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Original research
Simple screening model for identifying the risk of sleep apnea in patients on opioids for chronic pain
  1. Janannii Selvanathan1,2,
  2. Rida Waseem1,
  3. Philip Peng1,
  4. Jean Wong1,2,
  5. Clodagh M Ryan3 and
  6. Frances Chung1,2
  1. 1 Department of Anesthesia and Pain Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
  2. 2 Institute of Medical Science, University of Toronto Temerty Faculty of Medicine, Toronto, Ontario, Canada
  3. 3 Department of Medicine, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
  1. Correspondence to Dr Frances Chung, Department of Anesthesia, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, ON M5T 2S8, Canada; frances.chung{at}uhn.ca

Abstract

Background There is an increased risk of sleep apnea in patients using opioids for chronic pain. We hypothesized that a simple model comprizing of: (1) STOP-Bang questionnaire and resting daytime oxyhemoglobin saturation (SpO2); and (2) overnight oximetry will identify those at risk of moderate-to-severe sleep apnea in patients with chronic pain.

Method Adults on opioids for chronic pain were recruited from pain clinics. Participants completed the STOP-Bang questionnaire, resting daytime SpO2, and in-laboratory polysomnography. Overnight oximetry was performed at home to derive the Oxygen Desaturation Index. A STOP-Bang score ≥3 or resting daytime SpO2 ≤95% were used as thresholds for the first step, and for those identified at risk, overnight oximetry was used for further screening. The Oxygen Desaturation Index from overnight oximetry was validated against the Apnea-Hypopnea Index (≥15 events/hour) from polysomnography.

Results Of 199 participants (52.5±12.8 years, 58% women), 159 (79.9%) had a STOP-Bang score ≥3 or resting SpO2 ≤95% and entered the second step (overnight oximetry). Using an Oxygen Desaturation Index ≥5 events/hour, the model had a sensitivity of 86.4% and specificity of 52% for identifying moderate-to-severe sleep apnea. The number of participants who would require diagnostic sleep studies was decreased by 38% from Step 1 to Step 2 of the model.

Conclusion A simple model using STOP-Bang questionnaire and resting daytime SpO2, followed by overnight oximetry, can identify those at high risk of moderate-to-severe sleep apnea in patients using opioids for chronic pain.

Trial registration number NCT02513836.

  • chronic pain
  • diagnostic techniques and procedures
  • analgesics
  • opioid

Data availability statement

Data are available upon reasonable request.

https://doi.org/10.1136/rapm-2020-102388

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    Introduction

    Approximately 50 million American adults have chronic pain,1 and 20% of these individuals use opioids for their pain management.2 Opioids are associated with an increased risk of sleep apnea and contribute to respiratory depression and hypoxemia.3–5 Previous studies have shown that there is a high prevalence of sleep apnea (defined as Apnea-Hypopnea Index (AHI) ≥5 events/hour) among patients on opioids for chronic pain.6 7 Indeed, we recently conducted a multicenter cohort study in pain clinics and reported that 59% of participants on opioids for chronic pain had undiagnosed sleep apnea (72% obstructive, 20% central and 8% indeterminate sleep apnea), with a high prevalence of moderate (23.3%) and severe (30.8%) sleep apnea.6 These findings align with a recent meta-analysis reporting a pooled sleep-disordered apnea prevalence of 63% in patients recruited from pain clinics.7 In comparison, the prevalence of undiagnosed central sleep apnea in the general population is reported to be 0.9%,8 whereas a recent meta-analysis on the prevalence of obstructive sleep apnea was reported to be 9%–38%.9 Hence, the high prevalence of undiagnosed sleep apnea among patients on opioids warrants the development of an effective screening tool for pain clinics, for the earlier management and optimization of overall health and quality of life.

