http://orcid.org/0000-0001-7794-7495
Abstract
Aims: The aim of this study was to quantify how multiple sclerosis (MS) phenotypes differ from each other in respect of costs and quality-of-life.
Materials and methods: The study is based on survey data from Finnish patients with MS (n = 553). The information contained disease type, disease severity according to self-reported Expanded Disease Severity Scale (EDSS), healthcare resource use, and medication use. In addition, information related to employment and early retirement was collected. EQ-5D-VAS and Multiple Sclerosis Impact Scale-29 (MSIS-29) instruments were used to collect quality-of-life data, and Fatigue Severity Scale (FSS) instrument for evaluating fatigue. Patients were stratified based on their disease type (relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), primary progressive MS (PPMS)) and disease severity. The data were primarily analyzed using summary statistics.
Results: SPMS had the highest annual total cost (71,177€) followed by PPMS (51,082€) and RRMS (36,492€). Early retirement covered the greatest share of costs in RRMS (39%) and SPMS (43%). In PPMS, early retirement and professional care were the two most equally important cost drivers, contributing together 56% of the total costs. Direct healthcare costs were responsible for 33%, 19%, and 18% of total costs in RRMS, SPMS, and PPMS. The mean EDSS in RRMS, SPMS, and PPMS were 2.5, 5.5, and 5.9, respectively. Differences in the quality-of-life were observed with both disease specific (MSIS-29) and generic (EQ-5D-VAS) instruments. The mean utility value from EQ-5D among patients with RRMS, SPMS, and PPMS was 0.76, 0.52, and 0.49, respectively. In addition, patients with SPMS and PPMS were more likely to report fatigue than patients with RRMS.
Conclusions: MS phenotype has an impact on costs and quality-of-life of the patients. Early retirement seems to be one of the most important contributors to MS-related costs.
Introduction
Multiple Sclerosis (MS) is a neurodegenerative disease of the central nervous system. In MS the immune system attacks against the protective myelin surrounding nerve cells in the brain and spinal cord leading to a wide spectrum of symptoms. The disease nearly always affects physical functioning, and often impairs cognitive performance. Commonly observed clinical symptoms include fatigue, visual disturbances, limb weakness, gait problems, and bladder and bowel disturbances. Symptoms vary between patients, and type and severity of symptoms depend on location of the nerve damage and level of disease progression.
The global prevalence of MS has been reported to be 30/100,000Citation1. It is well known that Nordic countries have high prevalence of MS. In a recent epidemiological study a crude prevalence varied between 168 and 280/100,000 in a Finnish patient populationCitation2. It is estimated that in 2016 there were 2.2 million prevalent cases affected with MS globallyCitation1, and over 11,000 in Finland based on local MS registry. MS is most often diagnosed in relatively young patients aged between 20–50 years, and it is the major cause of non-traumatic disability among young adults.
MS consists of different disease phenotypes. The most common one is relapsing-remitting multiple sclerosis (RRMS), which is characterized by alternation of relapses and remission phases. Approximately 85% of patients with MS are initially diagnosed with RRMS, which over time transforms into secondary progressive multiple sclerosis (SPMS) in the majority of the patients. In SPMS, although patients might still have relapses, neurologic deficits accumulate independently of relapses, leading to continuous progression of disability. Around 15% of the patients are diagnosed with primary progressive multiple sclerosis (PPMS), where the illness progresses without relapses from the onset of the diseaseCitation3,Citation4.
Several studies have highlighted the burden of MS to patients, healthcare systems, and societyCitation5–9. Nevertheless, data are scarce in respect of different MS phenotypes, despite the significant clinical differences among these. It has been found that direct healthcare costs were significantly higher among patients with RRMS and SPMS than among those with PPMS, in ItalyCitation10. In another study, it was reported that drug costs and indirect costs were the biggest contributors to costs among patients with RRMS, while indirect costs were responsible for the major proportion of costs among those with SPMS and PPMS in SwedenCitation11. The current study continues from the previously published Defense-studyCitation12. The study presented the burden of disease for the overall Finnish MS population, and showed that the total costs increase, and the working ability as well as the quality-of-life (QoL) decreases in concert with disease progression among patients with MS. The aim of this current study is to further quantify how different MS phenotypes differ from each other in respect of costs and QoL.
