Maintenance chemotherapy for esophageal squamous cell carcinoma after standard concurrent chemoradiotherapy: a national propensity score matching cohort study.

Esophageal squamous cell carcinoma (ESCC) is a common and aggressive cancer, and its standard treatment is concurrent chemoradiotherapy (CCRT). Maintenance chemotherapy is often used to help prevent cancer recurrence, but its efficacy for patients with ESCC receiving CCRT remains unclear. We conducted a large head-to-head propensity score matching cohort study to estimate the effects of maintenance chemotherapy on overall survival and cancer-specific survival in patients with ESCC receiving standard CCRT. After propensity score matching (PSM), we recruited 2724 patients with ESCC (2177 in the maintenance chemotherapy group and 547 in the non-maintenance chemotherapy group). The adjusted hazard ratios (95% confidence intervals) of all-cause mortality and cancer-specific mortality for the maintenance chemotherapy group were 1.15 (1.06-1.26, P = 0.0014) and 1.08 (0.88-1.29, P = 0.1320), respectively, compared with the non-maintenance chemotherapy group. We also found that older age, relatively lower body mass index (BMI), higher American Joint Committee on Cancer clinical stage, and poor response to CCRT as measured using the Response Evaluation Criteria in Solid Tumors were poor independent predictors of all-cause mortality and cancer-specific mortality. Our findings indicated that maintenance chemotherapy may not improve the survival of patients with ESCC who have received CCRT. Additionally, we identified several key prognostic factors for patients with ESCC receiving CCRT, including relatively low BMI and poor response to CCRT. Further research is needed to understand the benefits and risks of maintenance chemotherapy in similar patient populations in order to identify new therapies that could improve treatment responses.

Correlation of maintenance chemotherapy and improved survival in patients with locally advanced unresectable pancreatic head adenocarcinoma receiving neoadjuvant chemotherapy and concurrent chemoradiotherapy.

To assess the efficacy of maintenance chemotherapy in the management of unresectable locally advanced pancreatic head adenocarcinoma (PHA) cancer after neoadjuvant chemotherapy and concurrent chemoradiation therapy (CCRT). This study, a large-scale head-to-head propensity score matching (PSM) cohort study, employed real-world data. PSM was used to evaluate the impact of maintenance chemotherapy on overall survival and cancer-specific survival in patients with unresectable locally advanced PHA who underwent neoadjuvant chemotherapy and CCRT. A total of 148 patients with locally advanced pancreatic head adenocarcinoma were included in the study after PSM. These patients were equally divided into two groups, those receiving maintenance chemotherapy and those who did not. Confounding factors were balanced between the groups. The adjusted hazard ratios for all-cause mortality and cancer-specific mortality were 0.56 (95% CI: 0.40-0.77; P = 0.0005) and 0.56 (95% CI: 0.40-0.78; P = 0.0007), respectively, in patients receiving maintenance chemotherapy compared to those who did not. Our large-scale, real-world study demonstrates that maintenance chemotherapy may enhance survival outcomes for patients with unresectable locally advanced pancreatic head adenocarcinoma who underwent neoadjuvant chemotherapy and concurrent chemoradiation therapy.

Cost-effectiveness of diabetic retinopathy screening for newly diagnosed type 2 diabetic patients: A nationwide population-based propensity score-matched cohort study.

AIMS: This study investigated the association between the frequency of screening for diabetic retinopathy (DR) versus the development of DR and corresponding medical expenses among patients newly diagnosed with type 2 diabetes mellitus (T2DM). METHODS: This longitudinal, population-based study used the Taiwan National Health Insurance Research Database (2004 to 2020) as a data source. Propensity score matching (PSM) (sex, age, comorbidities and concurrent medication use) was employed in the grouping of T2DM patients according to different frequency of DR screening. Outcome measures included the proportion of patients who developed DR, who received DR treatment, and the associated medical expenses and hospitalizations. RESULTS: The 17-year cohort included 337,046 patients. After PSM, three groups each containing 35,739 patients were assembled and analyzed. Compared to low-frequency screening, high-frequency screening was more effective in detecting patients requiring treatment; however, the net cost for treatment was significantly lower. Standard-frequency screening appears to provide the best balance in terms of DR detection, diagnosis interval, the risk of DR-related hospitalization, and DR treatment costs. CONCLUSIONS: In this real-world cohort study covering all levels of the healthcare system, infrequent screening was associated with delayed diagnosis and elevated treatment costs, while a fundus screening interval of 1-2 years proved optimal in terms of detection and medical expenditures.

