We hypothesized that propofol, a unique general anesthetic that engages N-methyl-D-aspartate and gamma-aminobutyric acid receptors, has antidepressant properties. This open-label trial was designed to collect preliminary data regarding the feasibility, tolerability, and efficacy of deep propofol anesthesia for treatment-resistant depression.
Ten participants with moderate-to-severe medication-resistant depression (age 18–45 years and otherwise healthy) each received a series of 10 propofol infusions. Propofol was dosed to strongly suppress electroencephalographic activity for 15 minutes. The primary depression outcome was the 24-item Hamilton Depression Rating Scale. Self-rated depression scores were compared with a group of 20 patients who received electroconvulsive therapy.
Propofol treatments were well tolerated by all subjects. No serious adverse events occurred. Montreal Cognitive Assessment scores remained stable. Hamilton scores decreased by a mean of 20 points (range 0–45 points), corresponding to a mean 58% improvement from baseline (range 0–100%). Six of the 10 subjects met the criteria for response (>50% improvement). Self-rated depression improved similarly in the propofol group and electroconvulsive therapy group. Five of the 6 propofol responders remained well for at least 3 months. In posthoc analyses, electroencephalographic measures predicted clinical response to propofol.
These findings demonstrate that high-dose propofol treatment is feasible and well tolerated by individuals with treatment-resistant depression who are otherwise healthy. Propofol may trigger rapid, durable antidepressant effects similar to electroconvulsive therapy but with fewer side effects. Controlled studies are warranted to further evaluate propofol’s antidepressant efficacy and mechanisms of action.
Treatment-resistant depression afflicts tens of millions of individuals worldwide, causing enormous suffering, economic costs, and mortality. Novel interventions are needed. This study provides the first evidence suggesting that propofol, a widely available anesthetic agent, has rapid and long-lasting antidepressant effects. Future studies are warranted to further evaluate propofol’s antidepressant efficacy and mechanisms of action.
Depression is among the most common and debilitating of mental disorders. Although many patients respond to currently available treatments, about one-third have a form of the illness that is not responsive to optimized treatment with antidepressant medications (Rush et al., 2006). Many individuals with severe, treatment-resistant depression pursue electroconvulsive therapy (ECT)—still considered the most effective treatment for depression—but the cognitive side effects of ECT cause many patients to avoid this treatment (Lisanby, 2007). Consequently, each year millions of individuals in the United States alone are debilitated by treatment-resistant depression and left with limited treatment options, at enormous societal costs (Mrazek et al., 2014).
The urgency of this problem has encouraged investigations of novel antidepressant interventions. Agents that target N-methyl-D-aspartate (NMDA) glutamate receptors and gamma-aminobutyric acid (GABA) receptors have appeared particularly promising. Substantial clinical evidence now supports the efficacy of ketamine for treatment-resistant depression (Berman et al., 2000; Zarate et al., 2006; McGirr et al., 2015), and a recent randomized controlled trial demonstrated antidepressant effects of nitrous oxide (Nagele et al., 2015). Several studies have suggested efficacy of another inhaled anesthetic, isoflurane, at high doses in humans (Langer et al., 1985, 1995; Weeks et al., 2013) and rodent models (Antila et al., 2017; Brown et al., 2018). Furthermore, positive GABA-A receptor modulators have shown promising antidepressant effects (Kanes et al., 2017; McMurray et al., 2018). These agents may share pharmacodynamic mechanisms, including inhibition of NMDA receptors and activation of GABAergic neurotransmission, as reviewed recently (Zanos et al., 2018). Indeed, ECT has been reported to reduce NMDA receptor expression and function (Fumagalli et al., 2010; Park et al., 2014), alter glutamatergic synaptic function (Stewart and Reid, 2000; Li et al., 2012), and increase cortical GABA levels in humans (Sanacora et al., 2003). The convergent observations among these diverse interventions suggest a new class of antidepressant agents that rapidly trigger plasticity within glutamate and GABAergic circuitry to induce antidepressant effects (Tadler and Mickey, 2018).
Propofol is a unique, intravenous, general anesthetic that potentiates the function of GABA-A and glycine receptors (Hales and Lambert, 1991) and inhibits the function of NMDA receptors (Orser et al., 1995; Yamakura et al., 1995; Kingston et al., 2006). It has been widely used for over 25 years for procedural sedation and general anesthesia. Propofol is known for its rapid onset and offset of action, tolerability, and safety (Lamperti, 2015). Similar to isoflurane, at high doses propofol induces burst-suppression, a state of intrinsic cortical hyperexcitability that is quantifiable using electroencephalography (EEG) and that is disrupted by blockade of GABA, NMDA, or α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (Steriade et al., 1994; Lukatch et al., 2005; Kroeger and Amzica, 2007; Ferron et al., 2009). Taken together, these properties of propofol led us to hypothesize that high-dose propofol would have antidepressant effects and a favorable side-effect profile. To collect preliminary evidence of feasibility, tolerability, and efficacy among individuals with treatment-resistant depression, we performed an open-label trial of deep propofol anesthesia.
Design and Participants
This open-label study was approved by the University of Utah Institutional Review Board and preregistered at ClinicalTrials.gov (NCT02935647). All participants provided written informed consent. We recruited outpatients who were seen for consultation in a referral clinic for treatment-resistant mood disorders. Assessment included a comprehensive psychiatric evaluation, full medical history, physical examination, screening blood tests (complete blood count, comprehensive metabolic panel, thyroid-stimulating hormone), 12-lead electrocardiogram, and urine pregnancy test as indicated. Inclusion and exclusion criteria (Table 1) were confirmed by a psychiatrist and an anesthesiologist. Importantly, we excluded many individuals with medications or conditions that increased risk of experiencing adverse effects during propofol treatments (e.g., advanced age, severe obesity, hypertension, heart disease). Bipolar depression was not excluded because previous studies of ECT and ketamine have shown similar response rates for bipolar and unipolar depression (Dierckx et al., 2012; Coyle and Laws, 2015; Haq et al., 2015). Of 249 patients screened, 36 met criteria for the study, 11 consented to participate in the study, and 10 received at least 1 treatment. Baseline assessments incorporated the Structured Interview Guide for the Hamilton Depression Rating Scale (HDRS) with Atypical Depression Supplement (Williams et al., 1988) and the Montreal Cognitive Assessment (MoCA) (Nasreddine et al., 2005; Srisurapanont et al., 2017). See supplementary Information for further details about participants.
Inclusion and Exclusion Criteria
Age 18–55 y, inclusive
Primary diagnosis of DSM-5 major depressive disorder or bipolar disorder
Current moderate-to-severe depressive episode
Minimum of 2 failed antidepressant medication trials of adequate dose and duration
(at least one trial within the current depressive episode)a
Quick Inventory of Depressive Symptomatology, Self-Rated, total score >10 at baseline
24-item Hamilton Depression Rating Scale total score >18b
Other current DSM-5 disorders, with the exception of anxiety disorders and attention deficit disorder
Electroconvulsive therapy within the past 6 months
Lifetime history of DSM-5 cognitive disorder
Body mass index >40
Daily use of angiotensin converting enzyme inhibitor or angiotensin receptor blocker **
Symptomatic coronary artery disease or congestive heart failureb
History of transient ischemic attack or neurologic signs during the past yearb
History of or susceptibility to malignant hyperthermiab
Any contraindication to propofolb
Diabetes requiring insulinb
Abnormal kidney functionb
Daily use of opioid medicationb
Daily use of benzodiazepine medication
Pregnant or breastfeeding
Psychiatric instability requiring a higher level of care
Incompetent to provide consent
Anesthesiologists administered a series of 10 propofol infusions at a frequency of 3 times per week (1 subject received only 9 treatments due to a holiday schedule conflict). The decision to deliver a series of treatments rather than a single treatment was based on prior experience with ECT, ketamine, and isoflurane, all of which appear to produce higher response rates with 6 to 12 administrations (Langer et al., 1995; Lisanby, 2007; Weeks et al., 2013; Coyle and Laws, 2015). Monitoring included continuous EKG, pulse oximetry, blood pressure by noninvasive cuff, respiratory rate, and end-tidal carbon dioxide. A BIS Monitor (BIS VISTA Monitoring System, Aspect Medical Systems) was applied with a 4-electrode sensor (BIS Quatro, Covidien) to measure the left frontal EEG throughout the procedure. After preoxygenation, the anesthesiologist administered an induction dose of propofol (2,6-diisopropylphenol; Diprivan injectable emulsion; Fresenius Kabi) i.v., started a continuous infusion, and gave additional small boluses as needed. (Propofol dosing is described below.) A laryngeal mask airway and mechanical ventilation were employed. Trendelenburg positioning, IV fluids, and small boluses of pressors were used as needed for hypotension. During the recovery phase, a nurse monitored the participant in a postanesthesia care unit until discharge criteria were met. Further details about treatments are provided in supplementary Information.
Because brain concentrations and pharmacodynamic effects of a given dose of propofol vary substantially between individuals (Ludbrook et al., 2002), propofol dosing was guided by real-time EEG feedback via the BIS Monitor. This approach enabled us to produce relatively consistent pharmacodynamic effects across participants and across treatment sessions. Propofol induction (200–600 mg) was followed by a continuous infusion (300–650 µg/kg/min) and augmented with repeated small boluses (50–100 mg) as needed. Lower induction doses of 200 to 400 mg were used during each subject’s initial treatment to assure hemodynamic stability, and higher induction doses were introduced during later treatments as tolerated. After induction, the infusion rate was adjusted, and additional boluses were given with the goal of maintaining a burst-suppression state with a suppression ratio (SR) of 80% to 100% for 15 minutes. The SR is a metric calculated by the BIS Monitor that indicates the fraction of time the EEG is completely suppressed (isoelectric) during each 1-minute epoch. The rationale for suppressing EEG activity for 15 minutes was that previous studies of burst suppression using isoflurane anesthesia reported antidepressant effects using a similar protocol (Langer et al., 1995; Weeks et al., 2013).
After the procedure, EEG parameters calculated by the BIS Monitor were exported for off-line analysis. As shown in Figure 1, we defined the burst-suppression period of each treatment session as the interval during which SR was >50%. The duration at SR target was defined as the time during which SR was ≥80%. SR intensity was defined as the median SR value during the burst-suppression period. The integral of the SR curve (sum of SR values across all 1-minute epochs during the session) represented the cumulative time spent in the isoelectric state. The average signal quality index calculated by the BIS Monitor exceeded 95% throughout the burst-suppression period of all recording sessions.
