Exploring Healthcare Continuity in Pediatric-Onset Multiple Sclerosis in the United States.

Introduction

Shifting from pediatric to adult‐focused care is critical to any child's healthcare journey. It can influence medical decision‐making, insurance coverage, out‐of‐pocket costs, logistical planning, follow‐up, and adherence. Effective transition establishes lifelong medical independence and health conscientiousness. Otherwise, lifelong lapses in care can lead to poorer health‐related outcomes [ 1 , 2 ].

Transition of care is particularly challenging for children living with a chronic disease [ 3 , 4 ]. They rely on parents or guardians for medical management, minimize symptoms to avoid medical procedures and stigmatization, struggle with grasping and expressing complex medical concepts and symptoms, and underappreciate the potential consequences of not adhering to medical recommendations [ 5 ]. This can lead to discontinuations or delays in treatment, increasing morbidity and mortality [ 5 , 6 , 7 , 8 ].

Pediatric‐onset MS (POMS) occurs in at least 5% of MS. It is a lifelong debilitating demyelinating disease of the central nervous system requiring complex care coordination, immune‐modulating treatment, and close clinical, radiologic, and safety surveillance [ 9 , 10 , 11 ]. In addition to suffering from MS‐related disability, POMS patients struggle to access care, overcome social and emotional barriers, and succeed academically and professionally [ 12 , 13 , 14 ]. These challenges are likely compounded by transition to adult‐centered care. However, little research exists on transition of care and follow‐up in POMS [ 15 , 16 , 17 ].

Children with MS show higher disease activity early on than adults [ 18 , 19 , 20 , 21 ]. Although children recover better from individual relapses, they reach significant disability on average 10 years younger [ 22 , 23 , 24 ]. It is therefore important to prevent care lapses in this population. The US Network of Pediatric MS Centers (US NPMSC) encompasses the largest POMS research cohort in the country [ 25 ]. We aim to explore transition‐of‐care practices and how age and other factors impact follow‐up in the US POMS population.

Methods

Study Design

This was a two‐part study. First, surveys were distributed to 10 sites in the US NPMSC about transition‐of‐care practices. Next, we retrospectively analyzed prospectively collected data from the US NPMSC comparing 1‐ and 2‐year lapse in last follow‐up visit between the < 18‐ and ≥ 18‐year‐old age groups. Clinical information was collected at routine visits using standardized case report forms and recorded in an online database between May 1, 2011 and August 9, 2023. Information prior to 2011 was retrospectively entered according to available medical records.

Survey

Description

A 40‐question survey of transition‐of‐care practices was distributed by electronic mail to 10 sites in the US NPMSC by the University of Utah Data Coordinating Center on July 25, 2023. Respondents were required to have first‐hand, up‐to‐date knowledge of their pediatric MS center operations (either the center director or fellow) and to complete the survey according only to their pediatric‐focused practice. Questions were developed from working group discussions among POMS experts. Most questions required multiple choice responses, with additional free‐text options available for more complex questions. Question highlights included the age at which sites typically transitioned care, where they transitioned care to, how they prepared families for the transition process, what barriers they faced, and how they assessed transition readiness.

Analysis

Study Population

Patients diagnosed with POMS or clinically isolated syndrome (CIS) were included [ 26 ]. The diagnosis was confirmed by an on‐site neurologist with expertise in POMS, according to established consensus criteria (2010 Revised McDonald, 2013 International Pediatric MS Study Group, 2017 Revised McDonald) [ 26 , 27 , 28 ]. Participants with only a single recorded visit or who were reclassified in the registry as having myelin oligodendrocyte glycoprotein antibody‐associated disease were excluded.

Measurements

The primary comparison was between the < 18 and ≥ 18‐year‐old age groups. The primary outcomes were 1‐ and 2‐year lapse in last follow‐up visit, defined as greater than 1 and 2 years elapsing between the last recorded database follow‐up visit and the data lock date or, if ≥ 18 years, up to 1 year after a patient reached their site's typical transition age, if earlier (Figure 1 ). Baseline was the last recorded visit or, for the ≥ 18‐year age group, the last visit before the site's typical shift age plus 1 year, if earlier. Secondary outcomes included baseline factors associated with follow‐up lapse.