Development of a Machine Learning Algorithm to Correlate Lumbar Disc Height on X-rays with Disc Bulging or Herniation.

Lumbar disc bulging or herniation (LDBH) is one of the major causes of spinal stenosis and related nerve compression, and its severity is the major determinant for spine surgery. MRI of the spine is the most important diagnostic tool for evaluating the need for surgical intervention in patients with LDBH. However, MRI utilization is limited by its low accessibility. Spinal X-rays can rapidly provide information on the bony structure of the patient. Our study aimed to identify the factors associated with LDBH, including disc height, and establish a clinical diagnostic tool to support its diagnosis based on lumbar X-ray findings. In this study, a total of 458 patients were used for analysis and 13 clinical and imaging variables were collected. Five machine-learning (ML) methods, including LASSO regression, MARS, decision tree, random forest, and extreme gradient boosting, were applied and integrated to identify important variables for predicting LDBH from lumbar spine X-rays. The results showed L4-5 posterior disc height, age, and L1-2 anterior disc height to be the top predictors, and a decision tree algorithm was constructed to support clinical decision-making. Our study highlights the potential of ML-based decision tools for surgeons and emphasizes the importance of L1-2 disc height in relation to LDBH. Future research will expand on these findings to develop a more comprehensive decision-supporting model.

Effects of SGLT2 inhibitors on clinical cancer survival in patients with type 2 diabetes.

PURPOSE: According to the preclinical data, sodium-glucose cotransporter 2 (SGLT2) inhibitors (SGLT2is) may exert anticancer effects. Here, we clarified the cancer-specific mortality (primary outcome) and all-cause mortality (secondary outcome) of SGLT2is and their dose-dependency in patients with cancer undergoing standard curative treatments. METHODS: We analyzed data from patients with type 2 diabetes mellitus (T2DM) diagnosed with cancer between January 1, 2016, and December 31, 2018, enrolled from the Taiwan Cancer Registry database. Kaplan-Meier method was used to estimate all-cause mortality and cancer-specific mortality, comparing survival curves between SGLT2i users and nonusers using the stratified log-rank test. Cox proportional hazards regression was conducted to identify independent predictors for all-cause and cancer-specific mortality among the covariates. RESULTS: We performed 1:2 propensity score matching of our data, which yielded a final cohort of 50,133 patients with cancer; of them, 16,711 and 33,422 were in the SGLT2i user and nonuser groups, respectively. The adjusted hazard ratio (aHR) for cancer-specific and all-cause mortality in SGLT2i users compared with nonusers was 0.21 (95 % confidence interval [CI]: 0.20-0.22) and 0.22 (95 % CI: 0.21-0.23). We divided the patients into four subgroups stratified by quartiles (Q) of cumulative defined daily doses per year (cDDDs), and all-cause and cancer-specific mortality was noted to significantly decrease with increases in dosage (from Q1 to Q4 cDDDs) in SGLT2i users compared with in nonusers (P < 0.001). CONCLUSION: SGLT2is increase overall survival and cancer-specific survival in patients with cancer in a dose-dependent manner.

Data-driven, two-stage machine learning algorithm-based prediction scheme for assessing 1-year and 3-year mortality risk in chronic hemodialysis patients.

