The burden of multiple sclerosis 2015: Methods of data collection,assessment and analysis of costs,quality of life and symptoms

Introduction

Burden-of-illness studies on multiple sclerosis (MS) have a long history. The first study dates back to the 1980s in the United Kingdom, ¹ before the introduction of disease-modifying treatments (DMTs). Over the past 30 years, these studies have been continuously updated, ² and the methods and data have improved, along with the development of the general methods for and scientific discussions around cost-of-illness studies.^3–7

The strong interest in the cost of MS in early studies is explained by the high social cost of a disease where no treatment existed,^1,7 while later studies were triggered by the introduction of costly drugs and their potential for long-term cost-savings from early investment. Treatment in MS is essentially preventive, that is, the focus is on suppression of activity in the hope that further disability will be prevented. The development of new criteria leading to earlier diagnosis^8,9 and initiatives arguing for early diagnosis and early effective intervention to preserve brain loss reinforces this approach. ¹⁰ This raises, however, the question of the value to society of this early investment of public funds in view of the high cost of treatment, and this question can only be answered in the long term. In diseases such as MS, prevalence costs (i.e. total costs of MS in a given jurisdiction) do not directly answer the question of the development of costs over time (incidence costs). If related to disease development, they can, however, be used to model long-term costs and outcomes of the disease and its management. ¹¹

Models allow the combination of data on resource use and resource loss, patient outcomes and disease progression to estimate the long-term development under different scenarios, with different interventions, provided all data sets contain a relevant variable based on which the data can be merged. In MS, disability is routinely measured with the Expanded Disability Status Scale (EDSS), ¹² be it as an efficacy measure in clinical trials or as an outcome measure in patient registries. While the measure has a number of shortcomings, ¹³ it has been found to be well suited for use in economic studies as it has a clear link to both health-related quality of life (HRQoL) assessed as utility and costs. ¹¹ It thus answers the demand for a variable that allows linking HRQoL, costs (use and loss of resources) and disease progression.

A prerequisite for models are data. Models are only as good as the quality of the underlying data, and – regardless of the modelling approach – all models should give the same results if they are based on the same data. In a global health environment, one would clearly want to have data that are collected using the same methods and are recent at least when it comes to resource consumption. We have standard measures to collect HRQoL and in particular health state utility. ¹⁴ If linked to a given health state, for example, an EDSS level, utilities will not ‘age’ and are similar across countries if using the same reference system.^15,16 Costs, on the other hand, need to be updated regularly, as consumption changes with new management strategies, new treatments and changes in organisation of health care systems.

This study updates and expands the information on costs and HRQoL of patients with MS in 16 European countries, and summary results have been published. ¹⁶ It uses the same methods as an earlier survey in nine countries to allow, with a number of precautions, illustration of the development of costs over the decade. ¹⁵

This article details the study approach and methods used, while the results for the different countries are described in individual papers in this supplement.

Study approach

We collected individual patient data by level of disability (EDSS) to illustrate the development of costs and utilities as the disease progresses. Patient-level data can be analysed and used in several ways: together with epidemiological data on the distribution of disease severity in the population, they allow calculating the total cost of illness; combined with natural history data, they allow modelling the long-term costs of the disease; by adding clinical outcome data (from trials or registries), cost-effectiveness of different treatment strategies can be estimated. The basic requirement is to have large enough samples at each severity level to allow meaningful analysis.

Cost-of-illness studies can be ‘top-down’ or ‘bottom-up’. The former use national databases and research panels, including registries, for the estimations; the latter collect data directly from patients. The choice depends on the study question as both approaches have limitations. National databases never include all variables required for estimating the overall burden of illness and in the case of MS will lack differential diagnosis and disease severity; registries contain this information, but in many cases will have limited data on HRQoL, consumption and services. Collecting data from patients poses the challenges of self-assessment of disease severity and symptoms, recall bias and missing answers. But it has the advantage of providing complete information. Also, earlier studies have shown answers from patients to be reliable, for example, when comparing disability assessment in Sweden or hospitalisation and sick leave in Germany.^17,18

Data for such studies can be collected prospectively or retrospectively. Again, both approaches have advantages and shortcomings. Prospective data collection is very resource-intensive, takes a long time and generally limits the number of participants for cost reasons. Also, the mere fact of collecting the data may influence consumption. Retrospective data may include some recall biases and require strict rules for imputing missing values and handling outliers.

In all cases, however, the way patients are recruited influences the sample. Enrolling participants in MS clinics will tend towards a sample of early patients with active disease and on treatment; recruitment during a clinic visit will select the dependent variable of the study (resource consumption). Using advertising may include patients without MS, unless each participant’s diagnosis is verified. Recruiting from patient associations will provide a sample reflecting the association’s strategies, most often patients with more advanced disease but with active involvement. Internet surveys will increase participation towards younger participants in urban areas with better education and better employment opportunities but tend to yield lower answer rates than postal surveys that may, in turn, enrol older participants. This may result in samples that are not entirely representative of the patient population in a given country. However, depending on the question to be answered, this can be adjusted for if precise data on prevalence and severity distribution are available.

