Methods
Study design
We conducted a pragmatic RCT to evaluate the effectiveness of the Naloxone Navigator intervention among patients receiving LTOT. Participants were randomised to receive either the intervention or usual care. Participants in both arms could access naloxone by prescription from their physicians or directly from pharmacies without an individual prescription under a standing order. Participants were administered surveys at time 0 (T0), 4 months and 8 months to assess overdose risk behaviour and knowledge; electronic pharmacy records were used to measure naloxone uptake over a 12-month period from T0. We hypothesised that the intervention would not increase opioid risk behaviour, but that it would increase overdose prevention and naloxone knowledge, and increase naloxone uptake.
We used the Consolidated Standards of Reporting Trials guideline.
Study setting, participants and randomisation
All participants were members of Kaiser Permanente Colorado (KPCO), an integrated insurance and healthcare delivery system that serves more than 550 000 members across Colorado, with 29 outpatient pharmacies located within KPCO ambulatory medical offices. The KPCO patient population is demographically representative of Colorado.36 Under statewide standing order legislation,37 the study team collaborated with KPCO pharmacy operations to implement a naloxone standing order in January 2017, allowing pharmacists to dispense naloxone for take-home use without patients having to obtain individual prescriptions from their physicians.
Patients were recruited in 2-month waves starting on 21 December 2017. At the beginning of each wave, electronic pharmacy records were used to identify patients receiving LTOT, defined as three or more opioid dispensations in the previous 90 days with no more than a 5-day gap in opioid coverage. This included short-acting and long-acting opioid medications except for tramadol or medications used for opioid use disorder treatment. Patients also had to be 18 years or older, English-speaking and have internet access. Patients were ineligible if they were enrolled in hospice or had a do-not-resuscitate order, since the focus of this trial was the safety of chronic pain opioid management rather than end-of-life care. In each wave, a random sample of 500–1000 eligible patients were invited to participate first by mail, followed by email and telephone. Patients were directed to a study enrolment website where they could provide informed consent. After consent (T0), participants were randomised to the intervention or usual care arm at a 1:1 allocation ratio using the SAS/STAT (SAS Institute) procedure Proc Plan. The statistician and investigators were blinded to study arm assignment. Participants received a US$20 gift card for completing surveys at each time point, and all survey data were stored in a Research Electronic Data Capture database.38 39
Patient and public involvement
Patients and the public were not directly involved in the design of the study.
Usual care
Under usual care, patients could be prescribed naloxone by their physicians or request it from a pharmacist under a naloxone standing order, which made naloxone available without an individual prescription in all pharmacies in KPCO. Pharmacists were trained on standing order naloxone dispensing process, counselling points and cost quotes. Opioid prescribers were encouraged to prescribe naloxone to patients in continuing medical education sessions and made aware of the naloxone standing order in system-wide pharmacy communications. Member-facing system-wide communications informed members when naloxone was made available in system pharmacies under a standing order. The costs of naloxone ranged from US$0 to US$140, depending on the patient’s insurance plan. Patients in the usual care arm were not required to view a sham video intervention.
Intervention
Intervention participants received usual care and a web-based, 6 min animated educational video. The video presented standardised messages on how to prevent, recognise and respond to an opioid overdose through a first-person narrative of a patient prescribed opioids. It aimed to heighten patients’ overdose risk perceptions and increase their self-efficacy to acquire and use naloxone in an overdose emergency. Specifically, the video informed viewers about the overdose risk associated with opioid treatment for pain and encouraged them to purchase naloxone from the pharmacy under the standing order.
The intervention was iteratively developed using the integrate, design, assess and share (IDEAS) framework.40 Guided by the theory of planned behaviour41 and the health belief model,42 qualitative data were collected from semistructured interviews with patients prescribed high-dose opioid therapy and from focus groups with primary care staff19 22 to delineate key intervention targets. The intervention’s content was designed to address patients’ limited knowledge about naloxone, low overdose risk perception and reluctance to have difficult conversations about overdose with clinicians. The intervention was delivered as a video directly to patients—without requiring an appointment—to minimise clinician effort, reduce bias in patient identification and assuage clinician concerns that initiating conversations about overdose would stigmatise patients. The messages were conveyed as a fictional first-person narrative to elicit emotion and enhance recollection of the content, with foreshadowing and humour to help generate interest and sustain attention throughout the video.43 ,44
Study investigators wrote the draft content for the video, which was assessed for comprehension and acceptability in 11 cognitive interviews of patients and their caregivers, and further refined by the research team. Interviewees provided input on how the messages should be framed, whether they resonated and whether they were stigmatising. The research team then developed the animated video using Vyond Studio® software.45 The narrator is a female patient prescribed LTOT for chronic pain. After foreshadowing the overdose emergency, she visits her primary care physician, who provides clinical information on the signs of an overdose, risk factors and how to acquire and use naloxone. The narrator shares this information with her partner. She subsequently attends a party, uses both alcohol and prescribed opioid analgesics and accidentally experiences an overdose. Her partner revives her using naloxone he has with him, and she is transported to the emergency department. The couple later picks up a refill at the pharmacy under the standing order and engages in other preventive behaviours, including having a lock box for safer home opioid storage. Professional actors provided voiceovers for the main characters, and final editing was conducted using Adobe® Audition® software in a recording studio.46 The video was embedded within a password protected, single page website using WordPress® software and underwent usability testing with three patients.
Participants receiving LTOT who were randomised to the intervention arm received a link to the video and were required to play the video in its entirety at T0 to obtain renumeration for their research participation. One month after T0, participants in the intervention arm were emailed a weblink to view the video again with three additional weblinks that could be shared with family, friends or caregivers.
