A narrative review of the economic burden of myelin oligodendrocyte glycoprotein antibody-associated disease and analogous conditions

Background: Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is an inflammatory demyelinating disease of the central nervous system with a serious, debilitating presentation, including residual disability after relapses.

Objectives: To evaluate the economic impacts of MOGAD and analogous conditions, including direct costs, indirect costs and cost drivers.

Design: Systematic literature search and narrative review.

Data sources and methods: Search strings were designed to capture any study reporting health economic impacts of MOGAD or analogous autoimmune diseases of the nervous system. The costs of diagnostic tests and short- and long-term interventions were considered, and studies from both patient and institutional (public and private) perspectives were included. Searches were conducted using medical subject headings (MeSH) in PubMed in July 2023. Retrieved publications were screened initially based on title and abstract, then based on the full text. Data were extracted manually; findings are reported descriptively. All cost data were adjusted to 2024 US Dollars using the CCEMG-EPPI-Centre Cost Converter.

Results: Results from 40 studies of MOGAD and analogous autoimmune neurological conditions were extracted. In the only study that included patients with MOGAD (a cost investigation from Germany in which 166 patients had neuromyelitis optica spectrum disorder and 46 had MOGAD), the mean annualised cost of illness was $94,688 (direct medical costs 43%, direct non-medical costs 34%, indirect costs 23%). Across the conditions assessed, the annual total cost of illness per patient ranged widely, from $3,690 to $507,117 (among studies that reported types of cost, the range for direct costs was $1,981–$148,388; for indirect costs, $0–$942,707). The study that included patients with MOGAD identified the need for care, number of acute attacks, unemployment and disability as independent predictors of cost. Additional cost drivers (from all the conditions) included treatments (e.g., intravenous immunoglobulin), hospitalisation, disease severity, relapses and refractory disease.

Conclusion: Our search identified only one study that specifically examined costs associated with MOGAD. Results from this and studies of analogous autoimmune conditions suggest that inflammatory demyelinating diseases of the central nervous system including MOGAD are costly for the individual patient and place considerable burden on healthcare systems. Further evidence is needed for increased insight into the economic burden of MOGAD.

Introduction

Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is an inflammatory demyelinating disease of the central nervous system (CNS) (1, 2). MOGAD has some symptom overlap with other neuroinflammatory disorders such as multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) but there are important differences between these diseases in pathophysiology, prognosis and treatment response (1–4). Accurate and timely diagnosis of MOGAD is therefore needed to ensure that patients receive optimal therapy and minimise the risk of hospitalisation due to potentially preventable disease symptoms. This requires a standardised approach with thorough consideration of the clinical presentation and history along with the findings of serological tests and intracranial and spinal imaging (3–6). Knowledge and understanding of MOGAD have increased in recent years, allowing the development of an antibody test, unique international classification of diseases code (ICD-10-CM code G37.81) and updated diagnostic criteria (1, 2, 7, 8). Although ICD coding is not universally implemented, these developments should help enable gradual improvement in the patient pathway to diagnosis and treatment, which at present can be lengthy and challenging (9). Already they have paved the way for increased attention to the management of MOGAD. Understanding the direct and indirect costs associated with the disease and its treatment will benefit healthcare systems and will be important for determining the cost-effectiveness of future candidate treatments.

Despite being a rare disease, MOGAD is expected to represent a substantial economic burden; it has a serious debilitating presentation, with residual disability after relapses. Patients may present with one or more of optic neuritis, myelitis, acute disseminated encephalomyelitis (ADEM), cerebral mono-focal or multifocal deficits, brainstem or cerebellar syndromes or cerebral cortical encephalitis (1, 2, 10). Symptomatic attacks may be attributable to inflammation of the optic nerve, spinal cord or brain (11). Symptoms include loss of vision, eye pain, headaches, confusion, muscular stiffness or weakness, changes in bowel, bladder or sexual function, and seizures (11). Unlike MS, neurological deterioration in MOGAD does not usually progress without relapses, suggesting there is potential for effective treatment to reduce the accumulation of disability (1, 7).

The economic burden is likely to differ according to the nature of the clinical manifestation, the nerve region(s) impacted and the severity, number and frequency of attacks. Misdiagnoses and delayed treatment also play a role, as the heterogeneous presentations of MOGAD are not well recognised. The mean age of MOGAD onset is 28–30 years; however, approximately 30% of individuals with MOGAD are children (10). As a result, disability and sight loss can last for decades and the full economic impact is not known (1, 2).

There are currently no approved treatments for MOGAD. Clinical trials focused specifically on patients with MOGAD are currently ongoing. At present, treatment approaches to MOGAD in clinical practice are similar to those applied to other inflammatory demyelinating diseases. Case reports show that high-dose methylprednisolone (MP), intravenous immunoglobulin (IVIg) and plasma exchange are used to manage acute attacks, while methotrexate, azathioprine, corticosteroids, rituximab, IVIg and mycophenolate mofetil may be considered for maintenance therapy (2, 12, 13).

Given the current lack of evidence relating specifically to MOGAD, data from other antibody-mediated diseases where immunosuppression is commonly used to reduce inflammation and prevent relapse might provide greater insight. This decision was based on international recognition of extrapolation from related diseases as an acceptable approach to facilitate the approval of new medicines for rare diseases (14–17). We conducted a systematic literature search and here provide a narrative review of the economic impact of MOGAD. We included publications on autoimmune neurological conditions with similar clinical presentations or where similar treatments are used.

Methods

A systematic literature search was designed to gather information on the health economic impacts of MOGAD and analogous conditions. This narrative review aligned with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines (18).

Eligibility criteria

We selected any publication that presented the health economic impacts of MOGAD or analogous conditions as selected by the review team for inclusion (see author contributions for detail). Analogous conditions were selected based on comparison with MOGAD in terms of (a) clinical presentation, (b) treatments used in management, (c) pathophysiology and (d) incidence/prevalence; that is, they were autoimmune diseases of the nervous system with sufficient similarity in clinical presentation or management to MOGAD. Publications on MS were not included because of the higher prevalence and more mature treatment landscape of MS compared with MOGAD. Publications that did not specify a monetary amount but described predictors of cost (i.e., cost drivers) were eligible for inclusion. Exclusion criteria were as follows: non-English full text; lack of cost data for MOGAD or analogous condition (e.g., cost estimates spanning multiple neuroimmunological conditions); and data derived from cost modelling (e.g., Markov models) rather than true cost calculations. For hospitalisation costs, data relating to diagnostic costs alone were not included. Studies without annualised estimates were only included if drivers of cost were extractable.

