System-Level Variation in Multiple Sclerosis Disease-Modifying Therapy Utilization: Findings From the Multiple Sclerosis Continuous Quality Improvement Research Collaborative


Laetitia A N’Dri, PharmD1; Dexter D Waters, MSPH1; Karen Walsh, DHSc, MS, MBA1; Falguni Mehta, MS, MBA2; Brant J Oliver, PhD, MS, MPH, APRN-BC2–4; for the MS-CQI Investigators

Perm J 2021;25:21.025

Background: The Multiple Sclerosis Continuous Quality Improvement (MS-CQI) Collaborative is the first multicenter improvement research collaborative for multiple sclerosis (MS). The main objective of this study is to describe baseline system-level variation in disease-modifying therapy (DMT) utilization across 4 MS centers participating in MS-CQI.

Methods: Electronic health record data from the first year of the 3-year MS-CQI study were analyzed. Participants were adults ³ 18 years with MS presenting to any of the 4 MS-CQI centers. DMT utilization was categorized into oral, infusion, and injection types. Multinomial logistic regression was used to investigate associations between centers and DMT utilization.

Results: Overall, 2,029 patients were included in the analysis. Of those patients, 75.1% were female, mean age was 50 years, and 87.4% had relapsing-remitting MS. Overall, 32.7% were on an oral DMT, 23.5% on an infusion DMT, and 43.9% on an injection DMT. Overall, statistically significant differences (p < 0.01) were observed across centers for proportions of patients who received oral, infusion, and no DMTs. There were also overall sig nificant differences (p < 0.01) across MS types for proportions of encounters who received oral, infusion, injection, no DMTs, and mean age varied significantly across centers.

Conclusion: System-level effects on MS treatment and out- comes have not been previously studied and our findings con tribute initial evidence concerning system-level variation in DMT utilization. Results suggest system-level variation in DMT utiliza tion (ie, after adjusting for individual level factors, MS center or location of care a person with MS engages in care influences DMT treatment choices), resulting in a lack of standardization in DMT management. Continued research and improvement efforts targeting system-level performance could improve out- comes for people with MS.


Multiple sclerosis (MS) is an immune-mediated disease of the central nervous system in which damage to the myelin sheath leads to an inflammatory response.1,2 Brain function is impaired and various neurological symptoms such as fatigue, muscle weakness, difficulty walking, vision changes, pain, cognitive changes, and depression can be observed.3 There are 4 MS phenotypes: relapsing-remitting MS (RRMS), pri mary progressive MS (PPMS), progressive-relapsing MS (PRMS), and secondary progressive MS (SPMS).3 RRMS, the most common phenotype, is characterized by defined periods of attacks or exacerbations, followed by periods of partial or complete recovery—most disease-modifying therapies (DMTs) are indicated for this form of MS.4 Care management of MS can be grouped into three categories: DMT, relapse treatment, and symptom management. DMT can reduce the number of relapses, delay the progres sion of disability, and limit the development of new disease activity.5

Approved in 1993, the first DMT for RRMS treatment was interferon β-1b for injection.6 Since then, many DMTs have been made available in oral, injection, and infusion for- mulations (Table 1).7–9 Common anti-inflammatory mecha nisms of action of DMTs range from cytokine modulation to selective adhesion molecule inhibition, monoclonal anti- body inhibition activities, and immunosuppression.9 Oral and injection DMTs, such as interferon betas, glatiramer acetate, teriflunomide, and dimethyl fumarate are often considered first-line treatments.5 However, oral DMTs show a poorer safety profile than injectibles.9 Infusion DMTs are often secondary or tertiary options and are chosen when effectiveness is preferred over safety and convenience. Hence, choosing a DMT treatment option for a patient with RRMS is complicated and depends on several factors such as disease activity, comorbid cardiovascular diseases and potential cardiac and cerebrovascular complications while on therapy, safety concerns including serious infections, risk factors associated with poor prognosis, likelihood of adher ence, costs and patients’ values and preferences (tolerability, convenience, etc).5,6

Table 1. Selected examples of disease-modifying therapy treatment options, risk/benefit profiles, and treatment experience

