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Abstract

Objective:

To assess the feasibility of collaboration and retrospective data harmonization among three multiple sclerosis (MS) registries by investigating employment status.

Methods:

We used the Maelstrom guidelines to facilitate retrospective harmonization of data from three MS registries, including the NARCOMS (North American Research Committee on MS) Registry, German MS Register (GMSR), and United Kingdom MS (UK-MS) Register. A protocol was developed based on the guidelines, and summary-level data were used to combine results. Employment status and a limited set of factors associated with employment (age, sex, education, and disability level) were harmonized. A meta-analytic approach was used to pool estimates using a weighted average of logistic regression estimates and their variances in a random effects model.

Results:

Employment status, age, sex, education, and disability were mapped. The overall employment rate was 57% (11,143 employed out of 19,562 persons with MS) with the GMSR having the highest proportion of participants employed (66.2%), followed by the UK-MS (55.2%) and NARCOMS (43.0%) registries. As disability level increased, the odds of not being employed increased.

Conclusion:

Harmonization across registries was feasible. The Maelstrom guidelines provide a valuable roadmap for conducting high-quality harmonization projects. The pooling of data sources has the potential to be an important mechanism for conducting research in MS.

Keywords

Multiple sclerosis harmonization registry

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References

Kobelt

Berg

Atherly

, et al. Costs and quality of life in multiple sclerosis: A cross-sectional study in the United States. Neurology 2006; 66(11): 1696–1702.

Schiavolin

Leonardi

Giovannetti

, et al. Factors related to difficulties with employment in patients with multiple sclerosis : A review of 2002–2011 literature. Int J Rehabil Res 2013; 36(2): 105–111.

Raggi

Covelli

Schiavolin

, et al. Work-related problems in multiple sclerosis: A literature review on its associates and determinants. Disabil Rehabil 2016; 38(10): 936–944.

Larocca

Kalb

Scheinberg

, et al. Factors associated with unemployment of patients with multiple sclerosis. J Chronic Dis 1985; 38(2): 203–210.

Kornblith

La Rocca

Baum

HM.

Employment in individuals with multiple sclerosis. Int J Rehabil Res 1986; 9(2): 155–165.

Roessler

Fitzgerald

Rumrill

, et al. Determinants of employment status among people with multiple sclerosis. Rehabil Couns Bull 2001; 45(1): 31–39.

Bebo

Jr Fox

Lee

, et al. Landscape of MS patient cohorts and registries: Recommendations for maximizing impact. Mult Scler 2018; 24(5): 579–586.

Magalhaes

Wolfson

. Harmonization: A methodology for advancing research in multiple sclerosis. Acta Neurol Scand Suppl 2012(195): 31–35.

von Elm

Altman

Egger

, et al. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: Guidelines for reporting observational studies. J Clin Epidemiol 2008; 61(4): 344–349.

10.

Akobeng

AK.

Principles of evidence based medicine. Arch Dis Child 2005; 90(8): 837–840.

11.

Fortier

Raina

Van den Heuvel

, et al. Maelstrom research guidelines for rigorous retrospective data harmonization. Int J Epidemiol 2016; 46(1): dyw075.

12.

Pugliatti

Eskic

Mikolcic

, et al. Assess, compare and enhance the status of persons with multiple sclerosis (MS) in Europe: A European register for MS. Acta Neurol Scand Suppl 2012(195): 24–30.

13.

The Consortium of Multiple Sclerosis Centers (CMSC), http://www.mscare.org/?page=about_the_cmsc (accessed 11 June 2019).

14.

Marrie

Cutter

Tyry

, et al. Validation of the NARCOMS registry: Diagnosis. Mult Scler 2007; 13(6): 770–775.

15.

Flachenecker

Buckow

Pugliatti

, et al. Multiple sclerosis registries in Europe—Results of a systematic survey. Mult Scler 2014; 20(11): 1523–1532.

16.

DMSG German MS-Register, https://www.msregister.de/en/ms-registry/the-ms-registry/ (accessed 15 December 2018)

17.

