species of Bad Comment

You read a long-ish piece. Naturally your brain makes an overall summary of it, rather than transcribing it word-for-word into your memory. Then instead of responding to the actual piece, you respond to the impression it left on your brain. So you make a lot of arguments that are addressed, possibly refuted, in the original post. You say, “Nuh uh! What about X!?”, when a simple re-reading of the original piece, or even a text search for “X,” would turn up a paragraph-long discussion of X. Or you say, “But what about Y?”, when the original piece hedges, “Of course, this argument does not apply to Y.”

I see so many comments like this. The criticisms in the comment are actually addressed in the original piece, but the commenter overlooked it. The antidote for this is to read the piece carefully, re-read your comment before posting, and delete the comment without posting if your point has already been adequately addressed. It’s a simple case where careful communication leads to better communication. I understand that we’re all busy, but if you can’t be bothered to read someone’s argument carefully, you shouldn’t be pounding out a reply to it. There is no merit to this kind of dialogue.

My longer list of bad comments is here. Possibly the one described in this post is a subspecies of one in the older post. 

Who is Reading?

Just curious, because I get almost zero feedback. I know very little about who my readers are. If I just e-mailed someone a link or posted to Facebook I’ll sometimes see a spike in page views. In those cases it’s fairly obvious where my readers are coming from. But other than that I have no idea. Drop me a line or a comment. Or not. I’d love to hear from you! Or not! Lurkers welcome! I appreciate the views, input or no input. But I am curious, and I’m particularly curious about regular readers who I don’t personally know.

Age Distribution of Legitimate and Non-Medical Opioid Use

SAMHSA (the Substance Abuse and Mental Health Services Administration) very recently released the 2015 data for its annual drug abuse survey. (The National Survey on Drug Use and Health, NSDUH.) Detailed tables here. This year’s survey, for the first time, includes questions that distinguish legitimate use of prescription drugs from misuse. Previous incarnations only asked about “illicit use.”  The survey asks the user about “Any Use in the Past Year”, “Misuse in the Past Year”, and “Misuse in the past month.” (See table 1.23A in the link above.) This is the first time I’ve ever seen an age distribution for legitimate prescription opioid users, and for all I know it's the first that's ever been published.

There are competing theories about why opioid overdoses are rising. Are the extra deaths simply coming from normal users? Or are they coming from recreational users, who may not have a legitimate medical need for them? Or are they coming from illicit users who *once* had a legal prescription but were cut off by their pain doctor? (I explore several competing explanations here.)  My hope is that I can somehow match up the age distributions of the “users” and the “deaths.” We know the age distribution of prescription opioid deaths, because we know the age of every decedent for whom opioid poisoning was mentioned on the death certificate. The CDC tracks this data, and it’s publicly available. I was curious if this distribution better overlapped the age distribution of illicit users or the age distribution of legitimate users. (Click on the chart to get the best view of it.)

The “Legitimate Use” age distribution agrees best with the “age of mortality” distribution. Legitimate users skew older, and so do the deaths. (I’m assuming here that I can subtract “misuse in past year” from “Any use in past year” to get legitimate use in past year. The “Any Use” age distribution is on there for comparison. Most use is legitimate use; there are ~97 million “any” users and 12 million “misuse” users in the past year.) On the other hand, the “misuse” distribution skews young.  At first glance, this supports the theory that the deaths are coming mostly from legitimate users. But it’s not at all conclusive.

As I mentioned in this post age is a huge risk-factor in drug-related poisonings. The disjoint between the two distributions is pretty extreme for cocaine (click the link above for details). So I could still believe that most of the deaths are coming from illicit users of legal painkillers; the age distributions don’t help answer that question. The users can skew young while the deaths skew old because older people die from drug poisonings at a much higher rate. (I’m very glad I did this previous post, otherwise I might have been misled into thinking that this was some kind of smoking gun in support of the “there are simply more legitimate users who are dying at an unchanged mortality rate” theory.)

The reason I doubt the “illicit use is driving the excess deaths” story is that prescription painkiller abuse isn’t increasing. Total illicit use and even “substance abuse disorder” (people with especially severe drug habits) are basically flat. See page 7 figure 6 and page 26 figure 36 in this document. These certainly aren’t rising enough to explain the roughly three-fold increase in opioid poisoning deaths that’s happened in the past 15 years. I don’t totally discount this explanation, but on its face it’s contradicted by the survey data.

 There are important policy implications here; I'm not just engaging in meaningless number-crunching. If the true explanation for the increase in opioid poisonings is that there are just more normal users, who are overdosing at the same rates as 15 years ago, then I don’t think there’s any cause for alarm. A treatment with a known and acceptable risk has simply been expanded into a bigger population; it shouldn’t worry us that the raw numbers increase. If the explanation is that people are getting cut off from their legal prescriptions and turning to illegal dealers and dangerous routes of administration (injecting and snorting rather than pill swallowing, or resorting to heroin), then the solution is to back off enforcement and ensure that these drugs are readily available. Don't cut these people off of their legal supply and they won't engage in the risky behaviors that are leading to so many deaths. If the explanation is that people are getting addicted to their prescription opioids and their addictions are escalating into full-blown drug abuse, then only in this third case might it make sense to crack down on prescriptions. (And it’s by no means clear that a crack-down is in order; many other criteria would need to be met first.) Hopefully coroners and medical examiners start collecting relevant information for answering this question. Did the decedent have a legal prescription at the time of death? Did they recently have a prescription? Did they have a prescription at any time, and when? Knowing this, at least for a representative sample from a few hospitals, might answer the question once and for all. I’m trying to answer the question with disparate data sets that weren’t compiled for the purpose. This approach offers suggestions, but no clear answers. 

Trump's Popularity

Recently I was having donuts for breakfast at a local donut shop. There was a table populated by a bunch of old guys bullshitting with each other, eating donuts and drinking coffee. I thought to myself, “This is Trump!” None of them expressed a coherent worldview. They weren’t meticulously cost-benefit analyzing their political proposals. No, they were speaking from the “id” and trying to one-up each other on who had the cleverest political comment. If you had a transcript of all such conversations from this one group, you’d find the same people voicing support for opposite policies on different days (like Trump). Suppose you put these men in charge of government. Just sit them at a table, give them coffee and donuts, and turn each of their verbal ejaculations into a law. You would end up with an incoherent, self-contradicting hodgepodge. (Err…more so.) The bureaucrats who administer the law would be paralyzed by their inability to comply with contradicting orders. Watching these old guys talk gave me some insight, I think, into Trump’s popularity. And imagining them directing government gave me some insight into what a Trump presidency would look like. (Or any populist presidency, for that matter.)