Regional inequalities in excess mortality and its community determinants during the early COVID-19 pandemic in South Korea.

BACKGROUND: Excess mortality during the COVID-19 pandemic provides a comprehensive measure of disease burden, and its local variation highlights regional health inequalities. We investigated local excess mortality in 2020 and its determinants at the community level. METHODS: We collected data from 250 districts in South Korea, including monthly all-cause mortality for 2015-2020 and community characteristics from 2019. Excess mortality rate was defined as the difference between observed and expected mortality rates. A Seasonal Autoregressive Integrated Moving Average model was applied to predict the expected rates for each district. Penalized regression methods were used to derive relevant community predictors of excess mortality based on the elastic net. RESULTS: In 2020, South Korea exhibited significant variation in excess mortality rates across 250 districts, ranging from no excess deaths in 46 districts to more than 100 excess deaths per 100 000 residents in 30 districts. Economic status or the number of medical centres in the community did not correlate with excess mortality rates. The risk was higher in ageing, remote communities with limited cultural and sports infrastructure, a higher density of welfare facilities, and a higher prevalence of hypertension. Physical distancing policies and active social engagement in voluntary activities protected from excess mortality. CONCLUSION: Substantial regional disparities in excess mortality existed within South Korea during the early stages of COVID-19 pandemic. Weaker segments of the community were more vulnerable. Local governments should refine their preparedness for future novel infectious disease outbreaks, considering community circumstances.

The effect of dialysis modality on annual mortality: A prospective cohort study.

Despite numerous studies on the effect of each dialysis modality on mortality, the issue remains controversial. We investigated the hazard rate of mortality in patients with incident end-stage renal disease (ESRD) concerning initial dialysis modality (hemodialysis vs. peritoneal dialysis). Using a nationwide, multicenter, prospective cohort in South Korea, we studied 2207 patients, of which 1647 (74.6%) underwent hemodialysis. We employed the weighted Fine and Gray model over the follow-up period using inverse probability of treatment and censoring weighting. Landmark analysis was used for identifying the changing effect of dialysis modality on individuals who remained event-free at each landmark point. No significant difference in hazard rate was observed overall. However, the peritoneal dialysis group had a significantly higher hazard rate than the hemodialysis group among patients under 65 years after 4- and 5- year follow-up. A similar pattern was observed among those with diabetes mellitus. Landmark analysis also showed the higher hazard rate for peritoneal dialysis at 2 years for the education-others group and at 3 years for the married group. These findings may inform dialysis modality decisions, suggesting a preference for hemodialysis in young patients with diabetes, especially for follow-ups longer than 3 years.

Correction: The importance of investing in data, models, experiments, team science, and public trust to help policymakers prepare for the next pandemic.

[This corrects the article DOI: 10.1371/journal.pgph.0002601.].

Preservation of Topological Surface States in Millimeter-Scale Transferred Membranes.

Ultrathin topological insulator membranes are building blocks of exotic quantum matter. However, traditional epitaxy of these materials does not facilitate stacking in arbitrary orders, while mechanical exfoliation from bulk crystals is also challenging due to the non-negligible interlayer coupling therein. Here we liberate millimeter-scale films of the topological insulator Bi2Se3, grown by molecular beam epitaxy, down to 3 quintuple layers. We characterize the preservation of the topological surface states and quantum well states in transferred Bi2Se3 films using angle-resolved photoemission spectroscopy. Leveraging the photon-energy-dependent surface sensitivity, the photoemission spectra taken with 6 and 21.2 eV photons reveal a transfer-induced migration of the topological surface states from the top to the inner layers. By establishing clear electronic structures of the transferred films and unveiling the wave function relocation of the topological surface states, our work lays the physics foundation crucial for the future fabrication of artificially stacked topological materials with single-layer precision.

Is replacing missing values of PM2.5 constituents with estimates using machine learning better for source apportionment than exclusion or median replacement?

