He’s right. Likely Matthew is a greater expert on these matters than I am, and he pointed me to one of his earlier pieces that touches on the subject.* It’s about the, apparently nearly completely groundless, prosecution of Dr. Frank Fisher who prescribed opioid medications to poor patients in a rural California county.
Meanwhile, what do you think happened to the patients at his clinic, which was destroyed by this action? Go read the full interview with Fisher at DRCNet, but this is an extract about what happened to the people he was serving. As you might have guessed their transition from his care to that of others in that rural underserved area was not exactly smooth.
In the extract, Fisher is quoted,
The availability of pain management for poor people is even worse than for the rest of us. And it’s not good for the rest of us. Everyone who develops chronic pain is likely to be killed by it because of medical neglect. It’s a malignancy in the sense that if it is not controlled, it will spread and progress. My patients were effectively tossed out on the street to fend for themselves. The local medical clinic saw them as drug addicts who needed to be detoxed.
As for Anslinger, his Wikipedia entry includes that he “has been accused [source] of being responsible for racial themes in articles against marijuana in the 1930s.”
Had I done more research in this area and folded these themes into my piece, I’d have drawn a fairly similar conclusion. Narcotic painkillers remain both problematic (addictive, subject to diversion) yet opioids have some worthwhile uses (for pain and addiction treatment). These alone present substantial policy challenges. If the implementation of policy (whether good or bad) also has an implicit or explicit racial bias, that’s no less worthy of our attention.
* He also pointed me to this other one, but the link didn’t work as of the time I wrote this post. It does now. You can click through and read it for yourself. It’s short.
UPDATE: I removed Matthew’s full tweet and made some edits to accommodate that removal, as it included some criticism he later retracted.
Recently, I looked through what I published in 2014, which reminded me of some TIE milestones. Below are some of the TIE events and posts from 2014 I thought worth recalling. (In large part I had forgotten about these, so to me it’s almost as if someone else wrote them.) I’ll post about some others early in the new year.
As the accompanying timeline shows, since the late 19th century, there have been three eras in which opioid abuse has reached problematic levels and provoked policy responses.
Opioids now cause more deaths than any other drug, more than 16,000 in 2010. That year, the combination of hydrocodone and acetaminophen became the most prescribed medication in the United States. Patients hereconsumed 99 percent of the world’s hydrocodone, the opioid in Vicodin. They also consumed 80 percent of the world’s oxycodone, present in Percocet and OxyContin, and 65 percent of the world’s hydromorphone, the key ingredient in Dilaudid, in 2010. (Some opioids are also used to treat coughs, but that use doesn’t seem to be a major factor in the current wave of problems.)
Across the states and in the nation’s capital, policy makers are wrestling with ways to address the problems caused by high rates of opioid prescribing, while still permitting adequate access to these medications for patients who need them. Further complicating matters, while opioid-based medications are sometimes diverted to illegal markets and can be addictive, some formulations can also be helpful in treating those addictions.
Devising policy to manage the competing uses and risks of narcotic painkillers has been a century-long challenge, complicated by shifts in the government’s approach to drug regulation, the nation’s culture of illicit drug use and the role played by the pharmaceutical industry.
The most recent wave of opioid overuse can be traced back to at least the 1980s. Then, some doctors began reporting that addiction to them was rare, and drug companies vigorously promoted them as safe for a broad range of painful conditions. In a letter in The New England Journal of Medicine in 1980, Dr. Jane Porter reported that out of nearly 12,000 patients who had received a narcotic painkiller, only four became addicted. In a 1986 study published in the journal Pain, Dr. Russell Portenoy — at the time, a prominent proponent of narcotic painkillers whose work was backed by drug manufacturers — reported that only two of 24 patients treated with them for years had exhibited problems managing the medication. Other physicians expressed concern that by withholding opioid drugs, physicians could be under-treating pain.
Encouraged by these findings, doctors who once thought long-term use of narcotic painkillers was unsafe began to prescribe them in greater numbers. In 1993, The Times reported that chronic pain sufferers were now able to find relief with powerful narcotic drugs. “There is a growing literature showing that these drugs can be used for a long time, with few side effects and that addiction and abuse are not a problem,” Dr. Portenoy said.
In 1996, the American Pain Society — of which Dr. Portenoy would later be president — termed pain the “fifth vital sign,” to be routinely measured in patients along with the four traditional ones: body temperature, blood pressure, heart rate and breathing rate. Measurement of pain — for instance with a self-reported zero-to-10 rating — was recognized as a prerequisite to taking it seriously and treating it. Model guidelines developed in 1998 andupdated in 2004 were widely adopted by state medical boards and codified the use of opioids as standard pain treatment practice. For pain sufferers, these were welcome developments.
