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Updated: Jan 19, 2026

The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool
Published on: June 30, 2014
Validation of multiple sclerosis diagnoses in the Swedish National Patient Register
Chantelle Murley1, Emilie Friberg2, Jan Hillert3
1Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, 171 77, Stockholm, Sweden. chantelle.murley@ki.se.
Abstract:
Population-based registers are widely used in epidemiological studies. We aimed to estimate the validity of multiple sclerosis (MS) diagnoses registered in the Swedish National Patient Register (NPR) by two sequential register-based case-definition algorithms. Prevalent MS patients aged 16-64 years were identified from the in- and specialised out-patient NPR in 2001-2013, using International Classification of Diseases code G35. These identified MS diagnoses were validated through two sequential register-based case-definition algorithms, as the 'gold-standard' reference, by linking individual-level data longitudinally to other nationwide registers. The primary algorithm first sought to corroborate the MS diagnoses with MS-specific information in other nationwide registers. The exploratory secondary algorithm identified individuals with MS-related information in other registers and those who were unable to be followed sufficiently. Through multi-register linkage, we estimated the number of confirmed and uncertain individuals with an MS diagnosis recorded in the NPR. A total of 19,781 individuals (mean age at first visit 45.2 years; 69.5% women) had at least one MS diagnosis recorded in the NPR during 2001-2013. Using the two case-definition algorithms, 92.5% (n = 18,291) of the MS diagnoses recorded in the NPR were confirmed, while 7.5% (n = 1490) remained uncertain. Our findings indicate that a very high percentage of patients coded with an MS diagnosis in the Swedish NPR actually have MS, and supports the use of the NPR as a viable source to identify individuals with an MS diagnosis for population-based research. This exploratory methods paper suggests an alternative novel method to verify individuals' diagnoses in register-based settings.
Insights
The Swedish National Patient Register (NPR) accurately identifies multiple sclerosis (MS) diagnoses. This study confirms 92.5% of registered MS cases, supporting its use in epidemiological research.
Area of Science:
- Epidemiology
- Medical Informatics
- Neurology
Background:
- Population-based registers are crucial for epidemiological studies.
- The Swedish National Patient Register (NPR) is a key resource for health research.
- Accurate disease coding in registers is essential for reliable study outcomes.
Purpose of the Study:
- To validate the accuracy of multiple sclerosis (MS) diagnoses recorded in the Swedish National Patient Register (NPR).
- To assess the utility of two sequential register-based case-definition algorithms for MS diagnosis verification.
- To determine the reliability of the NPR for identifying MS patients in population-based research.
Main Methods:
- Utilized data from the Swedish National Patient Register (NPR) for individuals aged 16-64 with an International Classification of Diseases code G35 (MS) between 2001-2013.
- Employed two sequential register-based case-definition algorithms, including multi-register linkage, to validate MS diagnoses.
- The primary algorithm corroborated diagnoses with MS-specific information in other national registers; the secondary algorithm explored individuals with MS-related information.
Main Results:
- A total of 19,781 individuals had at least one MS diagnosis recorded in the NPR.
- The primary algorithm confirmed 92.5% (n=18,291) of the recorded MS diagnoses.
- 7.5% (n=1,490) of the MS diagnoses remained uncertain after applying the validation algorithms.
Conclusions:
- The Swedish National Patient Register (NPR) demonstrates high validity for identifying individuals with multiple sclerosis (MS).
- The findings support the use of the NPR as a reliable data source for population-based MS research.
- The study proposes a novel methodological approach for verifying diagnoses within register-based research settings.
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