Materials and methods
The current study is based on data from a retrospective, cross-sectional mail survey conducted during 2015. The study cohort consisted of a random sample of members (age ≥18 years) of the Finnish Neuro Society, which is the national patient association for patients with MS. The data collection was conducted using a structured questionnaire, in local language. The data covered self-reported information on resource use and health-related QoL, level of disability, and major symptoms such as fatigue, working capacity, and early retirement. Patient reported outcomes were collected using validated instruments the EQ5D-VAS, Multiple Sclerosis Impact Scale-29 (MSIS-29)Citation13, and Fatigue Severity Scale (FSS)Citation14. The current study briefly describes the methods, while more detailed methods along with inclusion and exclusion criteria are reported elsewhereCitation12.
The demographic variables included age, gender, employment status, and living conditions. In addition, information related to sick leave and reduced income due to MS were collected. Responders were also asked to fill in the year of diagnosis, age at the time of first symptoms, type of MS, occurrence of relapses, and disease severity.
In this study, disease severity was self-reported based on the written description of the Expanded Disease Severity Scale (EDSS) (Appendix 1). The self-assessment questionnaire is described in more detail elsewhereCitation15. In MS, the disease progression is measured with EDSS at a scale of 0–10Citation16. The higher the EDSS score, the higher is the disability and disease severity. For example, EDSS 0 represents normal neurological function, EDSS 3 moderate disability, EDSS 7 restriction to wheelchair, and EDSS 9 a bedridden patient with no ability to move.
In order to define the disease type, a set of symptom descriptions were given, among which the responders chose the one that described his/her disease most closely. The following descriptions were used to define the type of MS: (1) Relapsing-remitting MS: Relapses occur frequently, with new symptoms, but recovery after the relapse is almost complete. Between the relapses, the condition is stable; (2) Secondary progressive MS: After initial relapsing/remitting disease, the disease causes increasing limitations and physical disabilities, both during relapse and between relapses; (3) Primary Progressive MS: The disease causes increasing limitations and physical disabilities, without relapses; (4) I do not know. The clinical course descriptions of RRMS, SPMS, and PPMS were based on the 2013 consensus definitionsCitation4. Based on the responses the patients were stratified according to their respective MS phenotype. As the aim of this study was to report results among different MS phenotypes, the patients who did not know their disease type (n = 55) were excluded from the study cohort.
Resource use and costs
Total costs consist of three categories: direct healthcare costs, direct non-medical cost, and productivity costs. Direct healthcare costs included inpatient care and outpatient visits to healthcare professionals as well as rehabilitation and other services from professional caregivers. Also diagnostic tests, purchased over-the-counter medicines, as well as disease modifying treatment (DMT), and other MS related prescribed medication were included as direct healthcare costs. Non-medical resource use covered investments needed in daily life such as modifications at home as well as informal care from family and friends. Productivity losses were captured through sick leave, reduced income, and early retirement due to MS.
Responders were asked to report their MS related resource use and need for additional services on the structured questionnaire. Different recall periods were used for different items to reduce recall bias (e.g. 1 year for inpatient care and diagnostic investigations, 3 months for short-term sick leave and outpatient care, and 1 month for other medication use). Information on current DMT use was collected. These self-reported resource uses were converted to annual values where relevant. Corresponding unit costs were attached to each individual resource use parameter. The latest national healthcare unit costs or other relevant sources were used similarly to the original study, to enable comparability of the results. Costs were real valued to year 2017 using official indices from Statistics Finland. While medication prices do not follow cost index, the most recent available prices were used based on the Finnish medication price database (26.10.2018). A human capital approach was used when assessing the productivity losses for those employed. Here the productivity was valued using full-time gender-specific annual gross salary. The costs are included from a societal perspective. This means that all costs, irrespective of the payer, are taken into account.
