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.

Herbal medicine containing aristolochic acid and the risk of hepatocellular carcinoma in patients with hepatitis B virus infection.

It was suspected that aristolochic acid-induced mutations may be associated with hepatitis B virus (HBV), playing an important role in liver carcinogenesis. The purpose of this study was to investigate the association between the use of Chinese herbs containing aristolochic acid and the risk of hepatocellular carcinoma (HCC) among HBV-infected patients. We conducted a retrospective, population-based, cohort study on patients older than 18 years who had a diagnosis of HBV infection between January 1, 1997 and December 31, 2010 and had visited traditional Chinese medicine clinics before one year before the diagnosis of HCC or the censor dates. A total of 802,642 HBV-infected patients were identified by using the National Health Insurance Research Database in Taiwan. The use of Chinese herbal products containing aristolochic acid was identified between 1997 and 2003. Each patient was individually tracked from 1997 to 2013 to identify incident cases of HCC since 1999. There were 33,982 HCCs during the follow-up period of 11,643,790 person-years and the overall incidence rate was 291.8 HCCs per 100,000 person-years. The adjusted hazard ratios (HRs) were 1.13 (95% confidence interval [CI], 1.11-1.16), 1.21 (95% CI, 1.13-1.29), 1.37 (95% CI, 1.24-1.50) and 1.61 (95% CI, 1.40-1.84) for estimated aristolochic acid of 1-250, 251-500, 501-1,000 and more than 1,000 mg, respectively, relative to no aristolochic acid exposure. Our study found a significant dose-response relationship between the consumption of aristolochic acid and HCC in patients with HBV infection, suggesting that aristolochic acid which may be associated with HBV plays an important role in the pathogenesis of HCC.

Number of cholangitis episodes as a prognostic marker to predict timing of liver transplantation in biliary atresia patients after Kasai portoenterostomy.

BACKGROUND: Cholangitis may affect liver failure of biliary atresia (BA) patients after Kasai portoenterostomy (KP). We examined whether the number of cholangitis episodes could be a prognostic marker for liver transplant (LT) in children with BA after Kasai portoenterostomy (KP). METHODS: Data for BA patients born after 1998 and undergoing KP were obtained from National Health Insurance Research Database (NHIRD), Taiwan. Patients were followed up until the end of 2011. Incidence and the number of cholangitis episodes were recorded and compared between patients based on LT status. RESULTS: Ninety-six (26.8%) of the 366 BA patients underwent LT. More patients who underwent KP at < 60 days of age survived with their native liver (P = 0.007). The mean age at first cholangitis was 0.9 years and 0.8 years in the LT and non-LT groups, respectively (P = 0.868). The cumulative incidence of cholangitis within 2 years after KP did not differ between the groups (hazard ratio 1.2; 95% CI 0.9-1.6). However, the total number of cholangitis episodes was higher in the LT group within 2 years after KP (P < 0.001). CONCLUSIONS: Cholangitis occurrence was not related to LT in the first 2 years after KP in BA patients, but the number of cholangitis episodes could be a prognostic marker for future LT.

Statins and the risk of pancreatic cancer in Type 2 diabetic patients--A population-based cohort study.

The aim of this study was to determine whether statin use exerts a protective effect against pancreatic cancer in Type 2 diabetic patients. A retrospective population-based cohort study was designed to analyze the National Health Insurance Research database (NHIRD) from 1997-2010 in Taiwan. A total of 1,140,617 patients with a first-time diagnosis of Type 2 diabetes were enrolled. The event was defined as newly diagnosed pancreatic cancer. A Cox proportional hazards regression model with time-dependent covariates was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for risk of pancreatic cancer associated with statin use in the diabetic cohort. A total of 2,341 patients with newly diagnosed pancreatic cancer were identified in the diabetic cohort during the follow-up period of 6,968,217.1 person-years. In this cohort, 450,282 patients were defined as statin users (statin use ≥ 28 cumulative defined daily dose [cDDD] in 1 year) and 0.14% had pancreatic cancer; 690,335 patients were statin nonusers (statin use <28 cDDD in 1 year) and 0.25% had pancreatic cancer. Statin use significantly decreased the risk of pancreatic cancer (adjusted HRs: 0.78 in 28-83 cDDD per year; 0.48 in 84-180 cDDD per year; and 0.33 in >180 cDDD per year) after adjusting for multiple confounders. There was a significant dose-effect of statin use for the risk of pancreatic cancer (p for trend: <0.001). Statin use may be associated with a reduced risk of pancreatic cancer in Type 2 diabetic patients. More research is needed to clarify this association.

