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Background@#Although influenza poses substantial mortality burden, most studies have estimated excess mortality using time-aggregated data. Here, we estimated mortality risk and population attributable fraction (PAF) attributed to seasonal influenza using individual-level data from a nationwide matched cohort. @*Methods@#Individuals with influenza during four consecutive influenza seasons (2013–2017) (n = 5,497,812) and 1:4 age- and sex-matched individuals without influenza (n = 20,990,683) were identified from a national health insurance database. The endpoint was mortality within 30 days after influenza diagnosis. All-cause and cause-specific mortality risk ratios (RRs) attributed to influenza were estimated. Excess mortality, mortality RR, and PAF of mortality were determined, including for underlying disease subgroups. @*Results@#Excess mortality rate, mortality RR, and PAF of all-cause mortality were 49.5 per 100,000, 4.03 (95% confidence interval [CI], 3.63–4.48), and 5.6% (95% CI, 4.5–6.7%). Cause-specific mortality RR (12.85; 95% CI, 9.40–17.55) and PAF (20.7%; 95% CI, 13.2– 27.0%) were highest for respiratory diseases. In subgroup analysis according to underlying disorders, PAF of all-cause mortality was 5.9% (95% CI, 0.6–10.7%) for liver disease, 5.8% (95% CI, 2.9–8.5%) for respiratory disease, and 3.8% (95% CI, 1.4–6.1%) for cancer. @*Conclusion@#Individuals with influenza had a 4-fold higher mortality risk than individuals without influenza. Preventing seasonal influenza may lead to 5.6% and 20.7% reductions in all-cause and respiratory mortality, respectively. Individuals with respiratory disease, liver disease, and cancer may benefit from prioritization when establishing influenza prevention strategies.
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Background@#While the importance of mental health is well-recognized in the field of occupational health, implementation of effective strategies in the workplace has been limited by gaps in infrastructure, program comprehensiveness, coverage, and adherence. The authors developed a Screening, Brief Intervention, and Referral to Treatment (SBIRT) model based occupational mental health intervention, and implemented in a web-based format with a smartphone application. @*Methods@#The SBIRT-based intervention was developed by a multidisciplinary team, including occupational health physicians, nurses, psychiatrists, and software developers. The following mental health areas were included, based on outcomes of an epidemiological survey conducted: insomnia, depression, anxiety, problematic alcohol use, and suicidal risk. The viability of the two-step evaluation process utilizing a combination of the brief version and the full-length version of the questionnaire was examined using responses from the survey. The intervention was adjusted according to the survey results and expert opinions. @*Results@#The epidemiological survey included 346 employees who completed the long-form version of mental health scales. These data were the used to confirm the diagnostic value of using a combination of short-form and long-form version of the scales for screening in the SBIRT model. The model uses a smartphone application for screening, provision of psychoeducation, and for surveillance. The universal methods of the model ensure it can be implemented by all occupational managers, regardless of their specialization in mental health. In addition to the two-step screening procedure to identify employees at-risk for mental health problems, the model includes a stepped care approach, based on risk stratification, to promote mental health education, management, and follow-up for continuous care. @*Conclusion@#The SBIRT model-based intervention provides an easy-to-implement approach for the management of mental health in the workplace. Further studies are required to examine the effectiveness and feasibility of the model.