Quality-of-life
Quality-of-life was assessed through multiple self-reporting measurement tools. Both disease-specific and generic quality-of-life instruments were used to obtain a more thorough view of patient’s health-related quality-of-life. In EQ-5D, health status is measured in terms of five dimensions, and the result is expressed as a single utility value between 0 (death) and 1 (perfect health). Additionally, the Visual Analog Scale (VAS) records the patient self-rated current health at a scale of 0–100.
In addition to the generic quality-of-life measurement tool, MSIS-29 was used to capture the perceived physical and psychological impact of MS. MSIS-29 consists of 29 questions—20 questions measuring physical impact and nine questions measuring the psychological impact of the MSCitation13. Scores of both sub-scales are transformed to a 0–100 scale, where higher scores indicate more severe disease burden. FSS was used in scoring the severity of fatigue. It is a nine-item questionnaire where the mean score ≥4 indicates fatigueCitation14.
Statistical methods
The data were primarily analyzed using summary statistics. Summary statistics for continuous variables included the number of subjects, mean, standard deviation, median, minimum and maximum, and additionally for categorical variables frequencies and percentages were presented. The distribution of MS phenotypes and the proportion of patients on early retirement were presented in graphical format by EDSS categories. In addition to summary statistics, the possible differences between the MS phenotypes were statistically tested. While normality assumption of the data was violated, non-parametric Kruskal Wallis tests for independent samples were used to determine whether differences between the MS phenotypes were statistically significant (p-value <0.05). To overcome the limitations arising from sample size in different phenotypes, non-parametric bootstrapping was used in estimating the 95% confidence intervals.
In the present study, we used pre-existing dataCitation12 that were originally managed and analyzed with SAS 9.2 software. This data was further stratified into sub-groups using SAS EG 7.1 software. Outputs are listed by MS phenotype and level of disease severity.
Results
Demographics and disease information
Altogether 498 patients were included in the analyses, after exclusion of patients with an undefined type of MS. Demographics and disease information is summarized in . The study population consisted predominantly of females (77.7%). The mean age was 54 years, ranging from 21–88 years. Roughly half (49%) of the patients had RRMS, 32% SPMS, and 19% PPMS. Patients with RRMS tended to be younger (mean 48.9 years) than those with SPMS (mean 55.6 years) or PPMS (mean 63.5 years). In fact, the majority of the patients with RRMS and SPMS (89% and 78%, respectively) were still in the working age group (i.e. <63 years), whereas the respective proportion among patients with PPMS was only 36%. Mean time from first symptoms and diagnosis in the study population was 21 years and 16 years, respectively. Patients with RRMS were having a shorter time from both events when compared to those with SPMS and PPMS.
Table 1. Patient demographics.
Based on EDSS, 42% of the study population had mild-to-moderate disability (EDSS 0–3), 42% severe disability (EDSS 4–6.5), and 16% were restricted to a wheelchair or bed (EDSS 7–9). Nevertheless, there are statistically significant differences in EDSS distribution between the MS phenotypes (p < 0.001). The mean EDSS score in RRMS, SPMS, and PPMS were 2.5, 5.5, and 5.9, respectively. Among those with SPMS or PPMS, the most common EDSS score was 4–6.5, while the majority of patients with RRMS had an EDSS score 0–3 (). Thus, patients with PPMS or SPMS had more disability than those with RRMS. Despite the fact that around 16% of responders had EDSS scores of 7–9, only a few patients (2.8%) were accommodated in institutional care or required supportive housing.
Figure 1. Distribution of the study population (n = 498) according to MS phenotype and disease severity.
Relapses were apparent in the life of many patients. In total, 12% of RRMS, 10% of SPMS, and none of the patients with PPMS had experienced relapses during the previous 12 months (data not shown). Disease-modifying treatments (DMT) were used to some extent in all reported MS phenotypes. Around 65% of patients with RRMS were currently using DMT. The respective proportions among patients with SPMS and PPMS were 34% and 5%. Patients who were restricted to a wheelchair or bed (EDSS 7–9) seldom reported use of any DMT (data not shown).