We used < 18 versus ≥ 18 years of age to explore the effect age may have on follow‐up lapse in this study because 18 is the age at which patients in the United States are considered medically legal adults and expected to care for themselves more independently, including continuing medical visit follow‐up. Individual site‐defined transition age thresholds were used instead of a universal age cutoff since sites vary widely in transition age. We added a year onto the site‐reported transition cutoff age for the older group to ensure participants who were scheduled for their final visit shortly after reaching transition age were not counted as lapsing in follow‐up.

Baseline characteristics included age, sex, race, ethnicity, body mass index (BMI), highest maternal education level (proxy for household socioeconomic status) [ 29 , 30 , 31 ], primary insurance payer type, diagnosis (MS or CIS), disease‐modifying therapy (DMT) category (platform injectable, oral, intravenous, other, or none), and follow‐up time.

Statistical Analysis

The analysis plan was predefined. Summary statistics including mean, standard deviation, (SD), and frequency were used to analyze baseline characteristics of participants < 18 versus ≥ 18 years of age and 1‐ and 2‐year follow‐up lapsers versus non‐lapsers. Kruskal–Wallis test was used for continuous variables and chi‐squared test for categorical variables.

The primary outcome, lapse in last follow‐up visit, was analyzed using a standard adjusted and unadjusted Cox regression, time‐to‐event model. If a participant older than 18 years was enrolled before 18 years of age, that individual could contribute to both age groups; hence, the inclusion of a random effect for participant accounting for a correlation between a participant's possible observation in both age groups. Participants who were observed during both age periods were considered censored for the < 18‐year‐old portion of the observed period, as they were not observed to have dropped out during that time. The model was adjusted for preidentified confounders expected to be related to group allocation and outcome. These included age, sex, race, ethnicity, highest maternal education, primary insurance payer type, diagnosis, and DMT category. BMI was not included due to excessive missingness. Multiple imputation was not performed as missingness of the other variables was small.

Secondary outcomes exploring additional baseline factor associations with follow‐up lapse included sex, race, ethnicity, maternal education, DMT, diagnosis, and insurance payer type.

The same analysis plan was also carried out in the POMS‐only group (CIS excluded) to better characterize only those patients with confirmed MS.

Standard Protocol Approvals, Registrations, and Patient Consents

Prior to enrollment, informed consent, assent, and institutional review board approval were obtained in accordance with local study site regulations.

Results

Surveys

The survey results of transition‐of‐care practices at 10 sites in the US NPMSC are summarized in Tables S1 and S2 . Half of the sites reported typically shifting patients at 22 years of age. Almost all (90%) initiated transition discussions after patients turned 16 years of age. Seventy percent indicated they did not use a formal transition protocol. Half shifted patients to adult MS colleagues at the same institution, while 40% shifted patients to themselves in an adult clinic setting. Forty percent of the sites felt more psychology‐based supports were needed to help guide families through the transition process. All sites expressed an interest in a formal transition‐of‐care practice guideline.

Study Population

A total of 1166 POMS/CIS participants were included in the analysis (Table 1 ). There were 476 (41%) < 18‐year‐olds and 690 (59%) ≥ 18‐year‐olds. The mean age in the < 18‐year‐old group was 14.7 years and in the ≥ 18‐year‐old group 21.3. The ≥ 18‐year‐old group included a greater proportion of females, MS over CIS, and longer mean follow‐up. A higher percentage in the < 18‐year‐old group were on no DMT, while a lower percentage. Members of the < 18‐year‐old group were less likely to have received a DMT and less likely to be treated with an oral or intravenous DMT. The proportion of White individuals was slightly higher in the < 18‐year‐old group than in the > 18‐year‐old group (70% versus 66%), while the proportion of Black individuals was slightly higher in the > 18‐year‐old group (24% versus 18%), but neither comparison reached statistical significance.

