Farmeco 2020;21(1)49-59.html
Farmeconomia. Health economics and therapeutic pathways 2020; 21(1): 49-58
https://doi.org/10.7175/fe.v21i1.1476
Original Research
The Economic Burden of Different Multiple Sclerosis Courses: Analysis from Italian Administrative and Clinical Databases
Paolo Angelo Cortesi 1, Paolo Cozzolino 2, Ruggero Capra 3, Giancarlo Cesana 1, Lorenzo Giovanni Mantovani 1,2
1 Research Centre on Public Health (CESP), University of Milano-Bicocca, Monza, Italy
2 IRCCS Multimedica, Sesto San Giovanni, Italy
3 Multiple Sclerosis Centre, Spedali Civili di Brescia, Montichiari, Italy
Abstract
INTRODUCTION: Poor specific economic information are available for the different Multiple Sclerosis (MS) courses: relapsing remitting (RRMS), secondary progressive (SPMS) and primary progressive (PPMS). This study aims to fill this gap.
METHODS: A cost of illness study was conducted. Clinical information of patients treated in a major MS Center located in Lombardy, in the period 2004-2010, were linked with administrative data of Lombardy Healthcare System. We assessed the mean cost per patient-year and its association with different MS characteristics.
RESULTS: The study identified 869 patients (83.9% RRMS, 8.5% SPMS, 7.2% PPMS). RRMS reported the highest cost per patient-year with a mean of € 5,623 in Expanded Disability Status Scale (EDSS) 0-3, € 8,675 in EDSS 3.5-6.5, and € 7,451 in EDSS 7-9. The PPMS patients reported the lower annual mean cost per patient in all EDSS categories. The multivariate analysis reported a significant association between cost per patient-year and EDSS categories, relapse and use of Disease Modifying Therapies but not to MS courses, age and sex.
CONCLUSION: This study provides a complete picture of MS courses direct costs at the different disability levels. The results can help to better understand the burden of each MS courses and the cost-effectiveness of different interventions.
Keywords
Multiple sclerosis; Burden of disease; Direct costs; Italy
Corresponding author
Paolo Cozzolino
paolo.cozzolino@multimedica.it
Received: 23 April 2020
Accepted: 19 May 2020
Published: 5 June 2020
Background
Multiple sclerosis (MS) is the most prevalent chronic inflammatory disease of the central nervous system, affecting more than 2 million people worldwide [1]. MS is characterized by different symptoms as changes in mobility, balance, sensation, sphincter function, vision, and cognition [2,3]. People affected by MS can develop permanent disability, although the course is highly variable. Further, MS remains the major cause of neurological disability in young adults [1,3].
Based on initial course, MS is classified as relapsing-remitting (RRMS) or primary progressive (PPMS). RRMS is the most prevalent course, and is characterized by episodes of neurological dysfunction [3,4]. Recovery from this relapse is heterogonous and can be incomplete [4,5]. With time, RRMS may convert to a secondary-progressive MS (SPMS) course, characterized by a gradual increase in disability with or without relapses [2,5]. PPMS is also characterized by a slowly progressive increase in neurological disability over time, usually without relapses [2,5].
MS is associated to a high economic burden, with an estimated annual cost of approximately $ 10 billion in USA [6,7]. MS reported high direct costs associated to the health care resources consumption, but also high indirect costs associated to informal care, services and loss of productivity [7]. The direct and indirect costs are associated to the disability status of MS patients, with increased costs associated to increased disability [7]. The introduction of disease-modifying therapies (DMTs) over the past two decades has had profound effects on the management of RRMS reducing the progression of the diseases and significantly increasing the mean healthcare costs in the low level of disability [7,8]. However, the availability of treatment options and the high prevalence of RRMS have focused the interest of economic researches on this MS course, with the consequence of poor data available on the economic burden associated to progressive courses. Even a recent study assessing the burden and costs of MS in Europe, reported only aggregated data of all MS courses [7]. Further, economic impact of MS have been limited to information from questionnaires distributed to patients with a response rates low or not reported (range 16-64%) [9]. Only few studies, conducted in Scandinavian countries, have reported data on MS cost based on nationwide registers [10-13].
Considering the poor specific data available for the different MS courses and the new treatment opportunities that are becoming available [14], there is the need of obtaining specific economic information for each course based on administrative and clinical registries. The need of this data from the healthcare system perspective is even higher considering that the healthcare system is the main point of view applied to assess the cost-effectiveness of healthcare intervention by national/regional health technologies assessment agencies [15]. This study aims to fill this gap assessing the cost associated to the different MS courses and their differences from a universal health care coverage point of view as the Italian National Healthcare System (NHS).