Life expectancy is likely to be substantially reduced in patients undergoing chronic hemodialysis (CHD). However, machine learning (ML) may predict the risk factors of mortality in patients with CHD by analyzing the serum laboratory data from regular dialysis routine. This study aimed to establish the mortality prediction model of CHD patients by adopting two-stage ML algorithm-based prediction scheme, combined with importance of risk factors identified by different ML methods. This is a retrospective, observational cohort study. We included 800 patients undergoing CHD between December 2006 and December 2012 in Shin-Kong Wu Ho-Su Memorial Hospital. This study analyzed laboratory data including 44 indicators. We used five ML methods, namely, logistic regression (LGR), decision tree (DT), random forest (RF), gradient boosting (GB), and eXtreme gradient boosting (XGB), to develop a two-stage ML algorithm-based prediction scheme and evaluate the important factors that predict CHD mortality. LGR served as a bench method. Regarding the validation and testing datasets from 1- and 3-year mortality prediction model, the RF had better accuracy and area-under-curve results among the five different ML methods. The stepwise RF model, which incorporates the most important factors of CHD mortality risk based on the average rank from DT, RF, GB, and XGB, exhibited superior predictive performance compared to LGR in predicting mortality among CHD patients over both 1-year and 3-year periods. We had developed a two-stage ML algorithm-based prediction scheme by implementing the stepwise RF that demonstrated satisfactory performance in predicting mortality in patients with CHD over 1- and 3-year periods. The findings of this study can offer valuable information to nephrologists, enhancing patient-centered decision-making and increasing awareness about risky laboratory data, particularly for patients with a high short-term mortality risk.

Prevalence of Obstructive Sleep Apnea Using Home Sleep Test in Taiwan During the Coronavirus Disease Pandemic.

Background: Obstructive sleep apnea syndrome (OSAS) is a common disorder associated with serious sequelae. The current gold standard diagnostic method, polysomnography, is costly and time consuming and requires patients to stay overnight at a facility. Aim: This study aimed to reveal the prevalence of OSAS in general adult population using a home sleep test (HST) during the coronavirus disease 2019 (COVID-19) pandemic. Methods: This prospective cohort study was conducted by the Department of Otolaryngology, Taipei City Hospital, Taipei, Taiwan, between January 2020 and December 2021. A total of 1372 patients aged 30-70 years completed an HST using a Type 3 portable sleep monitor (PM). The apnea-hypopnea index (AHI) was analyzed to assess the association of OSAS with age, body mass index (BMI), sex, Epworth Sleepiness Scale (ESS) and the Sleep Apnea Risk Assessment questionnaire (STOP-Bang questionnaire) rating. Results: The mean age of the patients (782 men, 57%; 590 women, 43%) was 49.24 ± 11.04 years. OSAS was detected in 954 (69.5%) patients with 399 (29.1%) mild OSAS; 246 (17.9%) moderate OSAS; and 309 (22.5%) severe OSAS. Among these, the prevalence of moderate-to-severe OSAS was 143 (10.4%) in women and 412 (30.0%) in men. The mean age was the highest (51.29 ± 11.29) in the mild OSAS group and lowest (47.08 ± 10.87) in the healthy group. OSAS severity was greater with increasing BMI, 23.39 ± 3.44 in the healthy group and 29.29 ± 5.01 in the severe OSAS group. A positive correlation was also noted between the ESS/STOP-Bang questionnaire rating and OSAS severity. Conclusion: The prevalence of OSAS in Taiwan was 69.5% in our study. It showed strong evidence that OSAS has important public health consequences and PMs are simple, fast, feasible, and cost-effective tools for OSAS screening in the home environment, especially during the COVID-19 pandemic.

Impact of Pre-Transplant Parathyroidectomy on Graft Survival: A Comparative Study of Renal Transplant Patients (2005-2015).