    At present, patients on opioids for chronic pain are not routinely assessed for sleep apnea in clinical care,10 and guidelines for monitoring the effects of opioids among this population are inconsistent.11 12 The STOP-Bang questionnaire, a screening tool for sleep apnea,13 and resting daytime oxyhemoglobin saturation (SpO2) were found to be predictive factors for sleep apnea in patients on opioids for chronic pain.6 For every 1-unit increase in the STOP-Bang score, there is a 70% increase in the odds of moderate-to-severe sleep apnea (AHI ≥15 events/hour).6 For each 1% reduction in resting daytime SpO2, the odds of moderate-to-severe sleep apnea increased by 33%.6 Hence, the use of the STOP-Bang questionnaire and resting daytime SpO2 may assist identifying sleep apnea in patients taking opioids for chronic pain.6

    Although polysomnography is the gold standard for diagnosing sleep apnea, it is time-consuming, costly, and has limited accessibility. In specific populations such as those on opioid medications, home sleep apnea testing is not recommended due to limited evidence on whether it can accurately diagnose opioid-associated sleep apnea.14 15 However, it is impractical to refer all patients for polysomnography. Overnight home pulse oximetry has certain advantages over polysomnography, including lower expenses and greater ease of use.16 The Oxygen Desaturation Index is the average number of desaturation episodes per hour, an index measure provided by overnight home pulse oximetry. The addition of the Oxygen Desaturation Index with easy-to-use screening questionnaires has shown promising results for detecting moderate-to-severe obstructive sleep apnea in a primary care17 or sleep clinic setting.18

    The objective of the study is to assess the predictive performance of a user-friendly screening model to identify the risk of moderate-to-severe sleep apnea in patients on opioids for chronic pain. We hypothesize that a simple process using STOP-Bang questionnaire and resting daytime SpO2 as the first step, and the Oxygen Desaturation Index from overnight oximetry as the second step, will be effective for identifying sleep apnea among patients on opioids for chronic pain.

    Methods

    This was a planned post-hoc analysis of a multicenter prospective cohort study to examine the effects of opioids on sleep apnea in chronic pain patients (Op-Safe Trial, initial patient enrollment date: May 27, 2015, date registered: May 27, 2015, https://clinicaltrials.gov/ct2/show/NCT02513836) at five university-affiliated tertiary care pain clinics in Canada.6 Adults ≥18 years taking opioid medications for chronic pain for >3 months with a stable daily dose for >4 weeks were eligible to participate. We excluded participants with a prior diagnosis of sleep apnea, active neurological or psychiatric disorders, cancer, and those in whom an urgent sleep evaluation was deemed necessary for safety reasons. Detailed methods were previously described.6

    In each pain clinic, eligible participants completed the STOP-Bang questionnaire, and resting daytime SpO2 was measured (PULSOX-300, Konica Minolta Sensing, Osaka, Japan). The STOP-Bang questionnaire is a well-validated tool to screen for obstructive sleep apnea, based on established risk factors (Snoring, Tiredness, Observed apnea, high blood Pressure, higher Body mass index, older Age, higher Neck circumference, male Gender).13 Participant demographics were collected, including information regarding medication use and comorbid conditions. Daily opioid doses were converted to approximate morphine milligram equivalent (MME) according to the US Centers for Disease Control and Prevention.19 Participants were rested and seated for 5–10 min before measuring resting daytime SpO2. Participants also completed overnight pulse oximetry in their homes. The PULSOX-300i oximeter was used to measure an Oxygen Desaturation Index 4%, defined as the hourly average number of desaturation episodes with at least 4% decrease in saturation from the average saturation in the preceding 120 s and lasting more than 10 s. The PULSOX-300i oximeter has 1 Hz of sampling frequency, 3 s of averaging time, and 0.1% SpO2 resolution. Profox software (Profox Associates, Escondido, California, USA) was used to derive the Oxygen Desaturation Index 4% from overnight pulse oximetry.

    The participants then underwent an in-laboratory polysomnography. The level 1 polysomnography (Natus Medical, Sandman, Middleton, Wisconsin, USA) was performed at two university affiliated sleep study laboratories and was conducted and scored in accordance with the American Academy of Sleep Medicine recommendations,20 and reviewed by two sleep physicians at the respective laboratories. Both technologists and sleep physicians were blinded to the STOP-Bang score, resting daytime SpO2, and overnight oximetry data. Moderate-to-severe sleep apnea was classified as AHI≥15 events/hour.20