In this study, we collected all data retrospectively directly from patients, entirely anonymously, both on-line and with paper questionnaires, with the goal of including at least 500 patients per country covering the entire spectrum of the EDSS. In each country, a clinical expert participated and in particular reviewed and approved the study protocol and questionnaire and obtained ethical approval from the respective authorities.

Patient recruitment

The European Platform of MS Societies (EMSP) endorsed the study and encouraged its member associations to participate. As a result, 18 national MS societies in the 16 countries took part in the survey. They reviewed the study questionnaire and protocol and then invited their members using the means that were feasible and most efficient in their own setting: direct electronic mail, Internet site/forum/blog, alerts in regular news bulletins, inserts into information magazines and direct postal mailing. When needed, reminders were sent. In a small number of countries (Czech Republic, France, Portugal, Russia, Spain), the samples were complemented with random patients from clinics due to low response rates, most often the clinic of the clinical expert.

The survey was, in theory, open for respondents until a country had enrolled the required number of participants at all EDSS levels. In practice, answers were received within the 2 months following the invitation or during the month after a reminder, but the databases were closed only when analysis was to start. An exception was Germany where the survey was distributed to a very large number of members and was therefore open only for 2 months.

A total of 16,808 eligible patients participated in the study, after confirming informed consent. All disability levels were well represented in all countries, with between 6% and 14% of patients at each EDSS level (Figure 1). An exception was EDSS 9 where, as expected, fewer patients answered the survey.

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Figure 1.

Number of participants by disability level.

Data collection

The MS resource use questionnaire^© was developed in the late 1990s and adapted and improved over the past 15 years as it was used in different studies.^15,19,20 For this study, the questionnaire was revised with the help of an international clinical expert, and new questions relating to symptoms and their severity and the impact of MS on productivity while working were included. The questionnaire was translated into the different languages and verified by a second translator. Considering the availability of previous versions in most languages and their use in several studies, we did not back-translate and pre-test the questionnaire. It was then made available as paper/pdf version and on-line on a dedicated platform. Typically, the on-line data collection resulted in very few missing values as for most questions, the answer had to be provided before continuation to the next question was possible. Incomplete questionnaires were not included in the analysis. In addition, the range of inputs was controlled for many of the questions to avoid unreasonable answers.

The questionnaire collects data for MS, as opposed to data of patients with MS. Respondents were instructed to only include health care consumption relating to their MS, which, in view of the well-defined nature of the disease and the relatively young population with limited non-related co-morbidities, is considered to yield reliable answers. One way to formally exclude non-MS-related consumption would be to enrol a cohort matched on age, gender, education and socioeconomic status recruited from the population outside any contact with the health care system. Considering the size of our study and time involved, this was not judged to be feasible.

Data

The information requested included limited personal information, disease data including prevalent symptoms, HRQoL (utility), workforce participation, health care and service consumption and informal care. Time frames to which each question related were specified and adapted to minimise recall bias. Questions relating to the use or loss of resources or to specific disease manifestations were preceded by a yes/no question followed in the case of yes by more detailed questions. Examples are shown in Figures 2 and 3.

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Figure 2.

Sample questions.

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Figure 3.

Relapse assessment.

Considering the large number of countries in the study, country-specific questions had to be limited and a number of simplifications and compromises had to be made.

Disease data

Participants were asked about the year of their first symptoms and the year of diagnosis. For the older participants, this concerned events that happened 20 or more years ago, and the data are therefore approximate. Nevertheless, the information was felt to be important to detect a potential trend in the time to diagnosis over time. Indeed, it would appear that in younger patients with a more recent diagnosis, the time to diagnosis was shorter, ²¹ but the quality of the answers to this particular question and the variability between countries do not allow a more detailed analysis.

Although our focus was on EDSS, for completeness we also asked about the type of MS, providing a detailed description of relapsing-remitting, secondary progressive and primary progressive MS. The findings were not conclusive, with a high proportion of ‘unsure’, and did not represent what is considered to be the prevalence, similar to our earlier survey in 2005. ¹⁵ A number of factors may explain this. Patients may not always have been told about their type of MS, in particular when progressive disease starts, as DMTs are essentially limited to relapsing disease. Also, conversion to progressive disease is generally assessed retrospectively by neurologists, based on EDSS progression in the absence of relapses, and again not always communicated to patients. Finally, the complexity of the disease appears to make it difficult for patients to self-assess this.