Despite that most of the patients in the study population (75.7%) were of working age (<63 years), only 33.5% were employed. The employment rate was markedly higher among patients with RRMS (52%) than among those with SPMS (19%) or PPMS (11%). Overall, more than half of all responders (58%) had retired early to a disability pension due to MS. SPMS and PPMS were related to higher early retirement rates due to MS (79% and 83%, respectively) compared to RRMS (36%).
When looking at working age patients (), the proportion of patients with impaired working capacity seems to grow along with the disease severity. Interestingly, when compared to patients with RRMS, a high proportion of those with SPMS and PPMS (67% and 50%, respectively) had to retire early due to MS already at low EDSS scores (0–3). Age at early retirement varied widely, irrespective of the MS phenotype, the median age being 44.9 years among all responders (). The mean time from first symptoms to early retirement was ∼13 years among all patients with MS, and this was similar across all the MS phenotypes. However, according to the data patients with PPMS tended to retire sooner after MS diagnosis (5.5 years) than those with RRMS (6.3 years) or SPMS (7.9 years) ().
Figure 2. Effect of MS phenotype on working capacity. Histograms represent working-age population (age <63 years). Lines are indicating the proportion of working-age patients with MS on early retirement due to MS.
Table 2. Age at retirement and time to retirement for responders reporting early retirement due to MS.
Costs by MS phenotype and disease severity
The total mean annual costs per patient with RRMS were 36,492€ (n = 244), with SPMS 71,177€ (n = 160), and with PPMS 51,082€ (n = 94), respectively. The costs according to disease severity are presented in . The total costs increased along with the disease severity in all types of MS. Direct healthcare costs were responsible for 33% (12,232€), 19% (13,594€), and 18% (9,196€) of total costs in RRMS, SPMS, and PPMS, respectively. The corresponding values for direct non-medical costs were 6,172€ (17%), 23,590€ (33%), and 26,245€ (51%), and for productivity costs 18,087€ (50%), 33,992€ (48%), and 15,641€ (31%) in RRMS, SPMS, and PPMS, respectively. Interestingly, the increase in the direct non-medical costs seemed to increase most when moving from EDSS 4–6.5 to EDSS 7–9, while at the same time direct healthcare costs presented a more modest increase. It is noteworthy that patients with SPMS with relatively low disability (EDSS 0–3) had higher mean costs than respective patient groups with RRMS and PPMS, mainly due to higher productivity costs caused by early retirement. Productivity costs increased according to disease severity considerably in RRMS, whereas in SPMS and PPMS the costs remained relatively stable.
Table 3. Costs among patients with RRMS, SPMS, and PPMS according to disease severity.
When looking at the cost structure, there are clearly visible differences among different MS phenotypes, as illustrated in . Among the major cost drivers were early retirement, informal care, professional care, and DMTs. However, the ranking between these varied considerably between different MS phenotypes. Early retirement covered the greatest share of costs among RRMS (39%) and SPMS (43%). The second largest contributor to total costs in SPMS was professional care (19%). DMTs contributed 23% of healthcare costs among RRMS and only 6% among patients with SPMS. Among those with PPMS, early retirement and professional care were the two most important cost drivers, contributing together 56% of the total costs. ().
Figure 3. Composition of total costs among those with (a) RRMS, (b) SPMS, and (c) PPMS.
We further divided the responders according to DMT use. Among patients with RRMS, the mean annual total costs were 38,600€ for DMT users (n = 158), and 32,618€ for non-users (n = 86). Among DMT users (RRMS), productivity costs were responsible for 43%, direct healthcare costs 44%, and other direct costs 13% of total costs. Corresponding values among non-users (RRMS) were 64%, 11%, and 25%. Similar assessment was performed also for patients with SPMS, where the mean annual total costs were 72,023€ for DMT users (n = 54) and 70,745€ for non-users (n = 106). Among those with SPMS, the productivity costs represented the highest proportion of total costs among both DMT users (51%) and non-users (46%). Direct healthcare costs comprised 29% of the total costs among DMT users and 14% among non-users, in SPMS. Corresponding values for other direct costs among those with SPMS were 20% (DMT users) and 40% (non-users). There were only a few (n = 5) DMT users among the patients with PPMS, and, thus, these are not reported separately.