Baseline data
Demographic survey questions (race, ethnicity, education and income) were derived from the Behavioural Risk Factor Surveillance System and assessed at T0.47 Baseline (past year before T0) clinical characteristics were identified using the electronic health and pharmacy records.
Outcomes
We examined two primary outcomes: opioid risk behaviour and naloxone uptake. Opioid risk behaviour was assessed at T0, 4 months and 8 months with the Opioid-Related Behaviours In Treatment (ORBIT) instrument, a validated, self-administered 10-item scale.48 The ORBIT measures risk behaviours, such as using opioids for purposes other than pain or requesting early refills.19 ORBIT items are presented on a 5-point Likert scale and can be used to assess behaviour changes over time.48 49 We modified the ORBIT to measure behaviours over the previous 4 months. The ORBIT was administered to participants in the intervention prior to viewing the video at T0. Risk behaviour was analysed as a binary outcome, with a positive response defined as endorsing one or more risk behaviours on the ORBIT scale.50
Naloxone dispensings were ascertained over a 12-month follow-up period using National Drug Codes from electronic pharmacy data indicating that the products were sold by KPCO pharmacies and from claims demonstrating that a patient purchased naloxone from an external pharmacy. Participants were also asked on the survey if they or a family member had obtained naloxone. We conducted a post hoc analysis in which the survey naloxone uptake data were combined with the pharmacy and claims data, and participants who had received naloxone prior to T0 were excluded. For another post hoc analysis, naloxone uptake 12 months prior to T0 was compared with uptake 12 months after T0.
In addition to the primary outcomes, we assessed overdose and naloxone knowledge at T0, 4 months and 8 months with the Prescription Opioid Overdose Knowledge Scale (Rx-OOKS). Rx-OOKS is a validated 25-item scale measuring knowledge of overdose risks, overdose warning signs, steps to address overdose and appropriate use of naloxone. Rx-OOKS scores range from 0 to 25, with a higher Rx-OOKS score representing greater knowledge.51 Missing Rx-OOKS item responses received a score of zero. Rx-OOKS was administered to participants after they viewed the video at T0. It was analysed as a continuous variable.
We also used the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) to examine the following secondary risk behaviour outcomes: cannabis use, other drug use (heroin, cocaine, methamphetamine, hallucinogens, inhalants, other drugs) and non-medical sedative use.52 53 The ASSIST was assessed as a binary outcome, in which endorsement of one or more behaviour was considered a positive response. We assessed hazardous drinking with the Alcohol Use Disorders Identification Test–Concise (AUDIT-C).54 The AUDIT-C is scored on a scale of 0–12, where a score of 0 indicates no alcohol use. We assessed the AUDIT-C as a binary outcome, in which scores of ≥4 for men and ≥3 for women represented positive responses.
At 12 months, we compared the incidence of opioid overdose and all-cause mortality between the study arms. Opioid overdoses were identified using International Classification of Diseases-10 CM codes (online supplemental eTable 1) from emergency department and hospital records, and from Denver County paramedic records. To identify deaths and causes of deaths, identifiers for all patients were linked to the Colorado Department of Public Health and the Environment vital records. When available, participants’ medical records were reviewed to confirm opioid overdoses and the cause of death.
Statistical methods
A priori statistical power and sample size calculations were based on a continuous ORBIT score. There were approximately 8300 patients receiving LTOT who were eligible for the study, and prior work suggested that between 10% and 30% of these patients would participate in the trial. Based on a 10% participation rate and a two-sided α=0.05, we could detect a 0.31 difference in ORBIT scores between the intervention and usual care arms with 80% power. However, the ORBIT scores were skewed with little variability: mean at T0=1.6 (SD=2.3) on a scale of 0 to 40. For the analysis, we elected to dichotomise the ORBIT score as endorsing ≥1 opioid risk behaviour vs endorsing 0 behaviours.
Generalised linear mixed-effects models for repeated measures were used to assess the change in risk behaviour and knowledge scores between the study arms over time. The use of mixed models accounted for the correlation between observations made by the same participant across time. Primary and secondary risk behaviours were analysed as binary outcomes, using a binary distribution and log link function. The Rx-OOKS knowledge score was modelled as a continuous outcome, using a normal distribution and identity link function. Each model included the following variables: study arm (intervention or usual care), survey time point (T0, 4 months, 8 months), and interaction between the study arm and survey time point. Risk ratios or risk differences and 95% CIs comparing outcomes between study arms at each time point and the interaction p-values assessing change over time are reported. To account for missing survey data, we imputed 20 complete datasets by the method of fully conditional specification using all variables that could be potentially associated with the missing data. Each of the 20 complete datasets was analysed using a generalised linear mixed model with repeated measures, and the parameter estimates obtained from each analysed dataset were combined. We imputed missing data using PROC MI and combined parameter estimates using PROC MIANALZE in SAS. We also conducted sensitivity analyses for the ORBIT and Rx-OOKS that excluded missing survey data and only included individuals who completed the respective survey measures at each time point.
Receipt of at least one naloxone dispensation (in primary and post hoc analysis), opioid overdose and death were analysed as dichotomous outcomes using a log-binomial model with the study arm as the exposure variable. Risk ratios and 95% CIs were calculated. For the post hoc analysis comparing naloxone uptake 12 months prior to T0 to uptake 12 months after T0, we used generalised linear mixed-effects model. We analysed naloxone uptake as a binary variable and included the following variables in the model: study arm (intervention or usual care), study time point (preintervention, postintervention) and interaction between the study arm and study time point.
All outcomes were analysed by the intention-to-treat principle. A 5% significance level using two-sided tests was applied in all analyses, and data were analysed using SAS Studio Software V.3.8 (SAS Institute).