East Asian countries have been conducting source apportionment of fine particulate matter (PM2.5) by applying positive matrix factorization (PMF) to hourly constituent concentrations. However, some of the constituent data from the supersites in South Korea was missing due to instrument maintenance and calibration. Conventional preprocessing of missing values, such as exclusion or median replacement, causes biases in the estimated source contributions by changing the PMF input. Machine learning (ML) can estimate the missing values by training on constituent data, meteorological data, and gaseous pollutants. Complete data from the Seoul Supersite in 2018 was taken, and a random 20% was set as missing. PMF was performed by replacing missing values with estimates. Percent errors of the source contributions were calculated compared to those estimated from complete data. Missing values were estimated using a random forest analysis. Estimation accuracy (r2) was as high as 0.874 for missing carbon species and low at 0.631 when ionic species and trace elements were missing. For the seven highest contributing sources, replacing the missing values of carbon species with estimates minimized the percent errors to 2.0% on average. However, replacing the missing values of the other chemical species with estimates increased the percent errors to more than 9.7% on average. Percent errors were maximal at 37% on average when missing values of ionic species and trace elements were replaced with estimates. Missing values, except for carbon species, need to be excluded. This approach reduced the percent errors to 7.4% on average, which was lower than those due to median replacement. Our results show that reducing the biases in source apportionment is possible by replacing the missing values of carbon species with estimates. To improve the biases due to missing values of the other chemical species, the estimation accuracy of the ML needs to be improved.

Diabetic Neuropathy and Minimum Effective Anesthetic Concentration of Mepivacaine for Axillary Brachial Plexus Block: A Prospective Observational Study.

Nerves in patients with diabetic neuropathy (DN) show increased susceptibility to local anesthetics, potentially requiring a decreased dose. We investigated whether the minimum effective anesthetic concentration (MEAC) of mepivacaine for successful axillary block is lower in patients with DN than in those without. This prospective observational study included patients with DN (n = 22) and without diabetes (n = 22) at a tertiary care center. Patients received an ultrasound-guided axillary block with 30 mL of mepivacaine for anesthesia. The mepivacaine concentration used in each patient was calculated using Dixon's up-and-down method. A block was considered successful if all four sensory nerves had a score of 1 or 2 within 30 min with no pain during surgery. The primary outcome was the MEAC of mepivacaine, and the secondary outcomes included the minimal nerve stimulation intensity for the musculocutaneous nerve and the occurrence of adverse events. The MEAC50 was 0.55% (95% CI 0.33-0.77%) in patients without diabetes and 0.58% (95% CI 0.39-0.77%) in patients with DN (p = 0.837). The MEAC90 was 0.98% (95% CI 0.54-1.42%) in patients without diabetes and 0.96% (95% CI 0.57-1.35%) in patients with DN (p = 0.949). The stimulation threshold for the musculocutaneous nerve was significantly different between groups (0.49 mA vs. 0.19 mA for patients with vs. without diabetes; p = 0.002). In conclusion, the MEAC of mepivacaine for a successful axillary block is not lower in patients with DN.

Associations between breast cancer risk factors and mammographic breast density in a large cross-section of Korean women.

BACKGROUND: We investigated the association between established risk factors for breast cancer and mammographic breast density in Korean women. METHODS: This large cross-sectional study included 8 460 928 women aged >40 years, who were screened for breast cancer between 2009 and 2018. Breast density was assessed using the Breast Imaging Reporting and Data System. This study used multiple logistic regression analyses of age, BMI, age at menarche, menopausal status, menopausal age, parity, breastfeeding status, oral contraceptive use, family history of breast cancer, physical activity, smoking, drinking and hormone replacement therapy use to investigate their associations with mammographic breast density. Analyses were performed using SAS software. RESULTS: Of 8 460 928 women, 4 139 869 (48.9%) had nondense breasts and 4 321 059 (51.1%) had dense breasts. Factors associated with dense breasts were: earlier age at menarche [<15 vs. ≥15; adjusted odds ratio (aOR), 1.18; 95% confidence interval (CI), 1.17-1.18], premenopausal status (aOR, 2.01; 95% CI, 2.00-2.02), later age at menopause (≥52 vs. <52; aOR, 1.23; 95% CI, 1.22-1.23), nulliparity (aOR, 1.64; 95% CI, 1.63-1.65), never breastfed (aOR, 1.23; 95% CI, 1.23-1.24) and use of hormone replacement therapy (aOR, 1.29; 95% CI, 1.28-1.29). Women with a higher BMI and the use of oral contraceptives were more likely to have nondense breasts. CONCLUSION: Lower BMI, reproductive health and behavioral factors were associated with dense breasts in Korean women. Additional research should investigate the relationship between mammographic breast density, breast cancer risk factors and breast cancer risk.