Information sources

The search was performed in PubMed on 14 July 2023 using medical subject headings (MeSH) and keywords for journals published between 2000 and 2022.

Search strategy

The complete search strings, which include terms for cost analysis, economics, expenditure and expenses, are shown in Supplementary Table 1.

Selection process

A reviewer (LL) undertook initial screening of the titles and abstracts; any uncertainty when applying the eligibility criteria was mediated by two other independent reviewers. The same reviewer (LL) then screened the full-text publications, with the references of included studies further assessed for inclusion eligibility. Data (cost estimates and driver data) were manually extracted after eligibility screening was completed.

Data collection process and data items

Economic impacts included cost considerations from the patient or institutional (public or private) perspective, and costs associated with all stages of the patient journey. Diagnostic work-up, short-term interventions to aid recovery from an attack (e.g., hospitalisation, plasma exchange) and the management of long-term disability (e.g., carer costs, loss of income, support group services) were all considered. Reported costs were divided into direct costs, which denote those related to managing the condition in both the acute and the chronic setting, and indirect costs. The exhaustive list of considerations for direct costs included hospitalisation fees, post-discharge specialist fees, travel expenses for patients and caregivers, medication/diagnostic fees, at-home health services and support groups. For indirect costs, this study included morbidity (i.e., loss of productivity) and mortality (i.e., premature death from the illness). Mean annual costs per patient were extracted from each publication. Median costs were used where means were not available, and simple calculations were performed where needed to determine the annual cost per patient (e.g., when data were presented as monthly costs), and p-values for relevant comparisons were also extracted. No new statistical analyses were conducted. To ensure comparability, all reported costs were adjusted to 2024 US Dollars using the CCEMG-EPPI-Centre Cost Converter (19). The International Monetary Fund was used as the source dataset for purchasing power parities. If a cost year range was provided, the latter year was used for standardisation. If no cost year was provided, the publication year was used for standardisation. If the reporting country using Euros was not specified or if a pan-Europe study was conducted, Belgium was selected as the original study country. We referred to PRISMA in the reporting of our literature search, although owing to the range of conditions and study designs included, the literature search findings are summarised as a narrative review, and therefore not all PRISMA items were relevant.

Results

Literature search results

The search yielded 512 records, of which 443 were excluded during screening of the titles and abstracts. Of the remaining 69 records, 29 were excluded after full-text review with reasons presented in Figure 1. Results from the remaining 40 studies were extracted and tabulated.

Figure 1

Figure 1. Flow chart of the systematic literature search in this study.

Information on the methods, size and scope of the 40 included studies is provided in Figure 2 and Supplementary Table 2. In total, 76,255 patients were included, with all except four studies involving <2,000 patients (the four exceptions included 2,805, 3,341, 5,473 and 54,778 patients) (20–23). Most studies (n = 23) were cost analyses or cost-of-illness studies, four were cost minimisation analyses and there were four healthcare resource use studies. Fourteen of the studies were retrospective and 18 were non-comparative; there were four case–control studies but no randomised controlled trials. All studies except two were from a single country, and the countries with the largest numbers of studies were the USA, China, Germany and the UK. There were three studies each from India, Italy and the Netherlands, and additional countries in South America, Europe and Asia were represented by one or two studies only (the two international studies are included in these numbers). Most studies (n = 32) were from countries classified by the World Bank as high income; five were from countries with upper-middle incomes, two were from lower-middle income countries and one international study included countries of all four income levels (24).

Figure 2

Figure 2. Summarised results of the literature search. *The numbers shown include one study conducted in Belgium, Czech Republic, Greece, Italy, Netherlands, Spain and the UK (52) and one study conducted on a global basis (40); all other studies were conducted in a single country. ^†The three cost categories shown are not mutually exclusive (i.e., numerous studies contributed data to more than one category). AIE, autoimmune encephalitis; CIDP, chronic inflammatory demyelinating polyneuropathy; GBS, Guillain–Barré syndrome; MG, myasthenia gravis; MOGAD, myelin oligodendrocyte glycoprotein antibody-associated disease; NMO, neuromyelitis optica; NMOSD, neuromyelitis optica spectrum disorder.

The conditions represented by the 40 included studies were chronic inflammatory demyelinating polyneuropathy (CIDP; n = 11), myasthenia gravis (MG; n = 11), Guillain–Barré syndrome (n = 7), NMOSD (n = 5) and autoimmune encephalitis (n = 4) (Figures 2, 3). MOGAD was described in only one study (25). Seventeen studies provided an estimate of the total cost of disease, and total direct and indirect costs were reported in 11 and 8 studies, respectively. One study was published in 2023, eight in 2022, three in 2021, six in 2020 and four in 2019. Fifteen studies were from 2010–2018, and the remaining three were published before 2010.

Figure 3

Figure 3. Representation of MOGAD and analogues within the literature (n = 40 studies). MOGAD, myelin oligodendrocyte glycoprotein antibody-associated disease; NMO, neuromyelitis optica; NMOSD, neuromyelitis optica spectrum disorder.

Cost estimates

Tables 1–4 report cost estimates, grouped by disease. Studies reported different measures (i.e., not all studies reported total costs, direct costs or indirect costs) in different ways (most cost estimates were reported as means, but some were reported as medians; medians tended to be lower). The variability observed across the estimates can be attributed to the range of diseases and countries included in this review; there were generally not enough studies focusing on a given disease or from a given country to draw strong conclusions about specific diseases or countries.

Table 1

Table 1. Summary of publications with extractable cost estimates and cost drivers: total costs.

Table 2

Table 2. Summary of publications with extractable cost estimates: direct costs.

Table 3

Table 3. Summary of publications with extractable cost drivers: direct costs.