I feel obligated to share the results of my five-year-long investigation into the medical benefits of the cannabis plant. Before I started this worldwide, in-depth investigation, I was not particularly impressed by the results of medical marijuana research, but a few years later, as I started to dedicate time with patients and scientists in various countries, I came to a different conclusion.
Not only can cannabis work for a variety of conditions such as epilepsy, multiple sclerosis and pain, sometimes, it is the only thing that works. I changed my mind, and I am certain you can, as well. It is time for safe and regulated medical marijuana to be made available nationally. I realize this is an unconventional way to reach you, but your office declined numerous requests for an interview, and as a journalist, a doctor and a citizen, I felt it imperative to make sure you had access to our findings.
Mr. Sessions, there is an added urgency, as we are in the middle of a deadly opioid epidemic that has been described as the worst self-inflicted epidemic in the history of our country.
The drug overdose scourge claimed about 68,000 US lives in 2017, just over 45,000 of them from opioids alone. Every day, 115 Americans die from opioid overdoses. It has fueled a decline in an entire country’s life expectancy and will be remembered as a sad and tragic chapter in our collective history.
These are desperate times, and while some may consider making medical marijuana widely available to be a desperate measure, the evidence has become increasingly clear of the important role cannabis can have.
We have seen real-world clues of medical marijuana’s benefits. Researchers from the Rand Corp., supported by the National Institute on Drug Abuse, conducted “the most detailed examination of medical marijuana and opioid deaths to date” and found something few initially expected. The analysis showed an approximately 20% decline in opioid overdose deaths between 1999 and 2010 in states with legalized medical marijuana and functioning dispensaries.
It’s not the first time this association between medical marijuana and opioid overdose has been found. Though it is too early to draw a cause-effect relationship, these data suggest that medicinal marijuana could save up to 10,000 lives every year.
The science of weed
Cannabis and its compounds show potential to save lives in three important ways.
Cannabis can help treat pain, reducing the initial need for opioids. Cannabis is also effective at easing opioid withdrawal symptoms, much like it does for cancer patients, ill from chemotherapy side effects. Finally, and perhaps most important, the compounds found in cannabis can heal the diseased addict’s brain, helping them break the cycle of addiction.
Mr. Sessions, there is no other known substance that can accomplish all this. If we had to start from scratch and design a medicine to help lead us out of the opioid epidemic, it would likely look very much like cannabis.
A better, and safer, way to treat pain
The consensus is clear: Cannabis can effectively treat pain. The National Academies of Sciences, Engineering, and Medicine arrived at this conclusion last year after what it described as the “most comprehensive studies of recent research” on the health effects of cannabis.
Furthermore, opioids target the breathing centers in the brain, putting their users at real risk of dying from overdose. In stark contrast, with cannabis, there is virtually no risk of overdose or sudden death. Even more remarkable, cannabis treats pain in a way opioids cannot. Though both drugs target receptors that interfere with pain signals to the brain, cannabis does something more: It targets another receptor that decreases inflammation — and does it fast.
have seen this firsthand. All over the country, I have met patients who have weaned themselves off opioids using cannabis. Ten years ago, attorney Marc Schechter developed a sudden painful condition known as transverse myelitis, an inflammation of the spinal cord. After visiting doctors in several states, he was prescribed opioids and, according to our calculations, consumed approximately 40,000 pills over the next decade. Despite that, his pain scores remained an eight out of 10. He also suffered significant side effects from the pain medication, including nausea, lethargy and depression.
Desperate and out of options, Schechter saw Dr. Mark Wallace, head of University of California, San Diego Health’s Center for Pain Medicine, where he was recommended cannabis. Minutes after he took it for the first time, Schechter’s pain was reduced to a score of two out of 10, with hardly any side effects. One dose of cannabis had provided relief that 40,000 pills over 10 years could not.
I have seen this firsthand. All over the country, I have met patients who have weaned themselves off opioids using cannabis. Ten years ago, attorney Marc Schechter developed a sudden painful condition known as transverse myelitis, an inflammation of the spinal cord. After visiting doctors in several states, he was prescribed opioids and, according to our calculations, consumed approximately 40,000 pills over the next decade. Despite that, his pain scores remained an eight out of 10. He also suffered significant side effects from the pain medication, including nausea, lethargy and depression.
Desperate and out of options, Schechter saw Dr. Mark Wallace, head of University of California, San Diego Health’s Center for Pain Medicine, where he was recommended cannabis. Minutes after he took it for the first time, Schechter’s pain was reduced to a score of two out of 10, with hardly any side effects. One dose of cannabis had provided relief that 40,000 pills over 10 years could not.
Using marijuana to get off opioids
For Schechter, as with so many others, the seemingly insurmountable barrier to ending his opioid use was the terrible withdrawal symptoms he suffered each time he tried. When a patient stops opioids, their pain is often magnified, accompanied by rapid heart rate, persistent nausea and vomiting, excessive sweating, anorexia and terrible anxiety.
Here again, cannabis is proven to offer relief. As many know, there is longstanding evidence that cannabis helps chemotherapy-induced symptoms in cancer patients, and those symptoms are very similar to opioid withdrawal. In fact, for some patients, cannabis is the only agent that subdues nausea while increasing appetite.
Why we can’t ‘just say no’ to opioids
Finally, when someone is addicted to opioids, they are often described as having a brain disease. Yasmin Hurd, director of the Addiction Institute at Mount Sinai in New York City, showed me what this looks like in autopsy specimens of those who had overdosed on opioids. Within the prefrontal cortex of the brain, she found damage to the glutamatergic system, which makes it difficult for neural signals to be transmitted. This is an area of the brain responsible for judgment, decision-making, learning and memory.
Hurd told me that when an individual’s brain is “fundamentally changed” and diseased in this manner, they lose the ability to regulate opioid consumption, unable to quit despite their best efforts — unable to “just say no.”
It is no surprise, then, that abstinence-only programs have pitiful results when it comes to opioid addiction. Even the current gold standard of medication-assisted treatment, which is far more effective, still relies on less-addictive opioids such as methadone and buprenorphine. That continued opioid use, Hurd worries, can cause ongoing disruption to the glutamatergic system, never allowing the brain to fully heal. It may help explain the tragic tales of those who succeed in stopping opioids for a short time, only to relapse again and again.
This is precisely why Hurd started to look to other substances to help and settled on nonpsychoactive cannabidiol or CBD, one of the primary components in cannabis. Hurd and her team discovered that CBD actually helped “restructure and normalize” the brain at the “cellular level, at the molecular level.” It was CBD that healed the glutamatergic system and improved the workings of the brain’s frontal lobes.
This new science sheds lights on stories like the one I heard from Doug Campbell of Yarmouth, Maine. He told me he had been in and out of drug rehab 32 times over 25 years, with no success. But soon after starting cannabis, he no longer has “craving, desire and has not thought about (opioids) at all, period.”
For the past 40 years, we have been told that cannabis turns the brain into a fried egg, and now there is scientific evidence that it can do just the opposite, as it did for Campbell. It can heal the brain when nothing else does.
I know it sounds too good to be true. I initially thought so, as well. Make no mistake, though: Marc Schechter and Doug Campbell are emblematic of thousands of patients who have successfully traded their pills for a plant.
These patients often live in the shadows, afraid to come forward to share their stories. They fear stigma. They fear prosecution. They fear that someone will take away what they believe is a lifesaving medication.
Where do we go from here?
Mr. Sessions, Dr. Mark Wallace has invited you to spend a day seeing these patients in his San Diego clinic and witness their outcomes for yourself. Dr. Dustin Sulak could do the same for you in Portland, Maine, as could Dr. Sue Sisley in Phoenix. Staci Gruber in Boston could show you the brain scans of those who tried cannabis for the first time and were then able to quit opioids. Dr. Julie Holland in New York City could walk you through the latest research. All over the country, you will find the scientists who write the books and papers, advance the science and grow our collective knowledge. These are the women and men to whom you should listen. They are the ones, free of rhetoric and conjecture, full of facts and truth, who are our best chance at halting the deadly opioid epidemic.
Making medicinal marijuana available should come with certain obligations and mandates, just as with any other medicine. It should be regulated to ensure its safety, free of contamination and consistent in dosing. It should be kept out of the hands of children, pregnant women and those who are at risk for worse side effects. Any responsible person wants to make sure this is a medicine that helps people, not harms.
Recently, your fellow conservative John Boehner changed his mind after being “unalterably opposed” to marijuana in the past. If you do the same, Mr. Attorney General, thousands of lives could be improved and saved. There is no time to lose.
A bipartisan group of senators is introducing legislation Tuesday to address the opioid epidemic, framing it as a follow-up bill to the Comprehensive Addiction and Recovery Act (CARA) signed into law in 2016.
Dubbed CARA 2.0, the legislation includes a host of policy changes, such as establishing a three-day initial prescribing limit on opioids for acute pain, beefing up services to promote recovery and aiming to increase the availability of treatment.
The legislation is a mixture of policy changes and increased funding authorizations, in light of a two-year budget deal passed earlier this month that includes $6 billion for the opioid and mental health crises.
The bipartisan bill includes some measures similar to those removed from the original CARA bill passed in 2016, such as an initiative to bolster youth recovery support services and a provision requiring physicians and pharmacists to use their state prescription drug monitoring program before prescribing or dispensing opioids.
Additionally, the legislation would let states waive the cap on the number of patients a physician can prescribe buprenorphine — a medicine used to treat opioid addiction — and increase penalties for opioid manufacturers failing to report suspicious orders.
CARA 2.0 authorizes $1 billion in additional funding. Some $10 million would fund a national education campaign on opioids; $300 million would increase training for first responders and their access to an opioid overdose reversal drug; another $300 million would expand medication-assisted treatment; and $200 million would help build more recovery support services, for example.
To draft the first CARA bill, Portman and Whitehouse helped convene five national forums comprised of experts on prevention, treatment, law enforcement and recovery.
The opioid epidemic hasn’t shown signs of abating, as overdose deaths increased nearly 28 percent from 2015 to 2016, according to the latest data from the Centers for Disease Control and Prevention (CDC).
On the other side of the Capitol, the House Energy and Commerce Committee is working on legislation aimed at combating the opioid epidemic. Chairman Greg Walden (R-Ore.) hopes to pass the measures out of the House by Memorial Day weekend.
The British Journal of Psychiatry, Volume 2 February 2018, pp. 103-111
Depression contributes to persistent opioid analgesic use (OAU). Treating depression may increase opioid cessation.
To determine if adherence to antidepressant medications (ADMs) v. non-adherence was associated with opioid cessation in patients with a new depression episode after >90 days of OAU.