Outcomes of Various Classes of Oral Antidiabetic Drugs on Nonalcoholic Fatty Liver Disease.

Importance: Several oral antidiabetic drug (OAD) classes can potentially improve patient outcomes in nonalcoholic fatty liver disease (NAFLD) to varying degrees, but clinical data on which class is favored are lacking. Objective: To investigate which OAD is associated with the best patient outcomes in NAFLD and type 2 diabetes (T2D). Design, Setting, and Participants: This retrospective nonrandomized interventional cohort study used the National Health Information Database, which provided population-level data for Korea. This study involved patients with T2D and concomitant NAFLD. Exposures: Receiving either sodium-glucose cotransporter 2 (SGLT2) inhibitors, thiazolidinediones, dipeptidyl peptidase-4 (DPP-4) inhibitors, or sulfonylureas, each combined with metformin for 80% or more of 90 consecutive days. Main Outcomes and Measures: The main outcomes were NAFLD regression assessed by the fatty liver index and composite liver-related outcome (defined as liver-related hospitalization, liver-related mortality, liver transplant, and hepatocellular carcinoma) using the Fine-Gray model regarding competing risks. Results: In total, 80 178 patients (mean [SD] age, 58.5 [11.9] years; 43 007 [53.6%] male) were followed up for 219 941 person-years, with 4102 patients experiencing NAFLD regression. When compared with sulfonylureas, SGLT2 inhibitors (adjusted subdistribution hazard ratio [ASHR], 1.99 [95% CI, 1.75-2.27]), thiazolidinediones (ASHR, 1.70 [95% CI, 1.41-2.05]), and DPP-4 inhibitors (ASHR, 1.45 [95% CI, 1.31-1.59]) were associated with NAFLD regression. SGLT2 inhibitors were associated with a higher likelihood of NAFLD regression when compared with thiazolidinediones (ASHR, 1.40 [95% CI, 1.12-1.75]) and DPP-4 inhibitors (ASHR, 1.45 [95% CI, 1.30-1.62]). Only SGLT2 inhibitors (ASHR, 0.37 [95% CI, 0.17-0.82]), not thiazolidinediones or DPP-4 inhibitors, were significantly associated with lower incidence rates of adverse liver-related outcomes when compared with sulfonylureas. Conclusions and Relevance: The results of this cohort study suggest that physicians may lean towards prescribing SGLT2 inhibitors as the preferred OAD for individuals with NAFLD and T2D, considering their potential benefits in NAFLD regression and lower incidences of adverse liver-related outcomes. This observational study should prompt future research to determine whether prescribing practices might merit reexamination.

Prediction of low-density lipoprotein cholesterol levels using machine learning methods.

OBJECTIVE: Low-density lipoprotein cholesterol (LDL-C) has been commonly calculated by equations, but their performance has not been entirely satisfactory. This study aimed to develop a more accurate LDL-C prediction model using machine learning methods. METHODS: The study involved predicting directly measured LDL-C, using individual characteristics, lipid profiles, and other laboratory results as predictors. The models applied to predict LDL-C values were multiple regression, penalized regression, random forest, and XGBoost. Additionally, a novel 2-step prediction model was developed and introduced. The machine learning methods were evaluated against the Friedewald, Martin, and Sampson equations. RESULTS: The Friedewald, Martin, and Sampson equations had root mean squared error (RMSE) values of 12.112, 8.084, and 8.492, respectively, whereas the 2-step prediction model showed the highest accuracy, with an RMSE of 7.015. The LDL-C levels were also classified as a categorical variable according to the diagnostic criteria of the dyslipidemia treatment guideline, and concordance rates were calculated between the predictive values obtained from each method and the directly measured ones. The 2-step prediction model had the highest concordance rate (85.1%). CONCLUSION: The machine learning method can calculate LDL-C more accurately than existing equations. The proposed 2-step prediction model, in particular, outperformed the other machine learning methods.

