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Research Questions

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Define Hypothesis

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Identify Dependent and Independent Variables and Type of Data for the Variables

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Include what types of statistical tests were used for the variables.

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Report other potential assumptions and limitations of your review not listed by the author.

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Evaluate the article and identify potential ethical considerations that may have occurred when sampling, collecting data, analyzing data, or publishing results. Summarize your findings below in 250-500 words. Provide rationale and support for your evaluation.

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Contact patterns and costs of multiple sclerosis in the Swedish
healthcare system—A population‐based quantitative study.

Lind, Jonas (AUTHOR) Jonas.lind@rjl.se
Persson, Sofia (AUTHOR)
Vincent, Jonatan (AUTHOR)
Lindenfalk, Bertil (AUTHOR)
Oliver, Brant J. (AUTHOR)
Smith, Andrew D. (AUTHOR)
Andersson Gäre, Boel (AUTHOR)

Brain & Behavior. Jun2022, Vol. 12 Issue 6, p1-8. 8p.

Article

*MULTIPLE sclerosis
*QUANTITATIVE research
*MEDICAL care
*HOSPITALS
*MEDICAL care costs

co‐production
cost analysis
healthcare utilization
multiple sclerosis
patient acceptance of health care

https://library-gcu-edu.lopes.idm.oclc.org/

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primary health care
value architectures

622110 General Medical and Surgical Hospitals
622111 General (except paediatric) hospitals

Background: The burden of disease for persons with multiple sclerosis (MS) and society is changing due to new treatments.
Knowledge about the total need for care is necessary in relation to changing needs and new service models. Objective: The aim
of this study was to describe the contact patterns for MS patients, calculate costs in health care, and create meaningful
subgroups to analyze contact patterns. Methods: All patients diagnosed with MS at Ryhov Hospital were included. All contacts in
the region from January 1, 2018, until September 30, 2019, were retrieved from the hospital administrative system. Data about
age, sex, contacts, and diagnosis were registered. The cost was calculated using case costing, and costs for prescriptions were
calculated from medical files. Results: During the 21‐month period, patients (n = 305) had 9628 contacts and 7471 physical visits,
with a total cost of $7,766,109. Seventeen percent of the patients accounted for 48% of the visits. The median annual cost was
$7386 in the group with 10 or fewer visits, compared to $22,491 in patients with more than 50 visits. Conclusion: There are
considerable differences in the utilization of care and cost between patients with MS in an unselected population, meaning that
the care needs to be better customized to each patient’s demands. [ABSTRACT FROM AUTHOR]

Copyright of Brain & Behavior is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or
posted to a listserv without the copyright holder’s express written permission. However, users may print, download, or email
articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer
to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Section of Neurology, Department of Internal Medicine, County Hospital Ryhov, Jönköping, Sweden
Division of Neurobiology, Department of Biomedical and Clinical Sciences, Linköping University, Linköping,, Sweden
Department of Public Health and Health Care, Region Jönköping County, Jönköping, Sweden
Jönköping Academy for Improvement of Health and Welfare, School of Health and Welfare, Jönköping University, Jönköping,

Sweden
Departments of Community and Family Medicine, Psychiatry, and the Dartmouth Institute for Health Policy and Clinical Practice

at the Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover New Hampshire,, USA
Multiple Sclerosis Center, Dartmouth‐Hitchcock Medical Center and Geisel School of Medicine Dartmouth College, Lebanon

New Hampshire,, USA
Jönköping Academy for Improvement of Health and Welfare, School of Health and Welfare, Jönköping University, Jönköping and

Futurum, Region Jönköping County,, Sweden

5138

2162-3279

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10.1002/brb3.2582

157642334

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Contact patterns and costs of multiple sclerosis in the Swedish
healthcare system—A population‐based quantitative study