Patients with non-cancer, non-HIV pain (n = 2821), with a new episode of depression following >90 days of OAU, were eligible if they received ≥1 ADM prescription from 2002 to 2012. ADM adherence was defined as >80% of days covered. Opioid cessation was defined as ≥182 days without a prescription refill. Confounding was controlled by inverse probability of treatment weighting.
In weighted data, the incidence rate of opioid cessation was significantly (P = 0.007) greater in patients who adhered v. did not adhered to taking antidepressants (57.2/1000 v. 45.0/1000 person-years). ADM adherence was significantly associated with opioid cessation (odds ratio (OR) = 1.24, 95% CI 1.05–1.46).
ADM adherence, compared with non-adherence, is associated with opioid cessation in non-cancer pain. Opioid taper and cessation may be more successful when depression is treated to remission.
Long-term prescription opioid analgesic use (OAU) for chronic non-cancer pain is defined as ‘daily or near-daily’ use for >90 days.1,2 Between 1.4 and 10% of patients with a new opioid prescription develop chronic OAU,2,3 and a majority, 65–80%, of patients who have >90 days OAU, are still taking opioids 3–5 years later.4,5 These patients are more likely than those who take opioids short term to develop opioid use disorder and overdose. Chronic OAU is also associated with new depressive episodes (NDEs)3,6,7 and treatment-resistant depression.6 Because depression and OAU are mutually reinforcing,8 these patients may be in a cycle of persistent OAU, depression and pain. Research on treating depression to improve outcomes for chronic non-cancer pain is sparse. In Kroenke et al‘s9 Stepped Care for Affective Disorders and Musculoskeletal Pain (SCAMP) study, where patients were randomised to optimal depression treatment v. usual care, treating depression led to reduced pain severity and anxiety, improved functioning and better health-related quality of life.9 Although SCAMP was not designed to measure change in OAU, the findings raise the possibility that depression treatment could reduce use of opioids, possibly from reduced pain or improved functioning.9–11 Improved functioning can occur independent of pain11 and should follow depression treatment. Independent of changes in pain severity, the need for OAU to self-regulate mood should dissipate following reduction in depression.
We are not aware of any studies that report changes in OAU following adherence to antidepressant medication (ADM) treatment in patients with chronic non-cancer pain. Using a retrospective cohort design, it is possible to test the hypothesis that adherence to ADM treatment v. non-adherence is associated with OAU cessation without the ethical barrier of randomising to inadequate treatment. Adherence serves as an indicator of depression improvement because patients who are non-adherent are less likely to have decreasing depression symptoms.12 Among patients initiating ADMs, response to treatment by 24 weeks is much lower in non-adherent v. adherent patients (55.8 v. 82.5%).13 Although it would be ideal to have 9-item Patient Health Questionnaire (PHQ-9)14 scores for all patients at the time of ADM initiation and opioid cessation, such data was available from only a subset of patients. Therefore, we used adherence as a proxy for depression improvement. In a large cohort of Veterans Health Administration patients with NDE following >90 days of OAU, we tested the hypothesis that depression treatment adherence was associated with OAU cessation. Specifically, the objective of the current study was to determine whether patients who developed depression following chronic OAU were more likely to stop using opioids if they adhered to ADM treatment compared with patients who did not adhere to ADM treatment. In addition, exploratory analysis in a subset of patients with sufficient data was computed to assess the change in depression symptoms and pain scores over time in patients adherent to treatment with ADM compared with those who were non-adherent who did and did not stop OAU.
This retrospective cohort analysis used patient data extracted from the Veterans Health Administration electronic medical record for 1 Jan 2000 to 31 Dec 2012. Data included ICD-9-CM diagnostic codes,15 in-patient stays, out-patient visits, prescriptions dispensed records, vital signs and demographic information.
A random sample of 500 000 patients was taken from a cohort of 2 910 335 identified with at least one out-patient visit in both fiscal years 1999 and 2000, and aged 18–80 years. We excluded patients over 80 because they are more likely to receive prescription opioids for end-of-life pain management and cancer pain and the risk of misclassifying depression increases because of the greater prevalence of vascular depression and depression related to dementia. From this sample, we excluded 151 500 patients with a cancer and/or HIV diagnosis. Patients must have had at least one yearly visit in the 2-year ‘washout’ period (2000–2001) during which they must have been free of a medical record depression diagnosis (n = 266 901). We then selected patients with a NDE beginning in 2002–2011 and not occurring on the last out-patient visit date (n = 31 224). Because our previous reports indicate >90-day OAU is associated with up to twice the risk of NDEs,3,7 we limited the cohort to patients with >90 days of OAU or by the date of the NDE (n = 3075).
NDE was defined by the presence of a primary diagnosis (ICD-9-CM: 296.2, 296.3, 311) of depression in at least one in-patient stay or two out-patient visits within the same 12-month period. This algorithm has been shown to be a valid measure of depression when compared with self-report or written medical record information.16,17 Patients without ADM treatment on or after the NDE were excluded (n = 138). Patients must have had >3 months follow-up after NDE diagnosis to allow for the possibility of the occurrence of least one acute-phase depression treatment period (≥84 days)18 (n = 2843). The final sample included patients with complete demographic data (n = 2821). The cohort selection process is shown in Fig. 1.
Outcome – opioid cessation
Opioids included codeine, fentanyl, hydrocodone, hydromorphone, levorphanol, meperidine, oxycodone, oxymorphone, morphine and pentazocine. Both short-acting and long-acting formulations were included. Opioid prescription information included days supplied, quantity (e.g. pills or liquid volume) and unit dose (mg). OAU cessation was defined as a gap of at least 182 days from the end date of the last prescription.5 OAU cessation date was the first day of this gap.
Exposure – ADM adherence
ADMs included monoamine oxidase inhibitors (MAOIs), selective serotonin reuptake inhibitors (SSRIs), serotonin–norepinephrine reuptake inhibitors (SNRIs), tricyclics (TCAs) and non-classified ADMs. ADM adherence was defined using proportion of days covered (PDC) from NDE to opioid cessation or censor date.19,20 ADM prescriptions dispensed were used to create time arrays to identify days of follow-up that an ADM was available. If multiple ADMs were available in a day, that particular day was only counted once as a covered day. The PDC was calculated by taking the total number of covered days in follow-up by the total number of days in follow-up. PDC was dichotomised to standard thresholds for adherence (≥80%) and non-adherence (<80%).19–21 To determine if patient adherence was correlated with duration of ADM treatment, we computed the number of continuous weeks of treatment. ADM use was considered continuous if there was no gap of >30 days between prescriptions dispensed and duration for all periods of continuous use in follow-up were assessed to categorise duration as ever ≥24 weeks, 12 to <23 weeks or <12 weeks.
We included an OAU duration variable to control for duration of use at the date of NDE (3–6 months, >6 to 12 months, >12 to 24 months, >24 months). Duration was computed from the months of continuous OAU (no gap >30 days between prescriptions dispensed). The opioid morphine equivalent dose (MED) was calculated using standard conversion tables. Days supplied and quantity variables were used to calculate daily MED in follow-up. We modelled the maximum daily MED before the end of follow-up (1–50 mg, 51–100 mg, >100 mg). We controlled for comedication with benzodiazepines, which are associated with long-term opioid use,22 and muscle relaxants, which could improve pain and functioning. Benzodiazepines included alprazolam, clonazepam, diazepam, lorazepam, chlordiazepoxide and clorazepate. Muscle relaxants included carisoprodol, cyclobenzaprine, baclofen, dantrolene, metaxalone, methocarbamol, chlorzoxazone, tizanidine and orphenadrine. Demographic variables included age, gender, ethnicity (white v. other), marital status (married v. other) and insurance coverage (Veterans Health Administration only v. other sources).
To control for detection bias related to more healthcare encounters, we created a healthcare utilisation variable defined as average number of out-patient clinic visits per month in follow-up. The distribution of the mean was then dichotomised into high utiliser, >75th percentile, v. low utiliser, ≤75th percentile. We controlled for psychiatric and physical comorbidities associated with depression23 and/or OAU.24–26 Comorbidities were defined using ICD-9-CM diagnostic codes. Psychiatric comorbidities included post-traumatic stress disorder and any other anxiety disorder, a composite of panic disorder, generalised anxiety disorder, social phobia, obsessive–compulsive disorder and anxiety disorder not otherwise specified. We controlled for alcohol misuse or dependence; illicit drug misuse or dependence, including opioids; and nicotine dependence. Chronic physical conditions included type 2 diabetes mellitus, hypertension, cerebrovascular disease, obesity, low testosterone, sleep apnoea and cardiovascular disease. Cardiovascular disease was a composite of hyperlipidaemia, ischaemic heart disease, disease of pulmonary circulation, other heart disease, hypertensive heart disease and myocardial infarction.
Five separate pain condition variables were created based on over 900 ICD-9-CM codes.7,24 These conditions were arthritis, back pain, musculoskeletal pain, headaches and neuropathic pain. Pain scores, collected during routine care in the Veterans Health Administration, were on a numerical rating scale ranging from 0 to 10, with higher scores indicating greater pain intensity. In propensity score models, we adjusted for a time invariant maximum pain score before the end of follow-up to control for the highest pain level. As variability in pain scores in the Veterans Health Administration have been previously reported,27 a time-varying pain score for each month of follow-up was used in final survival models. For the time-varying pain score assessment, the pain score was assumed to be consistent across subsequent months until a new monthly assessment was available.
Propensity scores and inverse probability of treatment weighting (IPTW) were used to balance potential confounders listed in Table 1 between ADM adherent and non-adherent treatment groups to reduce the effect of bias by indication and other sources of confounding. The propensity score is the probability of ADM adherence, given covariates and was calculated using a binary logistic regression model. Propensity scores were used to apply IPTW approaches using stabilised weights.28–33 A stabilised weight is the marginal probability of ADM adherence divided by the propensity score for the adherent group, and (1 – marginal probability of ADM adherence) divided by (1 – propensity score) for the non-adherent group. It helps reduce bias associated with extreme weights of either individuals in the ADM adherent group with low propensity scores or those in the non-adherent group with high propensity scores. Extreme weights are associated with increased variability of the exposure effect, thus, stabilising weights helps reduce type II error rate.34 Stabilised IPTW also preserves original sample size (i.e. does not inflate sample size in pseudo-data) in analysis thereby also preserving the type I error rate.33 Stabilised weights were trimmed if they were ≥10, as well-behaved weights have a mean close to 1 and a maximum <10.30,35 The mean of stabilised weights should be close to 1, extreme values indicate the propensity-score model poorly specified predictors of treatment exposure. IPTW resulted in pseudo-populations for ADM adherence groups such that covariates balanced across the two groups. Covariate balance was assessed by comparing covariate distributions between ADM adherence groups. Balance is indicated with no significant differences in the distribution of covariates between groups and by standard mean differences <10%.34,36
Table 1 Characteristics of patients with long-term opioid use with a new depression episode (NDE) by antidepressant medication (ADM) adherence status in unweighted data, 2002–2012 (n = 2821)
Results in bold are significant.
