Epidemiology and Etiology of Elbow Pain Based on the Healthcare Bigdata Hub in Korea: A Longitudinal Observational Study

Abstract

This study investigated the epidemiological and etiological trends associated with elbow pain over the past decade in South Korea. Methods: Nationwide health statistics data from 2011 to 2020 pertaining to elbow pain-related diseases and soft tissue damages were sourced from the Healthcare Bigdata Hub with disease codes M771 (lateral epicondylitis), M770 (medial epicondylitis), S53 (elbow injury, dislocation, sprain), and G56 (mononeuropathies of the upper limb). The study assessed the annual fluctuations in the total medical cost and the number of patients associated with these codes. Trends over time were characterized by evaluating the crude and age-standardized prevalence rates and the annual percentage change. Changes in the proportion of medical expenses based on age distribution were also investigated. Results: A significant surge in medical costs was observed across all four codes. The M771, M770, and G56 codes experienced a pronounced increase in crude and age-standardized prevalence. Conversely, only S53 registered a significant drop in age-standardized prevalence. Moreover, within the total medical expenditures for the M771 code, the age bracket of 50 to 59 represented the largest proportion. Conclusion:The data suggest that the average age of patients reporting elbow pain is rising. Given this shifting trend in South Korea's health statistics concerning elbow pain, there will be an increasing need for socioeconomic support, which will in turn necessitate improving health policies that address allocating medical expenses and resources for elbow pain.

Mission and Goals of the New Editor of the Ewha Medical Journal

“…Dr. Youngbok Kim [8] states in his editorial that "this compilation of papers, encompassing a series of domestic epidemiological studies on elbow diseases, distinguishing factors for elbow pain, conservative and surgical treatments, and postoperative rehabilitation, is noteworthy as it provides a comprehensive overview of the understanding and treatment of elbow diseases." Big data analysis also revealed that the prevalence of diseases causing elbow pain was 114.21 per 100,000 for lateral epicondylitis, 32.82 for medial epicondylitis, 61.46 for elbow injury, dislocation, and sprain, and 39.15 for mononeuropathy of the upper limb [9]. The diagnosis, treatment, and rehabilitation of patients with elbow pain disorders will provide current and high-quality information for both specialists and general practitioners.…”