PREFACE

The VA Evidence-based Synthesis Program (ESP) was established in 2007 to provide timely and accurate syntheses of targeted healthcare topics of particular importance to clinicians, managers, and policymakers as they work to improve the health and healthcare of Veterans. QUERI provides funding for four ESP Centers, and each Center has an active University affiliation. Center Directors are recognized leaders in the field of evidence synthesis with close ties to the AHRQ Evidence-based Practice Centers. The ESP is governed by a Steering Committee comprised of participants from VHA Policy, Program, and Operations Offices, VISN leadership, field-based investigators, and others as designated appropriate by QUERI/HSR&D.

The ESP Centers generate evidence syntheses on important clinical practice topics. These reports help:

• Develop clinical policies informed by evidence;
• Implement effective services to improve patient outcomes and to support VA clinical practice guidelines and performance measures; and
• Set the direction for future research to address gaps in clinical knowledge.

The ESP disseminates these reports throughout VA and in the published literature; some evidence syntheses have informed the clinical guidelines of large professional organizations.

The ESP Coordinating Center (ESP CC), located in Portland, Oregon, was created in 2009 to expand the capacity of QUERI/HSR&D and is charged with oversight of national ESP program operations, program development and evaluation, and dissemination efforts. The ESP CC establishes standard operating procedures for the production of evidence synthesis reports; facilitates a national topic nomination, prioritization, and selection process; manages the research portfolio of each Center; facilitates editorial review processes; ensures methodological consistency and quality of products; produces “rapid response evidence briefs” at the request of VHA senior leadership; collaborates with HSR&D Center for Information Dissemination and Education Resources (CIDER) to develop a national dissemination strategy for all ESP products; and interfaces with stakeholders to effectively engage the program.

Comments on this evidence report are welcome and can be sent to Nicole Floyd, ESP CC Program Manager, at vog.av@dyolF.elociN.

EXECUTIVE SUMMARY

In Veterans, chronic pain may occur in up to 50% of those treated in primary care, and severe pain is more prevalent than in the general population. Chronic pain is a major public health challenge that is associated with serious physical and psychosocial impairment which costs the United States approximately $635 billion annually. Pain is a complex condition involving dynamic interactions between biological, psychological, and social factors unique to each individual. For this reason, pain care needs to be individually tailored, involving multiple care approaches and collaboration between primary and specialty care clinicians. Pain management guidelines, including those for the VHA, advocate for multimodal pain care. The VHA National Pain Management Strategy utilizes a stepped care model of pain management involving primary care and patient aligned care teams (PACTs), secondary consultation, and tertiary interdisciplinary pain centers. However, barriers to effective implementation of guideline-concordant care still exist, including: limitations in service accessibility; provider time constraints leading to fragmentation of the care process; complexity of treatment decisions due to variability in patients’ pain characteristics and multimorbidities; variability in patient education, activation, expectations, suspicion, and mistrust; provider training and burnout; and reimbursement limitations. Thus, there is a need to identify effective models of chronic pain care with system-based interventions aiming to improve the delivery of multimodal care.

Our objectives were to determine what multimodal care delivery models relieve chronic musculoskeletal pain and minimize unintended consequences, define key elements of and the resources required for these models, and identify patients who are most likely to benefit from these models.

This review found that 5 models coupling decision support —most commonly algorithm-guided treatment and/or stepped care — with proactive ongoing treatment monitoring have the best evidence from good-quality RCTs of providing clinically relevant improvement in pain intensity and pain-related function over 9 to 12 months (NNT range, 4.1 to 12.70), as well as variable improvement in other important core outcomes (Executive Summary Table). The strength of the evidence is generally low, however, as each intervention is only supported by a single study with imprecise findings. Findings from ESCAPE, SEACAP, SCAMP, and SCOPE are the most applicable to Veterans because they were studied in VAMC settings. We were unable to determine the patients who are most likely to benefit from these models due to under-reporting of key patient characteristics such as pain duration, opioid use at baseline and prevalence of common medical and mental health comorbidities. It is reasonable to consider wider implementation of one or more of these models across multiple VAMCs, with a clear plan for further evidence development to address shortcomings of previous research: (1) better characterization of patients’ pain duration, opioid use at baseline, prevalence of common medical and mental health comorbidities, co-interventions, and usual care; (2) more rigorous evaluation of model fidelity; (3) assessment of a broader range of clinically-relevant core outcomes per IMMPACT recommendations; (4) longer-term follow-up; and (5) inclusion of potentially underserved populations, such as rural settings and racial/ethnic minorities.