SMD %, standardised mean difference per cent.
a.Other anxiety disorders: panic disorder, obsessive–compulsive disorder, social phobia, generalised anxiety disorder, anxiety not otherwise specified.
b.Cardiovascular disease, hyperlipidaemia, ischaemic heart disease, diseases of pulmonary circulation, other heart disease, hypertensive heart disease, myocardial infarction.
Analyses were performed with SAS v9.4 at an alpha of 0.05. Bivariate analyses, using independent samples t-tests for continuous variables and chi-square tests for categorical variables, assessed the relationship of covariates with ADM adherence in unweighted and weighted data. A Poisson regression model was used in unweighted and weighted data to compare opioid cessation incidence rate (person-years) between ADM adherence groups. Unweighted and weighted Cox proportional hazards models were used to calculate hazard ratios and 95% confidence intervals for the relationship of ADM adherence and time to opioid cessation. Weighted analyses used stabilised weights in probability weighting. Confidence intervals and P-values in weighted analyses were calculated using robust, sandwich-type variance estimators. Follow-up time was defined as months from date of NDE to date of OAU cessation or censor date, which was the last available Veterans Health Administration encounter.
The final model included variables to control for pain diagnoses and changing pain score after the initiation of ADM treatment. All pain variables and ADM adherence were modelled as time dependent. This allows ascertainment of exposure status over the multiyear observation period and permits new diagnoses and change in pain scores to contribute to the outcome. Initial evaluation of each interaction term of each covariate and follow-up time confirmed that the proportional hazards assumption was met for ADM adherence (P = 0.11) and pain covariates (P > 0.05).
This project was approved by the institutional review boards of participating institutions.
In unweighted data, the average monthly change in PHQ-9 score and pain score across follow-up was computed using random intercept longitudinal mixed models (Proc Mixed, SAS v9.4) for four groups; (a) ADM adherent with OAU cessation (n = 5 PHQ-9; n = 213 pain scores), (b) ADM adherent without OAU cessation (n = 96 PHQ-9; n = 864 pain scores), (c) ADM non-adherent with OAU cessation (n = 14 PHQ-9; n = 354) and (d) ADM non-adherent without OAU cessation(n = 147 PHQ-9; n = 1390 pain scores). Because of the lack of PHQ-9 data prior to 2008, monthly changes in PHQ-9 scores in follow-up were computed for a subset of 262 patients with NDE occurring in 2008–2012 and with at least one PHQ-9 score before end of follow-up. Time was modelled as months since NDE and models included all available pain and PHQ-9 data in follow-up.
In unweighted data, a Fisher’s exact test of independence revealed ADM adherence and duration were highly related (P < 0.0001, results not shown). Among 1077 patients who were adherent, 0.2% received continuous ADM for less than 12 weeks, 4.4% received an ADM for 12 to <24 weeks and 95.5% for at least 24 weeks. Among 1744 patients who were non-adherent, 15.0% received ADM for less than 12 weeks, 19.0% for 12 to <24 weeks and 66.1% for at least 24 weeks.
Figure 2 shows that the overall unweighted incidence rate for OAU cessation was 48.4 per 1000 person-years, with no significant differences between ADM adherent (50.2/1000 person-years) and non-adherent (47.4/1000 person-years) groups (P = 0.496). However, after weighting data using IPTW techniques, the incidence rate of OAU cessation was higher for ADM adherent (57.2/1000 person-years) compared with non-adherent (45.0/1000 person-years ) groups (P = 0.007).
ADM, antidepressant medication.
Fig. 2 Antidepressant adherence, non-adherence and prescription incidence of opioids.
Unweighted distributions of covariates by ADM adherence are shown in Table 1. Among those individuals taking opioids for >90 day with a NDE receiving ADM treatment, almost half (46.9%) had taken opioids for more than 2 years (>24 months) at the time of the NDE. Almost two-thirds (63.0%) reached a maximum MED of >100 mg. Maximum dose achieved was similar (P = 0.704) in ADM adherent and non-adherent groups. Benzodiazepine comedication with ADM was significantly more prevalent among ADM adherent (59.0%) v. non-adherent (53.6%) groups. Comorbidities that were significantly more prevalent among ADM adherent compared with non-adherent groups were post-traumatic stress disorder, type 2 diabetes, hypertension, cardiovascular disease, low testosterone and sleep apnoea. Alcohol and illicit drug misuse/dependence were more prevalent among the non-adherent group. Patients in the ADM adherent compared with the non-adherent group were significantly older, White, married, more likely to have other insurance in addition to Veterans Health Administration insurance and have higher healthcare utilisation.
After applying IPTW, all covariates balanced and were not significantly different between the ADM adherent and non-adherent groups (Table 2). IPTW stabilised weights ranged from 0.54 to 3.15, with a mean of 1.00 (s.d. = 0.25) and median of 0.95 (interquartile range (IQR) = 0.83–1.11). The standardised mean differences (SMDs) after weighting were all <10%. Good balance was achieved given differences between treatment groups were all non-significant and all SMDs were <10%.
Table 2 Weighted association of covariates with antidepressant medication (ADM) treatment adherence, weighted by inverse probability of ADM treatment adherence, in patients with chronic opioid use (>90 days) at time of new depression episode (NDE, 2002–2012; n = 2821)
SMD %, standardised mean difference per cent.
a.Other anxiety disorders: panic disorder, obsessive–compulsive disorder, social phobia, generalised anxiety disorder, anxiety not otherwise specified.
b.Cardiovascular disease, hyperlipidaemia, ischaemic heart disease, diseases of pulmonary circulation, other heart disease, hypertensive heart disease, myocardial infarction.
Results of unweighted and weighted Cox proportional hazards models estimating the association between ADM adherence and time to OAU cessation are shown in Table 3. In unweighted data, there was no relationship of ADM adherence and time to OAU cessation (hazard ratio (HR) = 1.05, 95% CI = 0.88–1.24). However, after weighting and adjusting for changes in pain scores and pain diagnoses that could occur after ADM initiation, adherence was associated with a 24% increased likelihood of OAU cessation compared with non-adherence (HR = 1.24, 95% CI = 1.05–1.46)
Table 3 Results from Cox proportional hazards models estimating the association between antidepressant medication (ADM) adherence and opioid cessation among patients with chronic opioid use ( (>90 days) with a new depression episode (NDE, 2002–2012) (n = 2821).
Results in bold are significant.
b.Inverse probability of adherence weighted data to control for confounding factors shown in Table 1.
c.Additional adjustment for painful conditions and pain scores after date of ADM initiation.
Longitudinal linear growth curves for PHQ-9 score among a subset of 262 patients by ADM adherent and OAU cessation groups are shown in Fig. 3(a). At time of NDE, mean PHQ-9 scores were not significantly different between groups (P = 0.995). Overall, there was a trend for a monthly decrease in PHQ-9 score (P = 0.08). Average monthly decrease in PHQ-9 scores was largest for the ADM adherent group with OAU cessation (β = −0.60, 95% CI −1.33 to 0.12) followed by the ADM non-adherent group with OAU cessation (β = −0.21, 95% CI −0.53 to 0.10), ADM adherent group without OAU cessation (β = −0.13, 95% CI = −0.21 to −0.04), and ADM non-adherent group without OAU cessation (β = −0.06, 95% CI −0.12 to 0.01). However, time trend slopes were not statistically different (P = 0.23).
The four study groups were antidepressant medication (ADM) adherent, opioid continuation; ADM adherent, opioid cessation; ADM non-adherent, opioid continuation; and ADM non-adherent, opioid cessation.
Fig. 3 Change in (a) 9-item Patient Health Questionnaire (PHQ-9) scores and (b) pain scores over time in the study groups.
Longitudinal linear growth curves for pain scores in follow-up among the entire sample of 2821 patients are shown in Fig. 3b. The ADM non-adherent group with OAU cessation had significantly higher pain scores than the other three groups at time of NDE (P = 0.0003). Results indicated there was an overall significant monthly decrease in pain score across follow-up (P = 0.002) and that these monthly changes were different between groups (P = 0.01), however, these changes were relatively flat (β range −0.0004 to 0.009).
In a cohort of 2821 Veterans Health Administration patients who developed NDE after >90 days of prescription OAU and who received at least one ADM prescription, we observed adherence v. non-adherence to ADM treatment was associated with a 24% greater likelihood of opioid cessation. The ADM adherent v. non-adherent group had a significantly greater incidence rate of OAU cessation (57.2/1000 v. 45.0/1000 person years; P = 0.007). These results were observed after balancing factors associated with ADM. We also controlled for confounding by pain that could persist during ADM treatment.
Exploratory analysis indicates that the ADM adherent group who stopped taking opioids experienced a rapid and greater decline in depression symptoms compared with patients who did not stop taking opioids, regardless of adherence; however, these results are preliminary because cell sizes were very small for OAU cessation groups. Monthly pain scores were significantly higher among the ADM non-adherent group with OAU cessation compared with the other ADM adherent, non-adherent/opioid cessation-no cessation groups, but the size of the difference was not clinically meaningful. Adjusting for maximum pain scores after ADM initiation in the full Cox proportional hazard models did not change the association between ADM adherence and opioid cessation. Together, these results provide preliminary evidence that a reduction in depression may lead to OAU cessation. Stronger evidence indicates change in pain scores does not explain the association between ADM adherence and opioid cessation.
Interestingly, ADM adherent and non-adherent groups who stopped OAU had the steepest reductions in depression symptoms across follow-up. Because response to antidepressant treatment is markedly greater in those individuals who are adherent v. non-adherent,12,13 we speculate people who are ADM non-adherent may have decreased PHQ-9 scores because of OAU cessation. This would be consistent with the evidence for a bidirectional relationship between OAU and depression.8 Prospective studies are warranted to verify this finding.
Patients with comorbid pain and depression may remain on opioids in an attempt to self-medicate mood37 and to avoid depression during opioid withdrawal.38 Patients with depression are more likely to drop-out of opioid taper, and withdrawal symptoms are exacerbated in patients with current depression.38 Thus, another explanation for our findings may be related to improved depression leading to decreasing attempts to self-medicate mood and greater probability of completing opioid taper.
