OBJECTIVES: To analyze the trends of disease burden of lip and oral cancers in East Asia from 1990 to 2021 and its future projections. METHODS: We used the Global Burden of Disease 2021 database to conduct a comprehensive analysis of disease burden data from China (including Taiwan Province of China), Japan, Republic of Korea, Democratic People's Republic of Korea and Mongolia. The data were stratified by age, gender and major risk factors, and a Bayesian age-period-cohort model was employed to predict the future trends. RESULTS: From 1990 to 2021, the burden of lip and oral cancers in East Asian countries exhibited a steady increase. Taiwan Province of China experienced the most significant increases in incidence, prevalence, mortality, and disability-adjusted life years (DALYs), while Mongolia saw a decline in both mortality and DALYs. In 2021, Taiwan Province of China reported the highest rates of lip and oral cancer incidence (27.50 per 100 000), prevalence (137.92 per 100 000), mortality (9.59 per 100 000), and DALYs (292.07 person-years per 100 000), particularly among male and elderly populations. Tobacco use and alcohol consumption significantly exacerbated the disease burden in Taiwan Province of China and Japan. Future projections indicate that the incidence and prevalence of lip and oral cancer in China (excluding Taiwan Province of China) will continue to rise, while their mortality rates are expected to decline in most regions, except for Taiwan Province of China and Democratic People's Republic of Korea. CONCLUSIONS By the year 2035, the disease burden of lip and oral cancers in East Asia is expected to continue to increase, especially in Taiwan Province of China. To address this challenge, it is essential to implement effective measures to control major risk factors, promote early screening, and ensure equitable distribution of healthcare resources.
Humans ; Mouth Neoplasms/epidemiology* ; Incidence ; Lip Neoplasms/epidemiology* ; Asia, Eastern/epidemiology* ; Male ; Disability-Adjusted Life Years ; Prevalence ; Female ; Forecasting ; Risk Factors ; Cost of Illness ; Middle Aged ; Global Burden of Disease ; Aged ; Bayes Theorem

OBJECTIVES: To conduct a comprehensive analysis of the global burden of diarrheal diseases in children under 5 years. METHODS: The data from the Global Burden of Disease (GBD) 2021 were analyzed to assess the incidence, mortality rates and average annual percentage changes (AAPC) of diarrhea among children under 5 years across nations(regions) and GBD regions from 1990 to 2021 using joinpoint regression. Smoothed curve regression was employed to explore the correlation of diarrheal disease burden with the Social Development Index (SDI) and for analyzing the burden of specific diarrheal pathogens. The Slope and Concentration Indices quantified disparities across SDI levels and the future trend were projected by the Bayesian Age-Period-Cohort (BAPC) model. RESULTS: From 1990 to 2021, the global incidence (AAPC: -3.65) and mortality (AAPC: -5.15) rates of diarrheal diseases declined steadily in children below 5 years. In 2021, neonates (<28 days) were the most affected, with an incidence rate of 138 058.74 per 100 000 and a mortality rate of 251.14 per 100 000. Rotavirus was the leading cause of death. The incidence rate of diarrheal diseases was negatively correlated with SDI, and the Concentration Index decreased from -0.293 in 1990 to -0.314 in 2021 without a significant gender difference. The BAPC model suggested that the global incidence rate of diarrheal diseases tends to decrease progressively from 2022 to 2050, with a predicted rate of 23 448.04 per 100 000 for male and 29 932.59 per 100 000 for female by 2050. CONCLUSIONS Despite the reduction in the global burden of diarrhea and the projection of its further decline, diarrheal diseases disproportionately affect neonates and low-SDI regions. While rotavirus remains the primary etiological agent worldwide, the predominant pathogens vary by nations (regions) and GBD regions, and strengthened interventions targeting vulnerable populations are needed.
Humans ; Child, Preschool ; Diarrhea/mortality* ; Infant ; Incidence ; Infant, Newborn ; Global Burden of Disease/trends* ; Global Health ; Male ; Bayes Theorem ; Female

