Source: https://www.recoveryspotny.com/posts/
Timestamp: 2019-04-22 20:59:00+00:00

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But will that actually stem the deaths resulting from the opioid crisis? “There is a pervasive bias in the rehab network against this kind of medicine-assisted treatment,” Grinspoon said—but he’s optimistic that MAT and other medically based treatments might help reverse the upward spike of opioid deaths every year.
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.
For readers of our newsletter, this post might feel “different” from the others. Usually we like to talk about what you can do to help yourself or your loved one when it comes to addressing substance issues and finding support or treatment that will help changes last. In this post we are going to do something different because we are angry and scared about the misinformation that consumers are faced with everyday as they look for treatment for themselves or someone they care about.
An estimated 2 million Americans are dependent on opioids. The Centers for Disease Control reports that 115 Americans died daily in 2016 from opiate overdoses (42,249 deaths, five times higher in 2016 than 1999), and that 40% of these deaths involved a prescription opiate. Despite this deepening crisis, there is hope that a number of FDA approved, evidence-based Medication Assisted Treatments (e.g., buprenorphine, naltrexone/Vivitrol, methadone) for opioid use disorders can help. Studies have found that these medications support long-term change (including abstinence from opiates) and significantly reduce overdose rates. Yet the traditional drug and alcohol treatment industry has been shockingly slow to support their use. Even more astounding is that many doctors in the treatment industry have not been educated about their effectiveness or trained to use them despite comprehensive efforts such as the Surgeon General’s Report last year reiterating the important role of these treatments.
Why the delay? There continues to be a strong belief in the public and among many treatment providers that if you utilize medication to recover from a drug problem, you are not “really sober”. This bias persists even though mountains of evidence from well-conducted research studies demonstrate that medication-assisted treatments (MATs, like buprenorphine and naltrexone) save lives. Despite their benefits, MATs have been adopted in less than half of private treatment programs. Even in programs that do offer MAT options, only about a third of patients receive them.
In an effort to understand this at the ground level, we decided to do a little experiment. We know that when a family reaches out looking for treatment for a loved one, they are usually vulnerable, scared, and desperate for someone to offer advice and some hope. And often, in their fear, they will take the advice of the first person they speak to, assuming reasonably that that person is educated in substance use and has a clear understanding of current effective treatments. So we did what many people do: an internet search! Just like many desperate families do when they are looking for help. We typed in “top rehabs in the US” and “best rehabs in the country”, created a list of 34 rehabs that came up on these searches, and called them.
We called looking for treatment for “our brother” who was “in trouble with heroin”. In these calls we asked each program the same series of questions about their use of MATs like Vivitrol and Naltrexone (opiate blocking medications), and Suboxone (an opiate maintenance medication), including their treatment policies and stance on these medications. (We asked them a bunch of other questions but that is for another post)! Our mission? To see if the treatment landscape was changing now that multiple studies (over many years!) have found these medications to be life-saving. What we found is disturbing.
Hopeless addict for life? Really? Buprenorphine, which is a partial opioid agonist, does activate opioid receptors in the brain but does not produce the maximal effects that full opioid agonists (e.g., heroin) do. It does not produce feelings of euphoria, but it will produce enough effect to quell cravings, provide relief from withdrawal, and satisfy the brain into thinking it is receiving a full agonist. It is protective in lowering overdose risk as it blocks the receptor sites in the brain that opiates would otherwise attach to. It allows many opiate users to build a stable life without fighting off cravings to use everyday and protects them from overdosing if they relapse to opiate use (both “street” opiates like heroin and prescription opiates) while on the medication.
In these 34 contacted programs (“best of the best”!), openness to using Vivitrol was a little higher, with 65% of the rehabs saying that they could consider discharging someone on it. Vivitrol is the intramuscular injection (like a flu shot, for example) of Naltrexone, a medication that blocks opioids from binding with the opioid receptors in the brain, thereby eliminating any sense of a high. It last approximately 4 weeks and multiple studies have found that it contributes to reduced overdose rates among opiate users. It is not an opioid itself (unlike suboxone or methadone), which may account for it’s slightly more positive reception in our surveyed rehabs, by countering some of the (unfounded and stigmatizing) beliefs that an opiate user wanting to be on opioid medication is “just a drug user looking for another drug”.
Unfortunately, of the 12 programs that did not discharge clients on Vivitrol, 4 of them did not even know what it was! One of the programs we called said “We really try and get patients off of everything and we would not want to discharge him on Vivitrol” and another said “I don’t know what that is, but if you spell it for me I can look it up and see if we will use it”. We ask you: does your diabetes doctor not know what insulin is? Does your cancer doctor not believe in chemotherapy?
