Implementation of a Smart Teaching and Assessment System for High-Quality Cardiopulmonary Resuscitation.

The purpose of this study is to develop a smart training and assessment system called SmartCPR, for teaching and training cardiopulmonary resuscitation (CPR), based on human posture estimation techniques. In this system, trainees can automatically recognize and evaluate whether chest compressions during CPR meet the standard of high-quality CPR by simply using a device such as a smart phone. Through the system, trainees are able to obtain real-time feedback on the quality of compressions so that they can adjust the cycle, depth, frequency, and posture of compressions to meet the standard of high-quality CPR. In addition, the SmartCPR system is convenient for CPR trainers. Trainers can instantly and accurately assess whether the trainee's compressions meet the standard of high-quality CPR, which reduces the risk of manual assessment errors and also reduces the trainer's teaching pressures. Therefore, the SmartCPR system developed in this study can be an important tool for CPR teaching and training for physicians, which can provide training and guidance for high-quality CPR maneuvers and enable trainees to become more proficient in CPR and self-training.

Association between self-efficacy, spiritual well-being and the willingness to provide spiritual care among nursing staff in Taiwan: a cross-sectional study.

BACKGROUND: Spiritual care plays a significant role in holistic patient care, addressing not only physical ailments but also attending to patients' emotional and spiritual well-being. While the importance of spiritual care in nursing is widely recognized, there is often a gap in understanding nurses' willingness to provide such care. This cross-sectional study aimed to explore the association between self-efficacy, spiritual well-being, and willingness to provide spiritual care among nursing staff. METHODS: The study conducted a cross-sectional survey of full-time registered nurses at a hospital in Taiwan from January 2019 to December 2019. A sample comprising 168 nurses was selected for participation in the study through a random sampling method. In addition to collecting demographic variables, the assessment tools used in the study include the General Self-Efficacy Scale (GSES) for measuring self-efficacy, the Spiritual Index of Well-Being Chinese Version (SIWB-C) for evaluating spiritual well-being, and the Spiritual Care Needs Inventory (SCNI) to gauge willingness to provide spiritual care. RESULTS: Most participants in the study were female, accounting for 98.2% (n = 165). The mean age of all 168 nurses was 37.1 ± 9.3 years. Additionally, most participants held a Bachelor's degree (79.2%, n = 133) and possessed clinical experience was 10.5 ± 9.3 years. Through logistic regression analysis, it was found that regardless of whether participants have received sufficient spiritual care training, both GSES and SIWB-C remain influential factors in determining the provision of spiritual care. CONCLUSIONS: Collaboration between healthcare management and nursing staff is essential for fostering a healthcare environment that not only appreciates the physical and spiritual dimensions of patient care but also prioritizes the enhancement of nurses ' self-efficacy and well-being.

A model for predicting physical function upon discharge of hospitalized older adults in Taiwan-a machine learning approach based on both electronic health records and comprehensive geriatric assessment.

Background: Predicting physical function upon discharge among hospitalized older adults is important. This study has aimed to develop a prediction model of physical function upon discharge through use of a machine learning algorithm using electronic health records (EHRs) and comprehensive geriatrics assessments (CGAs) among hospitalized older adults in Taiwan. Methods: Data was retrieved from the clinical database of a tertiary medical center in central Taiwan. Older adults admitted to the acute geriatric unit during the period from January 2012 to December 2018 were included for analysis, while those with missing data were excluded. From data of the EHRs and CGAs, a total of 52 clinical features were input for model building. We used 3 different machine learning algorithms, XGBoost, random forest and logistic regression. Results: In total, 1,755 older adults were included in final analysis, with a mean age of 80.68 years. For linear models on physical function upon discharge, the accuracy of prediction was 87% for XGBoost, 85% for random forest, and 32% for logistic regression. For classification models on physical function upon discharge, the accuracy for random forest, logistic regression and XGBoost were 94, 92 and 92%, respectively. The auROC reached 98% for XGBoost and random forest, while logistic regression had an auROC of 97%. The top 3 features of importance were activity of daily living (ADL) at baseline, ADL during admission, and mini nutritional status (MNA) during admission. Conclusion: The results showed that physical function upon discharge among hospitalized older adults can be predicted accurately during admission through use of a machine learning model with data taken from EHRs and CGAs.

