The role of social support and social networks in smoking behavior among middle and older aged people in rural areas of South Korea: A cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Although the number of studies on anti-smoking interventions has increased, studies focused on identifying social contextual factors in rural areas are scarce. The purpose of this study was to explore the role of social support and social networks in smoking behavior among middle and older aged people living in rural areas of South Korea.</p> <p>Methods</p> <p>The study employed a cross-sectional design. Participants included 1,057 adults, with a mean age of 60.7 years, residing in rural areas. Information on participants' tobacco use, stress, social support, and social networks was collected using structured questionnaires. The chi-square test, the t-test, ANOVA, and logistic regression were used for data analysis.</p> <p>Results</p> <p>The overall smoking prevalence in the study was 17.4% (men, 38.8%; women, 5.1%). Overall, stress was high among women, and social support was high among men. Smokers had high levels of social support (t = -2.90, p = .0038) and social networks (t = -2.22, p = .0271), as compared to non- and former smokers. Those in the high social support group were likely to be smokers (AOR = 2.21, 95% CI 1.15-4.26). Women with moderate social ties were less likely to smoke (AOR = 0.18, 95% CI 0.05-0.61).</p> <p>Conclusion</p> <p>There was a protective role of a moderate social network level among women, and a high level of social support was associated with smoking behaviors in rural areas. Findings suggest the need for a comprehensive understanding of the functions and characteristics of social contextual factors including social support and social networks in order to conduct more effective anti-smoking interventions in rural areas.</p

Educational Inequalities in Self-Rated Health in Europe and South Korea

While numerous comparative works on the magnitude of health inequalities in Europe have been conducted, there is a paucity of research that encompasses non-European nations such as Asian countries. This study was conducted to compare Europe and Korea in terms of educational health inequalities, with poor self-rated health (SRH) as the outcome variable. The European Union Statistics on Income and Living Conditions and the Korea National Health and Nutrition Examination Survey in 2017 were used (31 countries). Adult men and women aged 20+ years were included (207,245 men and 238,007 women). The age-standardized, sex-specific prevalence of poor SRH by educational level was computed. The slope index of inequality (SII) and relative index of inequality (RII) were calculated. The prevalence of poor SRH was higher in Korea than in other countries for both low/middle- and highly educated individuals. Among highly educated Koreans, the proportion of less healthy women was higher than that of less healthy men. Korea’s SII was the highest for men (15.7%) and the ninth-highest for women (10.4%). In contrast, Korea’s RII was the third-lowest for men (3.27), and the lowest among women (1.98). This high-SII–low-RII mix seems to have been generated by the high level of baseline poor SRH

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash???Sutcliffe efficiency (NSE) &gt; 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values &gt; 0.79 and &gt; 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application

Staphylococcal enterotoxin sensitization in a community-based population : a potential role in adult-onset asthma

Background: Recent studies suggest that Staphylococcus aureus enterotoxin sensitization is a risk factor for asthma. However, there is a paucity of epidemiologic evidence on adult-onset asthma in community-based populations. Objective: We sought to evaluate the epidemiology and the clinical significance of staphylococcal enterotoxin sensitization in community-based adult populations. Methods: The present analyses were performed using the baseline data set of Korean adult population surveys, consisting of 1080 adults (mean age=60.2years) recruited from an urban and a rural community. Questionnaires, methacholine challenge tests, and allergen skin tests were performed for defining clinical phenotypes. Sera were analysed for total IgE and enterotoxin-specific IgE using ImmunoCAP. Results: Staphylococcal enterotoxin sensitization (0.35kU/L) had a prevalence of 27.0%. Risk factors were identified as male sex, current smoking, advanced age (61years), and inhalant allergen sensitization. Current asthma was mostly adult onset (18years old) and showed independent associations with high enterotoxin-specific IgE levels in multivariate logistic regression tests. In multivariate linear regressions, staphylococcal enterotoxin-specific IgE level was identified as the major determinant factor for total IgE level. Conclusions and Clinical Relevance: Staphylococcal enterotoxin sensitization was independently associated with adult-onset asthma in adult community populations. Strong correlations between the enterotoxin-specific IgE and total IgE levels support the clinical significance. The present findings warrant further studies for the precise roles of staphylococcal enterotoxin sensitization in the asthma pathogenesis

Comparison of three small-area mortality metrics according to urbanity in Korea: the standardized mortality ratio, comparative mortality figure, and life expectancy

Abstract Background This study aimed to compare three small-area level mortality metrics according to urbanity in Korea: the standardized mortality ratio (SMR), comparative mortality figure (CMF), and life expectancy (LE) by urbanity. Methods We utilized the National Health Information Database to obtain annual small-area level age-specific numbers of population and deaths in Korea between 2013 and 2017. First, differences in the SMR by urbanity were examined, assuming the same age-specific mortality rates in all small areas. Second, we explored the differences in ranking obtained using the three metrics (SMR, CMF, and LE). Third, the ratio of CMF to SMR by population was analyzed according to urbanity. Results We found that the age-specific population distributions in urbanized areas were similar, but rural areas had a relatively old population structure. The age-specific mortality ratio also differed by urbanity. Assuming the same rate of age-specific mortality across all small areas, we found that comparable median values in all areas. However, areas with a high SMR showed a strong predominance of metropolitan areas. The ranking by SMR differed markedly from the rankings by CMF and LE, especially in areas of high mortality, while the latter two metrics did not differ notably. The ratio of CMF to SMR showed larger variations in small areas in rural areas, particularly in those with small populations, than in metropolitan and urban areas. Conclusions In a comparison of multiple SMRs, bias could exist if the study areas have large differences in population structure. The use of CMF or LE should be considered for comparisons if it is possible to acquire age-specific mortality data for each small area