  Injection Oral Infusion
DMT category


Peg IFNβ-1a IFNβ-1b

Glatiramer acetate

Fingolimod (S1P receptor modulators)

Teriflunomide (pyrimidine synthesis inhibitors)

Dimethyl fumarate, Cladribine (purine antimetabolites)

Diroximel fumarate

Natalizumab Alemtuzumab Ocrelizumab

 Positioning and risk/

 benefit profile

First-line: Best safety profile but lower effectiveness, injection-related adherence and tolerability problems First or second-line: More risk and more benefit than injectables but less effective than infusibles, oral administration is very convenient

Second or third line: Most risk and

best effectiveness, high adherence due to low dosing frequency and supervised administration

Treatment experience10,11 Preferred when administration frequency and side effects are low or when safety concerns are high Convenient; preferred when side effects and frequency of other routes are held constant Administered by health care professionals; high adherence rates, preferred when need for effectiveness is highest

DMT= Disease Modifying Therapy, INFB= Interferon-beta, Peg = Pegylated

Setting: The MS Continuous Quality Improvement Collaborative

Improvement collaboratives have been shown to facilitate system-level performance improvement because they facilitate benchmarking and transparency of system-level performance, accelerate the rate of learning, and motivate rapid cycle improvement efforts.12 Collaboratives are defined as a group of two or more clinical care delivery systems which are focused on the same population and care delivery type (eg, MS). Centers agree to share aggregated system-level performance data, conduct benchmarking, and engage in group learning to enable accelerated improvement for all participating sites. Improvement coaching facilitates, guides, and organizes improvement efforts within collaboratives.

The MS Continuous Quality Improvement Research Collaborative (MS-CQI) is a multicenter MS improvement research initiative comprised of 4 MS centers, which has been following approximately 5,000 people with MS since 2017. Using electronic health records (EHRs) and patient-reported outcomes data, MS-CQI aims to understand system-level effects on MS care utilization and delivery and to study the effect of improvement interventions on popula tion health outcomes for people with MS. The following describes system-level effects on DMT utilization variation in the first year of the MS-CQI study (baseline “usual care”/ pre-intervention “current state”).


This was a cross-sectional study of baseline year EHR- abstracted clinical encounter data from 4 centers participating in the 3-year MS-CQI study. Our analysis aims to describe the utilization of oral, infusion, and injection DMTs across centers using descriptive statistics and multi- nomial logistic regression. We were specifically interested in conducting an initial evaluation of system-level variation in DMT treatment in MS and to determine whether system- level variation exists in DMT utilization to establish empiri cal rationale for continued systems-level research and improvement initiatives in MS care.


De-identified encounter level data were obtained from EHR abstraction for all adults with a diagnosis of RRMS, PPMS, PRMS, or SPMS aged 18 years or older presenting to any of the 4 centers in year 1 of the MS-CQI study (July 1, 2017 through June 30, 2018). Patients with missing or incorrectly input demographic or clinical information were excluded (n = 75). We analyzed 2 population groups for MS-CQI care centers 1-4. To describe DMT types, a total of 2,679 patients were retrieved, and patients on multiple DMTs as well as those on no DMTs were included. The group for the multinomial regression consisted of 2,029 patients. Appropriate power tests were conducted to ensure β = 0.80 with significance set at α < 0.05.

Data and Variables

Data were indexed in the first quarter of the study with the previous three quarters of utilization data captured (each patient pre-index phase analyzed for benchmarks). EHR data were abstracted from each of the 4 participating centers, input into the RedCap database management system, and then converted to SAS 9.4 format for statistical analyses. Baseline EHR data were collected between July 1, 2017 and June 30, 2018 from 4 participating MS centers: an urban academic center, a rural academic center, a rural community hospital, and an urban private practice. DMT use was cate- gorized into oral agents (Aubagio, Extavia, Revia, Tecfidera, Zinbryta), infusible agents (Lemtrada, Novantrone, Ocrevus, Rituxan, Tysabri), injectable agents (Avonex, Betaseron, Copaxone, Gilenya, Glatopa, Plegridy, Rebif, Vivitrol), and no DMT. Patient characteristics included age, sex, MS type, various comorbidities, and relapse as documented by the examining neurologist at the time of visit, based on history and clinical exam.