Executive Summary Table

Summary of Findings.

INTRODUCTION

METHODS

To identify relevant articles, we searched MEDLINE® (Ovid) and CINAHL using terms for chronic pain and multimodal care through October 2016. Additional sources searched were Agency for Healthcare Research and Quality (AHRQ), Canadian Agency for Drugs and Technologies in Health (CADTH), Cochrane Database of Systematic Reviews, ECRI Institute, Health Technology Assessments (HTA), National Institute for Health and Care Excellence (NICE) Guidance and Evidence Services, National Library of Medicine, CADTH Grey Matters, Conference Papers Index, The New York Academy of Medicine’s Grey Literature Report, National Institutes of Health (NIH) ClinicalTrials.gov, Clinical Trial Results, World Health Organization (WHO) International Clinical Trials Registry Platform, Research Portfolio Online Reporting Tools (RePORT), National Repository of Grey Literature (NRGL), OpenGrey, Turning Research Into Practice (TRIP), metaRegister of Controlled Trials (mRCT), Scopus, Google Scholar, Google, American Pain Society, University of Southern California Pain Center, Patient-Centered Outcomes Research Institute (PCORI), American Academy of Pain Management, VA HSR&D publications, Australian Government Department of Veterans’Affairs’ Medicines Advice and Therapeutics Education Services (Veterans’ MATES), American Chronic Pain Association, The Pain Community, University of New Mexico, UK’s National Back Pain Association’s Backcare, Pain Association Scotland, and University of New Mexico Project TeleECHO (ECHO Pain). See Appendix A in the supplemental materials for complete search strategies. Additional citations were identified from hand-searching reference lists and consultation with content experts. We limited the search to articles involving human subjects available in the English language. Study selection was based on the eligibility criteria described above. Titles and abstracts and full-text articles were reviewed by one investigator and checked by a second investigator. All disagreements were resolved by consensus. We used predefined criteria to rate the internal validity of all studies. For controlled trials, we used the Drug Effectiveness Review Project methods.³² For cohort studies, we used Cochrane’s Risk of Bias Tool.^33–35 We abstracted data from all included studies and results for each included outcome. All data abstraction and internal validity ratings were first completed by one reviewer and then checked by another. All disagreements were resolved by consensus.

We graded the strength of the evidence based on the AHRQ Methods Guide for Comparative Effectiveness Reviews.³⁶ This approach incorporates 5 key domains: risk of bias (includes study design and aggregate quality), consistency, directness, precision of the evidence, and reporting biases. Ratings range from high to insufficient, reflecting our confidence that the evidence reflects the true effect. Strength of evidence ratings were first completed by one reviewer and then checked by another, and we resolved disagreements using consensus.

Models of multimodal chronic pain care differ substantially in the types of systems interventions they used to promote guideline-concordant multimodal chronic pain management in the primary care setting, and components of each intervention had varying breadth, intensity, frequency, and duration. This type of heterogeneity is often characteristic of complex multicomponent interventions and can be a challenge to constructing a framework for organizing the evidence synthesis. This is because interventions can be conceptually lumped or split by various types of characteristics and there is no agreed-upon single best approach for doing so.³⁷ Figure 1 illustrates how we classified the interventions into 4 categories based on the most common ways that the models attempted to change primary care processes regarding chronic pain management.

Figure 1

Four Categories of Most Common System Intervention Components.