Use of artificial intelligence in triaging of chest radiographs to reduce radiologists' workload.

OBJECTIVES: To evaluate whether deep learning-based detection algorithms (DLD)-based triaging can reduce outpatient chest radiograph interpretation workload while maintaining noninferior sensitivity. METHODS: This retrospective study included patients who underwent initial chest radiography at the outpatient clinic between June 1 and June 30, 2017. Readers interpreted radiographs with/without a commercially available DLD that detects nine radiologic findings (atelectasis, calcification, cardiomegaly, consolidation, fibrosis, nodules, pneumothorax, pleural effusion, and pneumoperitoneum). The reading order was determined in a randomized, crossover manner. The radiographs were classified into negative and positive examinations. In a 50% worklist reduction scenario, radiographs were sorted in descending order of probability scores: the lower half was regarded as negative exams, while the remaining were read with DLD by radiologists. The primary analysis evaluated noninferiority in sensitivity between radiologists reading all radiographs and simulating a 50% worklist reduction, with the inferiority margin of 5%. The specificities were compared using McNemar's test. RESULTS: The study included 1964 patients (median age [interquartile range], 55 years [40-67 years]). The sensitivity was 82.6% (195 of 236; 95% CI: 77.5%, 87.3%) when readers interpreted all chest radiographs without DLD and 83.5% (197 of 236; 95% CI: 78.8%, 88.1%) in the 50% worklist reduction scenario. The difference in sensitivity was 0.8% (95% CI: - 3.8%, 5.5%), establishing noninferiority of 50% worklist reduction (p = 0.01). The specificity increased from 86.7% (1498 of 1728) to 90.4% (1562 of 1728) (p < 0.001) with DLD-based triage. CONCLUSION: Deep learning-based triaging may substantially reduce workload without lowering sensitivity while improving specificity. CLINICAL RELEVANCE STATEMENT: Substantial workload reduction without lowering sensitivity was feasible using deep learning-based triaging of outpatient chest radiograph; however, the legal responsibility for incorrect diagnoses based on AI-standalone interpretation remains an issue that should be defined before clinical implementation. KEY POINTS: • A 50% workload reduction simulation using deep learning-based detection algorithm maintained noninferior sensitivity while improving specificity. • The CT recommendation rate significantly decreased in the disease-negative patients, whereas it slightly increased in the disease-positive group without statistical significance. • In the exploratory analysis, the noninferiority of sensitivity was maintained until 70% of the workload was reduced; the difference in sensitivity was 0%.

The importance of investing in data, models, experiments, team science, and public trust to help policymakers prepare for the next pandemic.

The COVID-19 pandemic has brought about valuable insights regarding models, data, and experiments. In this narrative review, we summarised the existing literature on these three themes, exploring the challenges of providing forecasts, the requirement for real-time linkage of health-related datasets, and the role of 'experimentation' in evaluating interventions. This literature review encourages us to broaden our perspective for the future, acknowledging the significance of investing in models, data, and experimentation, but also to invest in areas that are conceptually more abstract: the value of 'team science', the need for public trust in science, and in establishing processes for using science in policy. Policy-makers rely on model forecasts early in a pandemic when there is little data, and it is vital to communicate the assumptions, limitations, and uncertainties (theme 1). Linked routine data can provide critical information, for example, in establishing risk factors for adverse outcomes but are often not available quickly enough to make a real-time impact. The interoperability of data resources internationally is required to facilitate sharing across jurisdictions (theme 2). Randomised controlled trials (RCTs) provided timely evidence on the efficacy and safety of vaccinations and pharmaceuticals but were largely conducted in higher income countries, restricting generalisability to low- and middle-income countries (LMIC). Trials for non-pharmaceutical interventions (NPIs) were almost non-existent which was a missed opportunity (theme 3). Building on these themes from the narrative review, we underscore the importance of three other areas that need investment for effective evidence-driven policy-making. The COVID-19 response relied on strong multidisciplinary research infrastructures, but funders and academic institutions need to do more to incentivise team science (4). To enhance public trust in the use of scientific evidence for policy, researchers and policy-makers must work together to clearly communicate uncertainties in current evidence and any need to change policy as evidence evolves (5). Timely policy decisions require an established two-way process between scientists and policy makers to make the best use of evidence (6). For effective preparedness against future pandemics, it is essential to establish models, data, and experiments as fundamental pillars, complemented by efforts in planning and investment towards team science, public trust, and evidence-based policy-making across international communities. The paper concludes with a 'call to actions' for both policy-makers and researchers.