Quality-of-life
The overall quality-of-life, measured with EQ-5D, was affected both by disease type and severity (). The mean utility value from EQ-5D among patients with RRMS, SPMS, and PPMS was 0.76, 0.52, and 0.49, respectively. The corresponding VAS results in RRMS, SPMS, and PPMS were 75, 59, and 55, respectively. When disease severity increased, the quality-of-life decreased similarly in all types of MS. It is noteworthy that quality-of-life in SPMS and PPMS was consistently lower when comparing the corresponding disease severity groups in RRMS.
Table 4. Quality-of-life and patient reported outcomes measurements by disease type and severity.
The MS-specific MSIS-29 instrument was also used for quality-of-life assessment. Although MSIS-29 physical scores increased along the disease severity in all types of MS, patients with SPMS and PPMS reported higher physical burden in all disease severity groups in comparison to patients with RRMS. The psychological scores are showing a somewhat distinct pattern. Only patients with SPMS reported consistently higher psychological burden in all disease severity groups when compared to corresponding groups in RRMS.
Based on FSS scores, fatigue was present in all MS phenotypes and across all levels of disease severity. In SPMS and PPMS, the vast majority of patients (70–81%) with relatively low disability (EDSS 0–3) reported fatigue. On the contrary, in RRMS only 49% of patients reported fatigue at the corresponding level of disability group. Thus, a notable increase in the proportion of patients suffering from fatigue along disease severity was seen only among patients with RRMS.
Discussion
Multiple sclerosis places a considerable burden on society as well as the affected individuals, their families, and caregivers. Various studies have demonstrated economic and quality-of-life burden caused by MS in several countriesCitation5–9. In this study, we wanted to further elaborate how MS phenotypes differ from each other in respect of costs and quality-of-life. Our current study continued from the previously published Defense-studyCitation12, which estimated the overall burden of MS in respect of costs and quality-of-life in Finland. According to the results, the MS phenotype has an impact on costs and quality-of-life of the patients.
Based on our study, direct healthcare costs in Finland are higher in RRMS (12,232€) and SPMS (13,594€) compared to PPMS (9,196€). The biggest contributor to costs being DMTs in RRMS, and professional services in both SPMS and PPMS. Findings of similar magnitude were made in an Italian studyCitation10, where the direct healthcare costs were significantly higher for both RRMS (15,991€) and SPMS (14,099€) than for PPMS (10,825€). In their study, the expenses were largely due to the use of DMTs and immunosuppressive drugs—the corresponding costs being 12,855€ in RRMS, 10,654€ in SPMS, and 9,015€ in PPMS.
In our study, we have presented mean annual costs in different MS phenotypes irrespective of the time spent with the disease. A recent Swedish studyCitation11 explored the progression of all-cause costs over time among patients with different MS phenotypes after diagnosis (RRMS, PPMS) or after conversion from RRMS to SPMS. During the first year, mean direct costs were ∼9,200€ (SEK 96,744) for RRMS, ∼10,400€ (SEK 109,537) for SPMS, and ∼4,800€ (SEK 49,928) for PPMS. Nevertheless, the cost structure changed during the follow-up time. Similarly to our findings, drug costs and indirect costs were driving the cost for RRMS, while indirect costs were responsible for major costs in SPMS and PPMS. Interestingly, drug costs during the first year were higher in SPMS (SEK 72,450) compared to RRMS (SEK 52,628) and PPMS (SEK 10,885). Nevertheless, during subsequent years these drug costs doubled in size in RRMS, while steadily declining in SPMS, and remaining stable in PPMS. The cost structure in the initial phases of SPMS resembled those of RRMS, but, over time, indirect costs replaced direct medical costs as the main cost driver. Despite the differences in the study methodology and included costs, these data support our findings. It seems that the use of DMTs was more common during the early years of SPMS, which also reflects the current Finnish treatment guidelines. Thus, the costs related to DMTs in our study may be underestimations, while our data could not capture the potential time-dependence of costs.