A draft version of this report was reviewed by peer reviewers as well as clinical leadership. Their comments and our responses are presented in the Supplemental Materials.

The complete description of our full methods can be found on the PROSPERO international prospective register of systematic reviews (http://www.crd.york.ac.uk/PROSPERO/; registration number CRD42016050272).

RESULTS

LITERATURE FLOW

Study Design and Quality

The literature flow diagram (Figure 2) summarizes the results of the search and study selection processes. Searches resulted in 901 potentially relevant articles. Of these, we included 8 RCTs (in 10 publications)^{1–3,5–7,38–42} and 1 retrospective cohort.⁴ Detailed reasons for study exclusion are provided in Appendix B in the supplemental materials.

Figure 2

Literature Flowchart. * 1 secondary study included but not synthesized

Overall, most studies were fair or good quality. Three studies were rated poor.^2,4,38 Common limitations among fair-quality studies included greater than 20% attrition and baseline differences in potential prognostic factors. Poor-quality studies also had high levels of exclusions from analyses (34% to 47%). Assessment of intervention fidelity was limited to attendance at group or individual appointments or number of patient contacts. Figure 3 displays the quality indicators of the included RCTs. Despite strong methodology, the strength of the evidence is generally low as each intervention is only supported by a single study with imprecise findings (full details in Appendix C in the supplemental materials). All but one study⁵ were randomized at the patient level. Most interventions were compared to usual care, which was often minimally described as regular access to primary and specialty care.

Figure 3

Risk of Bias Assessment of Included RCTs.

Setting and Subjects

We identified 9 diverse models of multimodal chronic pain care. Most studies involved multiple primary care practices in the USA^{1,3,5,8,38,41} or England.^6,7 Four interventions were evaluated within either the Indianapolis (Roudebush) VAMC^3,8,41 or the Portland VAMC.⁵ Two studies took place in single centers in Canada.^2,4 Table 1 gives the characteristics of the included studies (full details in Appendix C in the supplemental materials). Sample sizes were ≤ 250 patients in the majority of the studies (range, 63 to 1066). Follow-up duration was 12 months in the majority of studies (range, 6 to 18 months). The proportion of male patients ranged from 31% to 92%, with higher proportions in those studies within the VA. The mean patient age ranged from 37 to 62 years old. Most studies reported baseline pain intensity which ranged from 5.1 to 7.7 on a 10-point scale. Most commonly reported mental health comorbidities were major depressive disorder, post-traumatic stress disorder, and substance use disorder, but baseline prevalence of these conditions was low in most studies (range: 1% to 24%). The exception was that one study specifically targeted patients with comorbid musculoskeletal pain and depression.⁸

Table 1

Characteristics of Included Studies.