Analytic Plan

Descriptive statistics were conducted including frequency distributions for categorical variables and means for normally distributed continuous variables. Demographic data were analyzed for differences across centers. Chi-squared tests were used to analyze differences in proportions of patients utilizing each DMT type across centers and MS diagnostic phenotypes. Multinomial logistic regression was used to investigate associations between center and type of DMT utilization (system-level effects), controlling for significant individual level covariates. Multinomial logistic regression is an extension of logistic regression that allows for more than two categories of the dependent or outcome variable.13


There were 2,029 unique patients included in our analysis for DMT utilization. Of those patients, 75.1% were female, mean age was 50 years, and 87.4% had RRMS; 23.9% were not on DMT, and a majority of those (77%) had RRMS. Among those utilizing DMT, 32.7% were on an oral DMT, 23.5% on an infusion DMT, and 43.9% on an injection DMT. The proportion on oral, infusion, and injection DMTs ranged from 23% to 41.9%, 15.9% to 35.8%, and 34.6 to 55.3% across sites, respectively. Overall, statistically significant differences (p < 0.01) were observed across centers for proportions of patients who received oral, infusion, and no DMTs. Significant differences (p < 0.01) were also found across MS types for proportions of encounters who received oral, infusion, injection, and no DMTs and mean age varied significantly across centers. Cancer and depression were statistically different (p < 0.01) across centers (Table 2).

Table 2. Multinomial regression model patient characteristics (n = 2,029)

  Center A Center B Center C Center D MS-CQI (Total)
Patients, n (%) 747 (36.8) 316 (15.6) 443 (21.8) 523 (25.8) 2029 (100)
Phenotype, n (%)          
PPMS 25 (3.3) 27 (8.5) 30 (6.8) 11 (2.1) 93 (4.6)
PRMS 0 (0) 0 (0) 0 (0) 9 (1.7) 9 (0.4)
RRMS 675 (90.4) 260 (82.3) 344 (77.7) 494 (94.5) 1773 (87.4)
SPMS 47 (6.3) 29 (9.2) 69 (15.6) 9 (1.7) 154 (7.6)
Age, mean (SD) 51.2 (11.3) 48.1 (12.2) 48.6 (11.7) 49.7 (11.6) 49.8 (11.7)
Sex, n (%)          
Male 161 (21.6) 93 (29.4) 133 (30.0) 118 (22.6) 505 (24.9)
Female 586 (78.4) 223 (70.6) 310 (70.0) 405 (77.4) 1524 (75.1)
DMT type, n (%)          
Oral 313 (41.9) 93 (29.4) 102 (23.0) 155 (29.6) 663 (32.7)
Infusion 119 (15.9) 74 (23.4) 96 (21.7) 187 (35.8) 476 (23.5)
Injection 315 (42.2) 149 (47.2) 245 (55.3) 181 (34.6) 890 (43.9)
Comorbidities, n (%)          
Cancer 16 (2.1) 30 (9.5) 40 (9.0) 46 (8.8) 132 (6.5)
Depression 297 (39.8) 55 (17.4) 145 (32.7) 164 (31.4) 661 (32.6)

To examine the differences in DMT types between centers among patients receiving DMT, we used multinomial logis tic regression analysis to investigate associations between center and DMT type. Table 3 highlights patient characteristics for the regression model. Age, MS type, sex, and relapses were controlled for in the model, and injection DMTs were used as a reference group. Results showed decreased odds of receiving an oral DMT at center 2 (odds ratio [OR]: 0.59, 95% confidence interval [CI]: 0.43-0.81) and center 3 (OR: 0.39, 95% CI: 0.29-0.52) relative to center 1 (Table 3). Patients had increased odds of receiving an infusion DMT at center 4 (OR: 2.80, 95% CI: 2.06-3.81) relative to center 1.