Association with Combined Occupational Hazards Exposure and Risk of Metabolic Syndrome: A Workers' Health Examination Cohort 2012-2021.

Background: This study aimed to evaluate the association between exposure to occupational hazards and the metabolic syndrome. A secondary objective was to analyze the additive and multiplicative effects of exposure to risk factors. Methods: This retrospective cohort was based on 31,615 health examinees at the Pusan National University Yangsan Hospital in Republic of Korea from 2012-2021. Demographic and behavior-related risk factors were treated as confounding factors, whereas three physical factors, 19 organic solvents and aerosols, and 13 metals and dust were considered occupational risk factors. Time-dependent Cox regression analysis was used to calculate hazard ratios. Results: The risk of metabolic syndrome was significantly higher in night shift workers (hazard ratio = 1.45: 95% confidence interval = 1.36-1.54) and workers who were exposed to noise (1.15:1.07-1.24). Exposure to some other risk factors was also significantly associated with a higher risk of metabolic syndrome. They were dimethylformamide, acetonitrile, trichloroethylene, xylene, styrene, toluene, dichloromethane, copper, antimony, lead, copper, iron, welding fume, and manganese. Among the 28 significant pairs, 19 exhibited both positive additive and multiplicative effects. Conclusions: Exposure to single or combined occupational risk factors may increase the risk of developing metabolic syndrome. Working conditions should be monitored and improved to reduce exposure to occupational hazards and prevent the development of the metabolic syndrome.

Characteristics of fatal occupational injuries in migrant workers in South Korea: A machine learning study.

Objective: Analysis of occupational injuries is essential for developing preventive strategies. However, few studies have evaluated severe occupational injuries in migrant workers from the perspective of gender. Therefore, using a new analytical method, this study was performed to identify gender-specific characteristics associated with fatal occupational injuries among migrant workers; the interactions between these factors, were also analyzed. In addition, we compared the utility of explainable artificial intelligence (XAI) using SHapley Additive exPlanations (SHAP) with logistic regression (LR) and discuss caveats regarding its use. Materials and methods: We analyzed national statistics for occupational injuries among migrant workers (n = 67,576) in South Korea between January 1, 2007, and September 30, 2018. We applied an extreme gradient boosting model and developed SHAP and LR models for comparison. Results: We found clear gender differences in fatal occupational injuries among migrant workers, with males in the same occupation having a higher risk of death than females. These gender differences suggest the need for gender-specific occupational injury prevention interventions for migrant workers to reduce the mortality rate. Occupation was a significant predictor of death among female migrant workers only, with care jobs having the highest fatality risk. The occupational fatality risk of female workers would not have been identified without the performance of detailed job-specific analyses stratified by gender. The major advantages of SHAP identified in the present study were the automatic identification and analysis of interactions, ability to determine the relative contributions of each feature, and high overall performance. The major caveat when using SHAP is that causality cannot be established. Conclusion: Detailed job-specific analyses stratified by gender, and interventions considering the gender of migrant workers, are necessary to reduce occupational fatality rates. The XAI approach should be considered as a complementary analytical method for epidemiological studies, as it overcomes the limitations of traditional statistical analyses.

Directed acyclic graphs for clinical research: a tutorial.

Directed acyclic graphs (DAGs) are useful tools for visualizing the hypothesized causal structures in an intuitive way and selecting relevant confounders in causal inference. However, in spite of their increasing use in clinical and surgical research, the causal graphs might also be misused by a lack of understanding of the central principles. In this article, we aim to introduce the basic terminology and fundamental rules of DAGs, and DAGitty, a user-friendly program that easily displays DAGs. Specifically, we describe how to determine variables that should or should not be adjusted based on the backdoor criterion with examples. In addition, the occurrence of the various types of biases is discussed with caveats, including the problem caused by the traditional approach using p-values for confounder selection. Moreover, a detailed guide to DAGitty is provided with practical examples regarding minimally invasive surgery. Essentially, the primary benefit of DAGs is to aid researchers in clarifying the research questions and the corresponding designs based on the domain knowledge. With these strengths, we propose that the use of DAGs may contribute to rigorous research designs, and lead to transparency and reproducibility in research on minimally invasive surgery.