Prediction of risk of ischemic heart disease in first-diagnosed systemic lupus erythematosus patients in taiwan: is air pollution exposure a risk factor?

BACKGROUND: Air pollution is a key public health factor with the capacity to induce diseases. The risk of ischemia heart disease (IHD) in those suffering from systemic lupus erythematosus (SLE) from air pollution exposure is ambiguous. This study aimed to: (1) determine the hazard ratio (HR) of IHD after the first-diagnosed SLE and (2) examine the effects of air pollution exposure on IHD in SLE for 12 years. METHODS: This is a retrospective cohort study. Taiwan's National Health Insurance Research Database and Taiwan Air Quality Monitoring data were used in the study. Cases first diagnosed with SLE in 2006 cases without IHD were recruited as the SLE group. We randomly selected an additional sex-matched non-SLE cohort, four times the size of the SLE cohort, as the control group. Air pollution indices by residence city per period were calculated as the exposure. Life tables and Cox proportional risk models of time-dependent covariance were used in the research. RESULTS: This study identified patients for the SLE group (n = 4,842) and the control group (n = 19,368) in 2006. By the end of 2018, the risk of IHD was significantly higher in the SLE group than in the control group, and risks peaked between the 6th and 9th year. The HR of incidence IHD in the SLE group was 2.42 times that of the control group. Significant correlations with risk of developing IHD were noted for sex, age, CO, NO2, PM10, and PM2.5, of which PM10 exposure had the highest risk of IHD incidence. CONCLUSIONS: Subjects with SLE were at a higher risk of IHD, especially those in the 6th to 9th year after SLE diagnosis. The advanced cardiac health examinations and health education plan should be recommended for SLE patients before the 6th year after SLE diagnosed.

Risk Factors Surrounding an Increase in Burnout and Depression Among Health Care Professionals in Taiwan During the COVID-19 Pandemic.

OBJECTIVES: This study aimed to investigate the risk factors surrounding an increase in both burnout levels and depression among health care professionals in Taiwan through use of a longitudinal study design. DESIGN: This is a 2-year observational study that took place from January 2019 to December 2020. SETTING AND PARTICIPANTS: Data among health care professionals were extracted from the Overload Health Control System of a tertiary medical center in central Taiwan. METHODS: Burnout was measured through use of the Chinese version of the Copenhagen Burnout Inventory (C-CBI), whereas depression was ascertained by the Taiwanese Depression Questionnaire. Each participant provided both burnout and depression measurements during a nonpandemic period (2019) as well as during the COVID pandemic era (2020). Risk factors surrounding an increase in burnout levels and depression were analyzed through a multivariate logistic regression model with adjusting confounding factors. RESULTS: Two thousand nineteen participants completed the questionnaire over 2 consecutive years, including 132 visiting doctors, 105 resident doctors, 1371 nurses, and 411 medical technicians. After adjustments, sleeplessness, daily working hours >8, and stress due to one's workload were all found to be risk factors for an increase in depression levels, whereas sleeplessness, lack of exercise, and stress due to one's workload were all found to be risk factors for an increase in personal burnout level. Being a member of the nursing staff, a younger age, sleeplessness, and lack of exercise were all risk factors for an increase in work-related burnout levels. CONCLUSIONS AND IMPLICATIONS: Poor sleep, lack of exercise, long working hours, and being a member of the nursing staff were risk factors regarding an increase in personal burnout, work-related burnout levels and depression among health care professionals. Leaders within the hospital should investigate the working conditions and personal habits of all medical staff regularly and systematically during the COVID-19 pandemic and take any necessary preventive measures, such as improving resilience for nursing staff, in order to best care for their employees.

A model for predicting fall risks of hospitalized elderly in Taiwan-A machine learning approach based on both electronic health records and comprehensive geriatric assessment.