Table 3. Multinomial Regression Results: Odds of DMT Utilization

Effect Odds ratio 95% CI
Sex (ref = female)    
Oral vs injection 0.81 0.64, 1.03
Infusion vs injection 0.84 0.64, 1.10
Oral vs injection 0.98 0.98, 0.99
Infusion vs injection 0.97 0.96, 0.98
Center (referent group = 1)    
Center 2    
Oral vs injection 0.59 0.43, 0.81
Infusion vs injection 1.11 0.77, 1.60
Center 3    
Oral vs injection 0.39 0.29, 0.52
Infusion vs injection 0.80 0.57, 1.12
Center 4    
Oral vs injection 0.85 0.65, 1.17
Infusion vs injection 2.80 2.06, 3.81
MS types (ref = RRMS)    
Oral vs injection 1.00 0.54, 1.84
Infusion vs injection 4.70 2.84, 7.77
Oral vs injection 1.16 0.07, 18.88
Infusion vs injection 7.71 0.91, 65.4
Oral vs injection 1.10 0.73, 1.67
Infusion vs injection 2.00 1.30, 3.09
Relapse (ref = yes)    
Oral vs injection 0.95 0.67, 1.35
Infusion vs injection 0.82 0.56, 1.21
Cancer (ref = yes)    
Oral vs injection 1.36 0.87, 2.11
Infusion vs injection 1.64 1.04, 2.61
Depression (ref = yes)    
Oral vs injection 1.18 0.94, 1.47
Infusion vs injection 1.42 1.11, 1.83

Increasing age was associated with decreased odds of receiving oral DMT (OR: 0.98, 95% CI: 0.98, 0.99) and decreased odds of receiving infusion DMT (OR: 0.97, 95% CI: 0.96, 0.98). Cancer comorbidity was associated with increased likelihood of receiving infusion (OR: 1.64, 95% CI: 1.04, 2.61), as was depression (OR: 1.42, 95% CI: 1.11, 1.83). PPMS (OR: 4.70, 95% CI: 2.84, 7.77) and SPMS (OR: 2.00, 95% CI: 1.30, 3.09) were associated with increased likelihood of infusion relative to RRMS.


We observed significant differences in DMT utilization type by center, controlling for known covariates, including age, sex, disease subtype, relapse rate, and depression and cancer comorbidities across centers. Findings suggest that system-level variation exists in DMT utilization and that a system-level focus is needed to further study and address variation and optimize outcomes. We also observed that increased age was associated with decreased odds of receiving an oral agent; whereas, the presence of cancer and depression were associated with increased likelihood of receiving an oral or infusion agent. While it has been observed that DMT utilization decreases overall with age,14–17 often due to progression to secondary progressive disease, our findings suggest the possibility of a more selective pattern. Medication adherence barriers to injection or oral DMTs in MS patients is influenced by patient and pro vider beliefs, comorbidities (such as depression), and cultural interpretations and may lead to small area variation effects.18 Among injectables, interferon β-1a and -1b sub- cutaneous injections showed the highest percentage of non- adherence in our sample; compared to glatiramer acetate, which had the lowest rate of lack of adherence.19 Temporary and permanent discontinuation was more frequent among subjects with interferon β-1a intramuscular compared to other DMTs. Increased age had a protective effect on non-adherence, DMT switching, and temporary discontinuations. Sex differences also played a role in non- adherence and temporary discontinuation, with males having lower risks of non-adherence in this sample.

System-Level Variation (Small Area Geographic Variation Effects)

Historically, MS care has been studied and improved at the basic science, individual, and population levels of analysis. However, the Institute of Medicine reports20 on quality and safety deficiencies in the US healthcare system and the Institute for Healthcare Improvement Triple Aim21 have called for a new systems-oriented focus and continuous improvement culture. Systems-oriented focus refers to a unit of analysis that is aggregated at a higher level than that of the individual clinician (eg, physician, nurse practitioner, etc) and which is focused at the level of the service delivery system (eg, clinic, department, hospital), but at a lower level than that of epidemiological studies of popula tions (eg, all MS patients in the United States). This level of analysis is a new focus in MS, which historically has focused on bench science, individual level, or population level analyses.

Wennberg’s seminal research on geographic variation,22 which established the Dartmouth Atlas Health Care,22 also demonstrated that local practice culture and patterns can displace evidence-based care and influence unwarranted utilization and increased costs.23,24 Without the use of bench- marking, transparency, and system-level improvement focus, local practice trends and cultures can remain staunchly resilient, and the well-intentioned efforts of hardworking individual clinicians to improve outcomes may result only in frustration. Additionally, the Affordable Care Act is influencing a shift from productivity to systems-level value- based reimbursement (“Accountable Care”) in many situations.25 Our findings suggest that Wennberg-type small area geographic (system-level) variation in DMT utilization exists and supports further system-level inquiry and improvement efforts in MS care.