BACKGROUND Hyperparathyroidism poses significant risks for patients prior to kidney transplantation. However, the outcomes of patients who undergo parathyroidectomy before renal transplantation compared to those without such a procedure remain uncertain. This real-world data study aimed to examine the clinical outcomes of both patient groups. MATERIAL AND METHODS Using the Taiwan National Health Insurance Research Database, we conducted a retrospective cohort study on patients who underwent renal transplantation between January 2005 and December 2015. The patients were divided into two groups: a case group (n=294) with parathyroidectomy and a control group (n=588) without the need for parathyroidectomy before kidney transplantation. The groups were matched based on age, sex, dialysis vintage, and baseline characteristics at a 1:2 ratio. Hazard ratios (HR) were estimated using the Cox regression model. The main outcomes assessed were graft failure, mortality, and major adverse cardiovascular events (MACE) recorded until December 2019. RESULTS During a mean follow-up period of 6 years, a significant difference was observed in graft failure (HR 1.40; 95% confidence interval 1.10-1.79, p=0.007) between the two groups. After further adjustment, graft failure remained significant (HR 1.52; 95% CI 1.07-2.15, p=0.019). Additionally, machine learning-based feature selection identified the importance of parathyroidectomy (ranked 9 out of 11) before kidney transplantation in predicting subsequent graft failure. CONCLUSIONS Our study demonstrates that severe hyperparathyroidism requiring parathyroidectomy before kidney transplantation may contribute to poor post-transplant graft outcomes compared to patients who do not require parathyroidectomy.

Improving Patient Safety in the X-ray Inspection Process with EfficientNet-Based Medical Assistance System.

Patient safety is a paramount concern in the medical field, and advancements in deep learning and Artificial Intelligence (AI) have opened up new possibilities for improving healthcare practices. While AI has shown promise in assisting doctors with early symptom detection from medical images, there is a critical need to prioritize patient safety by enhancing existing processes. To enhance patient safety, this study focuses on improving the medical operation process during X-ray examinations. In this study, we utilize EfficientNet for classifying the 49 categories of pre-X-ray images. To enhance the accuracy even further, we introduce two novel Neural Network architectures. The classification results are then compared with the doctor's order to ensure consistency and minimize discrepancies. To evaluate the effectiveness of the proposed models, a comprehensive dataset comprising 49 different categories and over 12,000 training and testing sheets was collected from Taichung Veterans General Hospital. The research demonstrates a significant improvement in accuracy, surpassing a 4% enhancement compared to previous studies.

Varicose veins are associated with an increased risk of mitral valve regurgitation: a nationwide population-based cohort study.

BACKGROUND: Varicose veins (VV) and mitral valve regurgitation (MR) are both common diseases. The aim was to investigate whether VV are associated with an increased risk of MR. METHODS: We conducted a nationwide cohort study to assess the association between VV and risk of developing MR. Drawn from the Taiwan National Health Insurance Research Database (NHIRD), the records of 56,898 patients with VV (the VV cohort) and 56,898 propensity score-matched patients without VV (the non-VV cohort) in the years 2007 to 2015 were identified. Follow-up duration was calculated from the date of entry in the cohort until the occurrence of a first MR diagnosis, death, or the end of the observation period (December 31, 2015), whichever occurred first. Hazard ratios (HRs) and accompanying 95% confidence intervals (CIs) derived from the Cox proportional hazards model were used to estimate the association between VV and MR risks. RESULTS: After multivariable adjustment, VV was associated with an increased risk of MR (adjusted HR, 1.63; 95% CI: 1.52-1.74). Notably, significant associations between VV and MR risk were evident in both genders and in all age groups. A trend of significant increase of MR risk was also observed with increasing frequency of annual clinical visits for VV. Within the VV cohort, the subgroup of MR presence had higher incidences of atrial fibrillation, heart failure, valve-related surgeries, and mortality (P<0.001). CONCLUSIONS: This population-based cohort study revealed that VV was associated with an increased risk of MR in a Taiwanese population. Vigilance of MR existence should be emphasized in patients of VV due to its potentially poor long-term outcomes.

Using the H2O Automatic Machine Learning Algorithms to Identify Predictors of Web-Based Medical Record Nonuse Among Patients in a Data-Rich Environment: Mixed Methods Study.