    The first step in the proposed model involved screening for the risk of sleep apnea with the STOP-Bang questionnaire and resting daytime SpO2. For STOP-Bang score, an optimal threshold of ≥3 had been shown to have adequate sensitivity and specificity in predicting the risk of sleep apnea.13 21 We compared the screening performance of various SpO2 cut-offs against polysomnography as the gold standard for detecting moderate-to-severe sleep apnea in our sample (online supplemental S-Table 1). We found that a cut-off of ≤95% had adequate diagnostic parameters with optimal sensitivity (72.6%, 95% CI: 61.7% to 81.9%) and specificity (51.5%, 95% CI: 46.4% to 55.9%) in predicting moderate-to-severe sleep apnea against polysomnography (online supplemental S-Table 1). For patients who met either of these thresholds in Step 1, their Oxygen Desaturation Index values from overnight oximetry were used as a second step to further assess the risk of moderate-to-severe sleep apnea. The screening model was compared against the AHI derived from polysomnography as the gold standard.

    Supplemental material

    Statistical analysis

    Data was analyzed using Stata/SE (Stata V.14.2).22 Demographic characteristics were presented using mean (SD) or median (IQR) for continuous variables and frequencies (percentage) for categorical variables. A Spearman correlation was performed to determine the relationship between AHI and Oxygen Desaturation Index, with a p value <0.05 considered as significant. The predictive performance of the screening model was validated against AHI from polysomnography using receiver operating characteristic analyses for different Oxygen Desaturation Index values of ≥5, ≥10, and ≥15 events/hour. Sensitivity, specificity, positive predictive value, negative predictive value (NPV), ORs, positive and negative likelihood ratios, and the area under curve (AUC) values were calculated for the screening model with polysomnography as the gold standard. The performance characteristics of the screening model to predict moderate-to-severe sleep apnea was also compared with the STOP-Bang questionnaire alone (threshold score of ≥3) and MME (≥50 or ≥90 mg per 24 hours).

    Results

    Demographics characteristics

    Three hundred and thirty-two participants consented for this study, of which 204 (61.4%) participants completed in-laboratory polysomnography (figure 1). Of these participants, 199 (97.5%) participants had data on the STOP-Bang questionnaire and resting daytime SpO2. Table 1 presents the demographic characteristics of this sample. Common pain conditions included back pain (25.1%), arthritis (17.1%), neuropathic pain (12%), and post-traumatic pain (9.5%). The overall mean age and body mass index of patients were 53±13 years and 29±6 kg/m2, respectively. The overall median (IQR) of AHI and Oxygen Desaturation Index values were 6.5 (2.3–19.4) events per hour and 5.8 (2.6–13.1) events/hour, respectively. There was a moderate association between AHI and Oxygen Desaturation Index (ρ=0.511, p<0.01) (data not shown).

    View this table:
    Table 1

    Patient demographics and characteristics (n=199)

    Figure 1
    Figure 1

    Study flowchart showing the number of participants involved in different phases. SpO2, oxyhemoglobin saturation.

    Step 1: clinical assessment with the STOP-Bang questionnaire and resting daytime SpO2

    In the first step, the STOP-Bang questionnaire and resting daytime SpO2 were used for screening for sleep apnea in patients on opioids for chronic pain. The threshold values for STOP-Bang questionnaire and resting daytime SpO2 were chosen to be ≥3 and ≤95%, respectively, as it optimized the sensitivity and specificity of these measures. When both the STOP-Bang questionnaire and resting daytime SpO2≤95% are included in one step (ie, a participant met either one of these thresholds), the sensitivity and specificity for predicting the risk of moderate-to-severe sleep apnea (AHI≥15 events per hour from polysomnography as the gold standard) is 95.4% and 23.1%, respectively (online supplemental S-Table 2). One-hundred fifty-nine (79.9%) participants met the threshold for Step 1, and their overnight home oximetry data was examined to further assess the risk of moderate-to-severe sleep apnea.

