Background: There are regional variations in the incidence of coronavirus disease 2019 (COVID-19), which means that some regions are more exposed to the risk of COVID-19 than others. Therefore, this study aims to investigate regional variations in the incidence of COVID-19 in Korea and identify risk factors associated with the incidence of COVID-19 using community-level data. Methods: This study was conducted at the districts (si·gun ·gu) level in Korea. Data of COVID-19 incidence by districts were collected from the official website of each province. Data was also obtained from the Korean Statistical Information Service and the Community Health Survey; socio-demographic factor, transmission pathway, healthcare resource, and factor in response to COVID-19. Community risk factors that drive the incidence of COVID-19 were selected using a least absolute shrinkage and selection operator regression. Results: As of June 2021, the incidence of COVID-19 differed by more than 80 times between districts. Among the candidate factors, sex ratio, population aged 20–29, local financial independence, population density, diabetes prevalence, and failure to comply with the quarantine rules were significantly associated with COVID-19 incidence. Conclusion This study suggests setting COVID-19 quarantine policy and allocating resources, considering the community risk factors. Protecting vulnerable groups should be a high priority for these policies.

Background: In monitoring exposure to environmental smoke (ETS), biomarkers can overcome the subjectivity and inaccuracy of self-reporting measurements, and have the advantage of reflecting ETS exposure in all places. This study aims to evaluate the effectiveness of ETS exposure measurement using biomarkers such as urine cotinine. Methods: This study used the Korea National Health and Nutrition Survey data from 2009 to 2018. A total of 28,574 non-smokers with urine cotinine data were selected for the study. The cotinine concentration and ETS exposure rate using urine cotinine was estimated and then compared with the self-reporting measurements. The degree of agreement among measurements of ETS exposure was confirmed. Results: As a result of measuring ETS exposure with urine cotinine, 23,594 (83.8%) out of 28,574 subjects were classified as to exposure groups. This estimate differs significantly from measurements made by self-reporting. In addition, the average concentration of cotinine in non-smokers has decreased to a 10th level over the past 10 years. Based on the biomarker, the sensitivity of the self-reporting was 8.5%–29.0%, the specificity was 16.4%–19.5%, and the kappa value was 2.0%–5.8%. Conclusion The findings of our study show that self-reporting measurement does not well reflect the extent to which non-smoker’s exposure to smoking materials. Whereas cotinine concentration has decreased significantly over the past 10 years, the ETS exposure rate has not reduced. It strongly suggests the need for intervention in the group of non-smokers exposed to low concentrations of smoke. Therefore, an assessment using biomarkers such as cotinine-based measurement should be made in the Health Plan 2030.

Digital health is rapidly growing worldwide and its area is expanding from wellness to treatment due to digital therapeutics (DTx). This study compared DTx in the Korean context with other countries to better understand its political and practical implications. DTx is generally the same internationally, often categorized as software as a medical device. It provides evidence-based therapeutic interventions for medical disabilities and diseases.Abroad, DTx support entailed state subsidies and fundraising and national health insurance coverage. In the case of national health insurance coverage, most cases were applied to mental diseases. Moreover, in Japan, DTx related to hypertension will possibly be under discussion for national health insurance coverage in 2022. In overseas countries, coverage was decided only when the clinical effects were equivalent to those provided by existing technology, and in the UK, real usage data for DTx and associated evaluations were reflected by national health coverage determination. Prices were either determined through closed negotiations with health insurance operating agencies and manufacturers or established based on existing technology. Concerning the current situation, DTx dealing with various diseases including hypertension are expected to be developed near in the future, and the demand for use and compensation will likely increase. Therefore, it is urgent to define and prepare for DTx, relevant support systems, and health insurance coverage listings. Several support systems must be considered, including government subsidies, science/technology funds, and health insurance.