Many of the inpatient/residential facilities we talked to made comments that showed they do not understand these life-saving medications, that they do not value clients’ experiences or wishes, and that they are using fear and scare tactics to pressure people into treatments that may not be appropriate for them. One intake coordinator said after a 5 minute description of the potential referral, “I don’t want to sound aggressive, but he could die today…” This kind of statement, made after a short phone call and aimed at a scared family, is preying on their fears in an effort to book a client. This is what you’d expect when you’re buying a used car, not what you’re hoping for when you’re looking for help for a life-threatening problem. Several of the programs used other tried and true sales techniques, like transferring callers to many different programs without explanation, or making suggestions that they close the deal and “get on a plane right now.” And others said the fictional brother clearly “needs a lot of treatment, possibly a year,” without offering to speak with him or gathering more information. And “he could die today”? The refusal to consider use of MATs will leave clients treated at these facilities more vulnerable to dying.
We’ve had countless people come to us at CMC who have been in multiple rehabs and never once were encouraged to be on medicatIon assisted treatments. This is in spite of the data that suggests as many as 90 percent of people detoxed completely off opiates relapse within the first 1-2 months unless treated with these medications.
The takeaway? If you or someone you love is one of the two million Americans estimated to be dependent on opioids, please demand better. Our nation’s opioid struggle is an undeniable tragedy, and is one of the worst public health crises in the nation’s history. It is made all the more tragic by the fact that there are many viable, proven treatments for the problem yet treatment programs continue to refuse to look at the science. This must change if we are to stem the tide of opioid addiction in this nation and prevent more loss of life.
Anyone looking for treatment needs to really investigate the different programs and ask lots of questions. Many people do more research about which car to buy than which treatment program to attend. That needs to be reversed. And clients should enter this process expecting their questions to be answered and answered well! If you’re not sure about something you heard, then it’s probably not right (or the right place for you). Demand better service, and you will have a better experience.
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.
Those introducing the bill include Sens. Rob Portman (R-Ohio), Sheldon Whitehouse (D-R.I.), Shelley Moore Capito (R-W.Va.), Amy Klobuchar (D-Minn.), Dan Sullivan (R-Alaska), Maggie Hassan (D-N.H.), Bill Cassidy (R-La.) and Maria Cantwell (D-Wash.).
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.
Health and Human Services Secretary Alex Azar is touting medication-assisted treatment (MAT) as a crucial component of stemming the opioid crisis plaguing the nation.
In his first extensive remarks on the opioid epidemic, set to be delivered Saturday, Azar announces two measures aimed at increasing this form of treatment.
Addiction experts have long touted medication-assisted treatment — which aims to couple medicine with therapy — as a gold standard of treatment for an opioid addiction.
“Under this administration, we want to raise that one-third number—in fact, it will be nigh impossible to turn the tide on this epidemic without doing so,” Azar says.
Buprenorphine is one form of medication-assisted treatment. In November, the FDA approved the first monthly injection of Buprenorphine, aimed at making it easier to adhere to the medication.
The FDA will draft guidance to clarify what kind of evidence manufacturers that are trying to develop new forms of the medication need in order obtain approval for monthly injectable forms of buprenorphine.
The agency will also draft guidance aimed at “encourag[ing] more flexible and creative designs of MAT studies.” Researchers will be tasked with developing new ways to evaluate the effects of MAT formulations.
The opioid epidemic has ravaged areas across the country, and has shown no sign of stopping. Deaths from opioid overdoses increased nearly 28 percent from 2015 to 2016, according to the latest data from the Centers for Disease Control and Prevention.
In late October, President Trump declared the epidemic a public health emergency. Months later, however, some advocates had expressed frustration that it hadn’t led to much concrete action. In mid-January, the administration extended the emergency declaration another 90 days.
Advocates had also been pushing for more funding, saying a robust infusion of federal dollars is needed to curb the crisis.
A budget deal passed earlier this month included $6 billion over two years for the opioid and mental health crises. Trump’s budget proposed $10 billion in funding to address the opioid epidemic for fiscal 2019.
Congress will also examine bills aimed at curbing the epidemic, as the House Energy and Commerce Committee will kick off its legislative push on Wednesday.
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‘s 9 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 pain 11 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.
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.
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 depression 23 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.
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).
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 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).
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 mood 37 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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