Methods
Data source
The study is a retrospective cost analysis of different courses of MS, based on the Lombardy Region health administrative databases and the clinical database of one of the biggest MS center in the Region (Centre of Montichiari Hospital). Lombardy is the most populated region in Italy, and with nearly 10 million inhabitants, accounts for more than 16% of the Italian population. Since 2000 to 2010, all data from administrative databases of Lombardy Region were collected in a data-warehouse, named DENALI, matching anonymized subject level data from different datasets with a probabilistic record linkage [16,17].
More in details, the different databased used to create DENALI included administrative datasets, demographic databases and the disease-specific exemption registry [17]. The available administrative datasets included information on: hospital discharges, pharmaceutical prescriptions, outpatient claims (laboratory and diagnostic examinations, specialist medical visits), and related costs covered by the Regional Health System (RHS). RHS is funded by the Italian NHS and provide a ‘‘universal coverage’’ to all its residents, who generally have to pay only a part of costs of drugs or services (ticket). In case people have a specific condition, such as a severe chronic disease like MS, they are exempted from co-paying.
Further, since 1980, all clinical data related to the patients accessing the MS Centre of Montichiari Hospital were collected prospectively; this data were merged with DENALI in order to obtain specific clinical information on age, sex, date of diagnosis, MS courses, relapse events, Kurtzke Expanded Disability Status Scale (EDSS) score, and diagnostic examination.
The data collected were managed through a unique and anonymous personal identification code, according to the Italian regulations regarding the conduct of observational analyses (Det. AIFA 20 March 2008). Therefore, neither Ethics Committee’s approval nor any informed consent were required.
Population
Patients with at least one medical contact during the years 1995-2010, registered in the clinical database held at the MS Centre of Montichiari Hospital, were initially selected keeping only those patients resident in Lombardy Region.
Patients with at least one hospital admission with MS diagnosis (ICD 9-CM 340), a disease modifying therapy (DMT) prescribed or an exemption code for MS according to the DENALI, and observed during the period 2004-2010 were selected. Based on this first selection, only those patients with at least one medical contact at the MS Centre of Montichiari Hospital were selected. This pre-processing allowed us to link the databases using the demographic information present in both data sources (birth date, sex and province of residence).
Patients included in the analyses were followed up from 1st January 2004 or date of MS diagnosis for patients diagnosed after that date, until 31st December 2010, death or exit from Regional Health System for any other reason (e.g. moved to another Region). All demographic and clinical data, medical contacts and events from 1st January 2004 have been included in the assessment. The observation period of the analysis started from 1st January 2004 due to the availability of DMTs data in DENALI data-warehouse from 2004.
The demographic and clinical data of identified patients were described with the use of proportions for categorical data, mean and/or median as central tendency parameters for continuous data, and standard deviation, minimum and maximum values as dispersion parameters.
Costs
All direct medical costs in charge to the RHS were considered in the analysis and were expressed as Euro per patient-year, using the unitary cost applied in Lombardy RHS in 2010. The direct medical costs were computed using charges that the RHS reimbursed to the providers of care and were stratified in 4 categories: hospitalizations (long stay and day case), DMTs, other drugs and outpatients (laboratory and diagnostic examinations, specialist medical visits, and all ambulatory care). Because of the highly skewed distribution of cost variables, confidence intervals for the estimates were calculated using the bootstrap method [18].
Costs were assessed stratifying patients by MS courses (RRMS, SPMS and PPMS) and by EDSS category (0-3, 3.5-6.5 and 7-9). Each patient provided data on costs based on the MS course and the time spent in each EDSS category in order to account for the possible switch from RRMS to SPMS during the follow-up period. The choice to stratify patients by EDSS score, without estimating cost from the time of the diagnosis, was made based on the correlation showed between cost and disability status in previous studies and the need of cost per EDSS level to assess cost-effectiveness of MS intervention [7,8,19]. Further, the impact of relapses on the direct costs was assessed stratifying patients by the presence of relapse within each EDSS categories and excluding DMT cost [20].
Costs analysis
As already mentioned, costs can be influenced by the patients’ characteristics; accordingly, we conducted univariate and multivariable regression models in which the dependent variable was total medical direct costs. As independent (explanatory) variables, the model included: age, gender, disease courses (RRMS, SPMS and PPMS), EDSS category (0-3, 3.5-6.5 and 7-9), use of DMT, and presence of relapses, in order to assess the significant association between patients’ characteristics and costs. The choice of these variables was made according to past experience in the areas of MS and of health economics, to the available literature, and according to the performance of the statistical models [7,8,20-23] A general linear models (GLM) was used to estimate annual direct mean costs per patient-year [21]. The univariate analysis was performed to assess the association between each patient’s characteristic and costs; while more multivariable models were explored with a stepwise procedure, in order to investigate and select the most efficient one according to logical consistency and statistical significance of the regression coefficients, and according to the goodness of fit model and the parsimony criterion.