BACKGROUND: With the advent of electronic storage of medical records and the internet, patients can access web-based medical records. This has facilitated doctor-patient communication and built trust between them. However, many patients avoid using web-based medical records despite their greater availability and readability. OBJECTIVE: On the basis of demographic and individual behavioral characteristics, this study explores the predictors of web-based medical record nonuse among patients. METHODS: Data were collected from the National Cancer Institute 2019 to 2020 Health Information National Trends Survey. First, based on the data-rich environment, the chi-square test (categorical variables) and 2-tailed t tests (continuous variables) were performed on the response variables and the variables in the questionnaire. According to the test results, the variables were initially screened, and those that passed the test were selected for subsequent analysis. Second, participants were excluded from the study if any of the initially screened variables were missing. Third, the data obtained were modeled using 5 machine learning algorithms, namely, logistic regression, automatic generalized linear model, automatic random forest, automatic deep neural network, and automatic gradient boosting machine, to identify and investigate factors affecting web-based medical record nonuse. The aforementioned automatic machine learning algorithms were based on the R interface (R Foundation for Statistical Computing) of the H2O (H2O.ai) scalable machine learning platform. Finally, 5-fold cross-validation was adopted for 80% of the data set, which was used as the training data to determine hyperparameters of 5 algorithms, and 20% of the data set was used as the test data for model comparison. RESULTS: Among the 9072 respondents, 5409 (59.62%) had no experience using web-based medical records. Using the 5 algorithms, 29 variables were identified as crucial predictors of nonuse of web-based medical records. These 29 variables comprised 6 (21%) sociodemographic variables (age, BMI, race, marital status, education, and income) and 23 (79%) variables related to individual lifestyles and behavioral habits (such as electronic and internet use, individuals' health status and their level of health concern, etc). H2O's automatic machine learning methods have a high model accuracy. On the basis of the performance of the validation data set, the optimal model was the automatic random forest with the highest area under the curve in the validation set (88.52%) and the test set (82.87%). CONCLUSIONS: When monitoring web-based medical record use trends, research should focus on social factors such as age, education, BMI, and marital status, as well as personal lifestyle and behavioral habits, including smoking, use of electronic devices and the internet, patients' personal health status, and their level of health concern. The use of electronic medical records can be targeted to specific patient groups, allowing more people to benefit from their usefulness.

H1-antihistamine use and head and neck cancer risk in type 2 diabetes mellitus.

This study aimed to examine the association between the use of H1-antihistamines (AHs) and head and neck cancer (HNC) risk in patients with type 2 diabetes mellitus (T2DM). Data from the National Health Insurance Research Database of Taiwan were analyzed for the period from 2008 to 2018. A propensity-score-matched cohort of 54,384 patients each in the AH user and nonuser groups was created and analyzed using Kaplan-Meier method and Cox proportional hazards regression. The results showed that the risk of HNC was significantly lower in AH users (adjusted hazard ratio: 0.55, 95% CI: 0.48 to 0.64) and the incidence rate was also lower (5.16 vs. 8.10 per 100,000 person-years). The lower HNC incidence rate in AH users (95% CI: 0.63; 0.55 to 0.73) suggests that AH use may reduce the risk of HNC in T2DM patients.

Bibliometric and Visualized Analysis of Good Death Research from 2001-2021.

Quantitative analysis via bibliometric field analyses is a recent, gradually emerging method. We conducted a bibliometric study to investigate the authors' scientific influence and contributions and evaluate trends and research foci in good death-related literature using the Web of Science (WOS) Core Collection. A total of 1,157 publications were selected for the analysis. There was a significant increase in annual publications per year (R2 = 0.79). The publication (317, 27.4%) and average citation (29.2) numbers were highest in the USA. Controlling for population number and GDP, the Netherlands had the highest number of articles per million persons (5.89) and US$ 1010 GDP (1.02). North American and Western European countries are leaders in the field, but some East Asian countries (Japan and Taiwan) perform well. Current research focuses on patient perspectives of good death and advance care planning among patients, families, and health care providers.