Table 4

Table 4. Summary of publications with extractable cost estimates and cost drivers: indirect costs.

Reported annual total cost of illness per patient, across all conditions assessed, ranged widely. The lowest annual total cost reported was a median of $3,690, based on conversion of the ~6.5 month reported cost to a full year in a Brazilian Federal District study of patients with Guillain–Barré syndrome (GBS), whereas the highest was a mean of $507,117 in a US study of GBS. Details of estimates of total annual costs are given in Table 1; for total direct costs, the corresponding low was a median of $1,981 to a high of mean $148,388 (Tables 2, 3) and for total indirect costs it was a median of $0 to a mean of $942,707 (Table 4). In most studies, direct costs appeared to comprise most (>70%) of the overall cost, although one study was an exception in this regard: a USA-wide study of GBS reported total direct medical cost as 14% of the total cost; the remaining 86% was attributable to indirect costs (20).

Reported costs for specific types of direct and indirect cost were similarly variable between studies, depending on country and disease. For example, reported costs for hospitalisation ranged from ~$0 to $93,039 (both medians) (26, 27). IVIg was an important contributor to direct medical costs in several diseases, accounting for 90% of the overall pharmacy cost in a study of CIDP and an estimated 85% of the total pharmacy cost in MG (28, 29). Several studies reported plasma exchange as less expensive or more cost-effective than IVIg (29–31), although others associated IVIg with reduced hospital stays and lower hospitalisation costs (32, 33).

In the only study of MOGAD (a cost investigation from Germany), most patients (n = 166) had NMOSD and 46 had MOGAD (25). The mean annualised cost of MOGAD/NMOSD was $94,688 (95% confidence interval, $81,418–$108,546), comprising direct medical costs (43%), direct non-medical costs (34%) and indirect costs (23%). Within direct medical costs, immunotherapy and inpatient hospital care were the significant drivers; those for direct non-medical costs and indirect costs were informal care and loss of salary, respectively. Need for care, number of acute attacks, unemployment, and disability measured by the Expanded Disability Status Scale (EDSS) were identified as independent predictors for cost of illness. The cost data were reported for NMOSD and MOGAD combined, as the authors found no cost differences between the two diseases stratified by disease duration and serostatus apart from in outpatient diagnostic tests (higher for MOGAD, but this was a small contributor to the overall cost of disease [<1%]). Although this study was performed prior to the publication of the 2023 MOGAD diagnostic guidelines, MOGAD was diagnosed according to international recommendations and MOG antibody positivity on a cell-based assay (34). Although studies comparing the 2023 MOGAD diagnostic criteria and the 2018 international recommendations published by Jarius et al. are sparse, one multicentre, retrospective study has found comparable sensitivity and specificity (35). One NMOSD study provided a total cost of illness with a mean annualised cost for the UK (calculated from the reported 3-month value) of $41,180 (36). In an NMOSD study from the USA, the median annual cost for IVIg was $29,247, the mean hospital cost was $34,893 and the mean cost for outpatient healthcare was $29,881 (37). Annual direct costs in NMOSD reported by other studies were $8,705 and $9,083 (mean hospital costs) (38, 39), $18,189 (mean healthcare cost) (39) and $5,248 (mean treatment cost; in this study, the term neuromyelitis optica [NMO] was used in preference to NMOSD) (40).

The authors of the MOGAD/NMOSD study compared the total cost of illness with that of MS and reported that it was higher for MOGAD/NMOSD ($94,688 vs. $65,495) despite disease severity and patient age being higher in the MS cohort (25). Our review did not include any studies focused specifically on MS.

Cost drivers

Cost drivers were explored in numerous studies and found to include treatment (e.g., IVIg), hospitalisation, disease severity, relapses, refractory disease, active disease, disability, loss of productivity and premature death (Tables 1–4).

Discussion

This narrative review confirms that data on cost of illness and health economic data for MOGAD are limited. We identified only one study with relevant cost data for MOGAD (25). This scenario is unsurprising given that MOGAD was only recently (October 2023) recognised as a separate entity within the ICD (8). The scarcity of economic data relating specifically to MOGAD supports our approach of examining data from analogous/proxy conditions; such methodology has been used by other groups to increase the robustness of economic modelling in rare diseases and is recognised by regulatory authorities in Europe and the USA (14–17).

Our results suggest that MOGAD and analogous conditions are associated with a range of direct and indirect costs likely to make them costly for the individual and place considerable burden on healthcare systems. As observed by Hümmert et al., the evaluation of disease-related cost is important for patients, their families and their physicians (25). It should also be a key consideration in decision-making by policy makers in the context of newly emerging treatment options for MOGAD (25). The aim of our study was to determine the overall economic impact of MOGAD. Therefore, we sought estimates of direct costs (those associated with healthcare) and indirect costs (those associated with reduced productivity) as well as the total cost. The long-term overall care requirements and effects of residual disability attributable to MOGAD are challenging to measure accurately (41–44). It is also important to note that the available economic evidence predates the new diagnostic criteria for MOGAD. Reported costs for other conditions may therefore have been affected by the inclusion of patients with MOGAD receiving an incorrect, non-MOGAD diagnosis. Unfortunately, no direct estimates of the economic impact of diagnostic insufficiencies were identified in our review. However, it is likely that an inaccurate or late diagnosis would result in delayed or inadequate treatment, slow/partial recovery between attacks and possible suboptimal management of residual disability – all of which have the potential to increase the economic burden of MOGAD. Previous studies have shown that diagnostic insufficiencies are common. It is estimated that only approximately 60% of patients receive their first consultation in less than 6 months after diagnosis, with 15% waiting over seven years; in addition, historically over half of patients have received an alternative (incorrect) diagnosis before confirmation of MOGAD (9, 45).

The extent to which results from different studies are comparable may be questioned. Costs may differ between studies according to the classification of specific elements as direct or indirect costs. Few studies provided a full and clear breakdown of either the elements included or their relative contributions to direct or indirect costs. Differences between countries in state funding can also have an effect, with costs that are borne by individual patients, their carers or their insurance companies in some countries being borne by the state healthcare system (and therefore wider society) in other countries.

