Patient identification
We identified patients diagnosed with MS from a large national insurance claims database during 1/1/2014 through 6/30/2019. We chose the most recent five and one-half years of data in order to capture a snapshot of current treatment practices rather than historical treatment practices which may no longer be relevant. More detail about the database is found in the ‘Data Source’ section below. Newly diagnosed multiple sclerosis was defined according to the presence of at least two claims with a diagnosis code for MS (ICD-9-CM 340 or ICD-10-CM G35) within one year or one inpatient hospitalization for MS. This is adapted from a validated study of multiple algorithms used to identify a population of MS patients in a Canadian administrative claims database [17]. There was no exclusion for comorbid conditions. The index date was the first observed medical claim in the database with a diagnosis of MS, resulting in a cohort of newly diagnosed patients. Patients were required to have at least 365 days of continuous enrollment in the database prior to and following their first diagnosis of MS, with no evidence of a prior treatment with a DMT.
Data Source
The analysis was executed in Optum© De-Identified Clinformatics® Data Mart Database, a US-based administrative claims database. Includes 84 million members with private health insurance, who are fully insured in commercial plans or in administrative services only and Medicare Advantage (Medicare Advantage Prescription Drug coverage. The population is representative of US commercial claims patients (0-65 years old) with some Medicare (65+ years old). At the time of this study data were available from May 31, 2000 through June 30, 2019.
The database contains data from adjudicated health insurance claims and health plan enrollment information. Data elements included were outpatient pharmacy dispensing claims (coded with National Drug Codes), inpatient and outpatient medical claims which provide diagnosis codes (coded in ICD-9-CM or ICD-10-CM) associated with a visit. The use of the Optum claims database was reviewed by the New England Institutional Review Board (IRB) and was determined to be exempt from broad IRB approval, as this research project did not involve human subjects research.
DMT treatment patterns
Treatment patterns included all FDA approved DMTs during the patient identification period except for siponimod and cladribine (both approved March 2019) due to an insufficient number of records in the database (most recently available data through June 2019). The DMTs included were glatiramer acetate, dimethyl fumarate, interferon beta-1a, interferon beta-1b, peginterferon beta-1a, fingolimod, teriflunomide, ocrelizumab, natalizumab, rituximab, alemtuzumab, daclizumab, mitoxantrone. Treatment sequences were captured from index date through all available follow-up, a minimum of 1 year. The term “treatment line” is used to describe the sequence of medication and combinations of medications received by patients during this time. Use of a specific medication was captured at the first instance and not counted again in later lines of therapy – for example an individual filling glatiramer, switching to natalizumab, and then moving back to glatiramer would only be captured as switching from glatiramer to natalizumab. The time a patient was continuously receiving medication is referred to as a drug era. Drug eras were calculated as the time from the first fill for a drug in a medication until discontinuation of that medication, allowing for gaps of up to 30 days beyond the days supply of a prescription (Appendix Figure 1). Combination therapy with multiple medications was defined as having at least 30 days of overlap in drug eras of more than one treatment. A fill for a medication following discontinuation of a previous drug or with fewer than 30 days of overlap was considered a switch.
Patient characteristics and comorbidities
Patient characteristics captured include demographics (age, gender, health plan type) on the index date, the Charlson comorbidity index for the year preceding the index date, and individual comorbid conditions during the one year following (and including) the index date. Comorbid conditions required just a single diagnosis and were defined using Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) classification system. The SNOMED CT classification allows mapping of various diagnostic languages across more than 80 countries, including, for example, ICD-9-CM, ICD-10-CM, and Read codes, to a single standardized set of concepts, and is used by the common data model leveraged for this study [18, 19], described in the next section.
Common data model
Data from all the database were mapped to standard concepts according to the Observational Medical Outcomes Partnership (OMOP) Common Data Model v5.0 [20] and the treatment sequence analysis was performed within the Observational Health Data Sciences and Informatics (or OHDSI, pronounced “Odyssey”) framework.