Results
Patients with at least one medical contact between 2004-2010 in the clinical database held at the MS Centre of Montichiari Hospital (n = 1,040) were linked with patients included in the DENALI database, resulting in a final cohort of 869 MS patients followed for a median time of 6.5 years. The mean (SD) age at inclusion date was 40.5 (12.0) and two-thirds were female (Table I). More than 75% of patients reported an age < 50 years and 70% reported an EDSS ≤ 3. Overall, 60.6% of patients were treated with DMTs (interferon beta-1a, interferon beta-1b, glatiramer acetate, natalizumab, peginterferon alfa-2a).
The majority of patients (81.5%) were RRMS, while 7.2% were PPMS. (Table I). Forty-two (5.9%) of 708 patients with RRMS develop SPMS during the follow-up, resulting in an incidence rate of SPMS equal to 0.94 per 100,000 person-years. PPMS and SPMS reported a higher mean age compared to RRMS, with 66.7% of PPMS and 46.9% of SPMS older than 50 years. Almost forty percent of patients with SPMS used DMTs, while only 9.5% of PPMS used at least a DMT one time (Table I).
In the cohort, relapses during the follow-up period were reported by 497 patients (57.2%); 442 (88.9%) of them were RRMS, 13 (2.6%) SPMS and 42 (8.5%) PPMS. The relapse rate over the follow-up period was estimated at 32.5 per 100 patients-year.
MS
RRMS
PPMS
SPMS
n (%)
869 (100.0)
708 (81.5)
63 (7.2)
98 (11.3)
Sex, n (%)
Female
582 (67.0)
492 (69.5)
30 (47.6)
60 (61.2)
Male
287 (33.0)
216 (30.5)
33 (52.4)
38 (38.8)
Age
Mean (SD)
40.5 (12.1)
37.9 (10.9)
54 (9.8)
50.9 (9.8)
Age class, n (%)
< 31 years
206 (23.7)
204 (28.8)
0 (0.0)
2 (2.0)
31-40 years
270 (31.1)
248 (35.0)
8 (12.7)
14 (14.3)
41-50 years
214 (24.6)
165 (23.3)
13 (20.6)
36 (36.7)
≥ 51 years
179 (20.6)
91 (12.9)
42 (66.7)
46 (46.9)
EDSS, n (%)
Mild (0-3)
599 (68.9)
578 (81.6)
14 (22.2)
7 (7.1)
Moderate (3.5-6.5)
170 (19.6)
93 (13.1)
28 (44.4)
49 (50.0)
Severe (7-9)
73 (8.4)
12 (1.7)
20 (31.8)
41 (41.8)
Missing
27 (3.1)
25 (3.5)
1 (1.6)
1 (1.0)
Treated with DMT1, n (%)
527 (60.6)
484 (68.4)
6 (9.5)
37 (37.8)
Patients with at least one relapse, n (%)
497 (57.2)
442 (63.4)
13 (20.6)
42 (42.9)
Table I. Baseline characteristics of identified patients
1 Interferon beta-1a, interferon beta-1b, glatiramer acetate, natalizumab, peginterferon alfa-2a
DMTs = disease-modifying therapies; EDSS = expanded disability status scale; MS = multiple sclerosis; RRMS = relapsing remitted multiple sclerosis; SPMS = secondary progressive multiple sclerosis
Figure 1. Direct medical cost of MS patients by EDSS categories
EDSS = expanded disability status scale
Costs
The mean (SD) cost for MS patient per year was € 6,008 (218), with DMT accounting for 68.6% of the total costs. In severe MS (EDSS 7-9) the cost of hospitalization was the main cost driver (61.0%); while, in the mild (EDSS 0-3) and moderate (EDSS 3.5-6.5) patients, DMTs was the main one, accounting for 80.8% and 60.1%, respectively (Figure 1).
The mean costs per patient-year by MS course and EDSS category are reported in Table II. PPMS reported the lowest cost in EDSS 0-3 (€ 1,013 per patient-year) and EDSS 3.5-6.5 categories (€ 3,028 per patient-year); while RRMS reported the highest cost in all three EDSS categories. An increased cost is associated to an increased EDSS severity in PPMS; with the biggest difference (€ 2,700) reported between EDSS categories 3.5-6.5 and 7-9. The same trend was reported in the SPMS, with a smaller increment of costs between moderate and severe EDSS categories. Conversely, a slight decrease in cost has been observed from EDSS 3.5-6.5 to 7-9 in RRMS.
The main cost driver in RRMS patients with EDSS between 0 and 6.5 was DMT; while in PPMS, the main cost driver was hospitalization. Similar percentages of total direct medical costs were estimated by DMT and hospitalizations in SPMS. In severe EDSS category, all three MS courses reported hospitalization as main cost driver.
