Approximately 2.8 million people worldwide are affected with multiple sclerosis (MS)—a chronic demyelinating and neurodegenerative disease of the central nervous system with increasing prevalence (1, 2). MS poses a major personal and socioeconomic burden: the average age of disease onset is 30 years—a time that is decisive for work and family planning; persons with MS die 7–10 years earlier and live on average almost 20 years with moderate and 30 years with severe disability (1, 3, 4). The condition has a heterogeneous presentation that can include sensory and visual disturbances, motor function impairments, fatigue, pain, and cognitive deficits (1, 5).

Within the MS population, the spectrum of disability ranges from essentially unaffected to highly disabled. The most common measure utilized to assess physical disability is the Expanded Disability Status Scale (EDSS) (6), which is based on a standardized neurological examination in combination with assessment of walking distance, arm function, speech, and utilization of walking aid and wheelchair; however, it is non-linear and some functional score items are evaluated subjectively. As the disability of patients increases, they become dependent on their family for carrying out their daily routines and activities, which leads to a reduction of their quality of life (7). Even in a population with low physical disability, MS is responsible for a substantial economic burden due to indirect and informal care costs (8). In addition, we previously reported that the average level of earnings was ten times lower and the average level of health-related benefits was four times higher when comparing patients with MS with severe and mild disability (9).

Cognitive decline is recognized as a prevalent and debilitating symptom of multiple sclerosis (10). Measurable cognitive dysfunction has been reported in up to 70% of patients (11). Various aspects of cognitive function can be detrimentally affected: difficulties with long-term and verbal memory, abstract and conceptual reasoning, fluency, planning, visuospatial perception, and reduced speed of information processing (11). In addition, the cognitive function affects the financial situation of persons with MS negatively, independently of physical disability, e.g., persons with MS in the highest Symbol Digit Modalities Test (SDMT) quartile earned more than two times annually compared with those in the lowest SDMT quartile (12).

Previously, we summarized the pronounced differences between patients with MS and the general population, e.g., 15–30% lower employment, lower earnings and higher social benefits, higher absenteeism and presenteeism proportions, and higher work disability (e.g., sick leave days) among persons with MS (13). However, besides underlying differences between MS and general population, persons with MS are quite different in terms of progression of physical disability, reduction of cognitive function, etc. As socioeconomic outcomes can be investigated in many ways (e.g., income, employment, marital status, sick leave days, etc.) a comprehensive overview is warranted. Thus, our aim was to systematically review the studies investigating the socioeconomic consequences in persons with MS in regard to their physical disability and cognition.

We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (14). The study protocol was registered in PROSPERO: International prospective register of systematic reviews (https://www.crd.york.ac.uk/prospero/), ID: CRD42020182085. Published studies on socioeconomic consequences of MS were systematically searched in Medline (Ovid), Embase, and Web of Science (Clarivate). A combination of relevant keywords to construct the search strategy, such as MS, socioeconomic outcomes, employment, income, earnings, benefits, disability pension, sickness absence, sick leave, and marital status, was used (full search strategy is available in Supplementary Material 1). The search was limited to English language and publications prior to July 2019.

One author (AK) conducted the first screening of potentially relevant records based on titles and abstracts, and two authors (AK and VDK) independently performed the final selection of included studies based on full text evaluation against the eligibility criteria. Rayyan, a web and mobile app for systematic reviews (https://rayyan.qcri.org/welcome) was used to facilitate the review process. Consensus between the two reviewers was used to resolve any disagreement.

The main eligibility criteria were:

Initially 4,957 studies were identified (Figure 1) and 4,783 records were screened after duplicates were removed. In total, 214 full-text articles (or abstracts) were assessed for eligibility and finally 19 studies were included in the qualitative synthesis. Only full articles were considered for the qualitative analysis. Studies that did not report any estimates (e.g., proportions and ratios), only pointing out to the direction (higher, lower) or association (e.g., significant and not significant) were not considered for the evaluation. Using a standardized data extraction form in Excel, study characteristics (as presented in the tables and Supplementary Material 2) were extracted from the included studies. In case of possible overlap in a study population, the most recent study was selected.

Three reviewers (AK, VDK, and SB; two per each study) independently assessed the quality of the included studies focusing on such study characteristics as study design (cohort studies prioritized over cross-sectional), data sources (registries and databases prioritized over surveys and interviews), timeline, and the size of the study population. Accordingly, on the basis of the aforementioned study characteristics, each study was evaluated by points, whereby this is reflected in the final grading (A—high quality; B—moderate quality; C—low quality). Consensus was used to resolve any disagreement. The quality assessment was performed ensuring that at least one of the reviewers is not among the co-authors (Supplementary Material 2).