Statin use and the risk of cirrhosis development in patients with hepatitis C virus infection.

BACKGROUND & AIMS: Several animal studies have shown that statins can inhibit the progression of cirrhosis; however, few clinical studies have been conducted. Previous studies have indicated that statins can prevent the progression of hepatic fibrosis in patients with hepatitis C virus (HCV) infection and advanced hepatic fibrosis, however data is lacking on patients who have yet to progress to cirrhosis. This study investigated the association between the use of statin and the risk of cirrhosis development in patients with HCV infection. METHODS: We conducted a population-based cohort study by using the Taiwan National Health Insurance Research Database. A total of 226,856 patients with HCV infection were included as the study cohort. Each patient was followed from 1997 to 2010 to identify incident cases of cirrhosis. A Cox proportional hazard regression was performed to evaluate the association between statin use and cirrhosis risk. RESULTS: A total of 34,273 cases of cirrhosis were identified in the cohort with HCV infection during the follow-up period of 2,874,031.7 person-years. The incidence rate was 445.5 cases of cirrhosis per 100,000 person-years (95% confidence interval (CI), 423.3 to 465.7) for statin users (defined as those who used more than 28 cumulative defined daily doses (cDDD)), and 1311.2 cirrhosis cases per 100,000 person-years (95% CI, 1297.1 to 1325.6) for non-users. A dose-response relationship between statin use and cirrhosis risk was observed. The adjusted hazard ratios were 0.33 (95% CI, 0.31 to 0.36), 0.24 (95% CI, 0.22 to 0.25), and 0.13 (95% CI, 0.12 to 0.15) for statin use of 28 to 83, 84 to 365, and more than 365 cDDD, respectively, relative to no statin use (<28 cDDD). CONCLUSION: Among the patients with HCV infection, statin use was associated with a reduced risk of cirrhosis development in a dose-dependent manner. Further clinical research is required.

Incidence and survival of cancers among 1,054 hemophilia patients: A nationwide and 14-year cohort study.

As life expectancy increases in persons with hemophilia (PWH), more age-related diseases such as cancer emerge among this patient group. The aim of this study was to investigate incidence and survival of cancers among PWH in Taiwan. We analyzed data of 1,054 PWH retrieved from Taiwan's National Health Insurance Research Database between 1997 and 2010, by comparing variables to 10540 age- and gender-matched healthy individuals from the general population. There were 43 PWH and 178 individuals of general population with newly diagnosed cancer (RR 2.42, 95% CI 1.74-3.35). The cumulative incidences of cancer in PWH and the general population were 4.7 and 1.9%, respectively. Hepatocellular carcinoma (HCC) was the major type of cancer (17 cases) in PWH; cancer rate was still increased when HCC and HIV-related cancers were excluded (RR 1.66, 95% CI 1.06-2.59). There was no significant difference observed in lung, colorectal, or prostate cancer occurrence. Compared to the general population, PWH were younger at the time of cancer diagnosis (45.1 vs. 57.2 years old, P value < 0.001), and had fewer co-morbidities. Nineteen PWH with cancers died during the study period, and no bleeding-related death was recorded among these patients. The survival rate was not different between PWH and the general population, P = 0.86. In conclusion, the cumulative incidence of cancer among PWH was higher than the general population. PWH with cancer were younger and had fewer comorbidities, but the survival rates were similar in the two groups.