The costs of hospitalization, visits, diagnostic exams and other drugs were higher in patients who experience relapse during the observational period (Figure 2). In EDSS 0-3 category, the patients with relapse reported an average cost of € 5,036 per patient-year compared to € 693 in patients without relapse. A similar difference was reported by patient in EDSS 3.5-6.5, with € 5,719 in patients with relapse and € 2,105 in patient without relapse.
Cost per patient-year, mean € (95% CI)
RRMS
PPMS
SPMS
EDSS 0-3
Hospitalization1
365 (361-369)
436 (416-455)
148 (138-159)
Visits and diagnostic exam
572 (570-574)
406 (391-422)
516 (501-532)
DMT
4,554 (4,534-4,573)
0 (-)
1,518 (286-312)
Other drugs
132 (131-133)
171 (165-177)
299 (286-312)
Total
5,623 (5,601-5,644)
1,013 (979-1,047)
2,481 (2,391-2,571)
EDSS 3.5-6.5
Hospitalization1
1,752 (1,726-1,777)
2,076 (2,032-2,119)
1,740 (1,703-1,777)
Visits and diagnostic exam
714 (709-720)
432 (428-437)
523 (513-533)
DMT
5,948 (5,889-6,008)
14 (13-16)
2,969 (2,909-3,030)
Other drugs
261 (258-264)
506 (499-513)
633 (620-646)
Total
8,675 (8,598-8,752)
3,028 (2,981-3,075)
5,865 (5,770-5,960)
EDSS 7-9
Hospitalization1
3,257 (3,157-3,358)
4,345 (4,232-4,458)
4,755 (4,671-4,839)
Visits and diagnostic exam
553 (543-562)
336 (332-341)
392 (388-397)
DMT
3,155 (473-499)
344 (320-368)
1,198 (1,156-1,239)
Other drugs
486 (473-499)
703 (693-713)
925 (912-938)
Total
7,451 (7,290-7,613)
5,728 (5,607-5,849)
7,270 (7,169-7,370)
Table II. Direct costs of MS courses stratified by EDSS categories
1 It includes both long stay and day case
DMTs = disease-modifying therapies; EDSS = expanded disability status scale; RRMS = relapsing remitted multiple sclerosis; SPMS = secondary progressive multiple sclerosis
Figure 2. Direct medical costs of MS patients by EDSS categories in patient who experience (with relapse) and not experience (no relapse) relapses during the observational period
EDSS = expanded disability status scale
Costs analysis
The univariate analysis showed a significant association between costs and all variables tested, except sex and EDSS categories (Table III). While the multivariate analyses showed a statistically significant (p < 0.01) increase of mean direct medical associated to EDSS categories, DMTs and relapse. MS courses, age, and sex were not included in the final multivariate models because they reduced the model’s goodness of fit and resulted no statistical significant associated to costs.
Based on the multivariate analyses, EDSS 3.5-6.5 and EDSS 7-9 were associated to a significant increment of costs, 1.4 and 3.3 times higher than EDSS 0-3, while being treated with DMT and experience relapse were associated to a significant increased costs 4.2 and 6.0 times higher, respectively.
Explanatory variables
Univariate model coefficient (95% IC)
Univariate model exponential coefficient (95% IC)
p-value
Multivariate model coefficient (95% IC)
Multivariate model exponential coefficient (95% IC)
p-value
Age
-0.022
(-0.032 – -0.011)
0.979
(0.968 – 0.989)
<0.001
-
-
-
Male sex
0.071
(-0.214 – 0.357)
1.074
(0.805 – 1.432)
0.626
-
-
-
MS course
SPMS
1.040
(0.239 – 1.842)
3.080
(1.628 – 5.830)
0.001
-
-
-
RRMS
1.125
(0.483 – 1.767)
2.830
(1.282 – 6.249)
0.011
-
-
-
EDSS category
3.5-6.5
0.070
(-0.249 – 0.390)
1.073
(0.787 – 1.462)
0.667
0.360
(0.151 – 0.568)
1.433
(1.172 – 1.752)
0.001
7.0-9.0
-0.006
(-0.489 – 0.478)
0.994
(0.630 – 1.570)
0.982
1.179
(0.750 – 1.609)
3.252
(2.130 – 4.965)
<0.001
DMTs
1.733
(1.459 – 2.007)
5.658
(4.235 – 7.560)
<0.001
1.438
(1.249 – 1.628)
4.214
(3.464 – 5.125)
<0.001
Relapse
1.923
(1.716 – 2.130)
6.842
(5.548 – 8.436)
<0.001
1.783
(1.599 – 1.966)
5.945
(4.972 – 7.108)
<0.001
Constant term
-
-
-
7.310
(7.139 – 7.481)
1494.897
(1252.936 – 1783.585)
<0.001
Table III. Factors associated to MS costs: results of the univariate and multivariate models (Reference groups: Sex - Female; MS course – PPMS; EDSS category –0.0-3.0; DMT – Not DMTs; Relapse –no relapse)
DMTs = disease-modifying therapies; EDSS = expanded disability status scale; RRMS = relapsing remitted multiple sclerosis; SPMS = secondary progressive multiple sclerosis
Discussion
This study combined Regional administrative databases and clinical database, creating a reliable source of data for assess costs stratified by clinical characteristics with the median follow up of 6.5 years per patient. The study results give a complete picture of the health care costs associated to the different MS courses, highlighting the difference based on the disability status and the treatment management. Further, the cohort included in the study was representative of MS patients in charge of reference MS center [24].