The POMS‐only cohort (CIS excluded) consisted of 959 patients (Table 2 ). Similar patterns were observed as in the combined analysis. In the < 18‐year‐old group, there was a greater proportion of Hispanic/Latino ethnicity with government over private insurance. The < 18‐year‐old group contained a lower proportion of Black (20% in < 18, 24% in ≥ 18) and higher proportion of other races (12% in < 18, 10% in ≥ 18). None of these reached statistical significance.

When comparing POMS/CIS 1‐ and 2‐year lapsers versus non‐lapsers (Table 3 ), lapsers included a larger proportion of males, non‐Hispanic/Latino ethnicity, private over government insurance, CIS over MS, platform injectable over intravenous DMT, lower average BMI, and shorter mean follow‐up.

The POMS‐only group showed a similar pattern, except there was no statistically significant difference in sex or BMI between lapsers and non‐lapsers (Table 4 ).

Association Between Age and Lapse in Last Follow‐Up in POMS/CIS

In the adjusted analysis for the combined POMS/CIS cohort (Figure 2 ), the primary outcome of ≥ 18 years of age was associated with an increased hazard of 1‐ and 2‐year follow‐up lapse compared with < 18. The estimated hazard of 1‐year lapse for ≥ 18 was 1.67 times the hazard for < 18 (95% CI: 1.35–2.08, p < .001). The estimated hazard of 2‐year lapse for ≥ 18 was 1.47 times the hazard for < 18 (95% CI: 1.14–1.89, p = 0.003).

Other Baseline Characteristics Associated With Lapse in Last Follow‐Up in PMS/CIS

In the secondary outcomes analysis for the combined POMS/CIS cohort, platform injectable DMT was associated with a greater hazard of 2‐year follow‐up lapse (hazard ratio [HR]: 1.62, 95% CI: 1.12–2.36, p = 0.010), while intravenous DMT was associated with a lower hazard of 1‐ and 2‐year lapse (1‐year HR: 0.64, 95% CI: 0.47–0.88, p = 0.005 and 2‐year HR: 0.66, 95% CI: 0.45–0.95, p = 0.025). MS (vs. CIS) was associated with a lower hazard of 1‐ and 2‐year lapse (1‐year HR: 0.38, 95% CI: 0.28–0.53, p < 0.001 and 2‐year HR: 0.66, 95% CI: 0.45–0.95, p = 0.025).

Association Between Age and Lapse in Last Follow‐Up in POMS‐Only (CIS Excluded)

In the adjusted primary outcomes analysis for the POMS‐only group, similar to the combined analysis, age ≥ 18 years was associated with an increased hazard of 1‐ and 2‐year follow‐up lapse compared with age < 18 (Figure 3 ). The estimated hazard of 1‐year lapse for age ≥ 18 was 1.70 times the hazard for < 18 (95% CI: 1.33–2.18, p < 0.001). The estimated hazard of 2‐year lapse for ≥ 18 was 1.49 times the hazard for < 18 (95% CI: 1.11–1.99, p = 0.007).

Other Baseline Characteristics Associated With Lapse in Last Follow‐Up in POMS‐Only (CIS Excluded)

In the secondary outcomes analysis for the POMS‐only cohort, similar to the combined analysist, platform injectable DMT was associated with a greater and intravenous a lesser hazard of 1‐ and 2‐ year follow‐up lapse (1‐year HR: 0.62, 95% CI: 0.45–0.86, p = 0.004 and 2‐year HR: 0.65, 95% CI: 0.44–0.95, p = 0.027). Unlike in the combined cohort, a trend in association was observed between Hispanic/Latino ethnicity and a greater hazard of follow‐up lapse (1‐year HR: 1.31, 95% CI: 0.96–1.79, p = 0.087 and 2‐year HR: 1.37, 95% CI: 0.96–1.95, p = 0.086).

Discussion

In this large US observational cohort study of POMS patients, prior to reaching a site's typical transition age, ≥ 18 years of age was associated with a greater hazard of 1‐ and 2‐year lapse in last follow‐up visit than < 18. Although prospective studies are needed to determine reasons for lapse (including transfer prior to a site's typically reported transfer age due to factors such as moving away for college or employment), the wide practice variability and barriers to transition of care reported by major US clinical centers suggest that standardized, targeted transition‐of‐care interventions may be beneficial. For example, emphasizing to young patients early on the importance of continuing consistent follow‐up with an MS specialist after turning 18 years of age (when parents may no longer be as involved in the child's care) could prove particularly useful. This topic is highly relevant, as over half of patients in the US NPMSC are now 18 years of age or older.