Based on our study, SPMS was causing the highest annual total costs of all MS phenotypes. This was mainly driven by productivity loss due to early retirement. Overall, patients with SPMS and PPMS had higher early retirement rates than patients with RRMS. One could speculate that this is caused by higher age of the PPMS and SPMS populations. However, in all MS types the mean age for early retirement was similar (44.9 years), and clearly less than the mean age in each of the MS type populations. Nevertheless, some patients with SPMS may have retired due to MS already in their relapsing remitting disease phase. In Sweden, 22% of total costs of MS in the year conversion from RRMS to SPMS were due to a disability pension in SPMS, while the corresponding values in the year of diagnosis were 3% in RRMS and 8% in the PPMS groups. Disability pension remained a major contributor to total costs during the entire follow-up period in SPMS, and increased over time in PPMSCitation11. Our data also indicates that patients with SPMS and PPMS had more disability compared to those with RRMS. The higher disability rates do not solely explain the early retirement. When looking at the early retirement rates according to disease severity, a significant proportion of the working age patients with SPMS and PPMS retired early, even though the motor disability captured by EDSS was relatively low (EDSS 0–3). This phenomenon was not seen in RRMS. Although early retirement was notable in PPMS, this did not reflect to productivity loss in a similar manner than in SPMS. One explanation for this can be the higher mean age of the PPMS population (63.4 years). In this study, productivity losses were calculated only for patients, who were employed (sick leaves and reduced income) or who were < 63 years of age and on early retirement due to MS (disability pension). Since 53% of the patients with PPMS were on retirement pension, the source for productivity losses was relatively small.
Early retirement was the largest contributor of total costs both in RRMS and in SPMS, and second largest in PPMS. In RRMS the second largest contributor to the costs were DMTs. On the contrary, in SPMS and PPMS DMTs were used less often. Instead, the need for professional and informal care seems to get higher. When this survey was conducted, licensed DMTs were not available either for PPMS or for SPMS. It is common clinical practice, though, to continue DMTs over the transition from RRMS to SPMS, and not to discontinue DMTs until no clinical benefit is observed, which explains the moderate use of DMTs in SPMS.
Since MS is a progressive disease, the aim of DMTs is to slow down the disease progression and worsening of the symptoms. It is estimated that ∼65–80% of patients with RRMS would turn into SPMS over timeCitation17,Citation18. SPMS is a debilitating condition associated with irreversible disability, and it negatively affects patient’s daily activities, and, based on our findings, is causing the highest costs of all MS phenotypes. It has been recently studied whether the choice and timing of DMTs has an impact on the transition from RRMS to SPMS. According to the results, patients initially treated with DMTs (glatiramer acetate, interferon beta, alemtuzumab, fingolimod, natalizumab) had decreased risk for transition compared to the untreated controls. In addition, initial treatment with high-efficacy DMTs (alemtuzumab, fingolimod, or natalizumab) reduced the risk for transitioning to SPMS by 34% when compared to glatiramer acetate or interferon betaCitation19.
In addition to physical disability, other symptoms, such as fatigue and cognitive impairment, contribute to MS disease burden. In this study, the vast majority (70–81%) of the patients with SPMS and PPMS suffered from fatigue, although their disability was still relatively low. Patients with SPMS and PPMS both reported consistently lower quality-of-life than patients with RRMS. This is in line with our previous findings showing that severity of fatigue correlates with quality-of-life and age does not seem to have a significant effect on fatigueCitation20. Interestingly, in this study, patients with SPMS and PPMS also reported higher psychological burden compared to those with RRMS. We have reported earlier that MSIS-29 psychological scores tend to increase at the low level of disability (EDSS 0–3) and then plateau and eventually decrease along with increasing disabilityCitation21. Although no cognitive impairment evaluations were done in our study, recent findings are suggesting that cognitive problems may be more pronounced and severe in the progressive forms of MS than in RRMSCitation22. Based on a Danish study, cognitive function seems to be more closely associated with quality-of-life than physical impairment in progressive forms of MSCitation23. It has also been shown that, of the psychosocial factors, fatigue and depression are the main correlates of cognitive impairmentCitation24.