The study showed a high prevalence of RRMS course, with PPMS that account for approximately 7%. Overall, the mean medical direct cost per patient-year was € 6,008, with higher costs associated to higher disability. PPMS reported the lower cost in EDSS categories 0-3 and 3.5-6.5; while RRMS reported the highest one. The differences of cost in mild and moderate MS courses were mainly related to the availability of DMT for relapsing form only. In EDSS category 7-9, the cost of MS courses was more similar with hospitalization resulted as the main cost driver due to complications associated to the advanced stage of disease and disability. Further, the study results provided a picture of the economic burden of PPMS before the availability of the new treatments [14], which could change the management and cost scenario, has done by DMTs in relapsing courses [8]. Finally, the study reported a higher cost in patients who experience relapses, with an increased cost per patient-year of approximately € 4,300 and € 3,700 in EDSS 0-3 and 3.5-6.5, respectively.
MS courses reported the same price within the same EDSS category, when adjusted for the DMT costs in the regression model. Different mean costs estimated for progressive and relapsing courses, without adjusting for confounding, were mainly due to the use of DMT. The costs in all MS courses (excluding DMT) were highly correlated with EDSS, showing increases in costs of nearly 1.4 times for EDSS 3.5-6.5 and 3 times for EDSS 7-9. This increase in costs is in line with what reported by the analysis performed in the review on cost of illness studies of Multiple Sclerosis by Ernstsson et al. [9].
Further, patients reporting relapse had a higher direct cost even in the adjusted model. This result suggests that the presence of one or more relapse during the observational period could identify patients with a different diseases activity who required a higher resource consumption to manage the condition and not the single event of relapse per se, as reported by Gyllensten et al. [10]. Indeed, the management of relapse in Montichiari center was mainly related to ambulatory visits and administration of steroids without hospitalization, resulting in a low impact on mean costs. Based on these results, an effective treatment able to reduce the relapse to zero should be assessed in future studies to understand the possible positive impact on hospitalization, visit and diagnostic examination costs even at low EDSS level. The study results confirm the association between increased costs and increased EDSS [7,8,20,22,23] and showed how this correlation is similar in all MS courses. However, our results had some important differences compared to previous studies [7,8,10,11,20,22,23].
A recent survey conducted in 16 European countries [8,20], assessing healthcare services and informal care, and indirect costs, reported a mean healthcare costs form approximately € 7,000 to € 20,000 per patient-year in EDSS 0-3, from € 8,000 to € 25,000 in EDSS 4-6.5, and from € 6,000 to € 30,000 in EDSS 7-9 [8]. Considering only the Italian setting, the mean healthcare costs reported was equal to € 20,132 per patient-year in EDSS 0-3, € 23,611 in EDSS 4-6.5, and € 15,670 in EDSS 7-9 [20]. The cost estimated in our study were significant lower, with a mean cost from € 1,013 (PPMS) to € 5,623 (RRMS) in EDSS 0-3, from € 3,028 (PPMS) to € 8,675 (RRMS) in EDSS 4-6.5, and from € 5,728 (PPMS) to €7,451 (RRMS) in EDSS 7-9. The differences in the average costs between our and previous studies could be associated to the studies’ designs, data sources and cost items included, as already reported in the comparison of other cost of illness study of MS [9]. First, the European study was based on a survey with patients invited to participate by patient organizations, while in our study we included all MS patients accessing an MS reference center. Second, in our study the EDSS score and relapse events was reported by expert Neurologists (Neurostatus certified) based on diagnostic examination performed during the clinical practice, while in the European study it was reported by patients. Third, our study estimated exactly what was paid by RHS using the regional administrative databases, while the European study estimation used resources consumption reported by patients in the previous 1-3 months and unit costs for individual resources taken from publicly available sources. Finally, the healthcare costs estimated in our study included hospital discharges, pharmaceutical prescriptions, outpatient claims (laboratory and diagnostic examinations, specialist medical visits) and related costs covered by the RHS, while in the European Study the healthcare costs included inpatient care, day admission, consultations, tests, medication and DMTs.