131I treatment for thyroid cancer and risk of developing primary hyperparathyroidism: a cohort study.

PURPOSE: To evaluate the association between (131)I therapy for thyroid cancer and risk of developing primary hyperparathyroidism. METHODS: This was a nationwide population-based cohort study of patients with thyroid cancer diagnosed during the period 1997-2008. The data were obtained from the Taiwan National Health Insurance Research dataset. The cumulative (131)I dose in each patient was calculated. Hazard ratios (HRs) were calculated using a proportional hazards model to estimate the effect of (131)I therapy on the risk of developing primary hyperparathyroidism in the cohort. RESULTS: A total of 8,946 patients with thyroid cancer were eligible for the final analysis. Among these patients, 8 developed primary hyperparathyroidism during the follow-up period that represented 38,248 person-years giving an incidence rate of 20.9 per 10(5) person-years. (131)I was used in the treatment of 6,153 patients (68.8%) with a median cumulative dose of 3.7 GBq. The adjusted HRs were 0.21 (95% CI 0.02-1.86) and 0.46 (95% CI 0.10-2.10) for those receiving a cumulative (131)I dose of 0.1-3.6 GBq and ≥3.7 GBq, respectively, compared to no therapy. The risk of developing primary hyperparathyroidism did not increase with increasing (131)I dose (test for trend p = 0.51). No interaction was found between (131)I dose and age (p = 0.94) or (131)I dose and sex (p = 0.99). CONCLUSION: (131)I treatment for thyroid cancer did not increase risk of primary hyperparathyroidism during a 10-year follow-up in this study population. Further research with a longer follow-up period is needed to assess late adverse effects beyond 10 years.

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.

Features and treatment modality of iliopsoas abscess and its outcome: a 6-year hospital-based study.

BACKGROUND: Percutaneous drainage (PCD) and surgical intervention are two primary treatment options for iliopsoas abscess (IPA). However, there is currently no consensus on when to use PCD or surgical intervention, especially in patients with gas-forming IPA. This study compared the characteristics of patients with gas-forming and non-gas forming IPA and their mortality rates under different treatment modalities. An algorithm for selecting appropriate treatment for IPA patients is proposed based on our findings. METHODS: Eighty-eight IPA patients between July 2007 and February 2013 were enrolled in this retrospective study. Patients < 18 years of age or with an incomplete course of treatment were excluded. Demographic information, clinical characteristics, and outcomes of different treatment approaches were compared between gas-forming IPA and non-gas forming IPA patients. RESULTS: Among the 88 enrolled patients, 27 (31%) had gas-forming IPA and 61 (69%) had non-gas forming IPA. The overall intra-hospital mortality rate was 25%. The gas-forming IPA group had a higher intra-hospital mortality rate (12/27, 44.0%) than the non-gas forming IPA group (10/61, 16.4%) (P < 0.001). Only 2 of the 13 patients in the gas-forming IPA group initially accepting PCD had a good outcome (success rate = 15.4%). Three of the 11 IPA patients with failed initial PCD expired, and 8 of the 11 patients with failed initial PCD accepted salvage operation, of whom 5 survived. Seven of the 8 gas-forming IPA patients accepting primary surgical intervention survived (success rate = 87.5%). Only 1 of the 6 gas-forming IPA patients who accepted antibiotics alone, without PCD or surgical intervention, survived (success rate = 16.7%). In the non-gas forming IPA group, 23 of 61 patients initially accepted PCD, which was successful in 17 patients (73.9%). The success rate of PCD was much higher in the non-gas forming group than in the gas-forming group (P <0.01). CONCLUSIONS: Based on the high failure rate of PCD and the high success rate of surgical intervention in our samples, we recommend early surgical intervention with appropriate antibiotic treatment for the patients with gas-forming IPA. Either PCD or primary surgical intervention is a suitable treatment for patients with non-gas forming IPA.

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.

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.

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.

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.

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.

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.

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.

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.