ADM adherence may be a proxy for overall adherence to medical treatment, the ‘healthy adherer’ effect.39 These patients may adhere to physician instructions to end OAU, adhere to other forms of pain management or begin opioid substitution treatment.
It is possible that unmeasured confounders were not included in the propensity score and we violated the exchangeability assumption.34 For instance we do not have personality measures or indicators of an orientation toward health that might predict both adherence to antidepressants and contribute to opioid cessation. Thus, unmeasured confounding is a limitation. The cohort was a majority male, Veterans Health Administration patient population, which could limit generalizability to non-Veterans Health Administration patients. However, we have previously found that the association between duration of OAU and NDEs in Veterans Health Administration patients was replicated in two private-sector cohorts,3 and the association between opioid use v. no use and risk of depression recurrence in Veterans Health Administration patients was replicated in a private-sector cohort.40 This suggests our findings could be replicated in private-sector cohorts. OAU was based on prescriptions dispensed and we were unable to determine whether patients took their medication as prescribed. Some OAU could be misclassified if patients transitioned to non-Veterans Health Administration or illicit sources for opioids.
Some antidepressants such as TCAs and duloxetine are used in pain management,41,42 but ADM management for analgesia is not designed to treat depression. Our conclusions were consistent in post hoc analysis comparing adherence to TCAs/duloxetine only v. adherence to other ADMs, indicating our findings were not as a result of adherence to only ADMs commonly used in pain management.
ADM adherence was associated with increased likelihood of OAU cessation in individuals with chronic use of opioids and this association was independent of duration of opioid use, maximum MED, pain and numerous comorbid conditions. Several studies have reported that the majority of people who take opioids for >90 days remain on opioids for 3–5 years.4,5 In our cohort, 47% took them for >2 years. Thus, OAU cessation following adherent ADM treatment occurred in a patient cohort with a low probability of OAU cessation. We computed the number-needed-to-treat and found for every 20 patients adherent to ADMs, one patient will stop OAU who would not have stopped if they were non-adherent.
Treatment of opioid dependence in patients with comorbid depression may be successful following effective depression treatment. Preliminary evidence suggests OAU cessation may also contribute to improvement in depression. Therefore, opioid taper paired with ADM could result in a faster reduction of depression symptoms and increase likelihood of successful OAU cessation. Prospective data collection to obtain detailed depression and functioning measures, change in OAU and treatment trials are needed to confirm our findings.
This study was supported by the National Institute of Mental Health, Prescription Opioid Analgesics and Risk of Depression, R21MH101389. The funding sources had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. The views expressed in this paper do not necessarily reflect those of the Veterans Health Administration.
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WASHINGTON — Deaths by drug overdose in the United States surged last year by more than 17 percent over 2015, another sign of the growing addiction crisis caused by opioids, according to a report released Friday by the Centers for Disease Control and Prevention.
Preliminary data from the 50 states show that from the fourth quarter of 2015, through the fourth quarter of 2016, the rate of fatal overdoses rose to nearly 20 people per 100,000 from 16.3 per 100,000. The C.D.C. had previously estimated that about 64,000 people died from drug overdoses in 2016, with the highest rates reported in New Hampshire, Kentucky, West Virginia, Ohio and Rhode Island.
Drug overdoses have become the leading cause of death for Americans under 50. In recent years, according to Dr. Robert Anderson, chief of the C.D.C. mortality statistics branch, the deaths have been driven by overdoses of synthetic opioids, mostly fentanyl, rather than heroin.
“The main message is the drug rate went up a lot again, and of course we’re worried about it,” Dr. Anderson said.
Dr. Anderson stressed that these are preliminary results. Although the report includes deaths by cancer, heart attack and most other causes through mid-2017, its section on drug deaths covers only 2015 through 2016, because of the complexity of toxicology reports and other information needed to confirm drug overdoses.
Dr. Andrew Kolodny, director of opioid policy research at Brandeis University, was not surprised by the numbers.
‘We have roughly two groups of Americans that are getting addicted,” Dr. Kolodny said. “We have an older group that is overdosing on pain medicine, and we have a younger group that is overdosing on black market opioids.”
The number of teenagers becoming addicted to pain killers is going down, Dr. Kolodny said. But those who are already addicted, in their 20s and 30s, are increasingly in danger because of the practice of mixing heroin with fentanyl or fentanyl being sold as heroin. Other government reports show that deaths by fentanyl have increased significantly in three years.
President Trump has declared the opioid crisis a “public health emergency,” but has not released additional funding to address it.
The C.D.C. report also showed that the rate of deaths from cancer were down slightly in the second quarter of 2017, at just under 180 deaths per 100,000 people; down from about 186 per 100,000 in the first quarter. And fewer people are succumbing to heart disease. The report shows that 187 people per 100,000 died in the second quarter, down from 217 per 100,000 in the first part of the year.
Yesterday afternoon, President Trump declared America’s opioid crisis a public health emergency, and for good reason: the American Society of Addiction Medicine estimates that there’s nearly 2.6 million Americans with an opioid addiction, and the communities affected include some of our poorest and most vulnerable. The problem is becoming critical, and solving it goes beyond politics to become one of basic human compassion.
The only problem: current guidelines by the FDA and CDC are ineffective, based on a factually‐faulty premise unsupported by evidence, and will almost certainly increase suffering and death without significantly improving the numbers for opioid addiction.
Patients must be prescribed opioids only for durations of treatment that closely match their clinical circumstances and that don’t expose them unnecessarily to prolonged use, which increases the risk of opioid addiction. Moreover, as FDA does in other contexts in our regulatory portfolio, we need to consider the broader public health implications of opioid use. We need to consider both the individual and the societal consequences.
(For this, and all future quotes, all bolding is my own emphasis.)
The CDC goes further with its current guidelines, including a clinical “reminder” that “opioids are not first-line or routine therapy for chronic pain”. It recommends to use as low of a dose as possible for as short of a time as possible, frequently reconsider its upkeep, and to only start them as a last resort.
Almost any statement made by public officials relating to the crisis is based on the same two premises: reducing the number of patients receiving long‐term opioid prescriptions is the most effective way to curtail the opioid epidemic, and most opioid addictions start as a result of use that began as legitimate.
Unfortunately, nearly all the evidence we currently have contradicts these foundations. If you’re interested in a detailed, academic look at the topic, there’s a fantastic article written by three doctors — including an Associate Professor at the University of Massachusetts Medical School — appropriately titled “Neat, Plausible, And Generally Wrong”:
Recommendations from the Centers for Disease Control and Prevention (CDC) for chronic opioid use, however, move away from evidence, describing widespread hazards that are not supported by current literature. This description, and its accompanying public commentary, are being used to create guidelines and state-wide policies.
These recommendations are in conflict with other independent appraisals of the evidence — or lack thereof — and conflate public health goals with individual medical care. The CDC frames the recommendations as being for primary care clinicians and their individual patients. Yet the threat of addiction largely comes from diverted prescription opioids, not from long-term use with a skilled prescriber in a longitudinal clinical relationship. By not acknowledging the role of diversion — and instead focusing on individuals who report functional and pain benefit for their severe chronic pain — the CDC misses the target.
We need to break some statistics down. According to the 2014 National Survey on Drug Use and Health, 74.9% of nonmedical opioid use happens as a result of people taking medication they were not prescribed, such as those obtained or stolen from a friend or drug dealer. A further 3.1% fraudulently obtained prescriptions from multiple doctors, a practice called “doctor‐hopping”. That’s a total of 78% of sources other than a relationship with a single doctor. That leaves 22% of those who were addicted who do receive their pills from a doctor, but that number must be put into perspective.
The findings of this systematic review suggest that proper management of a type of strong painkiller (opioids) in well-selected patients with no history of substance addiction or abuse can lead to long-term pain relief for some patients with a very small (though not zero) risk of developing addiction, abuse, or other serious side effects.
Signs of opioid addiction were reported in 0.27% of participants in the studies that reported that outcome.
A scant 0.27% of patients prescribed long‐term opioids for chronic pain showed signs of becoming addicted. The doctors above found similarly‐low rates of addiction with other sources of data.
Responses of addicted patients significantly differed from those of nonaddicted patients on multiple screening items, with the two groups easily differentiated by total questionnaire score. Further, three key screening indicators were identified as excellent predictors for the presence of addictive disease in this sample of chronic pain patients.
It’s important to recognize that scripts written properly aren’t the cause of the epidemic, because — beyond being ineffective — this mindset can actually lead to reduced access to treatment for those with addictions.
Buprenorphine is an opioid that’s used as maintenance therapy, as it has a far lower risk of causing respiratory depression, the primary killer in opioid overdose. Its use as a maintenance therapy is associated with a significantly lower chance of death than leaving addiction untreated; inexplicably, however, the use of buprenorphine was stifled by a 2000 law stating that only 30 patients could be treated at any one time per physician to begin with, and only after jumping through numerous bureaucratic hoops that made it more difficult to prescribe treatment for opioid addiction than the opioids themselves.
[As of June 2017,] only 35,894 providers are currently eligible to prescribe buprenorphine for addiction. Of those 35,894 only about 1/3 actively prescribe the treatment; and these few are further limited by the patient caps.
Researchers logged nearly 1,000 cases of doctors being either charged or administratively reviewed for the inappropriate prescription of opioid drugs over an eight year period, and specific high‐profile cases have been the subject of numerous documentaries. Pill mills prey on those susceptible to addiction, and prescribe indiscriminately:
Like the other pain clinics in Portsmouth where Volkman had worked, the clinic only accepted cash — no insurance, no Medicaid. In exchange for $150, patients could expect to receive high doses of pain medications, anti-anxiety agents, and muscle relaxers. In September 2005, according to a search warrant, one Portsmouth Police informant stopped in to see Volkman and received prescriptions for 180 oxycodone pills, 180 Lorcet (a hydrocodone-based painkiller) pills, 120 Soma (a muscle relaxer) pills, and 90 Xanax. Two days later, another informant received a prescription for 270 oxycodone pills, 270 Percocet, 120 Somas, and 60 Xanax. Volkman’s clinics brought in thousands of dollars in cash and pumped out thousands of pills in a region that was already being described in the Portsmouth Daily Times as “The OxyContin Capital of the World.”
It’s an operation driven by profit from the top down, and it’s easy to see how backing the CDC’s position of cracking down on opioids entirely becomes tempting, but there are ways to curtail excessive prescriptions without creating a devastating case of throwing the baby out with the bathwater.