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Background@#Numerous studies have shown the effect of particulate matter exposure on brain imaging markers. However, little evidence exists about whether the effect differs by the level of low-grade chronic systemic inflammation. We investigated whether the level of c-reactive protein (CRP, a marker of systemic inflammation) modifies the associations of particulate matter exposures with brain cortical gray matter thickness and white matter hyperintensities (WMH). @*Methods@#We conducted a cross-sectional study of baseline data from a prospective cohort study including adults with no dementia or stroke. Long-term concentrations of particulate matter ≤ 10 µm in diameter (PM10) and ≤ 2.5 µm (PM2.5) at each participant’s home address were estimated. Global cortical thickness (n = 874) and WMH volumes (n = 397) were estimated from brain magnetic resonance images. We built linear and logistic regression models for cortical thickness and WMH volumes (higher versus lower than median), respectively. Significance of difference in the association between the CRP group (higher versus lower than median) was expressed as P for interaction. @*Results@#Particulate matter exposures were significantly associated with a reduced global cortical thickness only in the higher CRP group among men (P for interaction = 0.015 for PM10 and 0.006 for PM2.5). A 10 μg/m3 increase in PM10 was associated with the higher volumes of total WMH (odds ratio, 1.78; 95% confidence interval, 1.07–2.97) and periventricular WMH (2.00; 1.20–3.33). A 1 μg/m3 increase in PM2.5 was associated with the higher volume of periventricular WMH (odds ratio, 1.66; 95% confidence interval, 1.08–2.56). These associations did not significantly differ by the level of high sensitivity CRP. @*Conclusion@#Particulate matter exposures were associated with a reduced global cortical thickness in men with a high level of chronic inflammation. Men with a high level of chronic inflammation may be susceptible to cortical atrophy attributable to particulate matter exposures.
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Purpose@#We propose the Lifelog Bigdata Platform as a sustainable digital healthcare system based on individual-centric lifelog datasets and describe the standardization of lifelog and clinical data in its full-cycle management system. @*Materials and Methods@#The Lifelog Bigdata Platform was developed by Yonsei Wonju Health System on the cloud to support digital healthcare and precision medicine. It consists of five core components: data acquisition system, de-identification of individual information, lifelog integration, analyzer, and service. We designed a gathering system into a dedicated virtual machine to save lifelog or clinical outcomes and established standard guidelines for maintaining the quality of gathering procedures. We used standard integration keys to integrate the lifelog and clinical data. Metadata were generated from the data warehouse after loading combined or fragmented data on it. We analyzed the de-identified lifelog and clinical data using the lifelog analyzer to prevent and manage acute and chronic diseases through providing results of statistics on analysis. @*Results@#The big data centers were built in four hospitals and seven companies for integrating lifelog and clinical data to develop the Lifelog Bigdata Platform. We integrated and loaded lifelog big data and clinical data for 3 years. In the first year, we uploaded 94 types of data on the platform with a total capacity of 221 GB. @*Conclusion@#The Lifelog Bigdata Platform is the first to combine lifelog and clinical data. The proposed standardization guidelines can be used for future platforms to achieve a virtuous cycle structure of lifelogging big data and an industrial ecosystem.
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Purpose@#Several artificial intelligence (AI) models for the detection and prediction of cardiovascular-related diseases, including arrhythmias, diabetes, and sleep apnea, have been reported. This systematic review and meta-analysis aimed to identify AI models developed for or applicable to wearable and mobile devices for diverse cardiovascular-related diseases. @*Materials and Methods@#The searched databases included Medline, Embase, and Cochrane Library. For AI models for atrial fibrillation (AF) detection, a meta-analysis of diagnostic accuracy was performed to summarize sensitivity and specificity. @*Results@#A total of 102 studies were included in the qualitative review. There were AI models for the detection of arrythmia (n=62), followed by sleep apnea (n=11), peripheral vascular diseases (n=6), diabetes mellitus (n=5), hyper/hypotension (n=5), valvular heart disease (n=4), heart failure (n=3), myocardial infarction and cardiac arrest (n=2), and others (n=4). For quantitative analysis of 26 studies reporting AI models for AF detection, meta-analyzed sensitivity was 94.80% and specificity was 96.96%. Deep neural networks showed superior performance [meta-analyzed area under receiver operating characteristics curve (AUROC) of 0.981] compared to conventional machine learning algorithms (meta-analyzed AUROC of 0.961). However, AI models tested with proprietary dataset (meta-analyzed AUROC of 0.972) or data acquired from wearable devices (meta-analyzed AUROC of 0.977) showed inferior performance than those with public dataset (meta-analyzed AUROC of 0.986) or data from in-hospital devices (meta-analyzed AUROC of 0.983). @*Conclusion@#This review found that AI models for diverse cardiovascular-related diseases are being developed, and that they are gradually developing into a form that is suitable for wearable and mobile devices.