Associations of water, sanitation, and hygiene with typhoid fever in case-control studies: a systematic review and meta-analysis.

BACKGROUND: Water, sanitation, and hygiene (WASH) play a pivotal role in controlling typhoid fever, as it is primarily transmitted through oral-fecal pathways. Given our constrained resources, staying current with the most recent research is crucial. This ensures we remain informed about practical insights regarding effective typhoid fever control strategies across various WASH components. We conducted a systematic review and meta-analysis of case-control studies to estimate the associations of water, sanitation, and hygiene exposures with typhoid fever. METHODS: We updated the previous review conducted by Brockett et al. We included new findings published between June 2018 and October 2022 in Web of Science, Embase, and PubMed. We used the Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I) tool for risk of bias (ROB) assessment. We classified WASH exposures according to the classification provided by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation, and Hygiene (JMP) update in 2015. We conducted the meta-analyses by only including studies that did not have a critical ROB in both Bayesian and frequentist random-effects models. RESULTS: We identified 8 new studies and analyzed 27 studies in total. Our analyses showed that while the general insights on the protective (or harmful) impact of improved (or unimproved) WASH remain the same, the pooled estimates of OR differed. Pooled estimates of limited hygiene (OR = 2.26, 95% CrI: 1.38 to 3.64), untreated water (OR = 1.96, 95% CrI: 1.28 to 3.27) and surface water (OR = 2.14, 95% CrI: 1.03 to 4.06) showed 3% increase, 18% decrease, and 16% increase, respectively, from the existing estimates. On the other hand, improved WASH reduced the odds of typhoid fever with pooled estimates for improved water source (OR = 0.54, 95% CrI: 0.31 to 1.08), basic hygiene (OR = 0.6, 95% CrI: 0.38 to 0.97) and treated water (OR = 0.54, 95% CrI: 0.36 to 0.8) showing 26% decrease, 15% increase, and 8% decrease, respectively, from the existing estimates. CONCLUSIONS: The updated pooled estimates of ORs for the association of WASH with typhoid fever showed clear changes from the existing estimates. Our study affirms that relatively low-cost WASH strategies such as basic hygiene or water treatment can be an effective tool to provide protection against typhoid fever in addition to other resource-intensive ways to improve WASH. TRIAL REGISTRATION: PROSPERO 2021 CRD42021271881.

Enhancing the reliability of particulate matter sensing by multivariate Tobit model using weather and air quality data.

Low-cost particulate matter (PM) sensors have been widely used following recent sensor-technology advancements; however, inherent limitations of low-cost monitors (LCMs), which operate based on light scattering without an air-conditioning function, still restrict their applicability. We propose a regional calibration of LCMs using a multivariate Tobit model with historical weather and air quality data to improve the accuracy of ambient air monitoring, which is highly dependent on meteorological conditions, local climate, and regional PM properties. Weather observations and PM2.5 (fine inhalable particles with diameters ≤ 2.5 µm) concentrations from two regions in Korea, Incheon and Jeju, and one in Singapore were used as training data to build a visibility-based calibration model. To validate the model, field measurements were conducted by an LCM in Jeju and Singapore, where R2 and the error after applying the model in Jeju improved (from 0.85 to 0.88) and reduced by 44% (from 8.4 to 4.7 µg m-3), respectively. The results demonstrated that regional calibration involving air temperature, relative humidity, and other local climate parameters can efficiently correct the bias of the sensor. Our findings suggest that the proposed post-processing using the Tobit model with regional weather and air quality data enhances the applicability of LCMs.

An Introduction to Causal Mediation Analysis With a Comparison of 2 R Packages.