According to the American Pain Society, there are over 25 million Americans who experience daily chronic pain — pain that affects quality of life every single day. Conditions like lupus, fibromyalgia, Ehlers‐Danlos syndrome, various forms of arthritis, and many more cause pain that is not only severe, but unending. That’s an important number, because it’s just about ten times the number of patients with an opioid addiction.
I’ve made two separate claims: increased suffering, and increased deaths. I’ll start with suffering.
“But Have You Tried Yoga?”
Invariably, you’ll find guidelines instructing doctors to tell patients to pursue “alternative” painkilling strategies first, as if there are myriad wells of untapped relief that chronic pain patients simply ignore.
We can save time and start with the easy ones: acupuncture doesn’t work. There’s some evidence for massage being somewhat effective for some conditions, but the fact that it’s rarely covered by insurance and the need for ongoing treatment renders the cost/benefit ratio bad. Marijuana is still federally illegal, unavailable in many states, and its presence on a drug test still precludes many employment opportunities for pain sufferers who are able to work. Chiropractic is ineffective pseudoscience that hurts more than it helps. Supplements can cause harm, interact with real medicine, and extraordinarily few have any high‐quality evidence for any condition, including almost none for chronic pain.
That leaves the big one: exercise. “Exercise helps reduce chronic pain!” is repeated often and adamantly, as if it is long‐accepted fact that has a long, positive background in research. Does it?
As recently as April of this year, Cochrane looked over twenty‐one of their reviews of studies regarding exercise for chronic pain, and found that the evidence was generally insufficient:
The quality of the evidence examining physical activity and exercise for chronic pain is low. This is largely due to small sample sizes and potentially underpowered studies. A number of studies had adequately long interventions, but planned follow-up was limited to less than one year in all but six reviews.
There were some favourable effects in reduction in pain severity and improved physical function, though these were mostly of small-to-moderate effect, and were not consistent across the reviews. There were variable effects for psychological function and quality of life.
Additionally, participants had predominantly mild-to-moderate pain, not moderate-to-severe pain.
Beyond that, the correlation between chronic pain and chronic fatigue is massive, with everything from lupus and fibromyalgia to EDS, rheumatoid arthitis, and Raynaud syndrome causing heavy fatigue, and that in no way comprises a complete list. Despite what the CDC incorrectly insisted for years past being proven inaccurate, exercise invariably makes chronic fatigue, such as found in CFS, worse.
So strike all the chronic pain patients suffering from chronic fatigue. Of the remainder, what about the ones with a strong medical reason not to exercise? Chronic pain caused by physical damage, Ehlers‐Danlos patients unable to exercise or maintain yoga‐esque positions due to frequent joint dislocations, inflammatory conditions that forbid normal ranges of movement, those with heart conditions exacerbated by activity…
We’re left with a small slice of the pie chart for which exercise gets the chance to be effectively used at all, and I would posit that even if there was evidence for it, calling it a replacement for pain management and not something to do alongside it is an act of cruelty. This was stated earlier, but it necessitates repeating: chronic pain is daily. Chronic pain causes real suffering every day, and frequently every hour.
There are no “good days” where you don’t have pain, just days that are “somewhat better than usual”. Imagine this: every day, anywhere from “many parts” to “every part” of your body hurts; unrelenting, bone‐deep aching, nerves that light themselves on fire, jabbing pain coursing through your muscles upon the slightest activity.
If your pain levels average out to 7/10 on a daily basis, would you intentionally up the pain to 9/10 numerous days a week just to — after weeks and months of agony — potentially bring that daily average down to 6/10? Would you feel like you got good value on that proposition? Would you consider it worth it? Would you have the will to keep it up every week until your death, forever? It doesn’t matter if we assume the unsubstantiated claims of notable improvement are true; would you not want pain relief for the days in which your suffering was greatly increased?
Pain needs to be managed, and there are, unfortunately, limited ways to effectively do so. The single most efficacious non-pharmaceutical treatments involve mental mechanisms, such as mindfulness meditation and CBT, which do nothing to reduce the pain itself — merely one’s perception of it. They help, but they can take months to see improvement, and will always leave you with a certain baseline of pain that needs to be treated through some other avenue.
The End Of The Road
Depression affects up to half of all chronic pain patients. According to studies, risk of suicide is at least double that of controls, with up to 14% of CPP attempting suicide and around 20% of them experiencing suicidal ideation. Causes and levels of chronic pain can be disabling and prevent patients from maintaining gainful employment, or from participating in the hobbies and activities that were once important to them.
The comparison might seem trite, but it’s apropos: the worst part of chronic pain is not the “pain” — it’s the “chronic”. Relentless pain dominates your life in a way few healthy people appreciate. It demands schedules to be built around it; it demands plans to be canceled en masse when it unexpectedly flares; it holds you as a hostage in your own body and taunts you with its permanency. Make no mistake that even with opioids, it makes it more manageable, not absent; completely eliminating pervasive pain is nearly impossible.
If relief is taken away from chronic pain sufferers indiscriminately and under faulty pretenses, the question is not whether it will result in increased disability and suicide — the question is only by how much.
The opioid crisis needs solutions, and quickly, but it also needs those solutions to be factual, effective, and compassionate, and our current theories for how opioid addiction starts and how it needs to end are none of the above.
COLUMBUS, Ohio — Late last year, Manny Delaveris fell into a rut. He couldn’t shake his pessimism or stop worrying. His brain felt dark, all the time. It wasn’t that different from how he felt back when he was still using painkillers and heroin: empty.
Delaveris had been sober for a year following several stints in rehab, and he was on his way to making the dean’s list at Ohio State University. Yet he found himself tuning out at support group meetings and going out back to smoke cigarettes.
Then he ran into a friend he used to get high with.
“I said, ‘Fuck it. Let’s do it,’” Delaveris, 27, recalled. “I just felt so shitty. And I really thought it was going to be this quick, little, like, I’ll just do this, get it out of my system” — he snapped his fingers — “and get sober again right away. And it just was not that easy.”
For the next six months, Delaveris woke up nearly every day planning to stay sober. But after heading to classes in the morning, he’d find himself unable to think of anything else — until, finally, he would drive off-campus to buy painkillers from an acquaintance, and, because he couldn’t wait, use them on his way home. Then he’d spend the rest of the day in his room alone, watching YouTube videos on astrophysics or listening to One Direction.
“When I’d look at kids walking through campus, I would think to myself, ‘These kids wouldn’t last two minutes in my world. I do better than them in class and I get high,’” he said. “I didn’t know anybody else doing what I was doing, so I kind of used that to make myself feel better.”
The worsening opioid epidemic, which killed nearly 3,200 Americans under 25 in 2015, is drawing attention to the lack of help available to college students battling addiction. Campus has never been particularly friendly to sobriety, and students in recovery from drug and alcohol use have been overwhelmingly left to struggle on their own with a potentially deadly habit.
Yet that might be changing. After decades of ignorance, indifference, or resistance to the idea, programs that support students in recovery are popping up at schools all over the country. In 2013, there were just a couple dozen “collegiate recovery” programs — today, there are 186, according to Transforming Youth Recovery, a nonprofit dedicated to expanding resources for young people in recovery. Some colleges are even beginning to offer medication-assisted treatment to students, weaning them off opioids with the help of drugs like buprenorphine.
There’s scant data on college students with addictions to opioids or other drugs, since these students often leave school without telling officials why. But we do know that for the past decade, people younger than 25 have accounted for between 10 and 12 percent of all fatal opioid-linked overdoses — and that, after years of decline, such overdoses among kids ages 15 to 19 have spiked.
“We owe it to our students to have something for them when they come back from treatment,” said Matt Statman, the manager of the collegiate recovery program at the University of Michigan. “We’re setting them up for failure if we just send them off to treatment and then when they come back, we say, ‘Here’s an AA book. Have a nice day.’”
Delaveris is one of the lucky ones — he belonged to Ohio State University’s Collegiate Recovery Community, which offers a home to students struggling to stay sober. When he finally broke down and asked the program’s managers for help, “They were nothing but supportive,” he said. They helped him get into treatment and, he says, saved his life.
Looking for help
Collegiate recovery programs have been around since the ’70s, but for decades, advocates say, the stigma and lack of understanding surrounding addiction kept most school officials from starting one. Money is always scarce in higher education, and administrators didn’t necessarily want to take a chance on a potentially controversial program.
Christopher Hart, a consultant who works with Transforming Youth Recovery, summarized the longtime attitude among administrators as, “We don’t want to take the college tour of prospective parents by the location where students in recovery are gathering.”
Yet getting sober on campus can be uniquely challenging. Recovery researchers call college an “abstinence-hostile environment,” since it’s often seen as a consequence-free, “Animal House”–like oasis that revolves around finding the next party.
Nearly a third of full-time college students surveyed by the University of Michigan in 2015 said they’d had five or more drinks in a row, which qualifies as binge-drinking, at least once in the last two weeks. Other popular drugs include marijuana and stimulants, such as the ADHD drugs Adderall and Ritalin. About 3 percent of surveyed college students also said they’d used OxyContin, Vicodin, or Percocet without a doctor’s permission sometime during the past year.
“If I’m in a business class and the person sitting next to me is talking about who they can buy Adderall from, it’s very easy as a college student to say, ‘Hey, I’m actually looking for some of that too. Can you hook me up?’” said Alex Rager, 24, a University of Georgia student who’s in recovery from his addiction to stimulants. “There’s such a calm, collective, and kind of no-questions-asked culture.”
Brendan Saloner, who studies substance use treatment among young people at Johns Hopkins Bloomberg School of Public Health, said the general lack of collegiate recovery options is symptomatic of a broader problem. “We don’t have the infrastructure set up to really help anyone in most places. It’s sort of a bleak world,” he said. “People are desperate, and understandably so.”
When Tim Rabolt first stepped foot on George Washington University’s campus as a freshman in 2011, he’d assumed that such a large university would already have resources set up for students in recovery. He got hooked on prescription opioids after he got his wisdom teeth out at 15 and his dentist prescribed him what he called “an insane amount of pills.”
Rabolt’s assumption was wrong — instead, officials seemed caught off guard by his questions about recovery, as if no one had ever asked about it before.
“It was really just hard to believe,” said Rabolt, who got sober during his senior year of high school. “It’s not like they’ve never encountered addiction and alcoholism of students. They’d have to be blind not to see that, with the amount of partying that’s done in college and all the stress factors and everything.”
(Peter Konwerski, the school’s dean of student affairs, said GW has always offered services that students in recovery could use, such as counseling, but acknowledged, “As a university, we have come a long way and we’ve been much more open to being much more public and very vocal about addiction.”)
As he spiraled into depression and suicidal thoughts, Rabolt started leaving campus almost every day to attend support group meetings. At last, in the spring semester of his freshman year, a GW official set up an on-campus meeting for students in recovery.