The use of opioid-based medications for treatment of addiction and the use of antidotes for overdoses also advanced. In 2000, the Drug Addiction Treatment Act expanded opioid substitution therapy for treatment of addiction. Under the act, qualified physicians can prescribe opioid-based medications such as buprenorphine for use at home, an alternative to treatment with methadone at specialized clinics. In 2007, The Timesreported that naloxone, an antidote for an opioid overdose developed in the 1960s, had become an important, lifesaving tool in many cities and states.
About a decade ago, problems with narcotic painkillers began to surface.The Times reported in 2003 that, according to a government survey, more than 20 percent of 18- to 25-year-olds abused prescription pain medication, up from only 7 percent in 1992. A federal task force was formed to crack down on illegal sales of narcotics over the Internet. There were increasing reports of physicians prescribing narcotic painkillers in unusually large quantities; some were arrested after their patients diverted the painkillers to illegal drug markets. Some doctors reported that as many as 20 percent of their patients were involved in such diversion or addicted to opioids or other drugs. At the same time, studies found that as many as half of pain sufferers received insufficient treatment.
New studies showed that opioids may help only half of patients prescribed them, and many of those, only temporarily. The greatest proponents of narcotic pain killers have recognized their dangers. “Did I teach about pain management, specifically about opioid therapy, in a way that reflects misinformation? Well, against the standards of 2012, I guess I did,” Dr. Portenoy said that year in an interview with The Wall Street Journal. “We didn’t know then what we know now.”
Some physicians specializing in the treatment of pain are beginning to reduce therapeutic use of narcotics in favor of other treatments such as nonopioid drugs, physical therapy andacupuncture.
Despite the growing recognition of the limitations of opioids for pain, we are still in the midst of an epidemic. Policy makers continue to struggle to balance access to appropriate medications with their risks, while the pharmaceutical industry provides and promotes new formulations of narcotic painkillers. The tension was exemplified in 2011. That year, an Institute of Medicinereport characterized chronic pain as a disease in its own right, deserving of greater attention and treatment, including, as appropriate, properly monitored use of opioids. Also that year, the Obama administration released a plan to handle the “drug abuse crisis” fueled by prescription opioids, and the F.D.A. required a risk evaluation and mitigation strategy for extended-release and long-acting opioid medications.
As this third United States opioid epidemic continues, we can look back on its predecessors. The first peaked around the end of the 19th century, when opioid products were unregulated. Bayer Pharmaceuticals introduced heroin as a cough suppressant in 1898 and heroin was widely prescribed into the 1920s. One common medical use was the treatment of menstrual pain. In 1906 the Pure Food and Drug Act required the contents of drugs to be listed on their labels, including opioids. A 1911 New York Times article asserted that “at least one druggist out of every ten exists by means of profits from the sale of habit-forming drugs, of which, of course, opium and its derivatives are most important.”
Though use of opioids was already on the decline, the 1914 Harrison Narcotics Tax Act codified national policy makers’ ambition to curb their useby taxing them. The act also regulated medical applications, permitting opioids to be used for pain treatment, but not as maintenance treatment for addiction, which was not legalized by the Supreme Court until 1925.
Narcotics use spiked again in the middle of the 20th century. The Times documented growing use and overdose deaths in New York, including in a1951 article that noted a “tremendous increase” in teenage users admitted to local hospitals. Another article in 1969 reported that arrests for narcotics in the city were up 46 percent compared with the previous year. Also in 1969,Dr. Robert DuPont found that over 40 percent of people entering jails in the District of Columbia tested positive for heroin. In 1971, The Times reportedon widespread narcotic use and drug addiction by returning Vietnam veterans.
During this second wave of increased narcotic use and response, in 1961, the United Nations declared access to pain medication a human right, adding that countries should provide appropriate access to pain management, including opioids. This foreshadowed the third, current wave of opioid overuse, growing out of an effort to more fully recognize and treat pain.
Today, states and federal policy makers are offering new approaches to promote responsible, safe opioid use for pain and addiction treatment, which is highly cost-effective. All states but Missouri have or will soon have drug databases to track prescribers of opioid painkillers and those that use them. States that have required doctors to check the databases before prescribing the drugs have seen large decreases in prescribing. Physicians and F.D.A. officials have called for more tamper-resistant formulations that cannot be easily crushed for snorting or adapted for injecting. And states’ expansion of access to the overdose antidote naloxone could save many lives.