Our aim was to broadly review the studies with regards to the socioeconomic outcomes analyzed, thus we applied a comprehensive search strategy to include all possible operationalized definitions. In total, 21 different outcomes were identified. We grouped the outcomes into the categories of employment-, income-, work ability-, and relationship-related outcomes, also mapping the reported indicators for each of the included study (Table 1). Additionally, we extracted the information about the educational level from the selected studies. The employment domain consisted of such keywords as employment, unemployment, work, labor, occupational status; income domain—income, salary, earnings, benefits, compensation, remuneration; the work ability domain—work ability, work disability, sick leave, sickness absence, disability pension, absenteeism, presenteeism; relationship domain: marital status, relationship status, divorce, etc. (Supplementary Material 1). Results from the studies were qualitatively compared and summarized. We used the original categorization of EDSS and SDMT values, as it was reported in the studies. As many authors use different categories, we classified the lowest reported EDSS category into “lower EDSS” and the remaining into “higher EDSS,” similarly for SDMT.

A total of 19 studies were selected for inclusion into this systematic review of socioeconomic consequences of MS in relation to physical disability and cognitive function. Of them, 18 studies provided data from their respective countries, and one study was multi-center with the results from 16 European countries. The majority of studies (12 of 17) were conducted in Europe (with half of them in the Scandinavian countries, i.e., five in Sweden and one in Norway); four studies—in North America (three in USA and one in Canada); one-study in Hong Kong and one in New Zealand.

With respect to study design, three were cohort studies and 16—cross-sectional studies; four studies analyzed data from the registries, whereas 14 studies included data from surveys, questionnaires, or interviews (note, one study did not provided information about the data source used). The selected studies are summarized in the Tables 2–9 and categorized according to the functional domain (physical disability or cognitive function), and type of outcome.

In this systematic review of the socioeconomic consequences of MS, we summarized findings of differences between persons with MS with regards to the physical disability and cognitive function in terms of employment, income, work ability, family status, and education. All identified studies reported higher unemployment, higher early retirement, and higher odds for unemployment in relation to higher physical disability. In addition, cognitive function was found to be a predictor of employment (unemployment). The studies pointed out significant correlations between greater disability and lower earnings and higher income from benefits. Besides, one identified study showed the similar results with regard to the cognitive functions. The studies reported higher work disability in relation to higher physical disability and lower cognitive function.

Our results are in line with other studies that reported similar findings with regards to physical disability. For example, Findling et al. (22) reported median EDSS among fulltime working, part-time working, and among fulltime retired, which were 2.0, 3.0, and 5.0, respectively. Similarly, Koziarska et al. (26) reported a higher mean EDSS score among unemployed when compared with employed, 3.18 and 1.57, respectively. Corresponding results were also presented by Cadden et al. (34) (mean EDSS score among unemployed was 5.0 vs. 3.8 among employed), and Strober et al. (35) (4.62 and 3.13, respectively), as well as by Strober et al. (36) among women with MS (5.52 and 4.05, respectively). Lode et al. (37) reported mean EDSS score among those on disability pension—4.0 and among those not on disability pension—2.4.

In addition, Campbell et al. (25) and Lau et al. (23) reported mean SDMT among employed and unemployed: 53.3 vs. 39.5 and 50.73 vs. 33.35, respectively. Similar results, showing higher SDMT means among employed when compared with unemployed, were reported in several studies by Strober et al.: 43.69 vs. 34.28, respectively (35), and 57.03 vs. 48.03, respectively (38), or 53.59 vs. 45.52, respectively (36). Additionally, Goverover et al. (39) found that the SDMT score among those employed and able to cook was 57.4, among those unemployed but able to cook-−48.2, among employed who did not cook—42.6, and among unemployed who did not cook—44.5. Lode et al. (37) reported mean SDMT score among those on disability pension—40.0 and among those not on disability pension—49.7. Furthermore, Moore et al. (40) using a multinomial logistic regression revealed the factors most strongly predictive of employment status were disability level, years of education, disease duration, and fatigue.

This is in line with a study that showed employment status to be associated with the Processing Speed Test (adaptation of SDMT) (41), whereas EDSS and SDMT were among the strongest predictors of employment status (35). In another study, higher level of disability and lower level of education at baseline predicted loss of employment at follow-up, however, not cognitive function (self-reported) (42).

