Quantifying the Prevalence of Acute Vestibular Neuritis through Big Data Analysis

Abstract

Objectives: Big data analytics in healthcare research have gained momentum, offering unprecedented opportunities to investigate complex medical conditions like acute vestibular neuritis (AVN). However, an inappropriate definition can introduce bias and inaccuracies into prevalence estimation, making the results unreliable and hindering cross-study comparisons. The Health Insurance data in South Korea will be used to create a robust operational definition for AVN.Methods: The study utilized the National Patients Sample dataset from the Health Insurance Review and Assessment Service (HIRA) of the Republic of Korea. The operational definition of AVN was defined using the HIRA data, which includes specific codes for diagnosis, testing, and medications. The revised categorization scheme for AVN was presented as case 1 through case 5, with criteria for each category.Results: The optimal conditions are deemed to be those that encompass the outcomes of both case 5 and case 1-1, encompassing all conditions. The study also provided prevalence estimates for subgroups based on demographic factors (age, sex), and found a consistent pattern throughout all years, sex, and age.Conclusions: The study analyzed the prevalence of AVN in case 1 and case 5, which were similar to the reference prevalence of 3.5 per 100,000 people reported in other countries. The study’s results are encouraging for several reasons, including the validity of the operational definitions used, and the agreement between the study’s prevalence estimates and the reference prevalence. The operational definition in statistics, in the context of big data, serves as a precise and standardized criterion.