In light of the crisis, these are sensible responses. But it is instructive to remember how we got here. History shows both that it’s possible to overprescribe and misuse powerful narcotics, and that it’s possible to undertreat pain and addiction to them. Balancing the competing needs and risks is a continuing struggle.
Please share stories of research findings and evidence-based, improvements in care that would not have been possible, or will not be possible in the future, without these identifiable data at firstname.lastname@example.org! We will use these examples as we make a compelling case to the administration and other policymakers for these data’s release.
We must gather compelling evidence that the suppressed data is essential. If you’re aware of key studies that led to important findings affecting policy or practice, send them to the address above. Please make this easy for AcademyHealth by summarizing the work and its import — the more specific the better.
Of course, my state of residence—Massachusetts—and its neighbors are not immune to the growing opioid abuse problem. According to the Alanna Durkin, reporting for the Associated Press, in Maine, deaths from heroin overdoses quadrupled from 2011 to 2012; in New Hampshire the number of such deaths doubled from 2012 to 2013; in Vermont they doubled recently as well. New England governors suggested approaches to addressing this growing epidemic. Proposed solutions often include enhancing treatment programs and “hunt[ing] down drug dealers.”
These numbers serve to shock, but they do not address the pervasive misunderstandings of drug use and abuse in general and the opioid problem in particular. An insightful conversation between Harold Pollack and Keith Humphreys illuminated some hard truths. A few highlights:
Substance use disorders are not confined to Hollywood stars or the poor. Most people with drug problems have jobs; a huge proportion are “nice, middle class people.” They are your neighbors, friends, family members, coworkers, or you.
Though deaths due to drugs now rival those from AIDS at the peak of its epidemic, unlike the early outbreak of AIDS, substance use is not focused within a “pre-existing community of people. […] It’s spread all over.”
Detox that isn’t “followed up with careful interventions and monitoring” have relapse rates close to 100%. There are no quick fixes here.
Most opiate overdose deaths and growth in them can be traced back to prescriptions: “4 in 5 of people today who start using heroin began their opiod addiction on prescription opioids.” Clamping down on the supply of heroin alone will not address the problem.
U.S. clinicians “write more prescriptions for opiate painkillers each year than there are adults in the United States.” Think about that.
The health system itself plays a large role in the problem, and should be part of any solution. Recent work by Christopher Jones and colleagues, published in JAMA Internal Medicine, shows that while friends and relatives are the predominant, proximate source of opioid analgesics used for nonmedical purposes, about 20% are directly prescribed by physicians, the second most common source (others being stolen or bought from friends or relatives, bought from a drug dealer or stranger, and other). However, almost all opioid analgesics begin with a prescription, even if they’re later transferred by sale or theft.
What can be done? A full answer takes far more space than one post, and I’ll come back to it later (stay tuned). Ways to address the problem include alternative pain management, adjustments to prescribing practices, and expansion of access to a wider range of longer-term addiction treatment programs.
Responding to the overprescribing of opiate-based painkillers, in July 2012 Blue Cross Blue Shield of Massachusetts attempted to change prescribing patterns. It began requiring prior authorization for more than a 30-day supply within a two-month period. After 18 months, the insurer estimates that it cut prescriptions of opiate painkillers by 6.6 million pills.
In addition to attacking the problem on the front-end, there’s another way to save lives from the opioid epidemic. Better access to and protections for use of naloxone—an opioid agonist—could prevent death from overdose. Massachusetts Governor Deval Patrick recently made naloxone (brand name: Narcan) more available to first responders and to families and friends of drug abusers. Other states have already taken similar measures to promote life-saving naloxone use. Maine Governor Paul LePage is considering reversing his opposition to allowing health care providers prescribe naloxone to opioid addicts’ family members and allowing them to deliver it as required.
Here’s a spreadsheet that provides very detailed counts and percentages of suppressed records across Medicare claim types. It’s fairly consistent with what I posted previously, but with more detail. For instance about 4-5% of inpatient claims are suppressed, in general.
Here’s another that provides the same for Medicaid (MAX) files. As I suspected, the damage is greater than for Medicare. Up to 8% of inpatient claims are suppressed. I don’t know why the suppression proportion drops so much in 2011, but so do record counts. I’m guessing the analysis is based on incomplete files.
Remember, all the suppressed records are for claims related to substance use disorder treatment and diagnoses. So, this is far from a random sample. (It wouldn’t be that harmful if it were random.)
The spreadsheets come by way of ResDAC. Also, they use the term “suppression” in them and in the file names. I have mostly avoided that term, but I’m going to stop doing so. If this is what we’re calling it, then let’s all use the same terminology.