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Background: The burden of disease for persons with multiple sclerosis (MS) and society is changing due to new treatments. Knowledge
about the total need for care is necessary in relation to changing needs and new service models. Objective: The aim of this study was to
describe the contact patterns for MS patients, calculate costs in health care, and create meaningful subgroups to analyze contact patterns.
Methods: All patients diagnosed with MS at Ryhov Hospital were included. All contacts in the region from January 1, 2018, until September
30, 2019, were retrieved from the hospital administrative system. Data about age, sex, contacts, and diagnosis were registered. The cost was
calculated using case costing, and costs for prescriptions were calculated from medical files. Results: During the 21‐month period, patients
(n = 305) had 9628 contacts and 7471 physical visits, with a total cost of $7,766,109. Seventeen percent of the patients accounted for 48% of
the visits. The median annual cost was $7386 in the group with 10 or fewer visits, compared to $22,491 in patients with more than 50 visits.
Conclusion: There are considerable differences in the utilization of care and cost between patients with MS in an unselected population,
meaning that the care needs to be better customized to each patient’s demands.

Keywords: co‐production; cost analysis; healthcare utilization; multiple sclerosis; patient acceptance of health care; primary health care;
value architectures

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There are considerable differences in care utilization and cost between patients with multiple sclerosis in an unselected population. Less than
one‐fifth of the patients had half of the total number of visits, whereas one‐third accounted for 48% of the costs. The median cost was three
times higher in the group with more than 50 visits than in the group with less than 11 visits. Our data suggest that the care needs to be better
customized to each patient’s demands.

brb32582-gra-0001
.

INTRODUCTION
Multiple sclerosis (MS) can significantly diminish income‐earning ability, impose an extreme financial burden on patients and their families
(Owens, 2016), and substantially impact the quality of life and functional capability (Chruzander et al., 2014; Isaksson et al., 2005). Active
management, focusing on the person with MS, can minimize disease impact, maximize the quality of life, and espouse a wellness philosophy
(Thompson et al., 2018). To provide the best possible care with limited resources, it is crucial to prioritize the MS care that is of the highest
value in economic terms and patients’ perceived quality of life, ability to earn income, societal participation, and to decrease the burden on the
individual and the social welfare system (Boehmer et al., 2016; Eton et al., 2013).

MS is a costly and complex chronic disease, ranking second behind congestive heart failure in the United States (Adelman et al., 2013; Chen
et al., 2017). The annual costs stemming from MS in the healthcare system and society have been estimated to be 27,000 to 68,000$ per
patient in the European Union (Kobelt and Pugliatti, 2005; Kobelt et al., 2017). Cost estimates that are currently available have often been
based on processes during a limited period of time and are often focused on pharmacological therapy (Melendez‐Torres et al., 2017).

MS is a chronic disease affecting the central nervous system and is treated with drugs targeting the immune system. Cost studies cannot be
limited to MS specialty care alone but rather should include services in the entire healthcare system. Patients with MS have been reported to
have a high use of care in primary care, neurology, and rehabilitation departments in open care and as inpatients (Chruzander et al., 2015).
The reason for the contacts in care is usually expected to be MS, but because data about comorbidities are limited, it could also be due to a
condition other than MS (Marrie et al., 2015).

MS therapeutic development has occurred rapidly, thereby creating a greater opportunity for individualized treatment and better outcomes
(Piehl, 2014). Therefore, persons with MS may have different needs now than in the era before modern therapy. We aim to identify areas of

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need in persons living with MS and measure costs in the Swedish healthcare system, including other conditions than MS. A method
commonly used for distributing hospital costs is the Swedish version of case costing (CC). The method applies national standards for coding,
calculating, and distributing costs to single contacts and/or patients. This is incorporated into a system that calculates costs over time and
distributes costs according to national principles (Fitger, 2015).

The aim of this study was to describe the contact patterns for the MS population at a regional hospital in Sweden, serving a defined
population in a geographic area, calculate the costs for these persons in the healthcare system, and create meaningful subgroups of contact
patterns. This could establish a basis for future studies aiming to improve MS care by addressing care needs specific to each subgroup.