    Step 2: Oxygen Desaturation Index 4%

    Of the 159 participants who entered Step 2 of the model, 99 (62.3%), 56 (35.2%), and 42 (26.4%) participants had Oxygen Desaturation Index values ≥5, ≥10, and ≥15 events/hour, respectively. The median (IQR) Oxygen Desaturation Index was 5.8 (2.6–13.1) events/hour. The predictive performance of the different Oxygen Desaturation Index thresholds for predicting moderate-to-severe sleep apnea is presented in table 2. The AUC for utilizing Oxygen Desaturation Index from home oximetry to detect moderate-to-severe sleep apnea was 0.807 (95% CI: 0.736 to 0.877) (figure 2). Using Oxygen Desaturation Index ≥5 events/hour, Step 2 of the model identified the risk of moderate-to-severe sleep apnea (AHI ≥15 events/hour) with a sensitivity of 86.4% (95% CI: 76.5% to 93.3%) and specificity of 52% (95% CI: 46.2% to 56.0%) (table 2). A McNemar test revealed that using Oxygen Desaturation Index ≥5 events per hour as the threshold for Step 2 had significantly greater sensitivity at detecting moderate-to-severe sleep apnea in comparison to Oxygen Desaturation Index ≥10 events per hour (p<0.01). Using an Oxygen Desaturation Index ≥5 events per hour, 99 (62.3%) were identified to be at risk of moderate-to-severe sleep apnea and would require sleep studies to confirm a sleep apnea diagnosis (figure 1). Thus, there is a 37.7% reduction in the number of patients requiring sleep studies from Step 1 (159 participants) to Step 2 (99 participants). The predictive performance of Step 2 of the screening model to identify any sleep apnea (AHI ≥5 events per hour) and severe sleep apnea (AHI ≥30 events per hour) using Oxygen Desaturation Index ≥5, ≥10, and ≥15 events per hour is shown in online supplemental S-Table 3.

    View this table:
    Table 2

    Comparison of the screening model using different Oxygen Desaturation Index thresholds for predicting the risk of moderate-to-severe sleep apnea (Apnea-Hypopnea Index ≥15 events per hour from polysomnography as the gold standard)

    Figure 2
    Figure 2

    Receiver operating characteristic (ROC) curve for predicting the risk of moderate-to-severe sleep apnea (AHI ≥15 events/hour) using the Oxygen Desaturation Index from overnight home oximetry. AHI, Apnea-Hypopnea Index.

    Comparison of the screening model against the STOP-Bang questionnaire and opioid dose criteria

    Of the 204 participants with polysomnography data, 202 (99%) completed the STOP-Bang questionnaire, of whom, 143 (70.8%) participants had a STOP-Bang score of ≥3. A STOP-Bang score ≥3 had a sensitivity of 89.2% (95% CI: 80.1% to 95.0%) to predict moderate-to-severe sleep apnea, although the specificity was lower than our screening model at 38% (95% CI: 33.6% to 40.7%). Due to the higher specificity of the screening model versus the STOP-Bang alone, more participants without moderate-to-severe sleep apnea would be correctly identified by our model, resulting in fewer unnecessary referrals to a sleep clinic.

    Two hundred and three participants had complete data on MME dosage and AHI. One hundred and twenty-two (60%) and 91 (44.8%) participants were receiving a MME ≥50 and ≥90 mg per 24 hours, respectively. Both MME thresholds demonstrated lower sensitivities (60.9% for MME ≥50 mg and 48.4% for MME ≥90 mg) than the screening model. A MME threshold of ≥50 mg per 24 hours also showed lower specificity than our model at 40.3% (95% CI: 35.3%–45.0%), whereas a MME threshold of ≥90 mg had a similar specificity of 56.8% (95% CI: 51.9%–61.8%).

    Discussion

    We have demonstrated that a simple screening model comprizing STOP-Bang questionnaire and resting daytime SpO2 (Step 1), followed by overnight home oximetry (Step 2) performed well to detect undiagnosed moderate-to-severe sleep apnea in patients taking opioids for chronic pain. Using an Oxygen Desaturation Index ≥5 as the threshold in Step 2, a high sensitivity of 86% was found to detect undiagnosed moderate-to-severe sleep apnea. The proportion of patients that would require further testing was reduced by 38% between Step 1 and Step 2. Given that in-laboratory polysomnography is time-consuming and expensive, our model would provide a simpler approach for screening patients at pain clinics and expedite clinical care for chronic pain patients at risk for sleep apnea.