This tag pulls up all our posts on this issue. It’s the one you should share with colleagues to bring them fully up to speed.
This post uses charts to explain some concepts underlying economic interpretation of health care geographic variation studies. I learned most of the content from Amitabh Chandra and Jon Skinner. (This paper by Amitabh offers one, insightful review of the basics.) I thanks Garret Johnson for drafts of some of the charts below. Any errors are mine.
Productive and Allocative Efficiency
The only way to begin is with productive and allocative efficiency. For convenience, let’s imagine the health system as composed of geographic regions. (This is not always ideal. At the end, I will consider organizations within regions.) A region is productively efficient if its resources (inputs, spending) are used to produce the most health possible. Allocative efficiency is obtained within the health system if no rearrangement of the same, aggregate resources across regions can increase health.
Different policies are appropriate depending on whether the health system is allocatively inefficient or whether some regions are productively inefficient. What do geographic variations tell us about policy? It depends.
Consider the chart below, from a paper by by Chandra and colleagues. (I’ve discussed this chart before, here and here.) It shows regional health care inputs (e.g., spending) and outputs (survival/quality of life) for regions A-F. A production function — which relates inputs to outputs — for some of the regions is plotted. You can think of the production function as a codification of all the expertise, processes, and technology of a system. The one shown is also labeled “production possibility frontier,” which designates the most health (survival/quality of life) a region can produce for any level of resources (factor inputs).
Regions associated with points A and F are not productively efficient since they’re below the frontier. The policy goal is to improve productivity of A and F to get them up to the frontier (or at least closer). It’s not clear that either adding or subtracting inputs to/from A or F would help in this regard. They need to be made more efficient with whatever level of inputs they are provided.
Regions associated with points other than A or F share the same production function and happen all to be productively efficient (because they’re on the frontier). For those, policy intervention that reallocates inputs is clearly helpful. For the same total inputs, we can increase allocative efficiency by, for example, moving point D up the curve (rightward) and B down the curve (leftward). Because of diminishing marginal returns, for every additional unit of input allocated to D, greater additional survival/quality is achieved than is lost by subtracting a unit from B.
We cannot fully assess and address allocative inefficiency until we’ve addressed variation in productive inefficiency. Job one is to get regions A and F up to the same production function as the other regions.
Now let’s explore what geographic variations can tell us about productive or allocative inefficiency. Suppose all we know is mean and variation (ΔS, as illustrated in the really dumb chart just below) in spending across regions. Does that reveal whether there’s productive vs. allocative inefficiency? (No, but keep reading to see why.)
Let’s add axes (see the chart just below). “Quality” on the vertical axis is anything from process quality measures to hard outcomes (mortality, QALYs). Spending ($) is the horizontal axis. The vertical, dashed lines indicate what we can infer from spending variation ΔS (and mean): regional spending is in the range of values between the vertical lines (or most of it, if we’re talking some number of standard deviations, for instance). For simplicity, let’s imagine there are only two regions, one with spending somewhere on the left dashed line, the other on the right.
What does this imply about production functions? (Keep reading.)
One possibility is that the two regions could be on the same production function, as shown below. In that case, there’s allocative inefficiency because we can improve quality, on average, by moving the left point rightward and the right point leftward by the same amount along the common production function until the two points meet. (Technicality: All production functions are concave down, i.e. with non-positive second derivatives.) There may also be productive inefficiency if the common production function is not the production possibility frontier.
Another possibility is that the two regions could be on different production functions, as shown below. In that case, there’s definitely productive inefficiency: one region’s production function is below another. From spending variation alone, we can neither distinguish the chart immediate below from the one immediately above nor infer whether either region is operating at the production possibility frontier. We cannot conclude whether there is productive or allocative inefficiency. Hence, it’s not clear what the appropriate policy intervention should be.
Now let’s add quality variation (ΔQ) information, as in the chart just below. This lets us locate the regions’ operating points. They’re at the intersection of the dashed, horizontal and vertical lines. Notice that, in general, we still cannot tell whether these points lie on one production function or two. (These are the same locations of points used in the prior two charts.) So, we still don’t know very much about policy.
However, if we imagine we know just a bit more, we can make some progress. Let’s suppose we’ve measured quality in two ways: we’ve measured rates at which the two regions provide what I’ll call “costly never” and “cheap always” services. Here’s what I mean by those:
“Costly never” = high cost stuff that shouldn’t happen (or should be rare). Example: hospitalization for issues that could have been addressed with ambulatory care (aka, prevention quality indicators or ambulatory care sensitive conditions).