MATERIALS AND METHODS
Utilization of health care and costs in the healthcare system in Region Jönköping’s County (RJC) was analyzed in a retrospective
descriptive cross‐sectional study. All persons with a diagnosis of MS (International Classification of Diseases 10 code G35) at the Section of
Neurology at the Department of Internal Medicine at Ryhov from January 1, 2018, until September 30, 2019, were included (n = 305). The
region is responsible for primary and specialized care but not for nursing homes or care given in patients’ homes. Ryhov Hospital is
accountable for all persons with MS in three municipalities with a population of 160,800 in the southeastern part of Sweden ((SCB) SS, n.d.).
Anonymized data were retrieved from Diver (DivePort version 7.0, Dimensional Insight, Inc.), the information system for analysis and
reporting of care given. Data about age, sex, contact types, place of contact, the profession of caregiver, and diagnosis were collected. The
number of visits was defined as physical visits, and contacts were defined as physical visits, telemedicine, phone calls, and administrative
contacts for prescriptions or certificates.

CC was calculated as specified by the Swedish Association of Local Authorities and Regions (Fitger, 2015). CC is only used for the care
given at hospitals. Primary care costs were calculated based on the primary care contact type (visit at the clinic, a home visit, telemedicine, a
letter/telephone call) and performer, using average costs for the corresponding combinations in the regions’ primary care.

Costs for prescribed pharmaceuticals in outpatients were calculated from all prescriptions within RJC’s medical files during the study period.
All costs are presented as annual costs in USD, based on an exchange rate of 1 USD equal to 9.30 SEK, the exchange rate during the study
period.

Statistical analysis

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Data were initially analyzed using descriptive statistics. Subgroups were analyzed with the aid of graphical analysis and conditional
formatting. The patients were first divided into three equally large main groups based on the number of visits. The group with 26 or more

visits

had a wide range of visits and was therefore divided into two groups, one consisting of two‐thirds of the remaining patients and the last with
the one‐ninth of all patients with the most visits. Thereby the groups were corresponding to 3/9, 3/9, 2/9, and 1/9 of all patients.

Data were analyzed using Statistica Version 13.1, Dell Inc. For comparison between the two groups, a t‐test was used, and data were
presented as the mean and standard deviation (SD). For ordinal data and data not normally distributed, the median and interquartile range
(IQR) were given, and the Mann–Whitney U‐test was used to compare groups. For the nominal data, the chi2 test was performed. For groups
with fewer than five respondents, the analysis was completed with Fisher’s exact test. Kruskal–Wallis’s analysis of variance (ANOVA)
followed by the median test was used for comparisons between more than two groups. Correlations were tested using Spearman rank‐order
correlations. Analyses were completed with multiple linear regression. Differences were considered significant at p <.05.

Ethics
The study was approved by the Swedish Ethical Review Authority (Dnr 2020–03745).

RESULTS

Demographics/sampling
Three hundred five patients were included, 199 women with a mean age of 46.8 (SD 14.5) and 106 men with a mean age of 49.1 (SD 14.3;
not significant [n.s.] at p <.05 level). Patients divided into four groups according to age are presented in Table 1.

1 TABLEDemographics. The number of persons according to age and sex. Number of contacts and physical visits in each age group.
Distribution of patients according to subgroups based on age and number of visits during the study period

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WomenMenPersons Total
share

Contacts Physical
visits

Number of visits in
subgroups

Share of visits in
subgroups

Age
group

n (%) n (%) n (%) n n 0–
10

11–
25

26–
50

51+ 0–
10

11–
25

26–
50

51+

0–24 15 5% 4 1% 19 6% 506 409 2 12 5 0 11% 63% 26% 0%
25–44 73 24%34 11% 107 35% 2926 2266 39 32 29 7 36% 30% 27% 7%
45–64 84 28%55 18% 139 46% 4956 3908 46 44 24 25 33% 32% 27% 7%
65–99 27 9% 13 4% 40 13% 1308 888 14 15 7 4 35% 37% 18% 10%
Total 199 65%106 35% 305 100% 9696 7471 101 103 65 36

Contact patterns

All contacts
There were 9628 contacts and 7471 physical visits. The number of contacts and visits at the different clinics is presented in Table 2.
Physician’s letters or phone contacts were 1178, and 893 were from other staff; 667 of these were from primary care.