Of the 10 surveyed sites, most reported significant challenges in the care transition process. This is consistent with research on other pediatric chronic diseases, such as asthma, epilepsy, diabetes, rheumatological disorders, and cancer, which has spurred the development of disease‐specific transition‐of‐care guidelines. Yet, due to barriers to transition and differences in pediatric as opposed to adult onset of disease, pediatric subspecialty providers often care for patients well beyond 18 years of age [ 32 ]. The minimum typical transition age reported in our surveys was 20 years of age. Further research is needed to determine whether this approach translates into improved long‐term disease‐related outcomes for POMS patients.

Intravenous DMTs correlated with a lower hazard of 1‐ and 2‐year lapse in last follow‐up. Infusions typically require visits to specialized centers at least once every 6 to 12 months. The facilities are often close to affiliated pediatric MS clinics, supporting same‐day appointments [ 33 , 34 ]. Some infusion centers require patients be seen in clinic prior to each infusion, improving follow‐up continuity. Compared with platform injectable and oral DMTs, infusions are more costly and time‐intensive, portend a greater risk of more serious side effects, and are more frequently prescribed to patients with higher disease activity. As a result, there may be greater oversight from patients, families, insurance payers, and providers to prevent care lapses.

Platform injectable DMTs were associated with a greater hazard of lapse in follow‐up. This may be because platform injectables are more often prescribed to patients with less active disease [ 35 , 36 ]. Injectables can also cause needle fatigue and injection site reactions. They are less effective than newer options in controlling MS disease activity and show higher discontinuation rates. This may culminate in a decreased tendency for patients on platform injectable therapies to continue following up in their pediatric MS clinic [ 33 , 37 , 38 , 39 ].

MS was associated with a lower hazard of follow‐up lapse than CIS. CIS is a single clinical episode of demyelination and can be self‐limited [ 40 ]. Although CIS increases the risk of MS development, this concept can be confusing to families. As a result, they may find it harder to justify the need for ongoing follow‐up [ 41 ]. Clinicians also may not feel as strongly about continuing to see CIS patients long‐term. Finally, patients with CIS are less likely to be prescribed DMTs that would otherwise necessitate regular follow‐up visits to maintain an active prescription [ 36 , 42 ].

Of the 10 surveyed clinical sites, eight cited socioeconomic barriers, seven education and understanding, and nine logistics as important when assessing transition readiness. This could reflect social or language challenges limiting ongoing access to care. Individuals and families with limited English proficiency face greater difficulty navigating the US healthcare system [ 43 , 44 ]. Prior work from the US NPMSC demonstrated that individuals reporting difficulty enrolling and completing the necessary paperwork for inclusion in research were less likely to follow up for future visits [ 45 ]. Multiple studies have indicated that historically marginalized individuals living with MS are at greater risk of more active, rapidly progressive disease and worse outcomes, underscoring the importance of consistent follow‐up in these groups [ 46 , 47 , 48 ]. Although further research is needed, transition programs could benefit from offering multilingual support and culturally tailored resources to patients and families [ 49 , 50 ]. Additional studies on the role racial, ethnic, socioeconomic, and psychosocial factors may play in POMS follow‐up are warranted.