“Cheap always” = low cost stuff that should always happen (to the right people). Example: annual blood glucose testing for diabetics.
If we find evidence that each of the two health systems provide some “costly never” services while not always providing “cheap always” services and at different rates, that suggests two different production functions.* And, in that case, a reasonable policy goal would be to push the lower production function up towards the higher one, as indicated below. Importantly, it is not evident that it would be a good policy idea to withdraw resources from one of these regions and reallocate them to the other one because even if we know there are two production functions, we don’t know their shapes (which I’ve drawn arbitrarily): we don’t know which is operating in a region of higher marginal quality returns to resources.
Notice that as the lower production function is pushed up (and changes shape, perhaps), variation in some types of quality and spending could each grow or shrink. Degree of variation alone isn’t necessarily informative. But, as noted above, variation in certain kinds of services (e.g., “costly never” and “cheap always”) may be.
Imagine two health care systems: system 1 and system 2. (System 1 could be the Medicare and system 2 the VA, as in our study, for example.) The amount of spending variation across regions served in each system, ΔS1 and ΔS2, is illustrated in the chart below. What does this geographic variation in spending tell us about the efficiency of each system? Which system is more efficient, system 1 or system 2? (Not much by itself and can’t tell, but read on.)
Consider also variation in quality produced by each system. We’ll call that ΔQ1 and ΔQ2, which are illustrated by the lines shown along the vertical axis in the chart above. Now which system is more efficient? (In general, we still cannot tell, actually.)
It’s tempting to say that a system with greater variation in spending and/or quality (in this case system 2) is less efficient. Is that right? (Nope.)
Let’s draw in some possible production functions, as in the chart below. Looks like it’s possible for system 2 to be more efficient than system 1, even though variation is larger in the former than the latter. And, even though that’s the case, both systems harbor some inefficiency. We were able to draw these conclusions in our paper based on the kind of quality we measured and relying on prior work. But, in general, variation all by itself isn’t informative about type of and relative efficiency.
Positive and Negative Associations
Are you puzzled by some findings that show a negative association between spending and health while others show a positive association? Do you wonder which results are telling us the truth about the efficiency of our health system? Turns out, those types of results, by themselves, may not be very revealing. This is illustrated in the following chart from a paper by Amitabh Chandra.
Panel A shows regions that are all operating on the positive marginal return parts of their production functions. Yet the spending-health relationship across them is negative. Panel B shows regions all operating on the negative marginal return parts of their production functions. Yet the aggregate spending-health relationship is positive.
The deeper point about this and all of the forgoing discussion is that without a thorough analysis of production functions, we don’t know very much. Some types of geographic variation studies can be a bit revealing, depending on what they analyze, but geographic variation in general (by itself) is not informative about efficiency.
Within vs. Across Regions
One final note: I’ve couched all the above in terms of regions, but studies have shown that variation across providers (e.g., hospitals) within regions is larger than variation across regions. Each region can harbor both more and less efficient providers. This implies that region based policy is too crude. Now go back to the top of the post and read it all again replacing “regions” with “hospitals.”
* If the analysis is done right, meaning controlling for risk-adjusted costs across the two service types. A lot of stuff in this post only makes sense controlling for risk and if “spending” is interpreted as “real resource use.” Fuzzing out some technical details like this has pedagogical value.
Previously, I posted a spreadsheet of codes that I believed CMS was using to select Medicare and Medicaid claims for removal from research data. Today, a colleague sent me more details, in another spreadsheet obtained from General Dynamics Information Technology, which, I’m told, they characterized as “a CMS approved list based off of conversations had with the SAMHSA legal staff.”
I have not digested the full spreadsheet. (Actually, it’s a workbook with seven tabs.) Here’s some of what’s on the first tab, with my bold added:
If no criteria met, then SAMHSA suppress code = 0.
If any treatment code criteria found or primary DGNS [diagnosis] found, then SAMHSA suppress code = 1.
If any treatment code criteria found or any DGNS found, then SAMHSA suppress code = 2.
Option1 (Conservative): Suppress records with SAMHSA suppress code in(1,2).
Option 2 (Less Suppression): Suppress records with SAMHSA suppress code = 1.
In general, we used the conservative option when identifying substance abuse claims for suppression. That is, we exclude the claim for any treatment code criteria found, or primary diagnosis code found, or any diagnosis code found in the extract files of interest.
So, now you know. I’ve opened up comments to this post (for one week) so folks who go over the spreadsheets can weigh in on what they’re seeing, what they think it means or implies, and ask relevant questions.