2 TABLEAnnual cost and cost of prescriptions in different age groups

PersonsTotal cost Prescriptions

Age‐group n Median Interquartile range (IQR)Median IQR
0–24 19 8998 5984–15,307 140 52–201
25–44 107 10,279 5887–22,957 601 84–9502
45–64 139 12,839 6374–21,125 2064 466–9970
65–99 40 9291 4152–10,847 983 331–2358

1 Note: Costs are presented in USD.

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Four percent of the patients accounted for 15% of the contacts, and 19% accounted for 49% of healthcare contacts. Forty‐six percent of the
patients had only 16% of the contacts. There was no significant trend that the number of visits increased as patients got older (Spearman ρ
0.037, n.s. at p <.05 level).

Physical visits
The most common main diagnosis for outpatients was MS in 2622 visits. The number of visits at the different clinics ranged from 16 to 2562,
with most in primary care; see Table 2. Seventeen percent of the persons accounted for 48% of the visits, and 49% of the patients with the
least number of visits accounted for 16%. Ninety percent of the patients had visited at least three clinics (primary care considered one clinic),
and 27% had visited seven or more clinics. The mean number of clinics that patients had visited was 4.4 (SD 2.0), of which 39% were to a
physician. The number of visits to different professionals is presented in Table 3.

3 TABLEContact, visits, and costs for MS patients in different specialties

Mean number of visits
in subgroups based on

the total number of
visits

Annual
costs

Clinic Persons
with visits

Percent of
persons

Number of
contacts

Number of
physical

visits

0–10 11–25 26–
50

51+Prescriptions0001 CPP Total cost

Neurology 305 100% 2103 1441 2.6 4.7 6.1 8.3 $163,069 $1,266,883$2,903,953
Primary care 261 86% 3664 2599 2.0 5.7 11.5 29.3$87,209 $242,609 $329,817
Rehab center 145 48% 1230 1150 0.6 1.7 5.8 15.1$ – $103,208 $103,208
Rehabilitation
medicine

54 18% 906 875 0.0 0.9 4.3 13.9$4198 $394,690 $398,888

Ophthalmology 82 27% 259 237 0.9 0.9 1.0 1.6 $1867 $29,711 $31,578
Surgery 68 22% 215 169 0.2 0.3 0.9 1.6 $3507 $165,699 $169,207
Gynecology 99 32% 297 256 0.4 0.7 1.7 1.0 $4617 $41,348 $45,965
Psychiatry 14 5% 144 112 0.0 0.2 0.3 1.9 $2989 $56,604 $59,593
Orthopedics 42 14% 129 109 0.0 0.4 0.5 0.8 $828 $59,781 $60,609

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Mean number of visits
in subgroups based on

the total number of
visits

Annual
costs

Clinic Persons
with visits

Percent of
persons

Number of
contacts

Number of
physical

visits

0–10 11–25 26–
50

51+Prescriptions0001 CPP Total cost

Oncology 9 3% 136 101 0.0 0.1 0.5 1.4 $2850 $64,190 $107,040
Urology 37 12% 145 90 0.1 0.3 0.5 0.8 $1862 $47,635 $49,497
Dermatology 33 11% 94 85 0.1 0.3 0.6 0.3 $1959 $10,459 $12,418
Ear, nose, throat 36 12% 79 75 0.1 0.3 0.2 0.6 $149 $14,126 $14,275
Internal
medicine/geriatrics

19 6% 174 121 0.0 0.0 0.7 2.0 $5415 $106,412 $111,827

Other 18 6% 53 51 0.0 0.2 0.3 0.2 $4234 $35,659 $39,893
Total 305 7525 7471 6.3 17.0 35.1 72.7$1,798,753 $2,639,015$4,437,768

2 * Costs for drugs given at the day‐care unit are included in the CPP.

Hospitalizations
Seventy‐eight persons had 156 hospitalizations. The most common department was neurology, 65; followed by surgery, 25; urology, 14;
internal medicine and geriatrics, 14; rehabilitation medicine, nine; and psychiatry, nine. MS or demyelinating disease of the central nervous
system was the primary diagnosis in 37 of the hospitalizations; nine were due to urinary tract infection, eight were due to nephritis, four were
alcohol‐related disorders, and four were erysipelas. The patient with the most hospitalizations had nine episodes, mainly due to MS and
epileptic seizures.