Backgrounds: Falls are currently one of the important safety issues of elderly inpatients. Falls can lead to their injury, reduced mobility and comorbidity. In hospitals, it may cause medical disputes and staff guilty feelings and anxiety. We aimed to predict fall risks among hospitalized elderly patients using an approach of artificial intelligence. Materials and methods: Our working hypothesis was that if hospitalized elderly patients have multiple risk factors, their incidence of falls is higher. Artificial intelligence was then used to predict the incidence of falls of these patients. We enrolled those elderly patients aged >65 years old and were admitted to the geriatric ward during 2018 and 2019, at a single medical center in central Taiwan. We collected 21 physiological and clinical data of these patients from their electronic health records (EHR) with their comprehensive geriatric assessment (CGA). Data included demographic information, vital signs, visual ability, hearing ability, previous medication, and activity of daily living. We separated data from a total of 1,101 patients into 3 datasets: (a) training dataset, (b) testing dataset and (c) validation dataset. To predict incidence of falls, we applied 6 models: (a) Deep neural network (DNN), (b) machine learning algorithm extreme Gradient Boosting (XGBoost), (c) Light Gradient Boosting Machine (LightGBM), (d) Random Forest, (e) Stochastic Gradient Descent (SGD) and (f) logistic regression. Results: From modeling data of 1,101 elderly patients, we found that machine learning algorithm XGBoost, LightGBM, Random forest, SGD and logistic regression were successfully trained. Finally, machine learning algorithm XGBoost achieved 73.2% accuracy. Conclusion: This is the first machine-learning based study using both EHR and CGA to predict fall risks of elderly. Multiple risk factors of falls in hospitalized elderly patients can be put into a machine learning model to predict future falls for early planned actions. Future studies should be focused on the model fitting and accuracy of data analysis.

In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning.

The COVID-19 pandemic raises awareness of how the fatal spreading of infectious disease impacts economic, political, and cultural sectors, which causes social implications. Across the world, strategies aimed at quickly recognizing risk factors have also helped shape public health guidelines and direct resources; however, they are challenging to analyze and predict since those events still happen. This paper intends to invesitgate the association between air pollutants and COVID-19 confirmed cases using Deep Learning. We used Delhi, India, for daily confirmed cases and air pollutant data for the dataset. We used LSTM deep learning for training the combination of COVID-19 Confirmed Case and AQI parameters over the four different lag times of 1, 3, 7, and 14 days. The finding indicates that CO is the most excellent model compared with the others, having on average, 13 RMSE values. This was followed by pressure at 15, PM2.5 at 20, NO2 at 20, and O3 at 22 error rates.

Testosterone Level Reduction Increases the 10-Year Risk of Cardiovascular Diseases: A Retrospective Cohort Study in a Taiwanese Young Male Population.

Low testosterone levels are associated with increased risk of cardiovascular disease; however, most previous studies assessed the relationship of testosterone levels with a history of cardiovascular (CV) events rather than with CV risk prediction scores consequently neglecting the effect of testosterone on CV risk in healthy young individuals. The aim of this study was to investigate the relationship between testosterone levels and predict the 10-year risk of cardiovascular disease. This retrospective cohort study was conducted through a large medical health examination system in four metropolises in Taiwan. Two risk scores were used to predict the 10-year cardiovascular risk of participants: the Framingham Risk Score (FRS) (2008) and the Atherosclerotic Cardiovascular Disease (ASCVD) Risk Estimator (2013). Multivariate-adjusted logistic regression was used to calculate odds ratios (ORs) for the correlation of testosterone level reduction with the increase in predicted CV risk. We used the MJ Health Research Foundation database to collect reports of 125,414 individuals who underwent medical checkups between 2007 and 2016. The final sample size included 1,253 male participants. A reduction in testosterone level between two subsequent medical checkups was associated with higher CV risk estimated by the FRS and ASCVD Risk Estimator in young participants aged 30-49 years (OR = 0.804, 95% CI: 0.711-0.909, p < 0.01 and OR = 0.841, 95% CI: 0.742-0.953, p < 0.01, respectively). Reduction in total testosterone levels increases CV risk in men aged 30 to 49 years, while the CV risk is not influenced by low testosterone levels at baseline.

Biomonitoring of Urinary Nickel Successfully Protects Employees and Introduces Effective Interventions.