Identifying the predictors of severe psychological distress by auto-machine learning methods.

Social stress in daily life and the COVID-19 pandemic have greatly impacted the mental health of the population. Early detection of a predisposition to severe psychological distress is essential for timely interventions. This paper analyzed 4036 samples participating in the 2019-2020 National Health Information Trends Survey (HINTS) and identified 57 candidate predictors of severe psychological distress based on univariate chi-square and t-test analyses. Five machine learning methods, namely logistic regression (LR), automatic generalized linear models (Auto-GLM), automatic random forests (Auto-Random Forests), automatic deep neural networks (Auto-Deep learning) and automatic gradient boosting machines (Auto-GBM), were employed to model synthetic minority oversampling technique-based (SMOTE) resampled data and identify predictors of severe psychological distress. Predictors were evaluated by odds ratios in logistic models and variable importance in the other models. Forty-seven variables were identified as significant predictors of severe psychological distress, including 13 sociodemographic variables and 34 variables related to individual lifestyle and behavioral habits. Among them, new potentially relevant variables related to an individual's level of concern and trust in cancer information, exposure to health care providers, and cancer screening and awareness are included. The performance of each model was evaluated using five-fold cross-validation. The optimal model performance-wise was Auto-GBM with an accuracy of 89.75%, a precision of 89.68%, a recall of 89.31%, an F1-score of 89.48% and an AUC of 95.57%. Significant predictors of severe psychological distress were identified in this study and the value of machine learning methods in predicting severe psychological distress is demonstrated, thereby enhancing pre-prediction and clinical decision-making of severe psychological distress problems.

Comparison of medical resource consumption between oral cavity squamous cell carcinoma with and without sarcopenia: A nationwide population-based cohort study.

PURPOSE: No study has compared long-term medical resource consumption between patients with oral cavity squamous cell carcinoma (OCSCC) with and without sarcopenia receiving curative surgery. PATIENTS AND METHODS: Generalized linear mixed and logistic regression models were employed to evaluate the number of postoperative visits and medical reimbursement for head and neck cancer or complications and the number of hospitalizations for treatment-related complications over 5 years after curative surgery, respectively. RESULTS: The mean difference (95% CI) in total medical claims amounts between the nonsarcopenia and sarcopenia groups were new Taiwan dollars (NTD) 47 820 (35 864-59 776, p < 0.0001), 11 902 (4897-18 908, p = 0.0009), 17 282 (10 666-23 898, p < 0.0001), 17 364 (9644-25 084, p < 0.0001), and 8236 (111-16 362, p = 0.0470) for the first, second, third, fourth, and fifth years, respectively. CONCLUSION: The long-term medical resource consumption was higher in the sarcopenia group than in the nonsarcopenia group.

Effects of Statin Dose, Class, and Use Intensity on All-Cause Mortality in Patients with Type 2 Diabetes Mellitus.