Overview of Multimodal Chronic Pain Care Model Components

Table 2 summarizes the intervention components utilized in the multimodal chronic pain care models. All but one model⁷ involved multiple processes for improving pain care delivery. The majority of interventions included a decision-support component – most commonly algorithm-guided treatment and/or stepped-care – coupled with proactive ongoing treatment monitoring.^{1–3,5,6,8,38,39,41} In 2 studies the decision support was in the form of a stratified approach by way of prognostic screening with matched treatment pathways.^4,7 One stratified model⁷ focused on adults with back pain from 10 general practices within the Keele General Practice Research Partnership in England and used the validated Keele STarT Back Screening Tool, which is a 9-item inventory that queries patients about referred leg pain, comorbid pain, disability (2 items), bothersomeness, catastrophizing, fear, anxiety, and depression to categorize patients into low-, medium-, and high-risk groups.³⁵ The STarT Back Screening Tool is now also being evaluated in an ongoing study in 6 large primary care clinics in the integrated Group Health system in Washington State.⁴³ Alternatively, the stratified approach used in the Central Alberta Pain and Rehabilitation Institute (CAPRI), a single center in rural Alberta, triaged patients using an unspecified 1.5- to 2-hour assessment process to differentiate one of 4 care pathways based on the extent of their medication management, psychosocial, and/or comorbid medical illness issues (ie, minimal, high, complex).⁴ In the majority of studies, designated case managers from various disciplines delivered the treatment monitoring component of the intervention primarily via phone contacts at various frequencies. One notable exception was in the Stepped Care to Optimize Pain Care Effectiveness (SCOPE) study in which patients in the intervention group underwent automated symptom monitoring, either by interactive voice-recorded telephone calls or by internet, which prompted live case manager follow-up on an as-needed basis.⁴¹ Half of the models included active patient education, most of which was in the form of group education sessions. For 2 interventions, the main feature was increasing capacity for² and access to⁴ multimodal care. The McMaster Family Health Team (MFHT) in Hamilton, Ontario sought to use existing resources to increase capacity and access to multimodal care by centralizing services via weekly 2-hour group sessions that incorporated physician, pharmacist, dietician, and physiotherapist resource persons.² The CAPRI represents an example of a Canadian health region administration providing funding for developing a new multidisciplinary program designed specifically to increase access to multimodal chronic pain care in a previously underserved rural setting in Lacombe, Alberta. It featured decision support via risk stratification with matched treatment pathways and weekly symptom monitoring and weekly 5-hour group multidisciplinary education and activation sessions as needed.⁴

Table 2

Overview of Chronic Pain Care Model Components.

Specific Characteristics of Multimodal Chronic Pain Care Model Components

All interventions were comprised of multiple and heterogeneous components for improving the delivery of multimodal care. Table 3 describes the specific characteristics of the model components, such as the disciplines of the care management team members, frequency and duration of care management, and whether the provider and patient education was active or passive in nature (full detail in Appendix C in supplemental materials). Among the four VA-based models^{3,39, 5, 8, 41} decision support primarily involved weekly case management meetings to facilitate interaction between providers, plus either an analgesic^{3,39, 41} or antidepressant⁸ algorithm. Additionally, the SEACAP study from the Portland VAMC provided the most intense example of active provider education, in which providers participated in two 90-minute education sessions.⁵ In the category of care coordination, VA case management teams represented nursing and mental health and pain specialties. Active symptom monitoring ranged in frequency from biweekly to every 2 months. The VA SCOPE study was notable for including a health information technology component of interactive voice response monitoring.⁴¹ In the category of increasing access to multimodal care, mental health support was the most commonly added modality in the VA models,^{1,3,4,7,8,39,41} which was primarily optional. The majority of VA models included an active patient education component (workshops, individual counseling, etc).

Table 3

Specific Characteristics of Multimodal Chronic Pain Care Model Components.

Compared to VA-based models, in the remaining models conducted in other non-VA settings,^{1,38, 2,4,6,7} decision support components were less common, case management teams were more diverse – representing occupational therapy, social work, physical therapy, physiatry, pharmacy, physiotherapy, kinesiology, and dietary needs – mental health treatment was more often a required component, and patient self-management support was more often passive in nature.

Patient Outcomes

Decision Support Coupled with Case Management

Among the 6 models that coupled decision support with case management, the proportion of patients with clinically significant improvement in pain intensity or pain-related function based on a 30% or greater reduction in scores on the RMDQ, BPI, or OMERACT-OARSI was significantly increased in ESCAPE,^3,39 SEACAP,⁵ SCAMP,⁸ and SCOPE⁴¹ (NNT range, 4.1 to 12.7 in 12 months), unchanged in a model that emphasized enhanced pharmacy review and physiotherapy,⁶ and unmeasured in model that emphasized rapid assessment and management via computer-based assessment.¹ In the model that emphasized rapid assessment, pain intensity and function were measured based on the SF-36.¹ Change from baseline on the bodily pain score was greater in the intervention group compared to the control at 6 months (7.6 vs 2.2; P = 0.011), but not at one year (7.8 vs 3.6; P = 0.06). Change in functional interference estimate was reduced both at 6 months (0.96 vs −0.98; P = 0.027) and one year (1.5 vs 0.65; P = 0.02). Quality of life, depression, anxiety, sleep, opioid use, and unintended consequences were variably measured. Three of the models^5,8,41 also showed improvements on at least one of the additional important outcomes of quality of life,^8,41 depression,^5,8,41 anxiety,⁸ and sleep.⁴¹