Nickel is a heavy metal used in many industries. Nickel exposure can induce respiratory diseases and allergic reactions, and increase cancer risk. This study evaluated the introduction of a grinding and polishing system to prevent injuries from nickel toxicity in workers. We performed a controlled, interventional, before-and-after study from January 2018 to December 2019 at a faucet component industrial manufacturing site. Results from workplace environmental monitoring, questionnaire responses, and biomonitoring were collected before and after the intervention. Thirty-seven workers (100% men) aged 25.0 (interquartile range (IQR): 22.0-33.5) years were categorized into two groups, those with and without nickel exposure. In the exposed group, the median exposure time was 18.0 months (IQR 14.0-20.0 months). Urinary nickel concentration was lower in the exposed group than in the non-exposed group (13.8 (IQR 1.7-20.7); 23.1 (IQR 11.3-32.8) µg/g creatinine, respectively; p = 0.047). The median urinary nickel concentration was lower in the second year than in the first year (17.4 (IQR 2.2-27.4), 7.7 (IQR 4.3-18.5) µg/g creatinine, respectively; p = 0.022). Significant reductions in urinary nickel concentration were observed following the intervention and educational program. Thus, biomonitoring of urinary nickel concentration can successfully reflect the effectiveness of interventions and their relationship to nickel exposure.

The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA.

This paper proposed the forecasting model of Influenza-like Illness (ILI) and respiratory disease. The dataset was extracted from the Taiwan Environmental Protection Administration (EPA) for air pollutants data and the Centers for Disease Control (CDC) for disease cases from 2009 to 2018. First, this paper applied the ARIMA method, which trained based on the weekly number of disease cases in time series. Second, we implemented the Long short-term memory (LSTM) method, which trained based on the correlation between the weekly number of diseases and air pollutants. The models were also trained and evaluated based on five and ten years of historical data. Autoregressive integrated moving average (ARIMA) has an excellent model in the five-year dataset of ILI at 2564.9 compared to ten years at 8173.6 of RMSE value. This accuracy is similar to the Respiratory dataset, which gets 15,656.7 in the five-year dataset and 22,680.4 of RMSE value in the ten-year dataset. On the contrary, LSTM has better accuracy in the ten-year dataset than the five-year dataset. For example, on average of RMSE in the ILI dataset, LSTM has 720.2 RMSE value in five years and 517.0 in ten years dataset. Also, in the Respiratory disease dataset, LSTM gets 4768.6 of five years of data and 3254.3 of the ten-year dataset. These experiments revealed that the LSTM model generally outperforms ARIMA by three to seven times higher model performance.

Risk of Fractures, Repeated Fractures and Osteoporotic Fractures among Patients with Hemophilia in Taiwan: A 14-Year Population-Based Cohort Study.

The world is aging, and hemophilia patients are as well. The association between patients with hemophilia (PWH) and low bone mineral density is clear. However, the incidence of fractures in patients with hemophilia is inconclusive, and no research has yet explored repeated fractures among PWH. In this study, we investigated the incidence of all-site fractures, repeated fractures and osteoporotic fractures amongst PWH. The study compared the incidence of all-site fractures, repeated fractures and osteoporotic fractures occurring in all PWH who were enrolled in Taiwan's National Health Insurance Research Database between 1997 and 2013 with an age- and gender-matched group from the general population. Eight-hundred thirty-two PWH, along with 8320 members of the general population, were included in the final analysis. After multivariate COX regression analysis with an adjustment for confounding factors, it was found that PWH experienced a higher risk of osteoporotic fracture (HR: 1.25 with 95% CI of 1.03-2.52) but only saw a neutral effect with regards to both all-sites of fracture (HR: 1.00 with 95% CI of 0.92-1.09) and repeated fractures (HR: 1.01 with 95% CI of 0.92-1.10), when compared with the general population. This 14-year population-based cohort study showed that PWH had a higher risk of osteoporotic fracture, but that hemophilia only had a neutral effect in all-sites of fracture and repeated fractures. Screening, prevention and treatment for osteoporosis and further osteoporotic fractures among PWH, in order to improve quality of life and achieve healthy aging in this particular population, remain essential.

Comprehensive Geriatric Assessment and Clinical Outcomes in the Older People at the Emergency Department.