Traditional mediation analysis, which relies on linear regression models, has faced criticism due to its limited suitability for cases involving different types of variables and complex covariates, such as interactions. This can result in unclear definitions of direct and indirect effects. As an alternative, causal mediation analysis using the counterfactual framework has been introduced to provide clearer definitions of direct and indirect effects while allowing for more flexible modeling methods. However, the conceptual understanding of this approach based on the counterfactual framework remains challenging for applied researchers. To address this issue, the present article was written to highlight and illustrate the definitions of causal estimands, including controlled direct effect, natural direct effect, and natural indirect effect, based on the key concept of nested counterfactuals. Furthermore, we recommend using 2 R packages, 'medflex' and 'mediation', to perform causal mediation analysis and provide public health examples. The article also offers caveats and guidelines for accurate interpretation of the results.

Seroprevalence of Measles IgG Antibodies in Married Immigrant Women from Multicultural Families in Korea.

BACKGROUND: Although an effective vaccine has been available, measles still causes mast morbidity and mortality world widely. In Korea, a small number of measles cases have been reported through exposure to imported cases among young people with vaccine-induced measles immunity. Recently due to international migration including marriage, marriage migrants were the second-largest group of foreign population in Korea. Our study was carried out to obtain positive rate of measles antibody among married immigrant women from 12 countries in 10 Gun-Counties and 6 Cities, Korea. MATERIALS AND METHODS: A total of 547 blood samples were collected from maternal multicultural members from 12 countries. The measles-specific IgG antibody was measured by ELISA (Enzyme-linked immunosorbent assay; Enzygnost® Anti-measles virus/IgG, Siemens Healthcare Diagnostics Products GmbH, Marburg, Germany). We performed a simple logistic regression to test whether the measles antibody seroprevalence differed by participant age, location, or country of birth and then calculated the likelihood ratio statistics to determine whether measles antibody seroprevalence differed by country of birth. RESULTS: Overall positive measles seroprevalence was 75.3% (95% confidence interval: 71.7 - 78.9). Participants aged 20 - 24 years, 25 - 29 years, and 30 - 63 years has respective seropositivities of 52.5%, 55.3%, and 82.7%. In this study, the geometric mean titers of participants aged 21 - 29 years were slightly lower than those of participants aged over 30 years, which were 1,372 mIU/ml and 2,261 mIU/ml, respectively (average of total participants: 2,027 mIU/ml). CONCLUSION: The study provides detailed information about seroimmunity of the married immigrant population in Korea, which is important for measles elimination. Since the 1980s, most vaccine-preventable diseases including measles have been well-controlled. Nevertheless, sporadic measles outbreaks are still reported. Thus, special attention should be paid to the possible importation of infectious diseases such as measles by immigration.

Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease.

BACKGROUND: Information on electrocardiogram (ECG) has not been quantified in obstructive coronary artery disease (ObCAD), despite the deep learning (DL) algorithm being proposed as an effective diagnostic tool for acute myocardial infarction (AMI). Therefore, this study adopted a DL algorithm to suggest the screening of ObCAD from ECG. METHODS: ECG voltage-time traces within a week from coronary angiography (CAG) were extracted for the patients who received CAG for suspected CAD in a single tertiary hospital from 2008 to 2020. After separating the AMI group, those were classified into ObCAD and non-ObCAD groups based on the CAG results. A DL-based model adopting ResNet was built to extract information from ECG data in the patients with ObCAD relative to those with non-ObCAD, and compared the performance with AMI. Moreover, subgroup analysis was conducted using ECG patterns of computer-assisted ECG interpretation. RESULTS: The DL model demonstrated modest performance in suggesting the probability of ObCAD but excellent performance in detecting AMI. The AUC of the ObCAD model adopting 1D ResNet was 0.693 and 0.923 in detecting AMI. The accuracy, sensitivity, specificity, and F1 score of the DL model for screening ObCAD were 0.638, 0.639, 0.636, and 0.634, respectively, while the figures were up to 0.885, 0.769, 0.921, and 0.758 for detecting AMI, respectively. Subgroup analysis showed that the difference between normal and abnormal/borderline ECG groups was not notable. CONCLUSIONS: ECG-based DL model showed fair performance for assessing ObCAD and it may serve as an adjunct to the pre-test probability in patients with suspected ObCAD during the initial evaluation. With further refinement and evaluation, ECG coupled with the DL algorithm may provide potential front-line screening support in the resource-intensive diagnostic pathways.