    The current American Academy of Sleep Medicine statement on chronic opioid therapy recommends screening for the risk of sleep apnea in patients on opioids.14 A recent meta-analysis of nine studies found a high pooled prevalence (63%) of opioid-associated sleep apnea in patients recruited from pain clinics.7 We also recently reported that approximately 59% of patients on opioids for chronic pain had undiagnosed sleep apnea, with a high prevalence of moderate-to-severe sleep apnea.6 Opioids bind to inhibitory mu-opioid receptors located in brainstem regions that regulate breathing,5 leading to changes in hypoxic and hypercapnic ventilatory responses, respiratory depression, and sleep apnea.23 Opioid use may also affect respiration and rhythm generation by inhibiting the pre-Bötzinger complex in the medulla and Kölliker-Fuse nucleus in the pons, both of which are involved in inspiration and its transitions to expiration.24 Specifically, opioid-associated obstructive sleep apnea may be a result of opioid-induced inhibition of brain regions (eg, pre-Bötzinger complex) involved in activating upper airway muscles during breathing.23 Opioid-associated central sleep apnea may occur due to changes in the hypoxic and hypercapnic ventilatory drives.23 24 While opioid-induced depression of the hypercapnic ventilatory drive may cause hypercapnic-related central sleep apnea, long-term opioid-induced hypoxia may lead to an augmented hypoxic ventilatory drive as a compensatory effect.24 In turn, an increased hypoxic ventilatory drive may also lead to central sleep apnea by decreasing the carbon dioxide reserve (difference between the arousal and eupneic thresholds).24 A lower resting daytime SpO2 may be a marker of opioid-induced hypoxia, which leads to an elevated hypoxic ventilatory drive and central sleep apnea events.24

    The first step of our screening model comprises a STOP-Bang score ≥3 and resting daytime SpO2 ≤95%, because both factors are predictive of moderate-to-severe sleep apnea.6 25 Our study is the first to examine the screening performance of the STOP-Bang questionnaire or resting daytime SpO2 in patients using opioids for chronic pain. We demonstrated that Step 1 had a high sensitivity but low specificity for detecting the risk of moderate-to-severe sleep apnea. In order to enhance screening for sleep apnea in this population, we included a second step of overnight oximetry. In the past decade, oximetry has shown to have promising diagnostic validity in predicting sleep apnea.26 In the general and bariatric populations, the addition of overnight oximetry with a screening questionnaire was shown to improve the diagnostic parameters for detecting sleep apnea.17 18 27–29 Overnight oximetry using wearable pulse oximeters improve the convenience of monitoring patients’ oxygen levels during sleep in their natural environment (ie, at home). Given their lower costs, non-invasiveness, and easy-to-assemble nature, such devices could improve accessibility to screening in the pain clinic setting.

    Using an Oxygen Desaturation Index threshold ≥5 events/hour, our model reported a sensitivity of 86%. This threshold was chosen to maximize the sensitivity of our model, as a high sensitivity is critical in a clinical setting to enable detection of most patients with moderate-to-severe sleep apnea. The high NPV of 87% of the screening model is useful to rule out moderate-to-severe sleep apnea and expedite testing for those at greater risk (ie, patients with test-positive results). In comparison to our model, a STOP-Bang score ≥3 was able to correctly detect an additional four patients with moderate sleep apnea (AHI ≥15 and <30 events/hour). However, our model demonstrated a greater specificity than the STOP-Bang questionnaire (52% vs 38%), indicating that it is better able to detect the absence of disease resulting in less referral to sleep clinics while saving finite healthcare resources, time, and burden on sleep clinics and patients. Using MME ≥50 mg or 90 mg per 24 hours as screening parameters resulted in lower sensitivity, indicating these thresholds would be more likely to fail in identifying patients with risk of sleep apnea in comparison to the screening model.

    While patients using opioids for chronic pain are at greater risk of sleep apnea, many are not aware of this risk.10 One study reported that only 38% of patients with a STOP-Bang score ≥3 reported a discussion with their healthcare provider regarding their risk of sleep apnea and 31% had sleep studies.10 This suggests a knowledge gap for both patients and providers and/or reluctance to undergo sleep studies. As a result, the prevalence of undiagnosed sleep apnea is high in the chronic pain population,6 7 which reinforces the need to identify those at greater risk of sleep apnea and exclude those with lower/no risk in pain clinics. Our screening model is easy to implement and provides a quick turnaround that would assist in prioritizing patients who require confirmatory testing at a sleep clinic.

