Identifying Relapses in Multiple Sclerosis Patients through Administrative Data: A Validation Study in the Lazio Region, Italy

Abstract

Background: Relapse is frequently considered an outcome measure of disease activity in relapsing-remitting multiple sclerosis (MS). The objectives of this study were to identify relapse episodes in patients with MS in the Lazio region using health administrative databases and to evaluate the validity of the algorithm using patients enrolled at MS treatment centers. Methods: MS cases were identified in the period between January 1, 2006 and December 31, 2009 using data from regional Health Information Systems (HIS). An algorithm based on HIS was used to identify relapse episodes, and patients recruited at MS centers were used to validate the algorithm. Positive and negative predictive values (PPV, NPV) and the Cohen's kappa coefficient were calculated. Results: The overall MS population identified through HIS consisted of 6,094 patients, of whom 67.1% were female and the mean age was 41.5. Among the MS patients identified by the algorithm, 2,242 attended the centers and 3,852 did not. The PPV was 58.9%, the NPV was 76.3%, and the kappa was 0.36. Conclusions: The proposed algorithm based on health administrative databases does not seem to be able to reliably detect relapses; however, it may be a helpful tool to detect healthcare utilization, and therefore to identify the worsening condition of a patient's health.

Impact of the COVID‐19 pandemic on access to healthcare services amongst patients with multiple sclerosis in the Lazio region, Italy

“…With the recent lockdown and unavoidable changes to healthcare delivery, patients with MS may have had limited access to usual medical services (infusions, physical therapy, occupational therapy and homecare services), because of either unavailability or the fear of SARS-CoV-2 infection. In the Lazio region, health administrative databases are comprehensive and contain highquality information, allowing for region-wide public health monitoring and research [14,15]. A validated algorithm is used to identify MS cases within healthcare administrative databases, for healthcare planning and monitoring purposes [16].…”

A Systematic Literature Review of Health Information Systems for Healthcare

“…Globally, the need for HIS within the healthcare system is critical in the enhancement of healthcare. Many healthcare actions are dependent on the use of HIS [ 67 , 68 , 69 ]. This demand is substantiated by the offerings of HIS in tackling the transformation and digitalization confronting the healthcare system.…”

Validation of an Algorithm to Detect Multiple Sclerosis Cases in Administrative Health Databases in Piedmont (Italy): An Application to the Estimate of Prevalence by Age and Urbanization Level

“…Consequently, the use of administrative data to identify and monitor persons with MS at a population level is deemed highly useful [2]. Although some studies exploiting electronic health archives (EHAs) have been conducted in Italy to estimate the prevalence [4-8], the incidence [9], or the clinical course [10, 11] of the disease, few of the algorithms used to identify MS patients underwent a formal validation (i.e., provided sensibility and specificity) [12], and few of them considered the potential undercount (i.e., the completeness) of the estimate [7, 8].…”