We observed baseline characteristic differences between the younger and older POMS patient groups. Future research should clarify how POMS evolves and compares with adult‐onset MS long‐term. The higher proportion of females in ≥ 18‐year‐olds could be explained by equal sex distribution in MS before puberty [ 51 ]. Although our analysis did not reveal a significant association between sex and 1‐ and 2‐year follow‐up lapse, it will be important to explore whether tailoring transition practices to sex would be beneficial. The higher average BMI observed in the older group could reflect a physiological increase in BMI after puberty (possibly exacerbated by the obesity epidemic), although evidence for this is limited [ 52 ]. The older group included a greater proportion on oral DMTs, which could reflect a historical reluctance to prescribe newer DMTs to younger children or a child's inability to swallow pills [ 53 ]. The higher proportion on no DMT in the younger POMS/CIS cohort was not observed in the POMS‐only analysis, possibly due to the higher proportion of CIS patients in the younger group. Although one might attribute this to lower rates of DMT use in younger children, 63% of the younger POMS‐only patients were on newer DMTs (including 50% intravenous) compared with 71% in the older group (including 48% intravenous). This suggests that US NPMSC providers are comfortable prescribing newer higher‐efficacy DMTs, particularly infusions, to younger POMS patients. The proportion on platform injectables was similar between the two groups (15% in younger POMS‐only patients and 12% in older).

Strengths of this study included leveraging data from 10 major pediatric MS centers across the United States, incorporating transition practices taken from an initial survey phase into the analysis plan, and using reliable standardized database‐driven follow‐up visit information to assess follow‐up lapses. This study also had several limitations. Surveys on transition practices were not initially piloted, and we did not query electronic medical record utilization differences across transitional settings as a potential barrier to transition. Research‐based registry follow‐up is not equivalent to real‐world clinical follow‐up. Although the study period was restricted to the time during which patients were expected to continue following up at their pediatric MS center, we could not account for transfer before or after a site's reported typical transition age, such as moving away for college or employment. This is a major limitation of the study since transition age for individual patients is heterogenous and can depend on many factors such as insurance, financial factors, legal requirements, disease trajectory, family planning, clinic resources, and personal preferences. We also cannot exclude the possibility that visit data for some patients may have continued to be entered despite transferring care; however, this scenario would only occur if the shift occurred prior to a site's typical transition age. We also did not explore how zip code, increased awareness of transition shortcomings, measures of disease activity, DMT availability, or changes in MS/CIS incidence over time may influence follow‐up continuity. For example, the COVID‐19 pandemic likely increased rates of follow‐up lapses, especially during the peak years, and some patients may have then re‐established care. It is also unknown whether patients experienced poorer care or delays in treatment secondary to lapses in follow‐up, or why they did not follow up, representing critical areas of future study. We were unable to contact participants if they left the institutions their pediatric MS center was affiliated with due to registry protocol restrictions. Lastly, the US NPMSC consists of clinics at large academic institutions, which may not be generalizable to other clinical settings or countries.

All 10 major clinical sites surveyed expressed an interest in a standardized transition‐of‐care guideline. Future research should focus on site‐specific barriers and facilitators to guide implementation of transition best practices. Adopting strategies from sites with more consistent patient follow‐up and improving on methodology at sites with poorer follow‐up will be informative. Gathering patient and family perspectives, developing methods to track ongoing follow‐up, assessing long‐term outcomes and quality of life, and standardizing and validating transition‐of‐care readiness assessments specifically tailored to the POMS population will be important.

Author Contributions

Aaron W. Abrams: conceptualization, investigation, writing – original draft, methodology, visualization, writing – review and editing, project administration, supervision, resources. Michael Waltz: conceptualization, investigation, methodology, validation, writing – review and editing, software, formal analysis, data curation. Jonathan Race: conceptualization, investigation, methodology, validation, writing – review and editing, software, formal analysis, data curation. T. Charles Casper: conceptualization, investigation, methodology, validation, writing – review and editing, software, formal analysis, data curation. Kimberly O'Neill: writing – review and editing, investigation. Melissa A. Wright: investigation, writing – review and editing. Eva‐Chava M. Bernfeld: investigation, writing – review and editing. Grace Y. Gombolay: investigation, writing – review and editing. Mark P. Gorman: investigation, writing – review and editing. Jennifer S. Graves: investigation, writing – review and editing. Timothy E. Lotze: investigation, writing – review and editing. Soe Mar: investigation, writing – review and editing. Jayne Ness: investigation, writing – review and editing. Mary Rensel: investigation, writing – review and editing. Moses Rodriguez: investigation, writing – review and editing. Nikita Shukla: investigation, writing – review and editing. Jan‐Mendelt Tillema: investigation, writing – review and editing. Yolanda Wheeler: investigation, writing – review and editing. Emmanuelle Waubant: investigation, writing – review and editing. Drafting/revision of the manuscript: all authors. Acquisition of data: all authors. Study concept or design: Aaron W. Abrams, Michael Waltz, Jonathan Race, T. Charles Casper. Analysis or interpretation of data: Aaron W. Abrams, Michael Waltz, Jonathan Race.