“It felt like the final piece of the puzzle,” Rabolt recalled. “I finally had something like every week to look forward to. I could talk to them about classes and registering for courses or final exams. Everyone was in the same boat. … [I] felt a part of something, instead of apart from.”
Mandy, a University of Michigan senior who asked us not to use her last name, said she left campus during her sophomore year in 2014 to get sober from her addiction to alcohol. Shortly after she came back to school, she broke down in tears in a friend’s car.
“It was the fall, I had just moved back, and I was like, ‘I don’t know how I’m gonna do this, how I’m gonna stay sober, how I’m going to get through college and graduate and turn 21 and stay sober,’” Mandy, 22, recalled. She joined the school’s collegiate recovery program, which typically serves about 25 to 30 students, where she says she finally made friends outside of partying. “Somehow — just taking it one day at a time and with all the support of the people there — I was able to.”
Treating the problem
Collegiate recovery advocates attribute their field’s recent growth to a number of factors. One is the response to the opioid epidemic, which has helped reframe addiction as a public health issue instead of an individual moral failing. Another is Transforming Youth Recovery — started by a woman whose son died of an opioid-linked overdose — which, since 2013, has given out $10,000 seed grants to about 140 schools. Texas Tech University, a leader in the field, has also created a detailed blueprint to help other schools that want to replicate its collegiate recovery community.
And students in recovery who once took advantage of on-campus support are now graduating — and feeling compelled to start their own programs. Rabolt, for example, helped convince George Washington University to start an official collegiate recovery program before he graduated with a master’s degree in 2017. Today, he works as a consultant with the Association for Recovery in Higher Education, the field’s main advocacy and support organization. It counts 100 schools as members.
“You began to have this younger generation that was no longer accepting of the idea of staying in basement meetings, so to speak, but was coming out and talking about what it meant to be in recovery,” Hart, the consultant, said. “[They] were flat-out tired of their friends dying.”
For a month after he relapsed, Delaveris skipped the weekly support group meeting at Ohio State’s Collegiate Recovery Community out of embarrassment. When he returned, he initially revealed what had happened, but as he continued to use, he skirted actually lying to fellow members by keeping the details of his days vague. Growing up in Columbus, he had always dreamed of graduating as a Buckeye, and he was just a few months shy of his B.A. in psychology. He made a bargain with himself: He’d get treatment once he graduated.
After painkillers became too expensive for his dwindling savings, he started using heroin again. Eventually, he was using some kind of opiate every day.
In April, Delaveris went to dinner with his mom, who asked how he was doing. He couldn’t answer honestly. “I was just lying, just lying through my teeth, because I was dying inside, but I just have too much pride to say it,” Delaveris said. “The next day, I just woke up and couldn’t stop crying. It hurt so bad.”
He realized then that there was no way he’d make it to graduation without help. So he called Sarah Nerad, then the manager of the Collegiate Recovery Community. “‘We got you,’ is basically what she said,” Delaveris recalled. Within two days, he was in a Columbus in-patient treatment facility.
Ohio State’s Collegiate Recovery Community began in March 2013, after a student emailed administrators with pleas to help people in recovery. Today, it occupies a multi-room suite on the 12th floor of a tower overlooking the river that cuts through campus. There’s ’80s board games, foosball, free printing — a luxury on college campuses — and whole rooms devoted to meditation and watching television. Visiting members splay across the couches and put their feet on cushions with easy familiarity. With its fluorescent lighting and standard-issue dorm-room carpeting, the suite could be mistaken for any other student club’s meeting space.
The CRC has expanded rapidly, especially for the otherwise slow-moving world of academia, and now counts between 25 and 30 active members, including Delaveris. Like most collegiate recovery programs, it provides individualized help, support group meetings, and social outings like sober tailgates. It also created “Recovery House,” a dorm whose occupants must refrain from using drugs or alcohol on its premises, and spearheaded an initiative requiring Ohio State’s pharmacy and campus police to carry the anti-overdose drug naloxone. (Campus police told VICE News they haven’t had to use it yet.) Its “Recovery Ally” training, to fight stigma and help staff and faculty better understand substance use disorder and recovery, is also poised to become an industry standard.
“If we change the mentality of focusing on numbers and we start looking at these people as people again, we’re moving in the right direction,” said Rob Schilder, a 25-year-old senior at Ohio State University who belongs to the CRC (he’s recovering from opioids) and works there as a student assistant. “We service 25 to 30 students a semester who are in recovery, and at a school this large that may not sound like a lot. But that’s 30 people whose moms can sleep at night.”
Making it stick
Most collegiate recovery groups encourage members to abstain from using any substance. But that may begin to change in the next few years, supporters say, as the opioid crisis likely worsens and more evidence of the effectiveness of medication-assisted treatment emerges.
“There’s no reason why a self-help kind of program couldn’t be combined with an approach that is accepting of medication,” Saloner said. “The 12-step community” — which includes groups like Narcotics Anonymous — “is a little bit divided on this issue right now, so there’s still a lot of shunning of people who are on methadone or buprenorphine and want 12-step help, but there’s value in having peers who support you. There’s especially value in having that support network when you’re going through the college experience.”
Ohio State is one of the very few schools to create a medically-assisted treatment program. Students had complained about struggling to get to off-campus treatment between classes and without cars.
“We don’t ever want someone to have to make a choice between going to class and getting treatment,” said Gladys Gibbs, a physician and the head of Student Health Services, whose department helps oversee the MAT program. “We’re probably in a better position on campus to work around their schedules and be convenient to access.
“Our ultimate goal is abstinence,” Gibbs added, “but you have to take people where they are and not where you’d necessarily like them to be, in order to get them there.”
Since collegiate recovery programs and communities are still rare, there’s little research on their outcomes. But the available data indicates that even the abstinence-first models are effective at keeping students sober and in school.
The only nationwide survey of students in collegiate recovery programs, which was published in 2015 and looked at 29 schools, found that, on average, just 8 percent of students who’d been in recovery for about three years relapsed each year. Generally, people with substance use disorders must be in recovery for at least five years before their chances of relapse dip below 15 percent. It also found that students in collegiate recovery programs tend to have higher grade-point averages and graduation rates than other students at their schools.
Kitty Harris, who led Texas Tech’s collegiate recovery program for 12 years, says that’s likely because, after being written off as drug-addicted “losers,” students finally feel like they’re the type of people who can succeed.
“A lot of the students we’ve had through the years have been kicked out of other universities because of their grades or behavior,” she said. “A lot of these students feel like they’ve gotten a second chance.”
Since Delaveris got treatment in April, he says he’s had a good day pretty much every day. When he wakes up, he prays and meditates, and he takes Prozac to treat the longtime depression he’s been diagnosed with. During the school year, when Delaveris had a spare hour, he’d talk to his sponsor or head to one of the hundreds of support group meetings located around Columbus. He now goes to between one and three meetings a day; recovery is his primary extracurricular.
“I probably wouldn’t be alive” without the CRC, he said. “I know that’s a really bold statement, but the way things were headed, I probably wouldn’t be.”
Delaveris is now five months sober. He graduated in August.
Source: European College of Neuropsychopharmacology
What makes alcoholics drink? New research has found that in both men and women with alcohol dependence, the major factor predicting the amount of drinking seems to be a question of immediate mood. They found that suffering from long-term mental health problems did not affect alcohol consumption, with one important exception: men with a history of depression had a different drinking pattern than men without a history of depression; surprisingly those men were drinking less often than men who were not depressed
“This work once again shows that alcoholism is not a one-size-fits-all condition,” said lead researcher, Victor Karpyak (Mayo Clinic, MN, USA). “So the answer to the question of why alcoholics drink is probably that there is no single answer; this will probably have implications for how we diagnose and treat alcoholism.”
The work, presented at the ECNP congress by researchers from the Mayo Clinic*, determined the alcohol consumption of 287 males and 156 females with alcohol dependence over the previous 90 days, using the accepted Time Line Follow Back method and standardized diagnostic assessment for life time presence of psychiatric disorders (PRISM); they were then able to associate this with whether the drinking coincided with a positive or negative emotional state (feeling “up” or “down”), and whether the individual had a history of anxiety, depression (MDD) or substance abuse.
The results showed that alcohol dependent men tended to drink more alcohol per day than alcohol dependent women. As expected, alcohol consumption in both men and women was associated with feeling either up or down on a particular day, with no significant association with anxiety or substance use disorders. However, men with a history of major depressive disorder had fewer drinking days (p=0.0084), and fewer heavy drinking days (p=0.0214) than men who never a major depressive disorder.
Victor Karpyak continued: “Research indicates that many people drink to enhance pleasant feelings, while other people drink to suppress negative moods, such as depression or anxiety. However, previous studies did not differentiate between state-dependent mood changes and the presence of clinically diagnosed anxiety or depressive disorders. The lack of such differentiation was likely among the reasons for controversial findings about the usefulness of antidepressants in treatment of alcoholics with comorbid depression.
This work will need to be replicated and confirmed, but from what we see here, it means that the reasons why alcoholics drink depend on their background as well as the immediate circumstances. There is no single reason. And this means that there is probably no single treatment, so we will have to refine our diagnostic methods and tailor treatment to the individual. It also means that our treatment approach may differ depending on targeting different aspects of alcoholism (craving or consumption) and the alcoholic patient (i.e. man or a woman) with or without depression or anxiety history to allow really effective treatment.”
Commenting, Professor Wim van den Brink (Professor of Psychiatry and Addiction at the Academic Medical Centre, University of Amsterdam) said:
“This is indeed a very important issue. Patients with an alcohol use disorder often show a history of other disorders, including mood and anxiety disorders, they also often present with alcohol induced anxiety and mood disorders and finally the may report mood symptoms that do not meet criteria for a mood or anxiety disorder (due to a failure to meet the minimal number of criteria or a duration of less than two weeks). All these different conditions may influence current levels or patterns of drinking.
The current study seems to show that the current presence of mood/anxiety symptoms is associated with more drinking in both male and female alcoholics, whereas a clinical history of major depression in male alcoholics is associated with lower current dinking levels. Although, the study does not provide a clear reason for this difference, it may have consequences for treatment. For example, antidepressant treatment of males with a history major depression may have no effect on drinking levels. However, these findings may also result from residual confounding, e.g. patients with a history of major depression might also be patients with a late age of onset of their alcohol use disorder and this type of alcohol use disorder is associated with a different pattern of drinking with more daily drinking and less heavy drinking days and less binging. More prospective studies are needed to resolve this important but complex clinical issue.”