There is also some evidence that MS phenotype can also affect the income levels of the patients. In a Swedish study, patients with RRMS had an income level nearly twice as high as compared to those with SPMS and PPMSCitation25. Patients with SPMS earned slightly more than those with PPMS, but the difference was statistically non-significant. Patients with SPMS received benefits as a source of income more often than those with RRMS or PPMS. Thus, there is significant socioeconomic importance in preventing RRMS convert to SPMS.
Nevertheless, there are several potential limitations in our analysis. First, the patients were contacted through the local patient association. Members of patient organization may, in general, have more progressed disease, which is a potential source of responder selection bias. However, we believe that patients with such a debilitating disease as MS are well informed about the services and information provided through the association. In addition, the association is open to all patients with MS, irrespective of the severity (EDSS) or type of their disease. Thus, a sufficient number of all subtypes were included in the cohort in order to perform further analyses. However, it is likely that the distribution of MS phenotypes in these data cannot be directly generalized to the Finnish MS population. Consequently, in this study, we presented the mean values of both costs and QoL without an attempt to further generalize our results to the full population.
Another potential shortcoming arises from the study setting. As a survey, the study data (including medical condition, resource use, and quality-of-life), was solely based on self-reporting, and it did not include inputs from medical records. Thus, also the EDSS and MS phenotype are based on self-reporting. As a matter of fact, 10% of the responders were not able to classify their type of MS. Nevertheless, at the same time, it is worth noting that patients should be considered as primary sources of information concerning their disease and quality-of-life. Self-reporting may be considered as a strength, especially in respect of defining the MS phenotypes, since these may not be entered to medical records in a timely manner. This is true especially in the case of SPMS, where transition from RRMS to SPMS can take several yearsCitation26. In addition, confirmed diagnosis of SPMS may be delayed due to the fact that, in Finland, DMTs are reimbursed only for RRMS.
There were significant differences in patient-reported quality-of-life between the MS phenotypes. However, the interpretation of these results is not straightforward, and it is important to take into account the differences in the baseline disease severity among the MS phenotypes. Nevertheless, it seems that the type of MS may affect how patients observe their quality-of-life.
In conclusion, our study shows that MS phenotype has an impact on costs and quality-of-life of the patients. Early retirement seems to be one of the biggest contributors to MS related costs.
Transparency
Declaration of funding
This research was initiated and funded by Novartis Finland Oy.
Declaration of financial/other relationships
TP and TH are paid employees of Novartis Finland Oy. MT is a paid employee of StatFinn Oy, which provides statistical services for pharmaceutical companies. JR is a paid employee of the Finnish Neuro Society. A peer reviewer on this manuscript has disclosed that they are, or have been, a speaker for all manufacturers of MS DMTs. The peer reviewers on this manuscript have no other relevant financial relationships or otherwise to disclose.
Acknowledgements
None reported.
References
- GBD 2016 Multiple Sclerosis Collaborators. Global, regional, and national burden of multiple sclerosis 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18:269–285.
- Pirttisalo AL, Soilu-Hänninen M, Sipilä J. Multiple sclerosis epidemiology in Finland: regional differences and high incidence. Acta Neurol Scand. 2019;139(4):353–359.
- Lublin FD, Reingold SC. Defining the clinical course of multiple sclerosis: results of an international survey. National Multiple Sclerosis Society (USA) Advisory Committee on Clinical Trials of New Agents in Multiple Sclerosis. Neurology. 1996;46(4):907–911.
- Lublin FD, Reingold SC, Cohen JA, et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology. 2014;83(3):278–286.
- Brundin L, Kobelt G, Berg J, et al. New insights into the burden and costs of multiple sclerosis in Europe: results for Sweden. Mult Scler. 2017;23(2_suppl):179–191.