Conflicts of Interest

Several companies manufacture drugs for MS that are studied in this paper, including Bayer (interferon‐β1b SC), Biogen (interferon‐β1a IM, peginterferon‐β1a, dimethyl fumarate, natalizumab), EMD Serono (interferon‐β1a SC), Genentech/Roche (rituximab, ocrelizumab), Genzyme (alemtuzumab, teriflunomide), Novartis (interferon‐β1b SC, fingolimod), and Teva (glatiramer acetate). Aaron W. Abrams has received institutional funding for a pediatric MS clinical trial funded by Hoffmann‐La Roche and a pilot study from the National Multiple Sclerosis Society. Jonathan Race is supported by NMSS Grant SI‐2110‐38420, a grant awarded to the Utah DCAC that in turn provides funding for my contributions. T. Charles Casper has received research funding (for his institution) from Hoffmann‐La Roche and Biogen. Akash Virupakshaiah is supported by fellowship grants from Biogen, EMD Serono, and Novartis (2022–2024) and is a recipient of National Multiple Sclerosis Society Sylvia Lawry Physician Fellowship Award (Grant#FP‐2307‐41848, 2024–2026) and Young Investigator Award from the Race to Erase MS. Melissa A. Wright has received research funding (to institution) from Biogen. Eva‐Chava M. Bernfeld holds stock in Roche. Leslie A. Benson has participated in multicenter clinical trials funded by Roche and Biogen and received an honorarium for talks from Novartis. Tanuja Chitnis is supported by grant funding from Novartis, Genentech, EMD Serono, and Genzyme; received consulting fees (payment made to the institution) from Genentech, Novartis, and Biogen; received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Genentech and Novartis; and participates on a data safety monitoring board or advisory board for Genentech, Novartis, and Roche. Jennifer S. Graves has received research funding from EMD Serono. Grace Y. Gombolay has served as an associate editor for Annals of the Child Neurology Society . L. Krupp has received research funding from Novartis and Biogen; received royalties from various biopharmaceutical entities for use of the Fatigue Severity Scale; received consulting fees from Hoffman‐La Roche; and participated on a data safety monitoring board or advisory board for Novartis and Biogen. Soe Mar is the site PI for clinical trials for pediatric MS supported by Biogen and Roche and has served on the editorial board for Annals of the Child Neurology Society . Mary Rensel has received research funding from Roche, Novartis, Biogen, and Genentech; served on the data safety monitoring committee for Biogen, Novartis, and EMD Serono; and has been a speaker or consultant for EMD Serono, Novartis, Genentech, and Genzyme. Teri Schreiner has received consulting fees from Hoffman‐La Roche and participated o,n a data safety and monitoring board for Biogen. Bianca Weinstock‐Guttman has received personal fees from EMD Serono and grants from Biogen, Novartis, and EMD Serono. Emmanuelle Waubant has not received any pharmaceutical company honorarium and is the site PI for Biogen and Roche trials and serves on a Roche Steering Committee. John Rose has received research support from Biogen. The other authors declare no conflicts of interest.

Supporting information

Supporting File 1

Supporting File 2

Acknowledgments

We thank the National MS Society, dedicated research coordinators, and the patients and their families who participated in this study. All phases of this study were supported by a National Multiple Sclerosis Society grant, HC‐1509‐06233 (T. Charles Casper). The study was conducted in accordance with all IRB regulations, ethical standards, and all manuscript, research, and authorship integrity and professionalism requirements as stipulated by the Annals of the Child Neurology Society .

Data Availability Statement

Data for this manuscript will be made available upon reasonable request to the corresponding author.

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