Informing a System-Level Improvement Trajectory

Improvement opportunities identified by our findings include reduction of DMT utilization variation and increasing utilization of DMT in eligible patients. Perhaps the most striking finding in our analysis was that 23.9% of patients were not on DMT, and the majority of those were people with RRMS, the form of MS in which DMT is most strongly indicated and evidence based. Research enabled improvement collaboratives like MS-CQI have been shown to facilitate system-level performance improvement.12,26 Among these, the Cystic Fibrosis Foundation Learning and Leadership Collaboratives are perhaps the most notable exemplars and have inspired the design and purpose of MS-CQI. The Cystic Fibrosis Foundation Learning and Leadership Collaboratives have employed improvement collaborative, quality improvement, and improvement coaching methods to substantially improve cystic fibrosis care experience and outcomes.26


Our study should be regarded as an initial inquiry into system-level variation in DMT utilization. It is a cross- sectional study and only utilizes year 1 of the MS-CQI study to assess baseline “real life” current state performance prior to study intervention. Our initial cross-sectional analysis is vulnerable to temporal confounding and should be replicated with a longitudinal analysis, which will be possible over the next 2 years of the MS-CQI study. In addition, given that the MS-CQI consists of MS care centers in the eastern US, results may have generalizability limited to that geographic region. Finally, MS-CQI does not collect data on all possi ble factors that could have confounding or effect-modifying effects, including Extended Disability Status Score or MS disease duration. Lastly, those not on any DMTs were excluded from the multinomial adjusted regression analysis because addition of this group resulted in very poor multinomial regression model fit. Given that a majority of these patients had RRMS and are indicated for DMT, we intend to conduct a subsequent independent study of this subpopulation to inform targeted improvement initiatives focused on optimizing DMT access, utilization, and outcomes.


This is the first study we are aware of in MS that has investigated system-level variation in DMT utilization in a multi-center collaborative. We present initial evidence here of system-level (small area geographic) variation in DMT utilization in adjusted analyses even though evidence-based practice guidelines exist to guide MS care. We also found that of those not on DMT, a majority had RRMS and are indicated to be on DMT. These findings identify important areas for systems-level improvement in MS care and suggest that continued longitudinal system-level research is war- ranted in this area.

This focus on system-level, population health improvement is new in MS and offers a complementary perspective to established basic science, clinical trials, and epidemiological research approaches that are the predominant areas of inquiry currently pursued in the MS field.

The MS-CQI collaborative study, which was just recently completed in June 2020, also studied the effect of system-level improvement interventions on optimizing DMT utilization and other key population health out- comes, including relapse rate and acute care services utilization. Following precedents set by other successful national improvement efforts targeting complex, costly, chronic conditions, including cystic fibrosis, inflammatory bowel disease, rheumatology, and others, MS-CQI aims to drive a longitudinal improvement trajectory in MS care accompanied by rigorous population health outcomes research initiative.

Disclosure Statement

The author(s) have no conflicts of interest to disclose.

Funding Statement

The MS-CQI research study was supported by a 3-year research grant from Biogen (2017-2020).


Work on this project for Dr N’Dri and Mr Waters was supported as part of a Population Health fellowship program at the Jefferson College of Population Health, supervised by Dr Walsh and Dr Oliver.

The MS-CQI research collaborative is an improvement and research community of practice comprised of dedicated researchers, clinicians, coordinators, and PwMS. Findings disseminated from the MS-CQI study are authored acknowledging the combined efforts of the larger community of MS-CQI Investigators, which we wish to formally acknowledge below:

1. Dartmouth (Research Leadership Team, Hanover, NH): Brant Oli- ver (principal investigator), Fal Mehta (project manager, 2019- 2020), Randal Messier (improvement coach and co-investigator), Cathy Alexander (co-investigator), Hasna Hakim (co-investigator), Mary Smith (research assistant), Chandlee Bryan (research associ- ate), Lucy Fennesy (research associate), Amy Hall (project man- ager, 2017-2019), Troi Perkins (research assistant, 2017-2019), and James Curtin (database administrator).