Risk/Complexity-matched Treatment Pathways

Among the 2 models using risk stratification coupled with risk-matched treatment pathways,^4,7 only the model using the validated STarT Back screening tool for back pain resulted in greater clinically significant improvement in pain intensity or pain-related function (≥ 30% decrease in RMDQ scores) at 12 months.⁷ Patients screened with STarT Back and prescribed risk-matched treatment pathways also had greater improvement in depression scores and quality of life at 12 months. However, no differences in anxiety scores, or satisfaction with care were found between intervention and control at 12 months. This evidence is limited by moderate and different levels of attrition among risk groups and has thus far only been assessed in 851 people in England who were mostly female with a mean age of 50 years and with unknown mental health comorbidities. Thus, it is unclear how applicable this evidence is to Veterans. However, as previously mentioned, the STarT Back Screening Tool is now also being evaluated in an ongoing study in 6 large primary care clinics in the integrated Group Health system in Washington State.⁴³

The CAPRI’s stratified approach used in a single center in rural Alberta significantly reduced pain intensity scores (rated on a 0–10 scale) compared to medication management.⁴ However, this evidence is insufficient to determine true intervention effects because it was assessed in a single underpowered study (N=82) of poor quality due to lack of outcome assessor blinding, no adjustment for potential confounders, and differential loss to follow-up.

Increasing Access via Group Multidisciplinary Intervention Sessions

The McMaster Family Health Team (MFHT) in Hamilton, Ontario sought to increase access to and coordination of specialty services via their centralization in weekly group sessions.² After 6 months of follow-up, there was a statistically significant improvement in the SF-36 physical domain. But because this finding is supported by only a single underpowered study (N=63) with low adherence (50%), it provides insufficient evidence on which to draw conclusions about this model.

Table 4Summary of Findings (Intervention versus Control)