Visits by older people to the Emergency Department (ED) have increased in recent decades with higher revisiting and admission rates after discharge, particularly for those with frailties. This study used a before-after design aimed at evaluating Comprehensive Geriatric Assessment (CGA) screening in older ED patients (aged ≥ 75 years) during the 12-month preintervention period. Additionally, a CGA-based structured follow-up program after ED discharge was executed during the next 12-month intervention period. Amongst the 358 participants (median age 82 years), involving 122 in the preintervention period and 236 in the intervention period, 77 participants (21.5%) were identified as pre-frailty, while 274 (76.5%) were identified as frail using the Fried frailty phenotype. One-hundred ten (110) (30.7%) patients revisited the ED with 73 (20.4%) being admitted and 20 (5.6%) dying within three months after ED discharge. Compared with preintervention and intervention period, it was shown that the rates of admission at the index ED visit (50.8% vs. 23.1%), and mortality (10.7% vs. 3.0%), were both were significantly reduced. Using multivariate regression analysis, it was shown frailty was significantly associated with three-month mortality after adjusting for potential confounders. On the contrary, the program significantly decreased admission and death rate. It is suggested that frailty was prevalent amongst the older ED patients, and should be screened for in order to decrease revisits/admissions after ED discharge.

Late-career unemployment and risk of frailty among older adults in Taiwan: An 8-year population-based cohort study.

AIM: To evaluate whether late-career unemployment is associated with increased physical frailty among older adults in Taiwan by the design of a population-based cohort study. METHODS: Data were retrieved from the Taiwan Longitudinal Study on Aging. The study examined data from the period 1999 to 2007. A total of 652 subjects were included in the final analysis, and they were categorized as normally employed or unemployed depending on their employment status in 1999. Frailty was defined using the Fried criteria. Multiple confounding factors were adjusted in a multilevel analysis. RESULTS: The average age of the participants in 1999 was 59.0 years. A total of 491 participants were normally employed, and 161 participants were unemployed. After adjustment for gender, age, level of education, income, marital status, and number of chronic diseases, late-career unemployment was associated with increased frailty [odds ratio (OR) = 1.61; 95% confidence interval (CI) = 1.00-2.59]. The risk of developing frailty was higher for participants who were unemployed during late career and had poor self-rated health [OR = 3.54; 95% CI = 1.37-9.20]. CONCLUSIONS: Late-career unemployment is associated with increased frailty, especially for those who also have poor self-rated health. Older adults should be encouraged to maintain normal employment during the later stages of their career before retirement. Employers should apply strategies to prevent possible late-career unemployment, and the government should provide resources and help to unemployed older workers so that they can minimize poor health outcomes in late life. Geriatr Gerontol Int 2021; 21: 353-358.

Effect of ambient air pollution on the incidence of colorectal cancer among a diabetic population: a nationwide nested case-control study in Taiwan.

OBJECTIVES: An increasing number of studies had shown that air pollution exposure may aggravate blood glucose control in patients with diabetes, an independent risk factor for colorectal cancer (CRC) proposed by some researchers. This study aimed to investigate the impact of exposure to ambient particulate matter with aerodynamic diameters ≤2.5 µm (PM2.5) on the incidence of CRC among a diabetic population. DESIGN: A nested case-control study. SETTING: A subset data retrieved from the Taiwan's National Health Insurance Research Database. PARTICIPANTS: We identified patients with newly diagnosed diabetes (n=1 164 962) during 1999-2013. Participants who had subsequently developed an incident of CRC were placed into the case group, while controls were matched to the cases at a 4:1 ratio by age, gender, date of diabetes diagnosis and the index date of CRC diagnosis. METHODS AND OUTCOME MEASURES: All variables associated with the risk of CRC entered into a multinomial logistic regression model. The dose-response relationship between various average concentrations of PM2.5 exposure and the incidence of CRC was estimated by logistic regression. RESULTS: The study included a total of 7719 incident CRC cases matched with 30 876 controls of random sampling. The mean annual concentration of PM2.5 was 35.3 µg/m3. After adjusting for potential confounders, a dose-response relationship was observed between the CRC risks and each interquartile increase of PM2.5 concentration (Q1-Q2: 1.03 (0.95-1.11), Q2-Q3: 1.06 (0.98-1.15), ≥Q3: 1.19 (1.10-1.28) in model 2. The adjusted ORs (95% CI) of CRC incidence for each 10 µg/m3 increment of PM2.5 was 1.08 (1.04-1.11). Moreover, a faster growing adapted Diabetes Complications Severity Index (aDCSI) score was noticed in CRC group compared with the controls, which also showed a significant association in our multivariate analysis (adjusted OR=1.28, 95% CI 1.18 to 1.38). CONCLUSIONS: Long-term exposure to high concentrations of PM2.5 may contribute to an increased incidence of CRC among diabetic populations.

2019 novel coronavirus disease (COVID-19) in Taiwan: Reports of two cases from Wuhan, China.

