Search

The COVID-19 Epidemiological Investigation Support System (EISS) is a digital epidemiological tool, which utilizes location data from cellular base stations, credit card transactions records, and QR codes. It is a mass surveillance system that uses big data to track the entire infected population, featuring an extensive, automated, and speedy processing of data on personal location and the linkage of multiple databases from various governmental agencies. Based on interviews with people who have developed Korean digital epidemiology systems, this paper explores the technical, infrastructural, social, and institutional factors that have shaped Korean digital epidemiology since the 2014 avian flu crisis and examines the essential conditions of big data for digital epidemiology. The main findings are as follows: The feasibility of EISS goes beyond the matter of privacy; it is closely connected to technological infrastructures such as a high density of cellular base stations and private cloud systems; people's behavior such as a high rate of smartphone and credit card usage; and new forms of governance and institutions for speedy data processing. Multiple database linkage would develop EISS into a big data surveillance system that enables the prediction of risk-prone groups in a more preemptive manner. [ABSTRACT FROM AUTHOR]

Objective: To determine age-related risk factors for chronic rhinosinusitis (CRS) with asthma. Methods: Data were obtained from a national survey of non-hospitalized civilians conducted by the Korean Center for Disease Control and Prevention. CRS diagnosis was based on the guidelines of the European Position Paper on Rhinosinusitis and Nasal Polyps 2020. Asthma was judged based on whether the patient had been diagnosed with asthma in the past. Of the 45,811 survey participants, 26,335 were included in the cross-sectional study. Participants included in the study were divided into the control, CRS, and CRS with asthma groups. Age-related risk factors were analyzed in patients aged < 60 or > 60 years. Univariate logistic analyses were performed to evaluate the relationship between groups. Risk factors included age, sex, household income, residence, education level, occupation, and body mass index (BMI). Results: Education level (Odds Ratio [OR]: 0.342, P =.0003), BMI (OR: 1.09, P =.0082), and total IgE (TIgE) levels (OR: 5.582, P =.003) were significantly different between the control and the CRS with asthma group. Education level (OR: 0.478, P =.0016) and TIgE levels (OR: 4, P =.0218) were significantly different between the CRS and CRS with asthma groups under 60 years of age. BMI (OR: 1.087, P =.0443; OR: 1.104, P =.0224) showed a significant difference between all three groups with age > 60 years. Conclusion: Progression to CRS with asthma is influenced by education level, occupation, and TIgE levels in patients under 60 years of age. BMI was the only influencing factor associated with the progression to CRS with asthma in those aged > 60 years. [ABSTRACT FROM AUTHOR]