Background: Excess all-cause mortality is helpful to assess the full extent of the health impact, including direct and indirect deaths of coronavirus disease 2019 (COVID-19). The study aimed to estimate overall and regional excess all-cause mortality during the pandemic in Korea. Methods: We obtained all-cause death data and population statistics from January 2010 to December 2020. The expected mortality in 2020 was estimated using a quasi-Poisson regression model. The model included death year, seasonal variation, cold wave (January), average death counts in the previous month, and population. Excess mortality was defined as the difference between the observed mortality and the expected mortality. Regions were classified into three areas according to the numbers of COVID-19 cases. Results: There was no annual excess all-cause mortality in 2020 at the national and regional level compared to the average death for the previous ten years. The observed mortality in 2020 was 582.9 per 100,000 people, and the expected mortality was 582.3 per 100,000 people (95% confidence interval, 568.3–596.7). However, we found monthly and regional variations depending on the waves of the COVID-19 pandemic in Korea. While the mortality in August, October, and November exceeded the expected range, the mortality in September was lower than the expected range. The months in which excess deaths were identified differed by region. Conclusion Our results show that the mortality in 2020 was similar to the historical trend.However, in the era of the COVID-19 pandemic, it would be necessary to regularly investigate COVID-19-related mortality and determine its direct and indirect causes.

Background: Excess all-cause mortality is helpful to assess the full extent of the health impact, including direct and indirect deaths of coronavirus disease 2019 (COVID-19). The study aimed to estimate overall and regional excess all-cause mortality during the pandemic in Korea. Methods: We obtained all-cause death data and population statistics from January 2010 to December 2020. The expected mortality in 2020 was estimated using a quasi-Poisson regression model. The model included death year, seasonal variation, cold wave (January), average death counts in the previous month, and population. Excess mortality was defined as the difference between the observed mortality and the expected mortality. Regions were classified into three areas according to the numbers of COVID-19 cases. Results: There was no annual excess all-cause mortality in 2020 at the national and regional level compared to the average death for the previous ten years. The observed mortality in 2020 was 582.9 per 100,000 people, and the expected mortality was 582.3 per 100,000 people (95% confidence interval, 568.3–596.7). However, we found monthly and regional variations depending on the waves of the COVID-19 pandemic in Korea. While the mortality in August, October, and November exceeded the expected range, the mortality in September was lower than the expected range. The months in which excess deaths were identified differed by region. Conclusion Our results show that the mortality in 2020 was similar to the historical trend.However, in the era of the COVID-19 pandemic, it would be necessary to regularly investigate COVID-19-related mortality and determine its direct and indirect causes.

OBJECTIVES: The aim of this study was to test the applicability of haptic feedback using a smartwatch to the delivery of cardiac compression (CC) by professional healthcare providers. METHODS: A prospective, randomized, controlled, case-crossover, standardized simulation study of 20 medical professionals was conducted. The participants were randomly assigned into haptic-first and non-haptic-first groups. The primary outcome was an adequate rate of 100–120/min of CC. The secondary outcome was a comparison of CC rate and adequate duration between the good and bad performance groups. RESULTS: The mean interval between CCs and the number of haptic and non-haptic feedback-assisted CCs with an adequate duration were insignificant. In the subgroup analysis, both the good and bad performance groups showed a significant difference in the mean CC interval between the haptic and non-haptic feedback-assisted CC groups—good: haptic feedback-assisted (0.57–0.06) vs. non-haptic feedback-assisted (0.54–0.03), p < 0.001; bad: haptic feedback-assisted (0.57–0.07) vs. non-haptic feedback-assisted (0.58–0.18), p = 0.005—and the adequate chest compression number showed significant differences— good: haptic feedback-assisted (1,597/75.1%) vs. non-haptic feedback-assisted (1,951/92.2%), p < 0.001; bad: haptic feedbackassisted (1,341/63.5%) vs. non-haptic feedback-assisted (523/25.4%), p < 0.001. CONCLUSIONS: A smartwatch cardiopulmonary resuscitation feedback system could not improve rescuers' CC rate. According to our subgroup analysis, participants might be aided by the device to increase the percentage of adequate compressions after one minute.
Cardiopulmonary Resuscitation ; Health Personnel ; Heart Massage ; Humans ; Prospective Studies ; Simulation Training ; Smartphone ; Thorax