ObjectivesBusan is reported to have the highest mortality rate among 16 provinces in Korea, as well as considerable health inequality across its districts. This study sought to examine overall and cause-specific mortality and deprivation at the town level in Busan, thereby identifying towns and causes of deaths to be targeted for improving overall health and alleviating health inequality.MethodsStandardized mortality ratios (SMRs) for all-cause and four specific leading causes of death were calculated at the town level in Busan for the years 2005 through 2008. To construct a deprivation index, principal components and factor analysis were adopted, using 10% sample data from the 2005 census. Geographic information system (GIS) mapping techniques were applied to compare spatial distributions between the deprivation index and SMRs. We fitted the Gaussian conditional autoregressive model (CAR) to estimate the relative risks of mortality by deprivation level, controlling for both the heterogeneity effect and spatial autocorrelation.ResultsThe SMRs of towns in Busan averaged 100.3, ranging from 70.7 to 139.8. In old inner cities and towns reclaimed for replaced households, the deprivation index and SMRs were relatively high. CAR modeling showed that gaps in SMRs for heart disease, cerebrovascular disease, and physical injury were particularly high.ConclusionsOur findings indicate that more deprived towns are likely to have higher mortality, in particular from cardiovascular disease and physical injury. To improve overall health status and address health inequality, such deprived towns should be targeted.

Issues regarding air quality and related health concerns have prompted this study, which develops an accurate and computationally fast, efficient hybrid modeling system that combines numerical modeling and machine learning for forecasting concentrations of surface ozone. Currently available numerical modeling systems for air quality predictions (e.g., CMAQ) can forecast 24 to 48 h in advance. In this study, we develop a modeling system based on a convolutional neural network (CNN) model that is not only fast but covers a temporal period of two weeks with a resolution as small as a single hour for 255 stations. The CNN model uses meteorology from the Weather Research and Forecasting model (processed by the Meteorology-Chemistry Interface Processor), forecasted air quality from the Community Multi-scale Air Quality Model (CMAQ), and previous 24-h concentrations of various measurable air quality parameters as inputs and predicts the following 14-day hourly surface ozone concentrations. The model achieves an average accuracy of 0.91 in terms of the index of agreement for the first day and 0.78 for the fourteenth day, while the average index of agreement for one day ahead prediction from the CMAQ is 0.77. Through this study, we intend to amalgamate the best features of numerical modeling (i.e., fine spatial resolution) and a deep neural network (i.e., computation speed and accuracy) to achieve more accurate spatio-temporal predictions of hourly ozone concentrations. Although the primary purpose of this study is the prediction of hourly ozone concentrations, the system can be extended to various other pollutants.

Osteoporosis is the most common disease of the musculoskeletal system in old age. Therefore, research on osteoporosis risk factors is actively being conducted. However, whether socioeconomic inequality is associated with the prevalence and diagnosis experience of osteoporosis remains largely unexplored. This study aims to investigate whether socioeconomic inequality can be a risk factor for osteoporosis in postmenopausal women. Cross-sectional data of 1,477 postmenopausal women aged over 50 obtained from the Korea National Health and Nutrition Examination Survey V-2 were analyzed. Univariate analyses were performed to calculate the prevalence of osteoporosis and the rate of osteoporosis diagnosis experience according to the risk factor categories. Multivariate logistic regression analysis was performed to identify the independent variables’ associations with osteoporosis prevalence and diagnosis experience. The prevalence of osteoporosis was 34.8%, while the diagnosis experience rate was 22.1%. The higher the age, the higher the probability of osteoporosis presence and diagnosis experience. The lowest household income level was associated with a 1.63 times higher risk of osteoporosis. On the contrary, this factor was not significant for diagnosis experience. These results were similar for the 50–59 and 60–69 age groups. Among postmenopausal women, those who are older and have low socioeconomic levels are at a high risk of developing osteoporosis. Moreover, the lower the socioeconomic level, the lower the awareness of osteoporosis. Therefore, there is a need to develop more proactive preventive measures in postmenopausal women with low socioeconomic levels.

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Background: It is difficult to characterize extracranial venous malformations (VMs) of the head and neck region from magnetic resonance imaging (MRI) manually and one at a time. We attempted to perform the automatic segmentation of lesions from MRI of extracranial VMs using a convolutional neural network as a deep learning tool. Methods: T2-weighted MRI from 53 patients with extracranial VMs in the head and neck region was used for annotations. Preprocessing management was performed before training. Three-dimensional U-Net was used as a segmentation model. Dice similarity coefficients were evaluated along with other indicators. Results: Dice similarity coefficients in 3D U-Net were found to be 99.75% in the training set and 60.62% in the test set. The models showed overfitting, which can be resolved with a larger number of objects, i.e., MRI VM images. Conclusions: Our pilot study showed sufficient potential for the automatic segmentation of extracranial VMs through deep learning using MR images from VM patients. The overfitting phenomenon observed will be resolved with a larger number of MRI VM images.