We reported two cases with community-acquired pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) who returned from Wuhan, China in January, 2020. The reported cases highlight non-specific clinical presentations of 2019 novel coronavirus disease (COVID-19) as well as the importance of rapid laboratory-based diagnosis.

Modifiable risk factors related to burnout levels in the medical workplace in Taiwan: cross-sectional study.

OBJECTIVES: To detect the combined effects of lifestyle factors on work-related burnout (WB) and to analyse the impact of the number of weekend catch-up sleep hours on burnout risk in a medical workplace. DESIGN: Cross-sectional study. SETTING: Hospital-based survey in Taiwan. PARTICIPANTS: In total, 2746 participants completed the hospital's Overload Health Control System questionnaire for the period from the first day of January 2016 to the end of December 2016, with a response rate of 70.5%. The voluntary participants included 358 physicians, 1406 nurses, 367 medical technicians and 615 administrative staff. PRIMARY AND SECONDARY OUTCOME MEASURES: All factors that correlated significantly with WB were entered into a multinomial logistic regression after adjustment for other factors. The dose-response relationship of combined lifestyle factors and catch-up sleep hours associated with WB was explored by logistic regression. RESULTS: Abnormal meal time (adjusted OR 2.41, 95% CI 1.85 to 3.15), frequently eating out (adjusted OR 1.49, 95% CI 1.12 to 1.97), lack of sleep (adjusted OR 5.13, 95% CI 3.94 to 6.69), no exercise (adjusted OR 1.41, 95% CI 1.10 to 1.81) and >40 work hours (adjusted OR 2.72, 95% CI 2.08-3.57) were independently associated with WB (for high level compared with low level). As the number of risk factors increased (1-5), so did the proportion of high severity of WB (adjusted OR 1.39, 95% CI 0.45 to 4.27, to adjusted OR 32.98, 95% CI 10.78 to 100.87). For those with more than 7 hours' sleep on workdays, weekend catch-up sleep (≤0/>0 and ≤2/>2 hours) was found to be related to an increase of burnout risk (adjusted OR 4.91, 95% CI 2.24 to 10.75/adjusted OR 4.94, 95% CI 2.54 to 9.63/adjusted OR 6.74, 95% CI 2.94 to 15.46). CONCLUSION: WB in the medical workplace was affected by five unhealthy lifestyle factors, and combinations of these factors were associated with greater severity of WB. Weekend catch-up sleep was correlated with lower burnout risk in those with a short workday sleep duration (less than 7 hours). Clinicians should pay particular attention to medical staff with short sleep duration without weekend catch-up sleep.

Herbal Medicine Containing Aristolochic Acid and the Risk of Primary Liver Cancer in Patients with Hepatitis C Virus Infection.

BACKGROUND: We investigated the association between taking herbal medicine (HM) containing aristolochic acid (AA) and the risk of primary liver cancer (PLC) among patients with hepatitis C virus (HCV) infection. METHODS: This is a prospective study for the long-term follow-up of a nationwide population-based cohort of patients ages 18 years or older diagnosed with HCV infection during 1997 to 2010. A total of 223,467 HCV-infected patients were identified using the National Health Insurance Research Database in Taiwan. The use of HM containing AA was evaluated among patients who had visited traditional Chinese medicine clinics beginning from 1997 to 1 year prior to the diagnosis of PLC or dates censored (2003). We tracked each individual patient from 1997 to 2013 to identify incident cases of PLC since 1999. RESULTS: During the follow-up period of 3,052,132 person-years, we identified 25,502 PLC cases; this corresponded to an overall incidence rate of 835.5 PLCs per 100,000 person-years. The adjusted HRs were 1.21 [95% confidence interval (CI), 1.18-1.24], 1.48 (95% CI, 1.37-1.59), 1.50 (95% CI, 1.34-1.68), and 1.88 (95% CI, 1.61-2.19) for estimated AA usage groups: 1 to 250, 251 to 500, 501 to 1,000, and more than 1,000 mg, respectively, relative to no AA exposure (reference group). CONCLUSIONS: The current findings suggest that among HCV-positive patients, increasing exposure to AA poses an increased risk of acquiring PLC. IMPACT: AA may increase the risk of PLC in HCV-positive populations.

An implementation of real-time air quality and influenza-like illness data storage and processing platform.