PURPOSE: to examine the impact of statins on reducing all-cause mortality among individuals diagnosed with type 2 diabetes. This investigation explored the potential correlations between dosage, drug classification, and usage intensity with the observed outcomes. METHODS: The research sample consisted of individuals aged 40 years or older diagnosed with type 2 diabetes. Statin usage was determined as a frequent usage over a minimum of one month subsequent to type 2 diabetes diagnosis, where the average statin dose was ≥28 cumulative defined daily doses per year (cDDD-year). The analysis employed an inverse probability of treatment-weighted Cox hazard model, utilizing statin usage status as a time-varying variable, to evaluate the impact of statin use on all-cause mortality. RESULTS: The incidence of mortality was comparatively lower among the cohort of statin users (n = 50,804 (12.03%)), in contrast to nonusers (n = 118,765 (27.79%)). After adjustments, the hazard ratio (aHR; 95% confidence interval (CI)) for all-cause mortality was estimated to be 0.32 (0.31-0.33). Compared with nonusers, pitavastatin, rosuvastatin, pravastatin, simvastatin, atorvastatin, fluvastatin, and lovastatin users demonstrated significant reductions in all-cause mortality (aHRs (95% CIs) = 0.06 (0.04-0.09), 0.28 (0.27-0.29), 0.29 (0.28-0.31), 0.31 (0.30-0.32), 0.31 (0.30-0.32), 0.36 (0.35-0.38), and 0.48 (0.47-0.50), respectively). In Q1, Q2, Q3, and Q4 of cDDD-year, our multivariate analysis demonstrated significant reductions in all-cause mortality (aHRs (95% CIs) = 0.51 (0.5-0.52), 0.36 (0.35-0.37), 0.24 (0.23-0.25), and 0.13 (0.13-0.14), respectively; p for trend <0.0001). Because it had the lowest aHR (0.32), 0.86 DDD of statin was considered optimal. CONCLUSIONS: In patients diagnosed with type 2 diabetes, consistent utilization of statins (≥28 cumulative defined daily doses per year) was shown to have a beneficial effect on all-cause mortality. Moreover, the risk of all-cause mortality decreased as the cumulative defined daily dose per year of statin increased.

Statin Use During Concurrent Chemoradiotherapy With Improved Survival Outcomes in Esophageal Squamous Cell Carcinoma: A Propensity Score-Matched Nationwide Cohort Study.

INTRODUCTION: To determine the effect of statin use during concurrent chemoradiotherapy (CCRT) on overall survival and esophageal squamous cell carcinoma (ESCC)-specific survival in patients with ESCC receiving standard CCRT. METHODS: In this propensity score-matching cohort study, we used data from the Taiwan Cancer Registry Database and National Health Insurance Research Database to investigate the effects of statin use during the period of CCRT on overall survival and ESCC-specific survival. RESULTS: Statin use during the period of CCRT was found to be a considerable and independent prognostic factor for overall survival and ESCC-specific survival. The adjusted hazard ratio (aHR) for all-cause mortality in the statin group compared with that of the non-statin group was 0.65 (95% confidence interval: 0.51-0.84, p = 0.0009). The aHR for ESCC-specific mortality in the statin group compared with that of the non-statin group was 0.63 (95% confidence interval: 0.47-0.84, p = 0.0016). The use of hydrophilic statins such as rosuvastatin and pravastatin was associated with the greatest survival benefits. A dose-response relationship was also found, with higher cumulative defined daily doses and higher daily intensity of statin use associated with lower mortality. CONCLUSIONS: This study is the first to reveal that statin use during the period of CCRT for ESCC is associated with improvement in overall survival and ESCC-specific survival. In addition, we found that use of rosuvastatin, pravastatin, and simvastatin was associated with better survival outcomes for patients with ESCC receiving CCRT. Furthermore, we found a dose-response relationship of statin use associated with lower ESCC-specific mortality.

Gender Differences in the Nonspecific and Health-Specific Use of Social Media Before and During the COVID-19 Pandemic: Trend Analysis Using HINTS 2017-2020 Data.

The use of social media has changed since the outbreak of coronavirus disease 2019 (COVID-19). However, little is known about the gender disparity in social media use for nonspecific and health-specific issues before and during the COVID-19 pandemic. Based on a gender difference perspective, this study aimed to examine how the nonspecific and health-specific uses of social media changed in 2017-2020. The data came from the Health Information National Trends Survey Wave 5 Cycle 1-4. This study included 10,426 participants with complete data. Compared to 2017, there were higher levels of general use in 2019 and 2020, and an increased likelihood of health-related use in 2020 was reported among the general population. Female participants were more likely to be nonspecific and health-specific users than males. Moreover, the relationship of gender with general use increased in 2019 and 2020; however, concerning health-related use, it expanded in 2019 but narrowed in 2020. The COVID-19 global pandemic led to increased use of social media, especially for health-related issues among males. These findings further our understanding of the gender gap in health communication through social media, and contribute to targeted messaging to promote health and reduce disparities between different groups during the pandemic.