Information on relapses during the past 3 months was collected as a simple yes/no/unsure answer following a description of what could constitute a relapse. In the affirmative, we asked about the number of relapses and treatment with corticosteroids (oral or infusions). Again, this question appeared difficult for patients, and around 20% in all countries answered with ‘unsure’. It is also possible, however, that the simple inclusion of the ‘unsure’ option increased answers in this category. For these patients, we assumed that the answer was no, but nevertheless excluded them from the calculations of relapse costs.

Participants’ self-assessed disability was collected using a descriptive scale derived from the original EDSS ¹² and also the patient-assessed Patient Determined Disease Steps (PDDS) ²² developed for the NARCOMS database ²³ in the United States and disability descriptions used in the European Database for Multiple Sclerosis (EDMUS) registry (www.edmus.org) in France (Figure 4). A recent evaluation of the feasibility and reliability of self-assessed EDSS has found it acceptable. ²⁴

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Figure 4.

Patient-assessed EDSS.

It has, however, to be borne in mind that self-assessed EDSS (Figure 4) is essentially based on ambulation and does not represent overall mobility such as assessed in HRQoL instruments; it also does not take into account neurological factors.

Analysis

Responses were analysed as available without source data verification due to the anonymity of the survey.

Rules to handle missing data were defined, but very few imputations were made. In particular, no corrections for missing outcome data (HRQoL, symptoms) were made. When a patient indicated having used a resource but omitted the quantity, we assigned the mean quantity of the sample; in the inverse case, the patient was defined as a user.

Illogical answers, occurring mainly in the paper surveys, were adjusted. For clinical characteristics, issues were resolved in consultation with the overall clinical expert. For example, age at first symptoms had to precede age at diagnosis, and if this was not the case, the former was treated as missing. Furthermore, it would not be acceptable to have secondary progressive MS and report an EDSS below or equal to 3. In such cases, MS type was considered as missing. For relapses, we implemented a cap of three relapses for the 3-month period. For resource variables, we used relevant boundaries to correct impossible answers. As an example, the sum of inpatient days and day admissions could not exceed 90 days per quarter.

Outliers for continuous variables were checked using patient listings and graphical inspection (e.g. Box plots) and application of a pre-defined cut-off relative to a maximum value. Depending on the type of outlier, these were set to missing or replaced using trimmed means (where 5% of the extreme values are eliminated) plus 1 standard deviation. For resource utilisation data, this applied only to the upper values, in view of their skewness (i.e. a minority of patients consuming the majority of the resources). For patient-reported out-of-pocket costs as well as the number and distance of transportation use, we applied this replacement method to the top 5% of extreme values regardless of the presence of outliers.

Valuation

We calculated costs from the societal perspective where all costs, regardless of whose cost it is, are included. Patient co-payments and out-of-pocket expenses are thus included. Costs are a function of the quantity of resources used and their cost. Unit costs in each country were obtained from public sources, and the main costs are shown in Table 1. Costing of home care and DMTs presented particular challenges. Home care was collected as the number of days where care was needed and the number of hours per day. In many countries, home care is delivered after individual assessment of the circumstances and needs, and costs can be capitated by day, week or month. Also, services are often provided by private or not-for-profit organisations. It is thus difficult to define an average hourly cost. In these cases, telephone interviews were done with service providers to arrive at an average cost per hour of care. For DMTs, list prices are easily available, but in many countries discounts apply. As these are not public, we used list prices and may therefore overestimate the cost of DMTs, particularly in Central and Eastern European countries.

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Table 1.

Selected unit costs.