- Karampampa K, Gustavsson A, Miltenburger C, et al. Treatment experience, burden, and unmet needs (TRIBUNE) in multiple sclerosis: the costs and utilities of MS patients in Canada. J Popul Ther Clin Pharmacol. 2012;19(1):e11–25.
- Karampampa K, Gustavsson A, van Munster ET, et al. Treatment experience, burden, and unmet needs (TRIBUNE) in Multiple Sclerosis study: the costs and utilities of MS patients in The Netherlands. J Med Econ. 2013;16(7):939–950.
- Kobelt G, Pugliatti M. Cost of multiple sclerosis in Europe. Eur J Neurol. 2005;12(s1):63–67.
- Kobelt G, Thompson A, Berg J, et al. MSCOI Study Group; European Multiple Sclerosis Platform. New insights into the burden and costs of multiple sclerosis in Europe. Mult Scler. 2017;23(8):1123–1136.
- Patti F, Amato MP, Trojano M, et al. Multiple sclerosis in Italy: cost-of-illness study. Neurol Sci. 2011;32(5):787–794.
- Gyllensten H, Kavaliunas A, Murley C, et al. Costs of illness progression for different multiple sclerosis phenotypes: a population-based study in Sweden. Mult Scler J Exp Transl Clin. 2019;5(2):2055217319858383.
- Ruutiainen J, Viita AM, Hahl J, et al. Burden of illness in multiple sclerosis (DEFENSE) study: the costs and quality-of-life of Finnish patients with multiple sclerosis. J Med Econ. 2016;19(1):21–33.
- Hobart J, Lamping D, Fitzpatrick R, et al. The Multiple Sclerosis Impact Scale (MSIS-29): a new patient-based outcome measure. Brain. 2001;124(5):962–973.
- Krupp L, LaRocca N, Muir-Nash J, et al. The fatigue severity scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. Arch Neurol. 1989;46(10):1121–1123.
- Kobelt G, Berg J, Lindgren P, et al. Costs and quality of life in multiple sclerosis in Europe: method of assessment and analysis. Eur J Health Econ. 2006;7(S02):5–13.
- Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983;33(11):1444–1452.
- Tutuncu M, Tang J, Zeid NA, et al. Onset of progressive phase is an age-dependent clinical milestone in multiple sclerosis. Mult Scler. 2013;19(2):188–198.
- Weinshenker BG, Bass B, Rice GP, et al. The natural history of multiple Sclerosis: a geographically based study. I. Clinical course and disability. Brain. 1989;112(1):133–146.
- Brown JWL, Coles A, Horakova D, et al. Association of initial disease-modifying therapy with later conversion to secondary progressive multiple sclerosis. JAMA. 2019;321(2):175–187.
- Rosti-Otajärvi E, Hämäläinen P, Wiksten A, et al. Validity and reliability of the Fatigue Severity Scale in Finnish multiple sclerosis patients. Brain Behav. 2017;7(7):e00743.
- Rosti-Otajärvi E, Hämäläinen P, Wiksten A, et al. Validity and reliability of the Finnish version of the Multiple Sclerosis Impact Scale-29. Brain Behav. 2017;7(7):e00725.
- Brochet B, Ruet A. Cognitive impairment in multiple sclerosis with regards to disease duration and clinical phenotypes. Front Neurol. 2019;20(10):261.
- Højsgaard Chow H, Schreiber K, Magyari M, et al. Progressive multiple sclerosis, cognitive function, and quality of life. Brain Behav. 2018;8(2):e00875.
- Heesen C, Schulz KH, Fiehler J, et al. Correlates of cognitive dysfunction in multiple sclerosis. Brain Behav Immun. 2010;24(7):1148–1155.
- Kavaliunas A, Manouchehrinia A, Danylaite Karrenbauer V, et al. Income in multiple sclerosis patients with different disease phenotypes. PLoS One. 2017;12(1):e0169460.
- Katz Sand I, Krieger S, Farrell C, et al. Diagnostic uncertainty during the transition to secondary progressive multiple sclerosis. Mult Scler. 2014;20(12):1654–1657.