In our review, the best insight into MOGAD came from a patient survey performed by Hümmert et al. in Germany (25). Economic data were reported for the whole population (n = 212: 46 patients with MOGAD and 166 with NMOSD), with the authors reporting little difference between the two diseases. Comparability of treatment costs for the two diseases may have been coincidental, considering the likelihood of differences in prescribing. For example, rituximab and inebilizumab are established options for patients with NMOSD but less likely to be used in MOGAD (rituximab has been shown to have reduced efficacy in MOGAD vs. NMOSD, and there are no ongoing clinical trials of inebilizumab in MOGAD) (46, 47). Additionally, maintenance therapy is usually commenced following a relapse in MOGAD; however, there is evidence that initiating maintenance therapy early, from the first attack, may substantially reduce the risk of relapse in MOGAD (48, 49), which may potentially reduce the economic burden of MOGAD.

Outpatient costs were higher with MOGAD vs. NMOSD, but Hümmert et al. attributed this to a difference in disease duration (25). Severity of disease, use of medications such as IVIg, hospitalisation costs and reduced productivity were all identified as major cost drivers (25). The authors also reported ‘need for care’, number of prior acute attacks, unemployment and disability measured using EDSS as statistically significant predictors of cost (25). An association between EDSS-measured disability and cost has also been observed in MS, consistent with the relationship between these conditions (50). Of note, Hümmert et al. observed that the cost of MOGAD/NMOSD was higher than the cost associated with MS; however, a full comparison of costs between these conditions will only be possible after MOGAD treatment guidelines and evidence are better established.

Studies in the other diseases included in our review reported findings that appeared consistent with the results of Hümmert et al. Although the NMOSD/NMO studies (36–40) reported generally lower costs than the study of MOGAD/NMOSD (25), methodological differences mean that cost variations are to be expected. Studies in CIDP and MG suggested similar drivers to those identified for MOGAD/NMOSD (30, 51–55). Studies in NMOSD, MG, CIDP and MOGAD/NMOSD reported that reduced quality of life and increased disability were associated with greater costs (36, 53, 54).

Our review suggested that a relapse can have a major impact on cost. In particular, the German study of NMOSD reported that the hospitalisation cost was >20-fold higher during the active phase (defined as the 30-day period following a relapse or period of hospitalisation for NMOSD) compared with the inactive phase (all other times) (39). Delays in diagnosis and treatment-refractory presentation may also increase the disease cost by increasing the risk of disability accrual (26, 56).

The principal limitation of this study is the sparsity of MOGAD-specific data. No study examined MOGAD alone, and only one study provided data for a mixed population of patients with NMOSD and patients with MOGAD (the majority of whom had NMOSD). Owing to differences between MOGAD and the analogous diseases included in our review, the applicability of data from other diseases to MOGAD may be questioned (variability in clinical presentation between different patients with MOGAD should also be considered here). Despite the limited quantity of available evidence and the disparate nature of studies in our review, some examples of overlap between studies in the patient populations are possible (e.g., where more than one study of one condition was conducted in a country). We acknowledge small sample sizes within studies as a further limitation. Comparisons between studies may be confounded by differences in a range of aspects such as disease severity, management approaches (for instance, novel treatments may be available for some conditions and not others) and definitions of direct vs. indirect costs. Direct costs do not differentiate between short-term treatment used to aid recovery from an attack and long-term treatments administered to prevent attacks or manage residual disability. In addition, financial structures differ considerably between countries. Most studies in our review were conducted in high-income countries, and the economic impact of MOGAD could differ in countries with lower income levels. Information from the included studies provides minimal insight into the costs of diagnosis, and there were no estimates of the economic impact of diagnostic delay or misdiagnosis. This may be related to the fact that specific testing for MOGAD became available only recently. These limitations are consistent with those typically encountered with rare diseases. In this context, Pearson et al. identified limitations in natural history and epidemiological data as key challenges in estimating the economic impact of treatment (57).

In conclusion, informal care, drugs (mainly immunotherapies) and indirect costs such as loss of income/employment are likely to be key cost contributors in the management of MOGAD. However, insight into the economic burden of MOGAD is limited by the fact that only one study has provided data from patients with this disease, and even in that study most patients had NMOSD. Further research on the economic burden of MOGAD is urgently needed.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

LL: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. JB: Conceptualization, Data curation, Formal analysis, Methodology, Writing – review & editing. CH: Conceptualization, Data curation, Formal analysis, Funding acquisition, Writing – review & editing. HK: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Supervision, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This study was funded by UCB. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.

Acknowledgments

Medical writing support was provided by Niall Harrison of Apollo, OPEN Health Communications, and Ken Sutor, a contract writer working on behalf of OPEN Health Communications, and was funded by UCB. The authors thank Margarita Lens of UCB for publication and editorial support.

Conflict of interest

LL, HK, and JB have received research funding from UCB. CH was an employee of UCB at the time of the study.

Generative AI statement

The authors declare that no Gen AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fneur.2025.1506465/full#supplementary-material

References

1. Al-Ani, A, Chen, JJ, and Costello, F. Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD): current understanding and challenges. J Neurol. (2023) 270:4132–50. doi: 10.1007/s00415-023-11737-8

PubMed Abstract | Crossref Full Text | Google Scholar

2. Ambrosius, W, Michalak, S, Kozubski, W, and Kalinowska, A. Myelin oligodendrocyte glycoprotein antibody-associated disease: current insights into the disease pathophysiology, diagnosis and management. Int J Mol Sci. (2020) 22:100. doi: 10.3390/ijms22010100

PubMed Abstract | Crossref Full Text | Google Scholar

3. Rosenthal, JF, Hoffman, BM, and Tyor, WR. CNS inflammatory demyelinating disorders: MS, NMOSD and MOG antibody associated disease. J Investig Med. (2020) 68:321–30. doi: 10.1136/jim-2019-001126

PubMed Abstract | Crossref Full Text | Google Scholar

4. Shahriari, M, Sotirchos, ES, Newsome, SD, and Yousem, DM. MOGAD: how it differs from and resembles other neuroinflammatory disorders. AJR Am J Roentgenol. (2021) 216:1031–9. doi: 10.2214/ajr.20.24061