While survey with a bottom up approach are frequently used to assess the MS burden [9], two published studies assessed MS cost from the Sweden society point of view using a top down approach based on several nationwide registers [10,11]. These studies used source of data more similar to the administrative database used in our study. In the study by Gyllensten et al. [10] the annual direct costs (prescription drug, outpatient specialized healthcare used and inpatient healthcare use) of MS patients were similar in all EDSS category (from € 11,385 to € 13,575) while in our study the EDSS 0-3 category reported a lower price (€ 5,548) compare to high level of disability (~€ 7,000) [10]. The mean annual costs reported in the Sweden studies were higher than what reported in our analyses. The difference can be associated to healthcare intervention tariff and drug costs in the two countries but also to other issues: 1) inclusion of all patients age in our study compare to only 21-64 years in the other, 2) 6.5 years of median follow-up in our study compare to only 2013 data considered, and 3) the exact price used for each treatment or healthcare intervention provided by the RHS in our study compare the average price used for some drug and healthcare cost calculated from DRG codes and transformed using DRG weights and the national average cost per DRG. However, even these Swedish studies do not provide information on the different MS courses.
In the last 10 years, only two studies reported an analysis on costs associated to the different MS courses [22,23]. One study was conducted in Italy in 2009, via a web-based self-completed questionnaire [22,25], and the other one was conducted in UK in 2005 via a postal survey [23,26]. The first study reported mean direct medical costs per patient-year of € 22,952 in RRMS, € 12,927 in PPMS and € 38,554 in SPMS without analyzing the cost stratified by both EDSS category and MS courses [22]. This study reported higher costs of MS courses compared to our results. The second study used a multivariate linear regression model to assess the cost of EDSS level controlled by other factors, including the MS courses [23]. In this study the RRMS patients reported an annual medical cost between £ 85 and £ 850 in EDSS 0-3, £ 806 and £ 3,429 in EDSS 4-6.5, and £ 6,583 and £ 15,121 in EDSS 7-9. SPMS and PPMS were not significantly associated to a different cost compared to RRMS, adjusting for EDSS level, relapse occurrence and DMT use. Our analysis reported similar results; confirming that MS costs are associated to EDSS level, relapse and DMT use and not to MS courses. However, our study presents a more robust study design and data source that provides more reliable information. The two published studies reported a similar design used in the previously discussed European study [8,20], with the same limits associated to a survey that involved MS patients by mail, email, websites and social media platforms and that collected data retrospectively with a questionnaire self-completed only by patient.
Despite the benefit of using large, regional administrative datasets combined with a clinical database of a reference MS center, this study presents some limitations. First, the patients included in the analysis are in charge to only one MS center. The health care resources consumed by these patients can be affected by the specific patients’ management approach used by the center. However, in Lombardy Region, all MS patients are in charge to reference MS centers that share a high specialized environment that reduce the variability of patents’ management and treatments. Further, the drug price and the unit cost of the healthcare resources are the same in all centers. Second, the study includes a small number of PPMS and SPMS compare to RRMS. However, the MS courses prevalence reported in our study is in line with the epidemiology data in the literature [27]. Third, the observational time period used in the study was until December 2010 with the exclusion of the more recent years. However, recently published MS cost of illness studies are related to a time period of 2010 or 2013 [8,10,11,20]. Further, no significant change was observed in the treatment and management of SPMS and PPMS in the last years and no significant change was expected in the cost of these patients. While, in the recent years, new DMTs [28-31] become available for RRMS EDSS 0-3 and EDSS 4-6.5 patients with a possible impact on the overall cost. In the observational time period used for the study, effective DMTs were already available [32-34] and the switch from old to new drug should not lead to a substantial change in the overall patient cost; considering a similar price of new DMTs compare to what already available during our study and the possible impact of higher competition within treatments available [19,35,36]. However, future studies are required to estimate the costs of MS courses in recent years and the impact of disability levels, relapse, DMTs but also of other important characteristics as comorbidities.
In conclusion, this study provides a complete picture of the costs associated to all MS courses at the different disability level. This is the first study that estimates the MS costs from the national health care system point of view, combining administrative and clinical database. Further, this study provides costs estimation based on patient followed up to 7 years in MS reference center and not on data related to short time period collected retrospectively in a self-reported questionnaire filled in only by patients. The estimated cost for the different MS courses and EDSS level can help to better understand the burden of MS and the possible impact of interventions. This information is particularly important in the progressive courses where poor economic data are available in the literature and where new treatments are approaching the market. The estimated costs could be used as a reference to assess the cost-effectiveness of the new treatments for progressive MS and the relative impact on the economic burden.