Recently, air pollution has become the primary concern in Taiwan as it significantly affected people's health. Some air pollution monitoring, analysis, and prediction systems were proposed to solve the problem. However, there is very little research to see whether the air quality is associated with the Influenza-Like Illness (ILI) disease or not. In this study, a system is needed, in which the air quality data and the influenza-like illness data can be analyzed together to determine their associations accurately and effectively. In this work, a novel integrated platform was implemented by building a cluster environment based on Hadoop, Spark and a visualization environment with ELK Stack as well as a backup storage system based on Ceph object storage architecture. Also, Sqoop and Alluxio were used to solve the inefficiency problem in processing vast amounts of data. The experimental results showed the visualization of air quality and influenza-like illness data collected from 2016 to 2017 in Taichung, Taiwan. Besides, the association analyses and discussion between air quality and influenza-like illness were also presented.

Differences in Major Bleeding Events Between Patients With Severe Hemophilia A and Hemophilia B: A Nationwide, Population-Based Cohort Study.

There has been an ongoing debate as to whether hemophilia A (HA) is more severe than hemophilia B (HB), and there are studies supporting each side of the argument. The study aimed to investigate whether any differences in major bleeding events exist between patients with severe HA and HB. A nationwide, population-based retrospective cohort study using the National Health Insurance Research Database was conducted. We compared 658 patients with severe HA and 137 patients with severe HB without inhibitors from 1997 to 2013, during the period when adult patients older than 18 years old were treated with the on-demand therapy since birth. There was no significant difference between patients with severe HA and HB in the rate of major bleeding events, with an adjusted relative ratio of 0.79 (95% confidence interval [CI]: 0.36-1.71, P = .548). There was also no significant difference in the incidence rate of major bleeding events between adult patients with HA and HB with the on-demand therapy, and an adjusted hazard ratio (HR) of 0.82 (95% CI: 0.65-1.02). However, patients with HA had a lower incidence rate of intracranial hemorrhage, with an adjusted HR of 0.44 (95% CI: 0.25-0.79). In addition, no significant difference in the frequency of major bleeding events requiring hospitalization between patients with HA and HB was found, P > .05. In conclusion, the study demonstrated that patients with severe HB encountered a similar rate of major bleeding events to those with severe HA.

Association between dry eye disease and asthma: a nationwide population-based study.

BACKGROUND: Dry eye disease (DED), a chronic ocular disease, is associated with numerous medical issues, including asthma. However, studies on these associations are limited. In this study, we investigated the incidence of DED among patients with asthma and its correlation with other allergic comorbidities. METHODS: We retrospectively analyzed data from the National Health Insurance Research Database of Taiwan. We compared the data of 41,229 patients with asthma with those of 164,916 sex- and age-matched non-asthma controls. We followed up the patient and control groups from 1998 to 2010, and compared the rate of DED in these two groups. We further analyzed the allergic comorbidities and asthma-related medication use among the patients with asthma to verify whether these factors were associated with DED. RESULTS: The patients in the asthma group were more likely to have DED than were the controls (6.35% vs. 4.92%, p < 0.0001). In the asthma group, female had a higher risk of DED (odds ratio (OR) = 1.70, 95% confidence interval (CI) [1.57-1.85]) than males did. After adjustment for sex, age, income, urbanization, and the other two allergic comorbidities, patients with allergic rhinitis (adjusted OR = 1.58, 95% CI [1.46-1.72]) and urticaria (adjusted OR = 1.25, 95% CI [1.12-1.38]) were more likely to have DED, but not patients with atopic dermatitis (adjusted OR = 1.17, 95% CI [0.98-1.40]). Patients with asthma who had prescriptions of leukotriene receptor antagonists (LTRAs) (adjusted OR = 1.29, 95% CI [1.01-1.64]), oral antihistamines (adjusted OR = 2.02, 95% CI [1.84-2.21]), and inhaled corticosteroids (adjusted OR = 1.19, 95% CI [1.04-1.36]) exhibited association with DED. DISCUSSION: Our findings reveal that patients with asthma-particularly females-were more likely to have DED, with comorbidities such as allergic rhinitis and urticaria, and prescriptions including LTRAs, antihistamines, and inhaled corticosteroids. The results suggest that in clinical practice, physicians should pay attention to DED, particularly in patients with a high risk of DED.