Country (local currency)		Hospitalisations/medications				Consultations				Tests		Social/community services		Income
		Overnight stay in neurology	Day admission in neurology	Day visit in rehabilitation	Cortisone infusion (relapse treatment)	Neurologist	GP	MS nurse	Physiotherapist (30-min session)	MRI (brain)	MRI (spine)	Nurse visit (per day or per hour)	Home help (per hour)	Average national salary per hour – women	Average national salary per hour – men	Disposable income per hour
Austria	€	400 1	518 1	135 2	75 3	11 4	11 4	11 4	18 5	313 6	438 6	39/2690 7	34/2690 7	13 8	20 8	12 8
Belgium	€	284 9	32 9	101 9	190 9	63 9	21 9	36 10	22.3 9	93 9	93 9	18 9	33 9	22 11	24 11	13 11
Czech Republic	CZK	1890 12	1890 12	540 12	465 13	331 14	169 14	139 15	384 14	5076 14	6463 14	120 16	120 16	142 17	173 17	40 17
Denmark	DKK	5817 18	3164 18	997 18	1921 19	3164 18	128 20	128 20	207 21	3103 22	3103 22	370 23	130 24	167 25	210 25	123 25
France	€	279 26	456 26	250 26	162 27	43 28	24 28	10 29	21 28	69 28	69 28	370 30	22 31	18 32	22 32	4 32
Germany	€	388 33	309 34	49 35	100 36	47 36	21 35	10 37	20 38	125 39	125 39	8/54 40	14 40	22 41	27 41	15 41
Hungary	HUF	20885 42	12559 42	6306 42	1903 42	4050 42	1296 42	513 42	893 42	47193 42	46334 42	2944 42	1756 43	1532 44	1948 44	1024 44
Italy	€	156 45	234 45	111 45	125 46	21 45	21 45	12 47	19 45	248 45	150 45	18 48	8 49	15 50	19 50	10 50
The Netherlands	€	403 51	1642 51	213 51	302 52	226 51	18 53	93 54	40 55	247 56	244 56	74 57	23 57	17 58	28 58	11 58
Poland	PLN	214 59	728 59	77 59	163 60	32 59	25 59	12 59	38 61	470 62	675 62	20 63	20 63	23 64	28 64	15 64
Portugal	€	188 65	667 65	182 65	60.2 65	31 65	31 65	16 65	16 65	128 65	128 65	33 65	7 66	6 67	8 67	5 67
Russia	RUB	2358 68	1572 68	1040 68	98 69	1500 70	1500 71	1200 72	1500 71	1309 73	1309 73	261 74	261 74	183 75	246 75	175 75
Spain	€	566 76	280 76	24 76	35 77	87 76	43 76	17 76	15 76	157 76	157 76	20 78	10 79	14 80	16 80	12 80
Sweden	SEK	7325 81	4293 81	6061 81	1792 82	4075 81	1777 81	2505 81	1009 81	2944 81	2944 81	319 83	281 83	183 83	212 84	143 84
Switzerland	CHF	1355 85	2652 85	514 86	532 87	54 88	54 88	54 88	48 89	344 88	324 88	71 90	39 90	42 91	50 91	24 91
UK	GBP	558 92	433 92	266 92	187 93	176 94	65 95	52 95	52 94	138 96	138 96	43 97	17 97	13 98	15 98	9 98

GP: general practitioner; MS: multiple sclerosis; MRI: magnetic resonance imaging.

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Production losses were estimated using the human capital approach, where the production of an individual is valued at the market price (in this case, the cost of employment, that is, the average national gross wage including employers’ costs). ⁶ Costs are calculated only for patients of working age. The hourly cost used is presented for all countries in Table 1.

Informal care is estimated with the cost of leisure time, calculated as the disposable income, that is, net income after social contributions and taxes (Table 1). Contrary to our earlier studies, however, we have not assigned a cost to all hours of caregiving. As the cost is based on the national average salary for average working times, we have capitated costs at 8 hours (on average) per day. This may underestimate the costs for those very severe patients that indicated round-the-clock informal care that, in some cases, may involve more than one family member. On the other hand, family members may provide intermittent care over the course of a 24-hour day, and average working hours may represent the actual care time. This is similar to an employed caregiver on a monthly retainer. Our data do not allow exploring this further as we only collected the hours of care, not the number of caregivers, which in future studies should be included. We present the actual hours of informal care at each EDSS level separately from the cost.

Results

Results in the 16 countries depend very much on the composition of the samples in terms of age and hence in terms of disease severity, and employment situation that in turn will influence costs. The mean age in the countries ranged from 38.5 to 56.7 years, mean EDSS from 2.9 to 5.5, employment of patients below retirement age from 31% to 65% and DMT usage from 26% to 79%. Consequently, the mean total costs are not meaningful as a measure of the cost of MS in a given country, nor can they be compared among each other. We therefore present all results by individual EDSS level or for mild, moderate and severe disease (EDSS 0–3, 4–6.5 and 7–9, respectively). Figure 5 (reprinted with permission) shows the mean total annual costs per patient for these severity groups in all 16 countries. All costs have been converted into EUR 2015, using purchasing power parity exchange rates (PPP EUR 2015).

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Figure 5.

Mean total annual cost per patient by disease severity and resource type, PPP EUR 2015 (N = 16,808). ¹⁵

Conclusion

This study provides an update on patterns of resource use, costs by disability, HRQoL and prevalent symptoms for patients with MS across Europe. There is a wealth of data provided by 16,808 patients in 16 countries that will be useful in a number of ways, for clinicians, policy-makers, health technology assessment bodies and, not the least, patients.

As always, however, the study also triggers a number of questions that prompt further research. Examples would be the development of labour force participation and the content of work, the organisation of services to support people with disabilities, the impact of symptoms such as fatigue and cognitive deficits on work and activities of daily life and, most of all, health systems research into the reasons other than the needs of the disease that appear to drive health care consumption.