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Purpose@#The characteristic topography and climate often affect the occurrence of large-scale wildfires in the Eastern Gangwon-do region of Korea. However, there are no studies on the health effects of these wildfires in Korea. This study aimed to analyze the differences in medical use between a wildfire-affected area and an adjacent non-affected area before and after a wildfire in 2019 in Gangwon-do, Korea. @*Materials and Methods@#We used medical usage data from the Korean National Health Insurance Corporation. Rates of medical use were determined for citizens of a wildfire-affected area in the Eastern Yeongdong region and a non-affected area in the Western Yeongseo region. Logistic regression analysis was performed considering an increase in medical use per individual as a dependent variable; age, sex, income, smoking, drinking, and exercise were included as confounding variables. @*Results@#The odds ratio for medical use in Yeongdong region increased significantly after 3 days, 3 months, and 1 year after a fire occurred, compared with Yeongseo region. @*Conclusion@#The results of this study confirmed that the use of medical care increased for residents of a wildfire-affected area, compared with those of an adjacent non-affected area. This is the first study on the relationship between wildfires and inpatient medical use in Korea.
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Purpose@#We aimed to investigate variations in the risk of low back pain (LBP), lower extremity muscle pain, and whole body fatigue according to differences in prolonged standing work hours in relation to risk factor exposure and rest frequency. @*Materials and Methods@#From the fifth Korean Working Conditions Survey data collected in 2017, data for 32970 full-time workers who worked for more than 1 year at their present job were analyzed. We classified the workers according to exposure to fatigue or painful postures, carrying heavy objects, performance of repetitive movements that burden the musculoskeletal system, and how often they took a break. Relationships between time spent in a standing posture at work and risks of LBP, lower extremity muscle pain, and whole body fatigue were analyzed by multivariate logistic regression. @*Results@#Of the full-time workers in the survey, 48.7% worked in a standing position for more than half of their total working hours. A higher odds ratio (OR) value for lower extremity muscle pain was observed in female not exposed to carrying heavy objects [OR: 3.551, 95% confidence interval (CI): 3.038–4.150] and not exposed to performing repetitive movements (OR: 3.555, 95% CI: 2.761–4.557). @*Conclusion@#Changes in work methodologies are needed to lower the number of hours spent in a prolonged standing posture at work, including being able to rest when workers want to do so, to reduce pain and fatigue.
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Objectives@#Non-alcoholic fatty liver disease (NAFLD) is an increasingly prevalent metabolic disease. Muscle is known to influence NAFLD development. Therefore, this study aimed to determine the relationships among low muscle mass, NAFLD, and hepatic fibrosis using various definitions of low muscle mass and NAFLD diagnostic methods, including magnetic resonance imaging-based proton density fat fraction (MRI-PDFF). @*Methods@#This cross-sectional study included 320 participants (107 males, 213 females) from the Korean Genome and Epidemiology Study on Atherosclerosis Risk of Rural Areas in the Korean General Population cohort. Muscle mass was assessed using whole-body dual-energy X-ray absorptiometry and adjusted for the height squared, body weight, and body mass index (BMI). NAFLD was diagnosed using ultrasonography (US), MRI-PDFF, and the comprehensive NAFLD score (CNS). Hepatic fibrosis was assessed using magnetic resonance elastography. Multivariable logistic and linear regression analyses were performed to determine the aforementioned associations. @*Results@#According to US, 183 participants (57.2%) had NAFLD. Muscle mass adjusted for body weight was associated with NAFLD diagnosed using US (odds ratio [OR], 3.00; 95% confidence interval [CI], 1.70 to 5.31), MRI-PDFF (OR, 2.00; 95% CI, 1.13 to 3.53), and CNS (OR, 3.39; 95% CI, 1.73 to 6.65) and hepatic fibrosis (males: β=-0.070, p<0.01; females: β=-0.037, p<0.04). Muscle mass adjusted for BMI was associated with NAFLD diagnosed by US (OR, 1.71; 95% CI, 1.02 to 2.86) and CNS (OR, 1.95; 95% CI, 1.04 to 3.65), whereas muscle mass adjusted for height was not associated with NAFLD. @*Conclusions@#Low muscle mass was associated with NAFLD and liver fibrosis; therefore, maintaining sufficient muscle mass is important to prevent NAFLD. A prospective study and additional consideration of muscle quality are needed to strengthen the findings regarding this association.
