Evaluation of an air quality warning system for vulnerable and susceptible individuals in Korea: an interrupted time series analysis.

OBJECTIVES: This study was conducted to elucidate the effects of an air quality warning system (AQWS) implemented in January 2015 in Korea by analyzing changes in the incidence and exacerbation rates of environmental diseases. METHODS: Data from patients with environmental diseases were extracted from the National Health Insurance Service-National Sample Cohort database from 2010 to 2019, and data on environmental risk factors were acquired from the AirKorea database. Patient and meteorological data were linked based on residential area. An interrupted time series analysis with Poisson segmented regression was used to compare the rates before and after AQWS introduction. Adjustment variables included seasonality, air pollutants (carbon monoxide, nitrogen dioxide, sulfur dioxide, particulate matter less than 10 µm in diameter, and ozone), temperature, and humidity. RESULTS: After AQWS implementation, the incidence of asthma gradually decreased by 20.5%. Cardiovascular disease and stroke incidence also significantly decreased (by 34.3 and 43.0%, respectively). However, no immediate or gradual decrease was identified in the exacerbation rate of any environmental disease after AQWS implementation. Sensitivity analyses were performed according to age, disability, and health insurance coverage type. Overall, the AQWS effectively mitigated the occurrence of most environmental diseases in Korea. However, the relationships between alarm system implementation and reduced incidence differed among diseases based on the characteristics of vulnerable and sensitive individuals. CONCLUSIONS: Our results suggest that by tailoring the AQWS to demographic and sociological characteristics and providing enhanced education about the warning system, interventions can become an efficient policy tool to decrease air pollution- related health risks.

Changes in cesarean section rate before and after the end of the Korean Value Incentive Program.

BACKGROUND: The Korean government implemented a value incentive program providing incentives to providers based on C-section rates, with the rates being publicized. The program ended in 2014 after the administration decided that the effects of the incentive program were limited. In this report, we analyzed changes in C-section rates with the value incentive program. METHODS: The analysis used claim data from Korea's National Health Insurance. The study period (2011-2016) was divided into two phases: before and after the program. This study included 95 providers that were tertiary or general hospitals having more than 200 deliveries per year during the study period. The dependent variable was the risk-adjusted C-section rate. Independent variables included time and hospital characteristics such as hospital type, district, and ownership. Interrupted time series analysis was performed to analyze the data. RESULTS: Our results showed that risk-adjusted C-section rates increased immediately after the end of the incentive program for C-sections. The immediate effect of intervention, a change of 1.73% (P < .05), was statistically significant, as was the trend after intervention, at 0.21% (P < .0001). The slope showed an increase after the intervention to 0.25% per medical institution, which was contrary to the trend of the preintervention decline (negative slope). CONCLUSION: Risk-adjusted C-section rates increased immediately after the discontinuation of a value incentive program. Tertiary hospitals showed greater increases in C-section rates than general hospitals after the intervention.

Digital Biomarkers in Living Labs for Vulnerable and Susceptible Individuals: An Integrative Literature Review.

PURPOSE: The study aimed to identify which digital biomarkers are collected and which specific devices are used according to vulnerable and susceptible individual characteristics in a living-lab setting. MATERIALS AND METHODS: A literature search, screening, and appraisal process was implemented using the Web of Science, Pubmed, and Embase databases. The search query included a combination of terms related to "digital biomarkers," "devices that collect digital biomarkers," and "vulnerable and susceptible groups." After the screening and appraisal process, a total of 37 relevant articles were obtained. RESULTS: In elderly people, the main digital biomarkers measured were values related to physical activity. Most of the studies used sensors. The articles targeting children aimed to predict diseases, and most of them used devices that are simple and can induce some interest, such as wearable device-based smart toys. In those who were disabled, digital biomarkers that measured location-based movement for the purpose of diagnosing disabilities were widely used, and most were measured by easy-to-use devices that did not require detailed explanations. In the disadvantaged, digital biomarkers related to health promotion were measured, and various wearable devices, such as smart bands and headbands were used depending on the purpose and target. CONCLUSION: As the digital biomarkers and devices that collect them vary depending on the characteristics of study subjects, researchers should pay attention not only to the purpose of the study but also the characteristics of study subjects when collecting and analyzing digital biomarkers from living labs.

Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms.

In this study, we developed machine learning-based prediction models for early childhood caries and compared their performances with the traditional regression model. We analyzed the data of 4195 children aged 1-5 years from the Korea National Health and Nutrition Examination Survey data (2007-2018). Moreover, we developed prediction models using the XGBoost (version 1.3.1), random forest, and LightGBM (version 3.1.1) algorithms in addition to logistic regression. Two different methods were applied for variable selection, including a regression-based backward elimination and a random forest-based permutation importance classifier. We compared the area under the receiver operating characteristic (AUROC) values and misclassification rates of the different models and observed that all four prediction models had AUROC values ranging between 0.774 and 0.785. Furthermore, no significant difference was observed between the AUROC values of the four models. Based on the results, we can confirm that both traditional logistic regression and ML-based models can show favorable performance and can be used to predict early childhood caries, identify ECC high-risk groups, and implement active preventive treatments. However, further research is essential to improving the performance of the prediction model using recent methods, such as deep learning.