PubMed Abstract | Crossref Full Text | Google Scholar

5. Cortese, R, Prados Carrasco, F, Tur, C, Bianchi, A, Brownlee, W, De Angelis, F, et al. Differentiating multiple sclerosis from AQP4-neuromyelitis optica spectrum disorder and MOG-antibody disease with imaging. Neurology. (2023) 100:e308–23. doi: 10.1212/wnl.0000000000201465

PubMed Abstract | Crossref Full Text | Google Scholar

6. Xie, H, Shao, Y, Du, J, Song, Y, Li, Y, Duan, R, et al. Comparative analysis of clinical and imaging data between patients with myelin oligodendrocyte glycoprotein antibody disease and patients with aquaporin 4 antibody-positive neuromyelitis optica spectrum disorder. J Neurol. (2022) 269:1641–50. doi: 10.1007/s00415-021-10749-6

PubMed Abstract | Crossref Full Text | Google Scholar

7. Banwell, B, Bennett, JL, Marignier, R, Kim, HJ, Brilot, F, Flanagan, EP, et al. Diagnosis of myelin oligodendrocyte glycoprotein antibody-associated disease: International MOGAD Panel. Lancet Neurol. (2023) 22:268–82. doi: 10.1016/s1474-4422(22)00431-8

Crossref Full Text | Google Scholar

8. ICD List. (2023). ICD List free resource for ICD-10-CM and ICD-10-PCS coding. Available online at: https://icdlist.com/ (Accessed October 4, 2024)

Google Scholar

9. Santoro, JD, Gould, J, Panahloo, Z, Thompson, E, Lefelar, J, and Palace, J. Patient pathway to diagnosis of myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD): findings from a multinational survey of 204 patients. Neurol Ther. (2023) 12:1081–101. doi: 10.1007/s40120-023-00474-9

PubMed Abstract | Crossref Full Text | Google Scholar

10. Hor, JY, and Fujihara, K. Epidemiology of myelin oligodendrocyte glycoprotein antibody-associated disease: a review of prevalence and incidence worldwide. Front Neurol. (2023) 14:1260358. doi: 10.3389/fneur.2023.1260358

PubMed Abstract | Crossref Full Text | Google Scholar

11. Mayo Clinic. (2024). Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). Available online at: https://www.mayoclinic.org/diseases-conditions/mogad/symptoms-causes/syc-20560476 (Accessed October 4, 2024)

Google Scholar

12. Jurynczyk, M, Messina, S, Woodhall, MR, Raza, N, Everett, R, Roca-Fernandez, A, et al. Clinical presentation and prognosis in MOG-antibody disease: a UK study. Brain. (2017) 140:3128–38. doi: 10.1093/brain/awx276

PubMed Abstract | Crossref Full Text | Google Scholar

13. Sechi, E, Cacciaguerra, L, Chen, JJ, Mariotto, S, Fadda, G, Dinoto, A, et al. Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD): a review of clinical and MRI features, diagnosis, and management. Front Neurol. (2022) 13:885218. doi: 10.3389/fneur.2022.885218

PubMed Abstract | Crossref Full Text | Google Scholar

14. European Commission. (2024). Innovative designs, extrapolation, simulation methods and evidence – tools for rare diseases addressing regulatory needs. Available online at: https://cordis.europa.eu/project/id/101136365 (Accessed October 4, 2024)

Google Scholar

15. Garczarek, U, Muehlemann, N, Richard, F, Yajnik, P, and Russek-Cohen, E. Bayesian strategies in rare diseases. Ther Innov Regul Sci. (2023) 57:445–52. doi: 10.1007/s43441-022-00485-y

PubMed Abstract | Crossref Full Text | Google Scholar

16. Mishra, S, and Venkatesh, MP. Rare disease clinical trials in the European Union: navigating regulatory and clinical challenges. Orphanet J Rare Dis. (2024) 19:285. doi: 10.1186/s13023-024-03146-5

PubMed Abstract | Crossref Full Text | Google Scholar

17. National Library of Medicine. (2024). Regulatory processes for rare disease drugs in the United States and European Union: flexibilities and collaborative opportunities. Available online at: https://www.ncbi.nlm.nih.gov/books/NBK609394/. (Accessed January, 2025)

Google Scholar

18. Page, MJ, McKenzie, JE, Bossuyt, PM, Boutron, I, Hoffmann, TC, Mulrow, CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. (2021) 372:n71. doi: 10.1136/bmj.n71

PubMed Abstract | Crossref Full Text | Google Scholar

19. EPPI. (2024). CCEMG–EPPI Centre cost converter (v.1.7 last update: January 2024). Available online at: https://eppi.ioe.ac.uk/costconversion/ (Accessed March, 2025)

Google Scholar

20. Frenzen, PD. Economic cost of Guillain–Barré syndrome in the United States. Neurology. (2008) 71:21–7. doi: 10.1212/01.wnl.0000316393.54258.d1

Crossref Full Text | Google Scholar

21. Lin, T-Y, Zhang, X-Y, Fang, P-Q, and Min, R. Out-of-pocket expenses for myasthenia gravis patients in China: a study on patients insured by basic medical insurance in China, 2013–2015. Orphanet J Rare Dis. (2020) 15:13. doi: 10.1186/s13023-019-1289-9

PubMed Abstract | Crossref Full Text | Google Scholar

22. Rumalla, K, Reddy, AY, Letchuman, V, and Mittal, MK. Hyponatremia in Guillain–Barré syndrome. J Clin Neuromuscul Dis. (2017) 18:207–17. doi: 10.1097/cnd.0000000000000157

Crossref Full Text | Google Scholar

23. Darbà, J, and Marsà, A. Chronic inflammatory demyelinating polyneuropathy in Spain: a retrospective analysis of hospital incidence and medical costs. Expert Rev Pharmacoecon Outcomes Res. (2022) 22:665–70. doi: 10.1080/14737167.2022.2000862