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Purpose@#We aimed to investigate variations in the risk of low back pain (LBP), lower extremity muscle pain, and whole body fatigue according to differences in prolonged standing work hours in relation to risk factor exposure and rest frequency. @*Materials and Methods@#From the fifth Korean Working Conditions Survey data collected in 2017, data for 32970 full-time workers who worked for more than 1 year at their present job were analyzed. We classified the workers according to exposure to fatigue or painful postures, carrying heavy objects, performance of repetitive movements that burden the musculoskeletal system, and how often they took a break. Relationships between time spent in a standing posture at work and risks of LBP, lower extremity muscle pain, and whole body fatigue were analyzed by multivariate logistic regression. @*Results@#Of the full-time workers in the survey, 48.7% worked in a standing position for more than half of their total working hours. A higher odds ratio (OR) value for lower extremity muscle pain was observed in female not exposed to carrying heavy objects [OR: 3.551, 95% confidence interval (CI): 3.038–4.150] and not exposed to performing repetitive movements (OR: 3.555, 95% CI: 2.761–4.557). @*Conclusion@#Changes in work methodologies are needed to lower the number of hours spent in a prolonged standing posture at work, including being able to rest when workers want to do so, to reduce pain and fatigue.
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no abstract available
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PURPOSE: Metabolic syndrome (MetS) is a major public health problem related to increased risks of cardiovascular disease and type 2 diabetes. We investigated whether recommended levels of leisure time physical activity (LTPA) decrease the risk of MetS in a community-based prospective cohort study (Ansung and Ansan cohort).MATERIALS AND METHODS: A total of 3910 adults (1890 men, 2020 women) without MetS examined in 2001–2002 (baseline) were included in this study and followed up from 2013 to 2014. We measured LTPA energy expenditure using the standard metabolic equivalent value [metabolic equivalent of task (MET)-h/wk]. Individuals were categorized into five groups (physically inactive, 0.1 to <7.5, 7.5 to <15.0, 15.0 to <22.5, 22.5 to <40.0, and ≥40.0 MET-h/wk).RESULTS: The mean follow-up period was 11.8 years. Among the cohort, 482 men (25.5%) and 541 women (26.8%) were diagnosed with newly developed MetS. Multivariate logistic analysis revealed a 4.4% lower MetS risk among those performing two to three times the recommended minimum of 7.5 MET-h/wk [multivariate relative risk (RR), 0.956; 95% confidence interval (CI), 0.654–1.398] and a 21.9% lower risk among those performing three to four times the physical activity minimum (multivariate RR, 0.781; 95% CI, 0.574–1.063). This association was only significant in females performing three to four times the minimum (crude RR, 0.655; 95% CI, 0.432–0.994).CONCLUSION: We noted that physical activity at more than 2 times the recommended minimum prevents MetS. Healthcare professionals should encourage inactive adults to be physically active during their leisure time to take advantage of its health benefits.
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Adult , Female , Humans , Male , Cardiovascular Diseases , Cohort Studies , Delivery of Health Care , Energy Metabolism , Follow-Up Studies , Insurance Benefits , Korea , Leisure Activities , Metabolic Equivalent , Motor Activity , Prospective Studies , Public HealthABSTRACT
Objectives@#This study was conducted to determine the incidence and risk factors of myocardial infarction (MI) and stroke in farmers compared to the general population and to establish 5-year prediction models. @*Methods@#The farmer cohort and the control cohort were generated using the customized database of the National Health Insurance Service of Korea database and the National Sample Cohort, respectively. The participants were followed from the day of the index general health examination until the events of MI, stroke, or death (up to 5 years). @*Results@#In total, 734 744 participants from the farmer cohort and 238 311 from the control cohort aged between 40 and 70 were included. The age-adjusted incidence of MI was 0.766 and 0.585 per 1000 person-years in the farmer and control cohorts, respectively. That of stroke was 0.559 and 0.321 per 1000 person-years in both cohorts, respectively. In farmers, the risk factors for MI included male sex, age, personal history of hypertension, diabetes, current smoking, creatinine, metabolic syndrome components (blood pressure, triglycerides, and high-density lipoprotein cholesterol). Those for stroke included male sex, age, personal history of hypertension, diabetes, current smoking, high γ-glutamyl transferase, and metabolic syndrome components (blood pressure, triglycerides, and high-density lipoprotein cholesterol). The prediction model showed an area under the receiver operating characteristic curve of 0.735 and 0.760 for MI and stroke, respectively, in the farmer cohort. @*Conclusions@#Farmers had a higher age-adjusted incidence of MI and stroke. They also showed distinct patterns in cardiovascular risk factors compared to the general population.