Costs
The total cost of the MS patient group during the studied interval was $7,766,109, corresponding to an annual cost of $4,437,777 with a
median cost of $9937 (range $222–109,723, IQR $5923–20,400) per patient. There was no difference in cost between women and men
(median $9937 for women, and $12,760 for men, n.s. at p <.05 level). Costs in the age groups are presented in Table 2. There was no linear correlation between patients' age and cost (ρ = −0.06, n.s. at p <.05 level). In a Kruskal–Wallis ANOVA between the age groups, the difference was significant (p <.01), but the only significant differences were between the 65 or older group and the 25–44 and 45–64 groups.

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As expected, the cost increased with a higher number of visits (ρ = 0.36, p <.05). The total annual CC without cost for prescriptions was $2,639,015. The costs at different clinics are presented in Table 3.

Prescriptions
There were 4079 prescriptions at a total annual cost of $1,798,753. The median cost was $1098 (range $0–53,412, IQR 199–8444. The
highest number of prescriptions per year were paracetamol n = 121, zopiclone n = 85, dimethyl fumarate 55, oxycodone 53, and gabapentin n
= 51. The median cost of prescriptions was $840 (range: 0 −35,584) for men and $1354 (range 0–53,412) for women (n.s. at p <.05 level). The median cost in the different age groups is presented in Table 2. Kruskal–Wallis ANOVAs showed that the differences between groups were significant (p <.001).

Six drugs had yearly costs exceeding $60,000 dimethyl fumarate $586,036, fingolimod $480,732, glatiramer acetate $244,109, interferon
beta‐1a $ 183,484, interferon beta‐1 b $91,352, and teriflunomide $62,171. Pegylated interferon beta‐1a, encorafenib, binimetinib,
gabapentin, and sodiumoxybate all had costs exceeding $10,000. Encorafenib and binimetinib are used as treatments for malignant
melanoma.

Subgroups based on number of visits
Subgroups based on the number of visits were created. The results are presented in Tables 2–4. The group with more than 50 visits was
significantly older than patients with 25 to 50 visits (p =.019). There were no other differences in age between groups.

4 TABLEVisits at different professions and annual costs in subgroups based on the total number of visits

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Mean
number of
visits per

profession
(SD)

Median
cost in
USD
(IQR)

Subgroups
based on
the
number of
visits

Total
number
of
visits

Physician Nurse PhysiotherapistOccupational
therapist

PsychologistCounselorMidwifeOptician/orthopticianSpeech
therapist

Other Prescriptions

0–10 (n =
101)

622 2.9 2.1 0.4 0.3 0.0 0.1 0.3 0.0 0.0 0.1 $4948

(1.8) (2.0) (0.8) (0.6) 0 (0.6) (0.6) (0.1) (0.2) (0.6) (104–
13,110)

(965–
4774)

(4660–
15,624)

11–25 (n =
103)

1730 7.8 5.2 1.6 0.7 0.3 0.4 0.5 0.2 0.0 0.2 $721

(4.3) (4.3) (2.2) (1.3) (1.3) (1.7) (1.3) (0.5) (0.4) (0.5) (145–
6811)

(3725–
7253)

(5515–
16,095)

26–50 (n =
65)

2282 14.4 8.8 5.6 1.9 1.7 1.0 1.2 0.1 0.2 0.1 $1338

(8.2) (8.7) (7.5) (2.7) (4.1) (3.1) (3.2) (0.6) (1.2) (0.7) (239–
6997)

(6005–
17,185)

(9418–
21,463)

51+ (n =
36)

2837 24.6 29.3 17.1 1.0 2.1 1.8 0.2 0.3 0.1 1.9 $1151

(14.7) (22.8) (21.0) (1.8) (6.5) (4.2) (0.4) (0.5) (0.5) (4.6) (571–
3844)

(13,099–
28,696)

(15,161–
33,862)

Total
number of
visits

7471 2901 2370 1192 257 211 187 171 40 25 117

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The cost for prescriptions was not statistically significant between the subgroups; however, CC increased with an increasing number of visits
(p <.001 for all comparisons except 26 to 50 visits vs. the 51+ group; see Table 3). The total annual cost in the respective subgroups was $1,129,994, $1,186,286, $1,135,945, and $985,553. Figure 1 presents the subgroups' median values and IQR (p <.001 for all comparisons between separate groups, except 1–10 vs. 11–25 and 26–50 vs. 51–131 visits). The median annual cost in USD, the number of visits per year, and the number of persons in subgroups based on the total number of visits are presented graphically in Figure 2.