PubMed Abstract | Crossref Full Text | Google Scholar

24. World Bank. (2024). World Bank country classifications by income level for 2024–2025. Available online at: https://blogs.worldbank.org/en/opendata/world-bank-country-classifications-by-income-level-for-2024-2025 (Accessed October 4, 2024)

Google Scholar

25. Hümmert, MW, Schöppe, LM, Bellmann-Strobl, J, Siebert, N, Paul, F, Duchow, A, et al. Costs and health-related quality of life in patients with NMO spectrum disorders and MOG-antibody-associated disease: CHANCE (NMO) study. Neurology. (2022) 98:e1184–96. doi: 10.1212/wnl.0000000000200052

PubMed Abstract | Crossref Full Text | Google Scholar

26. Cohen, J, Sotoca, J, Gandhi, S, Yeshokumar, AK, Gordon-Lipkin, E, Geocadin, RG, et al. Autoimmune encephalitis: a costly condition. Neurology. (2019) 92:e964–72. doi: 10.1212/WNL.0000000000006990

Crossref Full Text | Google Scholar

27. Ignatova, V, Kostadinov, K, Vassileva, E, Muradyan, N, Stefanov, G, Iskrov, G, et al. Socio-economic burden of myasthenia gravis: a cost-of-illness study in Bulgaria. Front Public Health. (2022) 10:822909. doi: 10.3389/fpubh.2022.822909

PubMed Abstract | Crossref Full Text | Google Scholar

28. Guptill, JT, Bromberg, MB, Zhu, L, Sharma, BK, Thompson, AR, Krueger, A, et al. Patient demographics and health plan paid costs in chronic inflammatory demyelinating polyneuropathy. Muscle Nerve. (2014) 50:47–51. doi: 10.1002/mus.24109

PubMed Abstract | Crossref Full Text | Google Scholar

29. Guptill, JT, Marano, A, Krueger, A, and Sanders, DB. Cost analysis of myasthenia gravis from a large U.S. insurance database. Muscle Nerve. (2011) 44:907–11. doi: 10.1002/mus.22212

PubMed Abstract | Crossref Full Text | Google Scholar

30. Sonkar, KK, Bhoi, SK, Dubey, D, Kalita, J, and Misra, UK. Direct and indirect cost of myasthenia gravis: a prospective study from a tertiary care teaching hospital in India. J Clin Neurosci. (2017) 38:114–7. doi: 10.1016/j.jocn.2016.11.003

PubMed Abstract | Crossref Full Text | Google Scholar

31. Winters, JL, Brown, D, Hazard, E, Chainani, A, and Andrzejewski, C Jr. Cost-minimization analysis of the direct costs of TPE and IVIg in the treatment of Guillain-Barré syndrome. BMC Health Serv Res. (2011) 11:101. doi: 10.1186/1472-6963-11-101

PubMed Abstract | Crossref Full Text | Google Scholar

32. Mandawat, A, Kaminski, HJ, Cutter, G, Katirji, B, and Alshekhlee, A. Comparative analysis of therapeutic options used for myasthenia gravis. Ann Neurol. (2010) 68:797–805. doi: 10.1002/ana.22139

Crossref Full Text | Google Scholar

33. Tsai, CP, Wang, KC, Liu, CY, Sheng, WY, and Lee, TC. Pharmacoeconomics of therapy for Guillain–Barré syndrome: plasma exchange and intravenous immunoglobulin. J Clin Neurosci. (2007) 14:625–9. doi: 10.1016/j.jocn.2006.03.020

Crossref Full Text | Google Scholar

34. Jarius, S, Paul, F, Aktas, O, Asgari, N, Dale, RC, de Seze, J, et al. MOG encephalomyelitis: international recommendations on diagnosis and antibody testing. J Neuroinflammation. (2018) 15:134. doi: 10.1186/s12974-018-1144-2

PubMed Abstract | Crossref Full Text | Google Scholar

35. Cai, MT, Hua, Y, Lai, QL, Su, SY, Shen, CH, Qiao, S, et al. Performance of the 2023 diagnostic criteria for MOGAD: real-world application in a Chinese multicenter cohort of pediatric and adult patients. BMC Med. (2025) 23:40. doi: 10.1186/s12916-025-03875-9

PubMed Abstract | Crossref Full Text | Google Scholar

36. Hughes, DA, Bourke, S, Jones, A, Bhatt, R, Huda, S, Mutch, K, et al. Health utilities and costs for neuromyelitis optica spectrum disorder. Orphanet J Rare Dis. (2022) 17:159. doi: 10.1186/s13023-022-02310-z

PubMed Abstract | Crossref Full Text | Google Scholar

37. Exuzides, A, Sheinson, D, Sidiropoulos, P, Gholizadeh, S, Magrini, F, Surinach, A, et al. The costs of care from a US claims database in patients with neuromyelitis optica spectrum disorder. J Neurol Sci. (2021) 427:117553. doi: 10.1016/j.jns.2021.117553

PubMed Abstract | Crossref Full Text | Google Scholar

38. Beekman, J, Keisler, A, Pedraza, O, Haramura, M, Gianella-Borradori, A, Katz, E, et al. Neuromyelitis optica spectrum disorder: patient experience and quality of life. Neurol Neuroimmunol Neuroinflamm. (2019) 6:e580. doi: 10.1212/nxi.0000000000000580

PubMed Abstract | Crossref Full Text | Google Scholar

39. Knapp, RK, Hardtstock, F, Wilke, T, Maywald, U, Deiters, B, Schneider, S, et al. Evaluating the economic burden of relapses in neuromyelitis optica spectrum disorder: a real-world analysis using German claims data. Neurol Ther. (2022) 11:247–63. doi: 10.1007/s40120-021-00311-x

PubMed Abstract | Crossref Full Text | Google Scholar

40. Holroyd, K, Vogel, A, Lynch, K, Gazdag, B, Voghel, M, Alakel, N, et al. Neuromyelitis optica testing and treatment: availability and affordability in 60 countries. Mult Scler Relat Disord. (2019) 33:44–50. doi: 10.1016/j.msard.2019.05.013