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Background@#There have been no health-related studies of pre-employed firefighters without firefighter-specific job-related factors (FSJRF). This study aimed to evaluate the sleep quality of pre-employed firefighters and to examine the relationship between sleep quality and psychosocial factors. @*Methods@#We conducted a self-report questionnaire survey for 602 pre-employed firefighters at 3 Fire Service Academies after brief lecture about sleep. Sleep quality and psychosocial variables such as depression, anxiety, stress and social support were evaluated. The independent 2 sample t-test, χ2 test and multiple logistic regression analysis were used to evaluate the effect of the variables on the sleep quality of pre-employed firefighters. @*Results@#Among a total of 602 people, 347 (57.6%) had good sleep quality and 255 (42.4%) had poor sleep quality. Pittsburgh Sleep Quality Index score of them was 3.29 ± 1.41) and 7.87 ± 2.20), respectively. 24 (4.0%) were evaluated to have insomnia by Insomnia Severity Index.Logistic regression analyses showed that the depression (adjusted odds ratio [aOR]: 5.940, 95% confidence interval [CI]: 3.124–11.292), anxiety (aOR: 4.233, 95% CI: 2.138–8.381), stress (aOR: 2.880, 95% CI: 1.915–4.330) and social support (aOR: 0.959, 95% CI: 0.939–0.980) have a significant effect on sleep quality after adjusted by sex, age, smoking status, drinking status, caffeine intake, past shift working and circadian rhythm type. @*Conclusions@#Depression, anxiety, stress and social support were associated with sleep quality among pre-employed firefighters. Repeated follow-up studies of pre-employed firefighters are needed to further assess their change of sleep quality and identify the FSJRF that may affect the sleep quality of firefighters.
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Background@#There have been no health-related studies of pre-employed firefighters without firefighter-specific job-related factors (FSJRF). This study aimed to evaluate the sleep quality of pre-employed firefighters and to examine the relationship between sleep quality and psychosocial factors. @*Methods@#We conducted a self-report questionnaire survey for 602 pre-employed firefighters at 3 Fire Service Academies after brief lecture about sleep. Sleep quality and psychosocial variables such as depression, anxiety, stress and social support were evaluated. The independent 2 sample t-test, χ2 test and multiple logistic regression analysis were used to evaluate the effect of the variables on the sleep quality of pre-employed firefighters. @*Results@#Among a total of 602 people, 347 (57.6%) had good sleep quality and 255 (42.4%) had poor sleep quality. Pittsburgh Sleep Quality Index score of them was 3.29 ± 1.41) and 7.87 ± 2.20), respectively. 24 (4.0%) were evaluated to have insomnia by Insomnia Severity Index.Logistic regression analyses showed that the depression (adjusted odds ratio [aOR]: 5.940, 95% confidence interval [CI]: 3.124–11.292), anxiety (aOR: 4.233, 95% CI: 2.138–8.381), stress (aOR: 2.880, 95% CI: 1.915–4.330) and social support (aOR: 0.959, 95% CI: 0.939–0.980) have a significant effect on sleep quality after adjusted by sex, age, smoking status, drinking status, caffeine intake, past shift working and circadian rhythm type. @*Conclusions@#Depression, anxiety, stress and social support were associated with sleep quality among pre-employed firefighters. Repeated follow-up studies of pre-employed firefighters are needed to further assess their change of sleep quality and identify the FSJRF that may affect the sleep quality of firefighters.