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Author Year	Clinically significant* improvement in pain intensity or pain-related function	QOL	Depression	Anxiety	Sleep	Opioid use	Unintended consequences/treatment satisfaction
Decision Support Coupled with Case Management
Ahles 200138	NR	SF-36 mean: Pain Component: 59.7 vs 46.9, P<0.005 Role Physical: 54.8 vs 37.5, P<0.03 Role Emotional: 81.9 vs 62.0, P<0.001 Role Social: 79.5 vs 64.5, P<0.001	NR	NR	NR	NR	NR
Ahles 20061	NR	SF-36 mean change: Role Emotional: 13.9 vs 3.8, P=0.046 Vitality: 7.4 vs 3.7, P=0.048	NR	NR	NR	NR	NR
Bair 20153,39 (ESCAPE)	RMDQ: RR=1.52 (95% CI 1.22 to 1.99) NNT=7.5	NR	NR	NR	NR	NR	NR
Dobscha 20095 (SEACAP)	RMDQ: 21.9% vs 14.0%, P=0.04 NNT=12.70 (95% CI 12.48 to 12.74)	Mean change EQ-5D: −0.02 vs −0.04, P=0.17	Mean change PHQ-9: −3.7 vs −1.2, P=0.003	NR	NR	Any opioid prescribed: 65% vs 61%, P=0.56	Mean change global treatment satisfaction: −0.27 vs −0.36, P=0.44
Hay 20066	OMERACT-OARSI (high improvement): 27% vs 28%; P=0.8	NR	HADS depression:† 0.01 (95% CI −0.7 to 0.7)	HADS anxiety:† −0.23 (95% CI −1.1 to 0.6)	NR	NR	Satisfaction with treatment:† −19% (95% CI −32 to −4)
Kroenke 20098 (SCAMP)	BPI: 41.5% vs 17.3%; RR=2.4 (95% CI 1.6 to 3.2) NNT=4.1 (95% CI 3.0 to 6.5)	SF-36:** General health: 11.1 (95% CI 4.2 to 18.0) Social functioning: 6.1 (95% CI −1.3 to 13.5) Vitality: 8.8 (95% CI 3.6 to 14.0)	≥50% decrease in HSCL-20 from baseline: RR=2.3 (95% CI 1.5 to 3.2)	GAD-7: ** −2.2 (95% CI −3.5 to −0.9)	NR	Months of opioid use over 12 months: 3.5 vs 3.0, P=0.35	NR
Kroenke 201441 (SCOPE)	BPI: 51.7% vs 27.1%; RR=1.9 (95% CI 1.4 to 2.7) NNT=4.1 (95% CI 3.0 to 6.4)	SF-12:** Physical: 2.5 (95% CI 0.0 to 5.0) Mental: 0.2 (95% CI −2.9 to 3.3) SF-36:** Social functioning: 5.3 (95% CI −1.6 to 12.2) Vitality: 2.2 (95% CI −3.9 to 8.2)	PHQ-9:** −1.8 (95% CI −3.4 to −0.2)	GAD-7:** −0.7 (95% CI − 1.9 to 0.5	PROMIS sleep:** −1.0 (95% CI − 2.0 to 0.0)	Mean # of months taking opioids: 2.0 vs 1.6, P=0.27	NR
Risk/Complexity-matched Treatment Pathways
Burnham 20104 (CAPRI)	NR	NR	NR	NR	NR	NR	NR
Hill 20117	RMDQ: 65% vs 57%; OR=1.48 (95% CI 1.02 to 2.15) NNT=10.8 (95% CI 5.8 to 206	SF-12:** Physical: −2.93 (95% CI − 4.31 to −1.56) Mental: −0.69 (95% CI − 2.39 to 1.01)	HADS depression: ** 0.62 (95% CI 0.07 to 1.17)	HADS anxiety**: 0.45 (95% CI − 0.10 to 1.01)	NR	NR	Satisfaction with care (not satisfied): 27% vs 36%
Increasing Access via Group Multidisciplinary Intervention Sessions
Angeles 20132	NR	SF-36 mean change: Physical: −15.3 vs 3.4, P=0.01 Emotional: 2.6 vs 3.7, P=.92 Social: 3.2 vs 2.7, P=0.95 Mental: 3.6 vs 3.6, P=1.0	NR	NR	NR	NR	NR

Bold indicates statistical significance.

*

≥ 30% decrease from baseline;

**

between-group mean difference (intervention-control);

†

between-group mean difference (control-intervention)

Abbreviations: QOL = quality of life; RMDQ = Roland-Morris Disability Questionnaire; SF-36 = Short form-36; SF-12 = Short form-12; EQ-5D = EuroQol health-related quality of life; PHQ-9 = Patient Health Questionnaire-9; OMERACT-OARSI = Outcome measures in rheumatology-Osteoarthritis Research Society International; HADS = Hospital anxiety and depression; BPI = Brief pain inventory; HSCL-20 = Hopkins symptom checklist; GAD-7 = Generalized anxiety disorder; PROMIS = Patient-reported outcomes measurement information system; ESCAPE = Evaluation of Stepped Care for Chronic Pain; SEACAP = Study of the Effectiveness of a Collaborative Approach to Pain; SCAMP = Stepped Care for Affective Disorders and Musculoskeletal Pain; SCOPE = Stepped Care to Optimize Pain Care Effectiveness; CAPRI = Central Alberta Pain and Rehabilitation Institute

Emerging models

We identified several additional multimodal chronic pain care models that have shown promise for improving patient outcomes in single-arm before-after studies^44–49 (see Appendix D in supplemental materials for study details). The majority of these studies were small (N<65) and had short follow-up periods of 6 months or less. Most of the care models involved managed care with a multidisciplinary team. One model was unique in that it involved both group visits with a multidisciplinary team along with one-on-one visits with a primary care provider.⁴⁸ One study examined implementation of a stepped care model at a single VA center.⁴⁵ Although these pain care models have shown promise, they still need to be compared to a concurrent control group in larger samples of patients over longer-term durations to determine the true intervention effects.