Among the strengths of our study is that we assessed a wide spectrum of socioeconomic outcomes. As listed in Table 1, it is a heterogeneous field of investigation. Our mapping of these outcomes could help to define study outcomes when designing a study aiming at more comparable results and outcomes. Thus, we suggest reporting ratios (e.g., prevalence ratio for having income, OR for sick leave, IRR for work disability) instead of proportions.

Due to this wide spectrum, it was not possible to assess the extracted information from the studies in a quantitative manner. In addition, the majority of the studies were cross-sectional in design. It is important to underscore that due to well-known limitations of cross-sectional designs, the correlations revealed in the studies may not necessarily be causative (e.g., high cognitive reserve (i.e., high employment status), may protect against cognitive decline, just as cognitive decline may contribute to unemployment in persons with MS). Given the chronic and progressive nature of MS, more studies with longitudinal approach are needed for more robust measures. Additionally, we were specifically looking into physical disability as assessed by EDSS and cognition as assessed by SDMT, however, there are many more various assessments and evaluations used in the clinical practice. Furthermore, the generalizability of the studies may be limited to countries with a similarly functioning labor market and welfare system. One additional aspect that can be explored further is comorbidity, as a study in Denmark found that both psychiatric and somatic comorbidity implied an increased risk of a low income 10 years after MS onset (43). A study in Sweden also concluded that psychiatric diagnoses and medications are common among patients with MS and adversely affect risk for disability pension (44).

By reviewing and summarizing the studies investigating the socioeconomic consequences, we illustrate how such outcomes can be used to study MS. The high correlation between EDSS and both earnings and benefits indicate that these could be used as proxies for disability in registry studies investigating factors of importance for MS progression (9). Cognitive function affects the financial situation of persons with MS negatively and independently of physical disability. This warrants cognitive testing as a routine measure at follow-ups for persons with MS. This is remarkable since SDMT by no means covers more than some aspects of cognitive impairment in MS. In addition, SDMT has outstanding qualities (superior reliability, sensitivity, greater patient acceptance, better psychometric validity and ease of administration compared with other processing speed tests, good correlation with MRI data, and with activities of daily living and employment) (45, 46). Within a brief battery of cognitive tests, the SDMT was found to be the test that best predicted future cognitive decline (47). SDMT was the only neuropsychological test which predicted impaired money management in patients with MS (48). Thus, a full cognitive assessment is likely to be more predictive of reduction of earnings. To allow persons with MS to adapt optimally to their situation, mapping of cognitive function should be considered mandatory in healthcare services (12). Cognitive function is, to a high extent, associated with future work disability in persons with MS, after adjusting for other factors. An interesting aspect that has also arisen from the results is the possible association of EDSS and SDMT—as patients with MS in the highest SDMT quartile had lower EDSS scores, i.e., a median of 2.0, whereas the patients in the lowest SDMT quartile had the median EDSS score of 4.0. Whether these measures are of different construct or reflect disease progression in a similar way, as well as how they change through the clinical course in relation to each other, might be well explored in future studies (33).

In conclusion, this systematic review summarizes the pronounced differences in various socioeconomic outcomes between persons with MS in regard to their physical disability and cognitive function. In addition, we identified lack of studies with longitudinal design in this field that can provide more robust estimates with covariate adjustments, such as the disease modifying treatments.

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

AK and JH conceived and planned the study. AK, VD, and JH defined the search strategy. AK conducted the first screening of potentially relevant records based on titles and abstracts. AK and VD independently performed the final selection of included studies based on full text evaluation. AK, VD, and SB independently assessed the quality of the included studies. AK drafted the manuscript. VD, SB, and JH reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.

The study was financially supported by Biogen. Biogen courtesy reviewed the manuscript and provided feedback to the authors. The authors had full editorial control and provided approval to final content.

AK is also employed by Takeda Pharma AB. VD has received financial support from Stockholm County Council; Biogen (recipient of grant and scholarship, PI for project sponsored by); Novartis (Scientific Advisory board member, recipient of scholarship and lecture honoraria); Merc (Scientific Advisory Board member, recipient of lecture honoraria). SB has received speaker's fee and had travel and conference expenses paid by Biogen. JH received honoraria for serving on advisory boards for Biogen and Novartis and speakers fees from Biogen, MerckSerono, BayerSchering, Teva, and SanofiGenzyme. He has served as P.I. for projects sponsored by, or received unrestricted research support from Biogen, SanofiGenzyme, MerckSerono, TEVA, Novartis, and BayerSchering. His MS research is funded by the Swedish Research Council and the Swedish Brain Foundation. The authors declare that none of the funders were involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.

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