*This work was presented on Sunday 3rd September, 2017.
Overall increase driven by patients taking opioid medication for 90 days or longer
Source: Johns Hopkins University Bloomberg School of Public Health
A new study from the Johns Hopkins Bloomberg School of Public Health found that opioid prescription use increased significantly between 1999 and 2014, and that much of that increase stemmed from patients who’d been taking their medication for 90 days or longer.
Long-term use, which is associated with greater risk for addiction and overdose, increased threefold during the study’s time frame. In 1999-2000, less than half of the people who were taking prescription opioids were taking them for 90 days or more. By 2013-2014, more than 70 percent were taking opioid medication on a long-term basis.
The findings come as the U.S. grapples with a worsening opioid epidemic that on average is killing nearly 100 people a day, some from prescription opioids and others from illegal forms, primarily heroin. Last month, the Trump administration declared the opioid epidemic a public health emergency, a step that will allow the government to dispense additional federal funds for treatment.
The study, published online in the journal Pharmacoepidemiology and Drug Safety, draws from survey data gathered by the National Health and Nutritional Examination Survey, which the National Center for Health Statistics has conducted every two years since 1999-2000. Prescription opioid use, the paper found, rose from 4.1 percent of U.S. adults in 1999-2000 to 6.8 percent in 2013-2014, an increase of 60 percent. Long-term prescription opioid use, defined as 90 days or more, increased from 1.8 percent in 1999-2000 to 5.4 percent in 2013-2014.
“What’s especially concerning is the jump in long-term prescription opioid use, since it’s linked to increased risks for all sorts of problems, including addiction and overdoses,” says study author Ramin Mojtabai, MD, PhD, MPH, a professor in the Department of Mental Health at the Bloomberg School. “The study also found that long-term use was associated with heroin use as well as the concurrent use of benzodiazepines, a class of widely prescribed drugs that affect the central nervous system,” he says.
This is one of the paper’s more worrisome findings, Mojtabai notes, since combining opioids and benzodiazepines significantly increases the risk of overdose, even if the patient is taking a moderate dosage of opioid medication. Combining these drugs can also cause respiratory suppression, he says.
For the paper, Mojtabai examined eight consecutive biannual surveys, each of which included over 5,000 adults living throughout the U.S. Interviews were conducted via computer in participants’ homes. Participants were asked to identify prescription medication they’d taken in the past 30 days, and for what length of time. A total of 47,356 adults participated in the eight surveys, and the response rate ranged from 71 percent to 84 percent. If participants were taking more than one opioid medication, the study logged the duration for the longest-used medication.
Opioid medication use overall and long-term use were more common among participants on Medicaid and Medicare versus private insurance.
Despite the upward trend, there is scant evidence supporting benefits of longer-term prescription opioid use, Mojtabai says, with no randomized clinical trials that support their extended use, given the risks.
Prescription opioids were originally designed for shorter-term use, which involves fewer patient risks. Many patients who take opioid medication for weeks or months develop a tolerance that over time requires higher dosages for the medication to relieve pain. As a result, patients will take more medication to reduce their pain, setting them on the path to possible addiction. While there is no clear delineation as to when addiction kicks in, longer-term use is thought to be a risk factor.
The Centers for Disease Control and Prevention issued new guidelines last year, recommending that physicians prescribe opioids for chronic pain only after other options have been proven ineffective. The guidelines also recommend short-term use (three days instead of seven) and lower dosages. The impact of these new guidelines is not yet known, Mojtabai says.
The 2013-2014 survey asked participants for the first time to identify the main reason they were taking prescription opioid medication. Back pain was the leading reason, with over 42 percent, or 167 of 402 participants taking these medications in 2013-2014, reporting they were seeking relief for back pain. This was followed by arthritis and other joint pain, with 102, or 25.3 percent, identifying them as the reason for taking prescription opioids.
For those with back pain, nearly one half had been taking medication for over 90 days, while among those with arthritis or other joint pain, a quarter had been taking opioid medication for over 90 days. Other leading reasons for prescription opioid use included injury-related pain (14.4 percent) and muscle and soft tissue pain (11.4 percent).
“Given the urgency, it’s critical that we continue to try and understand what benefits, if any, exist for prescribing opioids for both short- but especially for longer-term consumption,” says Mojtabai, “There may be alternative treatments. We also need to understand what other factors contribute to the considerable risks of prescription opioid medication among different groups, especially those with other drug or alcohol use in their profiles.”
Johns Hopkins University Bloomberg School of Public Health. “Long-term opioid prescription use jumps threefold over 16-year period, study suggests: Overall increase driven by patients taking opioid medication for 90 days or longer.” ScienceDaily. ScienceDaily, 7 September 2017.
Three years ago, I checked myself out of a Colorado detox center against medical advice. I had nowhere to go but the broken-down van in which I’d been sleeping with my husband, but I was in the worst part of heroin withdrawal and all I could think about was ending the pain.
On my way out, the resident peer support specialist made one last attempt to stop me.
“The only way you can get sober is by working the steps,” he said, referring to the 12 steps of Narcotics Anonymous.
I told him I was going to try medication-assisted treatment instead. In response, he predicted that I was destined to be a “lifer” — someone who bounces between street drugs, prescribed medications, and brief periods of sobriety, but who never truly turns her life around.
He was right about one thing: I relapsed within hours of leaving the center. But the following month I enrolled in a buprenorphine program. It has worked for me. Today, I live drug-free in stable housing with my husband and our two daughters — and I’m still taking buprenorphine combined with naloxone, prescribed by my doctor.
Buprenorphine latches onto natural receptors in the brain, the same ones that heroin, oxycodone, and other opioids bind to. These receptors are involved in many of the body’s basic functions, like eating, breathing, sleeping, pleasure, and the perception of pain. Buprenorphine partially binds to these receptors, which is why it’s called a partial opioid agonist. It is prescribed as an alternative to methadone, which is a full agonist. Naloxone blocks the effects of opioids, and is added to prevent the abuse of buprenorphine.
Both medications stave off withdrawal symptoms and decrease physical cravings for drugs. They also deter people from abusing other opioids by preventing them from feeling their effects. Buprenorphine and methadone are recognized by World Health Organization as the most effective methods for lowering health problems, overdoses, and deaths related to opioid abuse.
Sadly, there’s a lot of misinformation out there about medication-assisted therapy for drug addiction. Take, for example, a comment made about medication assisted treatment by Tom Price, who recently resigned as secretary of Health and Human Services. “If we’re just substituting one drug with another,” he infamously said, “we’re not moving the dial much,” indicating his clear preference for faith-based and non-psychoactive interventions.
The most recognized providers of those kinds of interventions are the 12-step fellowships, which include Alcoholics Anonymous and Narcotics Anonymous. If that’s what the secretary of health said works best, we should count ourselves lucky that thousands of free 12-step meetings occur every day across the country. Right?
Wrong. These programs are making the opioid crisis worse by making recovery from opioid addiction harder than it already is. By turning their backs on people like me on medication-assisted therapy to kick opioid addictions, these programs are prolonging addiction and contributing to overdose deaths.
Here’s what a regional chairperson for Narcotics Anonymous told me. “People on methadone and buprenorphine are getting high every day, they’re just not buying it on the streets. It’s like you’re replacing one addiction with another.” (As part of their creed, service members of 12-step fellowships are required to maintain anonymity when speaking in the media. This individual agreed to be quoted anonymously.)
But that thinking about total abstinence is outdated. Dr. Mary Jeanne Kreek, who helped develop methadone as a treatment for addiction and who now heads Rockefeller University’s addictive diseases laboratory, believes it is necessary for habitual opioid users to take replacement therapy medications to correct endorphin deficiencies that developed during their use of opioids.
“You’re not going to treat genetics and brain changes with counseling and psychological support,” she told me by phone.
Writing in STAT, two Seattle-area addiction experts said that medication-assisted therapy helps stabilize brain receptors thrown out of whack by an opioid addiction, allowing the body and brain to establish a “new normal.”
Narcotics Anonymous and other 12-step programs describe themselves as wholly abstinence based, but claim to welcome anyone interested in pursuing addiction recovery. The reality, however, is that if someone in medication-assisted therapy seeks the support of a 12-step fellowship, he or she will most likely be met with a lecture or worse — denied the ability to speak during meetings.
I met with a Narcotics Anonymous secretary, who asked me to share his story under a pseudonym (I call him Jay) in keeping with the organization’s media guidelines and to protect his privacy.
Jay, who is in recovery from a 30-year opioid addiction, regularly attends 12-step meetings in Seattle — both AA and NA — and also takes buprenorphine. He recounted that when he first began attending meetings and mentioned his prescription, one member spent 15 minutes ranting that buprenorphine was “just a maintenance drug,” that Jay needed to “get off that crap,” and that he was “still a drug addict” as long as he continued to follow his doctor’s instructions.
“It really affected me,” Jay told me over coffee. “I was reaching out for help. It was really disheartening.” He admitted to relapsing shortly after leaving that meeting. “I thought: There’s no hope for me. I’m a drug addict.” Now sober from heroin for almost a year, he is very selective about where he shares information about his use of medication-assisted therapy.
Honesty and community support are essential to addiction recovery. Forced secrecy about medication-assisted therapy compromises an addicted person’s recovery by causing him or her to repeat patterns of deception implemented during active addiction. It is antithetical to every modern addiction treatment model. So why are we still relying on programs that vilify people who use evidence-based treatment for their recovery?
Narcotics Anonymous and Alcoholics Anonymous are not just widely available — they are often mandated by Drug Courts, the system that oversees many nonviolent drug-related offenders. Even costly rehab centers across the country employ 12-step programs and the accompanying abstinence-based approach.
This doesn’t make sense to me, since the science of addiction has evolved in recent years to include medication-assisted therapy as a cornerstone of treatment, while the 12-steps have not been touched by science since the 1940s.
As someone who has struggled with heroin addiction, I know how difficult recovery is. Beyond the physical and psychological discomforts, addicted individuals face being ostracized, sometimes even by our own families. Now, the stigma against using medication-assisted therapy is so rampant it’s even in the White House.
As we approach the second year of the Trump administration, the need for camaraderie between those of us in recovery is greater than ever. Many of us rely upon the 12 steps for our sobriety, but many also rely upon medication-assisted treatment.
The time has come for Narcotics Anonymous, Alcoholics Anonymous, and other 12-step programs to update their approach, or step aside. Abstinence-based models are too dangerous to rule the recovery community any longer.
Elizabeth Brico is a writer based in the Pacific Northwest who blogs at Betty’s Battleground. She is also a contributing writer for the HealthyPlace trauma blog.