Funding
This study was supported by an unrestricted research grant from Roche S.p.A., Italy.
Conflicts of interest
PAG reports personal fees from Roche spa and Pfizer, and grants from Shire now part of takeda, outside the submitted work.
PC, RC and GC have nothing to disclose.
GLM reports grants from Roche during the conduct of the study; grants and personal fees from Bayer, grants from Boehringer Ingelheim and Daiichi Sankyo, and personal fees from Pfizer outside the submitted work.
References
1. GBD 2015 Neurological Disorders Collaborator Group. Global, regional, and national burden of neurological disorders during 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Neurol 2017; 16: 877-97; https://doi.org/10.1016/S1474-4422(17)30299-5
2. Brownlee WJ, Hardy TA, Fazekas F, et al. Diagnosis of multiple sclerosis: progress and challenges. Lancet 2017; 389: 1336-46; https://doi.org/10.1016/S0140-6736(16)30959-X
3. Reich DS, Lucchinetti CF, Calabresi PA. Multiple Sclerosis. N Engl J Med 2018; 378: 169-80; https://doi.org/10.1056/NEJMra1401483
4. Polman CH, Reingold SC, Banwell B, et al. Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria. Ann Neurol 2011; 69: 292-302; https://doi.org/10.1002/ana.22366
5. Lublin FD, Reingold SC, Cohen JA, et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology 2014; 83: 278-86; https://doi.org/10.1212/WNL.0000000000000560
6. Adelman G, Rane SG, Villa KF. The cost burden of multiple sclerosis in the United States: a systematic review of the literature. J Med Econ 2013; 16: 639-47; https://doi.org/10.3111/13696998.2013.778268
7. 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: 1123-36; https://doi.org/10.1177/1352458517694432
8. Kobelt G, Berg J, Lindgren P, et al. Costs and quality of life of patients with multiple sclerosis in Europe. J Neurol Neurosurg Psychiatry 2006; 77: 918-26; https://doi.org/10.1136/jnnp.2006.090365
9. Ernstsson, O, Gyllensten H, Alexanderson K, et al. Cost of illness of multiple sclerosis - a systematic review. PLoS ONE 2016; 11: e0159129; https://doi.org/10.1371/journal.pone.0159129
10. Gyllensten H, Kavaliunas A, Alexanderson K, et al. Costs and quality of life by disability among people with multiple sclerosis: a register-based study in Sweden. Mult Scler J Exp Transl Clin 2018; 4: 2055217318783352; https://doi.org/10.1177/2055217318783352
11. Gyllensten H, Wiberg M, Alexanderson K, et al. Costs of illness of multiple sclerosis in Sweden: a population-based register study of people of working age. Eur J Health Econ 2018; 19: 435-46; https://doi.org/10.1007/s10198-017-0894-6
12. Svendsen B, Myhr KM, Nyland H, et al. The cost of multiple sclerosis in Norway. Eur J Health Econ 2012; 13: 81-91; https://doi.org/10.1007/s10198-010-0286-7
13. Jennum P, Wanscher B, Frederiksen J, et al. The socioeconomic consequences of multiple sclerosis: a controlled national study. Eur Neuropsychopharmacol 2012; 22: 36-43; https://doi.org/10.1016/j.euroneuro.2011.05.001
14. Montalban X, Hauser SL, Kappos L, et al.; ORATORIO Clinical Investigators. Ocrelizumab versus Placebo in Primary Progressive Multiple Sclerosis. N Engl J Med 2017; 376: 209-20; https://doi.org/10.1056/NEJMoa1606468
15. Angelis A, Lange A, Kanavos P. Using health technology assessment to assess the value of new medicines: results of a systematic review and expert consultation across eight European countries. Eur J Health Econ 2018; 19: 123-52; https://doi.org/10.1007/s10198-017-0871-0
16. Cortesi PA, Assietti R, Cuzzocrea F, et al. Epidemiologic and Economic Burden Attributable to First Spinal Fusion Surgery: Analysis From an Italian Administrative Database. Spine (Phila Pa 1976) 2017; 42: 1398-404; https://doi.org/10.1097/BRS.0000000000002118
17. Fornari C, Madotto F, Demaria M, et al. Record-linkage procedures in epidemiology: an Italian multicentre study. Epidemiol Prev 2008; 32 (3 Suppl): 79-88
18. Blasi F, Cesana G, Conti S, et al. The clinical and economic impact of exacerbations of chronic obstructive pulmonary disease: a cohort of hospitalized patients. PLoS One 2014; 9: e101228; https://doi.org/10.1371/journal.pone.0101228