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Subject(s)
Humans , Cardiovascular Diseases , Drug Costs , Fees and Charges , Firefighters , Health Expenditures , Hospitalization , International Classification of Diseases , Korea , Mental Disorders , Musculoskeletal Diseases , Myocardial Ischemia , National Health Programs , Occupational Groups , Outpatients , Police , Prevalence , Stomach NeoplasmsABSTRACT
40 years or new hires with less than 1 year of service.
Subject(s)
Humans , Brain , Cardiovascular Diseases , Cohort Studies , Firefighters , Follow-Up Studies , Health Surveys , Hospitals, University , Killer Cells, Natural , Korea , Magnetic Resonance Imaging , Mental Disorders , Mental Health , Neuropsychological Tests , Polycyclic Aromatic Hydrocarbons , Prospective Studies , Republic of Korea , Risk FactorsABSTRACT
BACKGROUND: The aim of this study was to determine whether there is a positive correlation between gamma-glutamyltransferase (GGT) levels and the prevalence of metabolic syndrome and whether GGT can be used as an easily checkable metabolic index using data from the large-scale Korean Genome and Epidemiology Study (KoGES).METHODS: We obtained data of 211,725 participants of the KoGES. The collected data included age, sex, height, weight, waist circumference, and various biochemical characteristics, including serum GGT levels. The data of study participants who ingested more than 40 g/day of alcohol and who were diagnosed with metabolic syndrome at baseline was excluded. We analyzed the prevalence of metabolic syndrome according to GGT quartiles in both genders.RESULTS: The GGT level was significantly higher in subjects with metabolic syndrome compared to normal subjects (37.92±48.20 mg/dL vs. 25.62±33.56 mg/dL). The prevalence of metabolic syndrome showed a stepwise increase with GGT quartiles in both male and female subjects. Compared to the lowest GGT quartile, the odds ratio was 1.534 (95% confidence interval [CI], 1.432 to 1.643), 1.939 (95% CI, 1.811 to 2.076), and 2.754 (95% CI, 2.572 to 2.948) in men and 1.155 (95% CI, 1.094 to 1.218), 1.528 (95% CI, 1.451 to 1.609), and 2.022 (95% CI, 1.921 to 2.218) in women with increasing GGT quartile. The cutoff value of GGT predicting risk of metabolic syndrome was 27 IU/L in men and 17 IU/L in women.CONCLUSION: We suggested that GGT could be an easily checkable marker for the prediction of metabolic syndrome.
Subject(s)
Female , Humans , Male , Epidemiology , gamma-Glutamyltransferase , Genome , Odds Ratio , Prevalence , Waist CircumferenceABSTRACT
BACKGROUND: Despite being an anti-obesity hepatokine, the levels of serum angiopoietin-like 6 (ANGPTL6) are elevated in various metabolic diseases. Thus, ANGPTL6 expression may reflect metabolic burden and may have compensatory roles. This study investigated the association between serum ANGPTL6 levels and new-onset metabolic syndrome. METHODS: In total, 221 participants without metabolic syndrome were randomly selected from a rural cohort in Korea. Baseline serum ANGPTL6 levels were measured using an enzyme-linked immunosorbent assay. Anthropometric and biochemical markers were analyzed before and after follow-up examinations. RESULTS: During an average follow-up period of 2.75 (interquartile range, 0.76) years, 82 participants (37.1%) presented new-onset metabolic syndrome and had higher ANGPTL6 levels before onset than those without metabolic syndrome (48.03±18.84 ng/mL vs. 64.75±43.35 ng/mL, P=0.001). In the multivariable adjusted models, the odds ratio for the development of metabolic syndrome in the highest quartile of ANGPTL6 levels was 3.61 (95% confidence interval, 1.27 to 10.26). The use of ANGPTL6 levels in addition to the conventional components improved the prediction of new-onset metabolic syndrome (area under the receiver operating characteristic curve: 0.775 vs. 0.807, P=0.036). CONCLUSION: Increased serum ANGPTL6 levels precede the development of metabolic syndrome and its components, including low high density lipoprotein, high triglyceride, and high glucose levels, which have an independent predictive value for metabolic syndrome.