PubMed Abstract | Crossref Full Text | Google Scholar

41. Deschamps, R, Pique, J, Ayrignac, X, Collongues, N, Audoin, B, Zéphir, H, et al. The long-term outcome of MOGAD: an observational national cohort study of 61 patients. Eur J Neurol. (2021) 28:1659–64. doi: 10.1111/ene.14746

PubMed Abstract | Crossref Full Text | Google Scholar

42. Dyer, SM, Crotty, M, Fairhall, N, Magaziner, J, Beaupre, LA, Cameron, ID, et al. A critical review of the long-term disability outcomes following hip fracture. BMC Geriatr. (2016) 16:158. doi: 10.1186/s12877-016-0332-0

PubMed Abstract | Crossref Full Text | Google Scholar

43. Grand, TS, Ren, S, Hall, J, Åström, DO, Regnier, S, and Thokala, P. Issues, challenges and opportunities for economic evaluations of orphan drugs in rare diseases: an umbrella review. PharmacoEconomics. (2024) 42:619–31. doi: 10.1007/s40273-024-01370-2

PubMed Abstract | Crossref Full Text | Google Scholar

44. Lourida, I, Bennett, HQ, Beyer, F, Kingston, A, and Jagger, C. The impact of long-term conditions on disability-free life expectancy: a systematic review. PLOS Glob Public Health. (2022) 2:e0000745. doi: 10.1371/journal.pgph.0000745

PubMed Abstract | Crossref Full Text | Google Scholar

45. Levy, M, Zappacosta, S, Narduzzi, S, Khellaf, M, Unsworth, M, Trenholm, E, et al. The patient journey in myelin oligodendrocyte glycoprotein antibody disease (MOGAD): results from cross-sectional patient and physician surveys. Neurology. (2024) 103:S64–5. doi: 10.1212/01.wnl.0001051416.97564.fa

Crossref Full Text | Google Scholar

46. Bai, P, Zhang, M, Yuan, J, Zhu, R, and Li, N. A comparison of the effects of rituximab versus other immunotherapies for MOG-IgG-associated central nervous system demyelination: a meta-analysis. Mult Scler Relat Disord. (2021) 53:103044. doi: 10.1016/j.msard.2021.103044

PubMed Abstract | Crossref Full Text | Google Scholar

47. Barreras, P, Vasileiou, ES, Filippatou, AG, Fitzgerald, KC, Levy, M, Pardo, CA, et al. Long-term effectiveness and safety of rituximab in neuromyelitis optica spectrum disorder and MOG antibody disease. Neurology. (2022) 99:e2504–16. doi: 10.1212/wnl.0000000000201260

PubMed Abstract | Crossref Full Text | Google Scholar

48. Deschamps, R, Guillaume, J, Ciron, J, Audoin, B, Ruet, A, Maillart, E, et al. Early maintenance treatment initiation and relapse risk mitigation after a first event of MOGAD in adults: the MOGADOR2 study. Neurology. (2024) 103:e209624. doi: 10.1212/wnl.0000000000209624

PubMed Abstract | Crossref Full Text | Google Scholar

49. Whittam, DH, Karthikeayan, V, Gibbons, E, Kneen, R, Chandratre, S, Ciccarelli, O, et al. Treatment of MOG antibody associated disorders: results of an international survey. J Neurol. (2020) 267:3565–77. doi: 10.1007/s00415-020-10026-y

PubMed Abstract | Crossref Full Text | Google Scholar

50. Dillon, P, Heer, Y, Karamasioti, E, Muros-Le Rouzic, E, Marcelli, G, Di Maio, D, et al. The socioeconomic impact of disability progression in multiple sclerosis: a retrospective cohort study of the German NeuroTransData (NTD) registry. Mult Scler J Exp Transl Clin. (2023) 9:20552173231187810. doi: 10.1177/20552173231187810

PubMed Abstract | Crossref Full Text | Google Scholar

51. Divino, V, Mallick, R, DeKoven, M, and Krishnarajah, G. The economic burden of CIDP in the United States: a case-control study. PLoS One. (2018) 13:e0206205. doi: 10.1371/journal.pone.0206205

PubMed Abstract | Crossref Full Text | Google Scholar

52. McCrone, P, Chisholm, D, Knapp, M, Hughes, R, Comi, G, Dalakas, MC, et al. Cost-utility analysis of intravenous immunoglobulin and prednisolone for chronic inflammatory demyelinating polyradiculoneuropathy. Eur J Neurol. (2003) 10:687–94. doi: 10.1046/j.1351-5101.2003.00701.x

PubMed Abstract | Crossref Full Text | Google Scholar

53. Mengel, D, Fraune, L, Sommer, N, Stettner, M, Reese, JP, Dams, J, et al. Costs of illness in chronic inflammatory demyelinating polyneuropathy in Germany. Muscle Nerve. (2018) 58:681–7. doi: 10.1002/mus.26315

PubMed Abstract | Crossref Full Text | Google Scholar

54. Schepelmann, K, Winter, Y, Spottke, AE, Claus, D, Grothe, C, Schröder, R, et al. Socioeconomic burden of amyotrophic lateral sclerosis, myasthenia gravis and facioscapulohumeral muscular dystrophy. J Neurol. (2010) 257:15–23. doi: 10.1007/s00415-009-5256-6

PubMed Abstract | Crossref Full Text | Google Scholar

55. Mahdi-Rogers, M, McCrone, P, and Hughes, RA. Economic costs and quality of life in chronic inflammatory neuropathies in southeast England. Eur J Neurol. (2014) 21:34–9. doi: 10.1111/ene.12245

PubMed Abstract | Crossref Full Text | Google Scholar

56. Harris, L, Allman, PH, Sheffield, R, and Cutter, G. Longitudinal analysis of disease burden in refractory and nonrefractory generalized myasthenia gravis in the United States. J Clin Neuromuscul Dis. (2020) 22:11–21. doi: 10.1097/cnd.0000000000000301

PubMed Abstract | Crossref Full Text | Google Scholar

57. Pearson, I, Rothwell, B, Olaye, A, and Knight, C. Economic modeling considerations for rare diseases. Value Health. (2018) 21:515–24. doi: 10.1016/j.jval.2018.02.008

