Suggestion of a simpler and faster influenza-like illness surveillance system using 2014-2018 claims data in Korea

Incidence, Severity, and Mortality of Influenza During 2010-2020 in Korea: A Nationwide Study Based on the Population-Based National Health Insurance Service Database

BACKGROUND

The epidemiology of influenza is commonly used to understand and establish relevant health policies for emerging respiratory infections, including coronavirus disease 2019 (COVID-19). However, Korea has no confirmed nationwide data on influenza incidence, severity, and mortality rate.

METHODS

We conducted a cross-sectional study to obtain epidemic data on influenza at the national level using National Health Insurance claims data during 2010 to 2020. Influenza cases were defined as 90-day timeframe episodes based on all inpatient and outpatient claims data with disease code J09, J10, and J11. Influenza incidence, severity, and mortality rate were calculated, and logistic regressions were performed to assess the associations of demographic characteristics and comorbidity with influenza-related hospitalization, severe illness, and death.

RESULTS

There were 0.4-5.9% influenza cases in the population from 2010 to 2020, with 9.7-18.9%, 0.2-0.9%, and 0.03-0.08% hospitalized, used in the intensive care unit, and dead, respectively. Age-standardized incidence and mortality rates were 424.3-6847.4 and 0.2-1.9 per 100,000 population, respectively. While more than half of the influenza cases occurred in populations aged younger than 20 years, deaths in older than 60 years accounted for more than two-thirds of all deaths.

CONCLUSION

This study provided the simplest but most important statistics regarding Korean influenza epidemics as a reference. These can be used to understand and manage other new acute respiratory diseases, including COVID-19, and establish influenza-related policies.

A Study on Customized Prediction of Daily Illness Risk Using Medical and Meteorological Data

This study selected the most common illnesses in children and older adults and aimed to provide a customized degree of daily risk for each illness based on patient data for specific regions and illnesses. Sample medical data of one million people provided by the National Health Insurance Corporation and information regarding the meteorological environment and atmosphere from the Korea Meteorological Administration and a public data portal using application programing interface were collected. Learning and predictions were carried out with machine learning. Models with high R2 were selected and tuned to determine the optimal hyperparameter for predicting the degree of daily risk of an illness. Illnesses with an R2 value greater than 0.65 were considered significant. For children, these consisted of acute bronchitis, the common cold, rhinitis and tonsillitis, and middle ear inflammation. For older adults, they consisted of high blood pressure and heart disease, the common cold, esophageal inflammation and gastritis, acute bronchitis, eczema and dermatitis, and chronic bronchitis. This study provides the degree of daily risk for the most common illnesses in each age group. Furthermore, the results of this study are expected to raise awareness of illnesses that occur in certain climates and to help prevent them.