1 Median yearly cost of health care in USD in subgroups based on the number of visits

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2 Median annual cost in USD, number of visits per year, and the number of persons in subgroups based on the total number of visits are
presented

DISCUSSION
We found considerable differences in care utilization and costs between persons in an unselected MS population, summarized in Figure 2.
Less than one‐fifth of the persons had half of the total number of visits. Our data do not provide an easy explanation, but overutilization of
care has been found in other groups of patients (Knapp et al., 2021). There was no correlation between age and number of visits when tested
with linear regression. The cost was lowest in patients older than 65 years, probably due to fewer persons on expensive disease‐modifying
treatments, which can be confirmed with data from the MS registry, where only 15 of the patients 65 years or older were on treatment and
then mostly on interferons (Swedish Neuro Registries, n.d.).

CC can help answer questions such as which group of patients costs the most at a clinic or the cost of a particular service for a specific group
of patients (Tan et al., 2014). It makes it possible to look at individual patients to follow the cost they create. Since the calculations are based
on each event, it is possible to measure change over time, both costs for specific patients or processes, and to better evaluate interventions
in economic terms. CC is also an excellent tool for making simulations.

We found that the variation in the number of visits is driven mainly by rehabilitation, with an increasing number of visits to the physiotherapist
in each subgroup based on the number of visits. In the group with up to 10 visits, only 8% of the visits were to a physiotherapist, while for the
group with more than 50 visits, the portion increased to 27%. However, there was no increase in the number of visits to an occupational

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therapist. Compared to previous reports, one crucial difference in our material is that hospitalizations, especially in rehabilitation medicine, are
far less common (Chruzander et al., 2015). This is most likely a combined result of improvements in treatment and a general change in
rehabilitation medicine from hospitalizations to different forms of outpatient rehabilitation.

To get a better understanding, patients were divided into four subgroups based on the number of visits (see Figure 2). Patients in the groups
with a higher number of visits had higher total costs, but the cost for prescriptions was not affected by the subgroup. The reasons for a patient
to have many visits and high costs have to be further explored. One explanation could be that there is a limit to how much a patient needs to
pay per year in the Swedish system, that is, after you reach that limit, care for the rest of the year is free for the individual, which might affect
both patients and caregivers in decisions about visits. The ability to cope with a chronic disease and comorbidities might be an essential
factor (Strober, 2017). The main number of visits for patients with many visits was related to MS, either at neurology, rehabilitation, or primary
care clinics, and might be attributed to disease activity or disabilities that have not required new expensive medications.

The most significant cost component for individuals with MS is disease‐modifying therapies, representing, for example, 53% of the MS‐
associated cost in Germany (Müller et al. 2020). The rising cost of MS treatment is a well‐known problem, with the cost of most disease‐
modifying drugs in the United States exceeding $70,000 a year (Hartung, 2017). Sweden has a high use of off‐label rituximab (Salzer et al.,
2016), with a lower annual cost of approximately $1300. According to the Swedish National Quality Registry for MS, 98 of the 305 patients in
this study received treatment with rituximab in March 2019 (Swedish Neuro Registries, n.d.). The rituximab cost is included in CC in our
study, as the treatment was given at the hospital.

This study differs from previously published studies about costs in MS, as all healthcare costs were retrieved from the region responsible
for all care in the area. A frequent problem in previous studies is which costs should be attributed to MS. One important finding is that
oncological therapies were among the most expensive drugs, even in this relatively young patient cohort. A recent community‐based study
from Spain found an increased risk for stroke, epilepsy, bipolar disorder, and depression among MS patients (Cardenas‐Robledo et al., 2021),
underlining the need to consider care and costs other than those directly attributed to MS.