Crossref Full Text | Google Scholar

58. Oliveira, AFM, Gallo, LG, Bastos, MM, Abrahão, AA, Garcia, KKS, de Carvalho, JKS, et al. Costs of Guillain–Barré syndrome in the Brazilian Federal District: the patients’ perspective. Trans R Soc Trop Med Hyg. (2022) 116:310–21. doi: 10.1093/trstmh/trab118

Crossref Full Text | Google Scholar

59. Fan, X, Xing, C, Yang, L, Wang, J, and Feng, L. Fatigue, self-efficacy and psychiatric symptoms influence the quality of life in patients with myasthenia gravis in Tianjin, China. J Clin Neurosci. (2020) 79:84–9. doi: 10.1016/j.jocn.2020.06.023

PubMed Abstract | Crossref Full Text | Google Scholar

60. Guptill, JT, Sharma, BK, Marano, A, Soucy, A, Krueger, A, and Sanders, DB. Estimated cost of treating myasthenia gravis in an insured U.S. population. Muscle Nerve. (2012) 45:363–6. doi: 10.1002/mus.22327

PubMed Abstract | Crossref Full Text | Google Scholar

61. Ting, A, Story, T, Lecomte, C, Estrin, A, Syed, S, and Lee, E. A real-world analysis of factors associated with high healthcare resource utilization and costs in patients with myasthenia gravis receiving second-line treatment. J Neurol Sci. (2023) 445:120531. doi: 10.1016/j.jns.2022.120531

PubMed Abstract | Crossref Full Text | Google Scholar

62. Cai, MT, Lai, QL, Zheng, Y, Fang, GL, Shen, CH, Xu, YF, et al. First-line immunotherapy of neuronal surface antibody-mediated autoimmune encephalitis: assessment of therapeutic effectiveness and cost-efficiency. Mult Scler Relat Disord. (2022) 66:104071. doi: 10.1016/j.msard.2022.104071

PubMed Abstract | Crossref Full Text | Google Scholar

63. Li, A, Gong, X, Guo, K, Lin, J, Zhou, D, and Hong, Z. Direct economic burden of patients with autoimmune encephalitis in western China. Neurol Neuroimmunol Neuroinflamm. (2020) 7:e891. doi: 10.1212/NXI.0000000000000891

PubMed Abstract | Crossref Full Text | Google Scholar

64. Sharp, CN, Fletcher, A, Muluhngwi, P, Snyder, J, Linder, MW, and Jortani, SA. A shared diagnostic stewardship approach toward improving autoimmune encephalopathy send-out testing utilization. J Appl Lab Med. (2021) 6:387–96. doi: 10.1093/jalm/jfaa123

PubMed Abstract | Crossref Full Text | Google Scholar

65. Allen, JA, Gelinas, DF, Freimer, M, Runken, MC, and Wolfe, GI. Immunoglobulin administration for the treatment of CIDP: IVIG or SCIG? J Neurol Sci. (2020) 408:116497. doi: 10.1016/j.jns.2019.116497

PubMed Abstract | Crossref Full Text | Google Scholar

66. Le Masson, G, Sole, G, Desnuelle, C, Delmont, E, Gauthier-Darnis, M, Puget, S, et al. Home versus hospital immunoglobulin treatment for autoimmune neuropathies: a cost minimization analysis. Brain Behav. (2018) 8:e00923. doi: 10.1002/brb3.923

PubMed Abstract | Crossref Full Text | Google Scholar

67. Perraudin, C, Bourdin, A, Vicino, A, Kuntzer, T, Bugnon, O, and Berger, J. Home-based subcutaneous immunoglobulin for chronic inflammatory demyelinating polyneuropathy patients: a Swiss cost-minimization analysis. PLoS One. (2020) 15:e0242630. doi: 10.1371/journal.pone.0242630

PubMed Abstract | Crossref Full Text | Google Scholar

68. Piscitelli, E, Massa, M, De Martino, BM, Serio, CS, Guglielmi, G, Colacicco, G, et al. Economic evaluation of subcutaneous versus intravenous immunoglobulin therapy in chronic inflammatory demyelinating polyneuropathy: a real-life study. Eur J Hosp Pharm. (2021) 28:e115–9. doi: 10.1136/ejhpharm-2020-002430

PubMed Abstract | Crossref Full Text | Google Scholar

69. Rajabally, YA, and Afzal, S. Clinical and economic comparison of an individualised immunoglobulin protocol vs. standard dosing for chronic inflammatory demyelinating polyneuropathy. J Neurol. (2019) 266:461–7. doi: 10.1007/s00415-018-9157-4

PubMed Abstract | Crossref Full Text | Google Scholar

70. Maheshwari, A, Sharma, RR, Prinja, S, Hans, R, Modi, M, Sharma, N, et al. Cost-minimization analysis in the Indian subcontinent for treating Guillain Barre syndrome patients with therapeutic plasma exchange as compared to intravenous immunoglobulin. J Clin Apher. (2018) 33:631–7. doi: 10.1002/jca.21646

PubMed Abstract | Crossref Full Text | Google Scholar

71. van Leeuwen, N, Lingsma, HF, Vanrolleghem, AM, Sturkenboom, MC, van Doorn, PA, Steyerberg, EW, et al. Hospital admissions, transfers and costs of Guillain–Barré syndrome. PLoS One. (2016) 11:e0143837. doi: 10.1371/journal.pone.0143837

Crossref Full Text | Google Scholar

72. Qi, CZ, Hughes, T, Gelinas, D, Li, Y, Goyal, A, Brauer, E, et al. Real-world utilization patterns of intravenous immunoglobulin in adults with generalized myasthenia gravis in the United States. J Neurol Sci. (2022) 443:120480. doi: 10.1016/j.jns.2022.120480

PubMed Abstract | Crossref Full Text | Google Scholar

73. Nosadini, M, Mohammad, SS, Suppiej, A, Sartori, S, and Dale, RC. Intravenous immunoglobulin in paediatric neurology: safety, adherence to guidelines, and long-term outcome. Dev Med Child Neurol. (2016) 58:1180–92. doi: 10.1111/dmcn.13159

PubMed Abstract | Crossref Full Text | Google Scholar