19. Iannazzo S, Iliza AC, Perrault L. Disease-Modifying Therapies for Multiple Sclerosis: A Systematic Literature Review of Cost-Effectiveness Studies. Pharmacoeconomics 2018; 36: 189-204; https://doi.org/10.1007/s40273-017-0577-2
20. Battaglia M, Kobelt G, Ponzio M, et al.; European Multiple Sclerosis Platform. New insights into the burden and costs of multiple sclerosis in Europe: Results for Italy. Mult Scler 2017; 23(2_suppl): 104-16; https://doi.org/10.1177/1352458517708176
21. Scalone L, Fagiuoli S, Ciampichini R, et al. The societal burden of chronic liver diseases: results from the COME study. BMJ Open Gastroenterol 2015; 2: e000025; https://doi.org/10.1136/bmjgast-2014-000025
22. Karampampa K, Gustavsson A, Miltenburger C, et al. Treatment experience, burden and unmet needs (TRIBUNE) in MS study: results from Italy. Mult Scler 2012; 18(2 Suppl): 29-34; https://doi.org/10.1177/1352458512441566c
23. Tyas D, Kerrigan J, Russell N, et al. The distribution of the cost of multiple sclerosis in the UK: how do costs vary by illness severity? Value Health 2007; 10: 386-9; https://doi.org/10.1111/j.1524-4733.2007.00192.x
24. Italian Association of Multiple Sclerosis (AISM). Barometro della sclerosi multipla 2018. AISM, 2018. Available at: https://www.aism.it/il_barometro_della_sm_2018 (last accessed July 2019)
25. Karampampa K, Gustavsson A, Miltenburger C, et al. Treatment experience, burden and unmet needs (TRIBUNE) in MS study: results from five European countries. Mult Scler 2012; 18(2 Suppl): 7-15; https://doi.org/10.1177/1352458512441566
26. Orme M, Kerrigan J, Tyas D, et al. The effect of disease, functional status, and relapses on the utility of people with multiple sclerosis in the UK. Value Health 2007; 10: 54-60; https://doi.org/10.1111/j.1524-4733.2006.00144.x
27. Nazareth TA, Rava AR, Polyakov JL, et al. Relapse prevalence, symptoms, and health care engagement: patient insights from the Multiple Sclerosis in America 2017 survey. Mult Scler Relat Disord 2018; 26: 219-34; https://doi.org/10.1016/j.msard.2018.09.002
28. O’Connor P, Wolinsky JS, Confavreux C, et al.; TEMSO Trial Group. Randomized trial of oral teriflunomide for relapsing multiple sclerosis. N Engl J Med 2011; 365: 1293-303; https://doi.org/10.1056/NEJMoa1014656
29. Kappos L, Radue EW, O’Connor P, et al.; FREEDOMS Study Group. A placebo-controlled trial of oral fingolimod in relapsing multiple sclerosis. N Engl J Med 2010; 362: 387-401; https://doi.org/10.1056/NEJMoa0909494
30. Coles AJ, Twyman CL, Arnold DL, et al.; CARE-MS II investigators. Alemtuzumab for patients with relapsing multiple sclerosis after disease-modifying therapy: a randomised controlled phase 3 trial. Lancet 2012; 380: 1829-39; https://doi.org/10.1016/S0140-6736(12)61768-1
31. Gold R, Kappos L, Arnold DL, et al.; DEFINE Study Investigators. Placebo-controlled phase 3 study of oral BG-12 for relapsing multiple sclerosis. N Engl J Med 2012; 367: 1098-107; https://doi.org/10.1056/NEJMoa1114287
32. Capra R, Cordioli C, Rasia S, et al. Assessing long-term prognosis improvement as a consequence of treatment pattern changes in MS. Mult Scler 2017; 23: 1757-61; https://doi.org/10.1177/1352458516687402
33. Furneri G, Santoni L, Marchesi C, et al. Cost-effectiveness analysis of delayed-release dimethyl-fumarate in the treatment of relapsing-remitting multiple sclerosis in Italy. Farmeconomia. Health economics and therapeutic pathways 2016; 17: 67-80; https://doi.org/10.7175/fe.v17i2.1251
34. Guarrera GM, Furian C, Eleopra R, et al. Health Technology Assessment: Fingolimod. QIJPH 2013; 11
35. Dorman E, Kansal AR, Sarda S. The budget impact of introducing delayed-release dimethyl fumarate for treatment of relapse-remitting multiple sclerosis in Canada. J Med Econ 2015; 18: 1085-91; https://doi.org/10.3111/13696998.2015.1076826
36. Furneri G, Marchesi C, Santoni L. Budget impact analysis of delayed-release dimethyl-fumarate in the treatment of relapsing-remitting multiple sclerosis in Italy. Farmeconomia. Health economics and therapeutic pathways 2016; 17: 29-39; https://doi.org/10.7175/fe.v17i1.1237