Thiel

Leypoldt

Röpke

, et al. Neuroimmunological registries in Germany. Neurol Int Open 2018; 02(01): E25–E39.

18.

Middleton

Rodgers

Chataway

, et al. Validating the portal population of the United Kingdom multiple sclerosis register. Mult Scler Relat Disord 2018; 24: 3–10.

19.

DerSimonian

Laird

. Meta-analysis in clinical trials. Control Clin Trials 1986; 7(3): 177–188.

20.

Smith-Warner

Spiegelman

Ritz

, et al. Methods for pooling results of epidemiologic studies. Am J Epidemiol 2006; 163(11): 1053–1064.

21.

OECD EU and UNESCO Institute for Statistics. ISCED 2011 operational manual: Guidelines for classifying national education programmes and related qualifications, 2015, http://hdl.voced.edu.au/10707/396687

22.

Kurtzke

JF.

Rating neurologic impairment in multiple sclerosis: An Expanded Disability Status Scale (EDSS). Neurology 1983; 33(11): 1444–1452.

23.

Learmonth

Motl

Sandroff

, et al. Validation of Patient Determined Disease Steps (PDDS) scale scores in persons with multiple sclerosis. BMC Neurol 2013; 13: 37.

24.

Marrie

Goldman

Validity of performance scales for disability. Mult Scler 2007; 13(9): 1176–1182.

25.

Hobart

Lamping

Fitzpatrick

, et al. The Multiple Sclerosis Impact Scale (MSIS-29): A new patient-based outcome measure. Brain 2001; 124(Pt. 5): 962–973.

26.

Hoogervorst

Zwemmer

Jelles

, et al. Multiple Sclerosis Impact Scale (MSIS-29): Relation to established measures of impairment and disability. Mult Scler 2004; 10(5): 569–574.

27.

Costelloe

O‘Rourke

Kearney

, et al. The patient knows best: Significant change in the physical component of the Multiple Sclerosis Impact Scale (MSIS-29 physical). J Neurol Neurosurg Psychiatry 2007; 78(8): 841–844.

28.

Higgins

JPT

Thompson

Deeks

, et al. Measuring inconsistency in meta-analyses. BMJ 2003; 327(7414): 557–560.

29.

Cohen

Trojano

Mowry

, et al. Leveraging real-world data to investigate multiple sclerosis disease behavior, prognosis, and treatment. Mult Scler 2020; 26: 23–37.

30.

Fortier

Dragieva

Saliba

, et al. Harmonization of the health and risk factor questionnaire data of the Canadian partnership for tomorrow project: A descriptive analysis. CMAJ Open 2019; 7(2): E272–E282.

31.

Peeters

van Munster

Van Wijmeersch

, et al. Multidisciplinary data infrastructures in multiple sclerosis: Why they are needed and can be done!. Mult Scler 2019; 25(4): 500–509.

32.

Raggi

Covelli

Schiavolin

, et al. Work-related problems in multiple sclerosis: A literature review on its associates and determinants. Disabil Rehabil 2016; 38: 936–944.

33.

Kobelt

Thompson

Berg

, et al. New insights into the burden and costs of multiple sclerosis in Europe. Mult Scler 2017; 23(8): 1123–1136.

34.

Vornholt

Villotti

Muschalla

, et al. Disability and employment—Overview and highlights. Eur J Work Organ Psychol 2018; 27(1): 40–55.

35.

Choi

Schalet

Cook

, et al. Establishing a common metric for depressive symptoms: Linking the BDI-II, CES-D, and PHQ-9 to PROMIS depression. Psychol Assess 2014; 26(2): 513–527.

36.

Marrie

Dufault

Tyry

, et al. Developing a crosswalk between the RAND-12 and the health utilities index for multiple sclerosis. Mult Scler 2020; 26: 1102–1110.

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Data harmonization for collaborative research among MS registries: A case study in employment

Abstract

Objective:

Methods:

Results:

Conclusion:

Keywords

Get full access to this article

References

Supplementary Material