    Due to the lower prevalence of sleep apnea and the absence of validation of this model in the general population,8 9 we do not suggest that our model be used to assess for sleep apnea in patients not taking opioids or prior to chronic opioid initiation. The use of sedating medications, such as benzodiazepines, should be prescribed with caution in patients with high-risk of moderate-to-severe sleep apnea, as these medications may increase the risk of opioid-associated sleep apnea or death.30 31

    Previous research has shown that opioid discontinuation or reduction may be associated with significant improvements in AHI, central apnea events, and mean SpO2 in patients using opioids for chronic pain management.25 Opioid-associated sleep apnea may persist after continuous positive airway pressure treatment.25 The use of alternative positive airway pressure therapies, such as adaptive servo-ventilation or bi-level positive airway pressure may be warranted.25 Of the patients in the Op-Safe cohort who received treatment for sleep apnea, 67% underwent positive airway pressure therapies, while the remaining participants received positional therapy, or opioid and/or benzodiazepine reduction.32

    Our study has some limitations. Given that only 61% of consented participants underwent polysomnography, participants experiencing symptoms may be more likely to complete the study creating selection bias. Patients who completed polysomnography had a significantly greater Epworth Sleepiness Score than those who did not undergo polysomnography.6 Also, we did not assess overnight transcutaneous or end-tidal carbon dioxide to determine whether patients had sleep-related hypoventilation, hypercapnia, or hypoxemia. Additionally, our model is not able to distinguish between obstructive sleep apnea and central sleep apnea, as the different types of sleep apnea can only be diagnosed by sleep studies.

    Conclusions

    In summary, a simple screening model comprizing of STOP-Bang questionnaire, resting daytime SpO2, and overnight oximetry at home was sensitive (86%) at identifying patients on opioids for chronic pain at an elevated risk of moderate-to-severe sleep apnea. Given the potential time and cost to healthcare, our novel model could provide an accessible, reliable, and easy modality of screening chronic pain population at risk of sleep apnea, thus alleviating the burden on both sleep clinics and patients to proceed with in-laboratory polysomnography.

    Data availability statement

    Data are available upon reasonable request.

    Ethics statements

    Patient consent for publication

    Ethics approval

    The research ethics board of each participating institution approved the research protocol. All participants provided written informed consent (Research Ethics Board approval numbers: 14–8611-AE, 15–0004-A, 2014 0122 and 106620).

    Acknowledgments

    We acknowledge the Op-Safe investigators for their assistance in the Op-Safe Trial: Geoff Bellingham MD, Gerald Lebovic PhD, Mandeep Singh MBBS, Philip Peng MBBS, Charles George MD, Andrea D Furlan MD, Anuj Bhatia MD, Hance Clarke MD, David N Juurlink MD, Muhammad Mamdani PharmD, Richard Horner PhD, Beverly A Orser MD, Neilesh Soneji MD, Paul Tumber MD, John Flannery MD, Dinesh Kumbhare MD, and Arsenio Avila MD. We also acknowledge the research staff for their assistance in data collection: Emad Al Azazi MD, Asmita Bhoite BSc, Rabia Jogezai BSN, Halema Khan BSN, Fatiha Mim MD, and Sazzadul Islam BSc.

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    Supplementary materials

    • Supplementary Data

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    Footnotes

    • Contributors JS, RW, and FC contributed substantially to the study concept, design, analysis, or interpretation of data for the manuscript. JW, CMR, and FC obtained funding and acquisition of data. JS and FC contributed substantially to the drafting of the manuscript. JS, RW, PP, JW, CMR, and FC contributed to the critical revision of the manuscript for important intellectual content and final approval of the version to be published. FC had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    • Funding Funded by Ontario Ministry of Health and Long-Term Care Innovation Fund; Department of Anesthesia and Pain Medicine, University Health Network-Mount Sinai Hospital, University of Toronto; and University Health Network Foundation.

    • Competing interests JS, RW, PP, and CMR did not report conflicts of interest. JW reports grants from the Ontario Ministry of Health and Long-Term Care, Merck Inc, and University of Toronto Merit Research Award. FC reports grants from the Ontario Ministry of Health and Long-Term Care, University Health Network Foundation, and UpToDate royalties. STOP-Bang questionnaire: proprietary to University Health Network.

    • Provenance and peer review Not commissioned; externally peer reviewed.

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