2. Jefferson College of Population Health (Data Analytics Center, Philadelphia, PA): Karen Walsh (chief data analyst), Dexter Waters (Academic Health Economics Outcomes Research (AHEOR) fellow, 2019-2020), Laetitia NDri (AHEOR fellow, 2019-2020), Arianna Kee (AHEOR fellow, 2019), Albert Crawford (researcher, 2019), and Marianna LaNoue (Director of Research).

3. Massachusetts General Hospital MS Clinic (Boston, MA): Eric Kla- witter (neurologist/site investigator), Anna Vaeth (study coordinator).

4. University of Vermont Medical Center MS Center (Burlington, VT): Andrew Solomon (neurologist/site investigator), Emily Azalone (study coordinator).

5. Concord Hospital Neurology MS Specialty Care Program (Con- cord, NH): Ann Cabot (neurologist/site investigator), Rick Lavallee (study coordinator, IT administrator).

6. MS Center of Greater Orlando (Maitland, FL): Tricia Pagnotta (MS specialist nurse practitioner/site investigator), Kelly Holley, RN (study coordinator).

7. MS-CQI Research and Improvement Advisory Committee (RIAC): The RIAC meets twice annually to advise and monitor the progress of the MS-CQI collaborative. The authors wish to thank the RIAC members for their important contributions and guidance: Heather Wishart (RIAC Chair, MS neuropsychologist, Dartmouth-Hitchcock Health), Cy Jordan (MD), Randy Messier (MT, MSA, PCMH-CCE), Dean Lea (PhD), Chris Rovinski-Wagner (MSN, APRN), Ann Cabot (DO), Elizabeth R McLure (PwMS), Alex Hoyt (PhD, RN), Jody Karp (PwMS), and Steven Triedman (PwMS).

Data Statement

The database for this study is de-identied and held via a secure RedCap repository by the Dartmouth research lead site and also locally at the Jefferson College of Population Health data analytics center per institutional review board-approved protocol and will be maintained securely for a period of 5 years post study completion or longer as required by regulatory policies. This dataset is not publicly available. Inquiries concerning the research dataset used in this study should be directed to the MS-CQI principal investigator (Dr Oliver).

Author Affiliations

1 Jefferson College of Population Health, Philadelphia, PA

2 Departments of Community and Family Medicine, Psychiatry, and the Dartmouth Institute for Health Policy and Clinical Practice (TDI), Geisel School of Medicine at Dartmouth, Hanover, NH

3 Dartmouth-Hitchcock Health, Lebanon, NH

4 Department of Veterans Affairs National Quality Scholars (VAQS) and Health Professions Education and Evaluation Research (HPEER) Advanced Fellowship Programs, White River Junction, VT and Houston, TX

Corresponding Author

Brant J Oliver, PhD, MS, MPH, APRN-BC (

Author Contributions

Brant J Oliver, PhD, MS, MPH, APRN-BC, served as the principal investigator and supervised all aspects of the MS-CQI core study design, conduct, data analysis, and manuscript preparation. Laetitia A N’Dri, PharmD, Dexter D Waters, MSPH, and Karen Walsh, DHSc, MS, MBA, developed and conducted the specific analysis of DMT outcomes for this manuscript. Karen Walsh, DHSc, MS, MBA, served as the lead data analyst and data custodian for the MS-CQI research database, oversaw the data analytic plan, data analyses, and participated in the interpretation of results and preparation of the manuscript. Falguni Mehta, MS, MBA, served as the project manager for the MS-CQI Collaborative and participated in manuscript reviews.


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Keywords: chronic conditions, neurology, population-based, quality, therapeutics

AbbreviationsDMT = disease-modifying therapy; EHR = electronic health record; MS = multiple sclerosis; MS-CQI = Multiple Sclerosis Continuous Quality Improvement Research Collaborative; PPMS = primary progressive multiple sclerosis; PRMS = progressive-relapsing multiple sclerosis; RRMS = relapsing-remitting multiple sclerosis; SPMS = secondary progressive multiple sclerosis


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