Lower coping capacity, impaired manual dexterity, and activity of daily living dependency at baseline, together with progress in MS disability,
predicted a higher use of care in a Swedish 10‐year population‐based study (Chruzander et al., 2015). To provide MS care that creates
maximal value for the patient individually and for the group, initiatives and research programs should improve their ability to assess and report
meaningful patient outcomes in many dimensions, including costs. One model for such balanced measures is the “Value Compass.” We are

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part of the COproduction VALUE creation in healthcare service (CO‐VALUE) study, which aims to find novel ways to use resources in the
best way and co‐produce and co‐design care with patients and the network around the patients (Oliver et al., 2020).

Limitations
A significant limitation is that the data were retrieved from the hospital’s reporting system without MS‐specific data, and we did not have
ethical approval to use personal identification numbers or other databases. This means that we cannot relate our findings to the disease
duration, clinical course, patient symptoms, or the Expanded Disability Status Scale, which in other studies have been found to affect costs
(Kobelt et al., 2017; Müller et al., 2020). This will be studied as our next step.

The data were retrieved from a single hospital, and the number of patients was limited; therefore, one should be careful to generalize the
results. However, the study is population‐based, including all patients with MS in the geographic area, since RJL is responsible for all health
care of persons living in the region.

The results for significance testing are presented without correction for multiplicity testing; hence, p‐values greater than.01 should be
interpreted with caution.

The contact data reported here represent the most conservative estimates, as all contacts were not registered, but using CC does not affect
the total cost, as these costs are included in the overhead cost, and the cost is allocated to the next visit. Phone calls may represent a
surrogate marker of unmet needs, and the more calls that come in, the more nursing utilization there is, which then increases related costs
allocated to subsequent care visits.

Future directions
Our study raises many questions about population‐level variation in care utilization in Sweden. Healthcare systems in Sweden and
elsewhere often aim to move a person with a chronic disease from the group needing specialist care to primary care and to involve peer
support from networks and, when, if possible, to self‐care (Suutari et al., 2019).

In a diverse population of patients with MS, it might be more cost‐efficient to design care for the individual person by adapting the service
configuration and use of telemedicine when appropriate. We are part of an international collaboration exploring co‐production (Oliver et al.,
2020), and based on that, value configurations and service offerings will be further evaluated through qualitative inquiry with patients from the

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four subgroups. The results will be evaluated using Fjeldstad’s theories about the value configurations of the value chain, value shop, and
value network health in care (Fjeldstad et al., 2020).

According to the Batalden co‐production of health, “the interdependent work of users and professionals who are creating, designing,
producing, delivering, assessing, and evaluating the relationships and actions that contribute to the health of individuals and populations. At
its core are the interactions of patients and professionals in different roles and degrees of shared work” (Batalden, 2018). Evaluation and
outcomes related to co‐production can be challenging (Voorberg et al., 2014). It has been argued that outcome measures such as patient
satisfaction may overlook the real value created for patients and staff and are too narrow in their construct. Future studies of coevaluation in
health care should include clinical outcomes, patient‐reported outcomes from multiple service processes and outcomes, and cost‐
effectiveness (Clarke et al., 2017). We would add the lived experiences of persons living with MS in a deeper sense than just traditional
questioning. A model for co‐production value in MS is being developed to improve care (Smith et al., 2020). In addition, we intend to study
the effect of using a national initiative on making co‐designed care contracts with patients.

CONCLUSION
Persons with MS are very diverse and have very different needs in relation to the healthcare system. Understanding care needs and
utilization patterns can inform targeted co‐production and co‐design approaches to meet the specific care needs of identified individuals and
subgroups and improve outcomes in a broad sense.

ACKNOWLEDGMENT
This study was supported by a grant from Futurum, Region Jönköping, Sweden.

CONFLICT OF INTEREST
The authors declare that there is no conflict of interest.

DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.

PEER REVIEW
The peer review history for this article is available at https://publons.com/publon/10.1002/brb3.2582

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~~~~~~~~

By Jonas Lind; Sofia Persson; Jonatan Vincent; Bertil Lindenfalk; Brant J. Oliver; Andrew D. Smith and Boel Andersson Gäre

Reported by Author; Author; Author; Author; Author; Author; Author

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