We also identified several ongoing studies by recognized researchers including Matthew Bair, MD, Dan Cherkin, PhD, Jordan Karp, MD, Lynn Debar, PhD, and Erin Krebs, MD, which may fill gaps in existing research, or provide further support for various models of pain care (see Appendix D in supplemental materials for full listing of identified ongoing studies). Several ongoing studies examine models in new settings, including one national VA study examining telecare for integrated pain management. Previous studies within the VA have been limited to a single center. Another ongoing study is assessing the STarT Back Tool for risk stratification in the US healthcare system,⁴³ which has been previously studied in the UK.⁷ Several other ongoing studies examine care models with common elements such as case management with some form of medication optimization algorithm, or collaborative care programs which increase access to specialty care. Additionally, the Nova Scotia Chronic Pain Care Collaborative Network has been implemented which provides pain and addiction specialist mentors to primary care providers. Through personal correspondence with the principal investigator, we are aware of preliminary findings available in abstract form but have not gained access to them at the time of this draft.

SUMMARY AND DISCUSSION

To our knowledge, this is the first review to focus exclusively on evaluating the effectiveness of models to improve the delivery of multimodal chronic pain care in the primary care setting. We analyzed the models based on the 4 most common ways they promoted guideline-concordant multimodal chronic pain management: decision support, additional care coordination resources, enhanced patient education and activation, and increased access to a broader range of treatments. The 9 models we identified were evaluated in mostly good-quality RCTs comprised of 3,816 individuals primarily from 5 US States. The top 5 models that provided clinically relevant improvement in pain intensity and pain-related function over 9 to 12 months (NNT range, 4.1 to 12.70), as well as variable improvement in quality of life, depression, anxiety, and sleep, coupled a decision-support component – most commonly algorithm-guided treatment and/or stepped care – with proactive ongoing treatment monitoring: ESCAPE,^3,39 SEACAP,⁵ STarT Back,⁷ SCAMP,⁸ and SCOPE.⁴¹ Findings from ESCAPE,^3,39 SEACAP,⁵ SCAMP,⁸ and SCOPE⁴¹ have the highest applicability to Veterans because they were studied in VAMC settings. As each of the top 5 models is only supported by a single study with imprecise findings, however, current evidence leaves us with sufficient doubt about their findings to recommend further evidence development.

Acknowledgements

We would like to thank Julia Haskin, MA for editorial support and Gail Betz, MLIS for searching support.

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Supplemental Materials

Prepared for: Department of Veterans Affairs, Veterans Health Administration, Quality Enhancement Research Initiative, Health Services Research & Development Service, Washington, DC 20420.

Prepared by: Evidence-based Synthesis Program (ESP), Coordinating Center, Portland VA Health Care System, Portland, OR, Mark Helfand, MD, MPH, MS, Director.

Recommended citation: Peterson K, Anderson J, Bourne D, Erickson K, Mackey K, Helfand M. Evidence Brief: Effectiveness of Models Used to Deliver Multimodal Care for Chronic Musculoskeletal Pain. VA ESP Project #09-199; 2017.

This report is based on research conducted by the Evidence-based Synthesis Program (ESP) Coordinating Center located at the Portland VA Health Care System, Portland, OR, funded by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Quality Enhancement Research Initiative. The findings and conclusions in this document are those of the author(s) who are responsible for its contents; the findings and conclusions do not necessarily represent the views of the Department of Veterans Affairs or the United States government. Therefore, no statement in this article should be construed as an official position of the Department of Veterans Affairs. No investigators have any affiliations or financial involvement (eg, employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties) that conflict with material presented in the report.