OBJECTIVE: To identify the optimal machine learning algorithm for predicting post-stroke epilepsy (PSE) within one year following acute stroke, establish a nomogram model based on this algorithm, and perform external validation to achieve accurate prediction of secondary epilepsy. METHODS: A total of 870 acute stroke patients admitted to the emergency department of Xiang'an Hospital of Xiamen University from June 2019 to June 2023 were enrolled for model development (model group). An external validation cohort of 435 acute stroke patients admitted to the Fifth Hospital of Xiamen during the same period was used to validate the machine learning algorithms and nomogram model. Patients were classified into control and epilepsy groups based on the development of PSE within one year. Clinical and laboratory data, including baseline characteristics, stroke location, vascular status, complications, hematologic parameters, and National Institutes of Health Stroke Scale (NIHSS) score, were collected for analysis. Nine machine learning algorithms such as logistic regression, CN2 rule induction, K-nearest neighbors, adaptive boosting, random forest, gradient boosting, support vector machine, naive Bayes, and neural network were applied to evaluate predictive performance. The area under the curve (AUC) of receiver operator characteristic curve (ROC curve) was used to identify the optimal algorithm. Logistic regression was used to screen risk factors for PSE, and the top 10 predictors were selected to construct the nomogram model. The predictive performance of the model was evaluated using the ROC curve in both the model and validation groups. RESULTS: Among the 870 patients in the model group, 29 developed PSE within one year. Among the nine algorithms tested, logistic regression demonstrated the best performance and generalizability, with an AUC of 0.923. Univariate logistic regression identified several risk factors for PSE, including platelet count, white blood cell count, red blood cell count, glycated hemoglobin (HbA1c), C-reactive protein (CRP), triglycerides, high-density lipoprotein (HDL), aspartate aminotransferase (AST), alanine aminotransferase (ALT), activated partial thromboplastin time (APTT), thrombin time, D-dimer, fibrinogen, creatine kinase (CK), creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), serum sodium, lactic acid, anion gap, NIHSS score, brain herniation, periventricular stroke, and carotid artery plaque. Further multivariate logistic regression analysis showed that white blood cell count, HDL, fibrinogen, lactic acid and brain herniation were independent risk factors [odds ratio (OR) were 1.837, 198.039, 47.025, 11.559, 70.722, respectively, all P < 0.05]. In the external validation group, univariate logistic regression analysis showed that platelet count, white blood cell count, CRP, triacylglycerol, APTT, D-dimer, fibrinogen, CK, CK-MB, LDH, NIHSS score, and cerebral herniation were risk factors for PSE one year after acute stroke. Further multiple logistic regression analysis showed that APTT and cerebral herniation were independent predictors (OR were 0.587 and 116.193, respectively, both P < 0.05). The nomogram model, constructed using 10 key variables-brain herniation, periventricular stroke, carotid artery plaque, white blood cell count, triglycerides, thrombin time, D-dimer, serum sodium, lactic acid, and NIHSS score-achieved an AUC of 0.908 in the model group and 0.864 in the external validation group. CONCLUSIONS The logistic regression-based prediction model for epilepsy one year after acute stroke, developed using machine learning algorithms, showed optimal predictive performance. The nomogram model based on the logistic regression-derived predictors showed strong discriminative power and was successfully validated externally, suggesting favorable clinical applicability and generalizability.
Humans ; Machine Learning ; Stroke/complications* ; Nomograms ; Epilepsy/etiology* ; Algorithms ; Male ; Female ; Logistic Models ; Middle Aged ; Aged ; Risk Factors ; Bayes Theorem

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms

Progressive pulmonary fibrosis (PPF) is a progressive and lethal condition with few effective treatment options. Improvements in quality of life for patients with PPF remain limited even while receiving treatment with approved antifibrotic drugs. Traditional Chinese medicine (TCM) has the potential to improve cough, dyspnea and fatigue symptoms of patients with PPF. TCM treatments are typically diverse and individualized, requiring urgent development of efficient and precise design strategies to identify effective treatment options. We designed an innovative Bayesian adaptive two-stage trial, hoping to provide new ideas for the rapid evaluation of the effectiveness of TCM in PPF. An open-label, two-stage, adaptive Bayesian randomized controlled trial will be conducted in China. Based on Bayesian methods, the trial will employ response-adaptive randomization to allocate patients to study groups based on data collected over the course of the trial. The adaptive Bayesian trial design will employ a Bayesian hierarchical model with "stopping" and "continuation" criteria once a predetermined posterior probability of superiority or futility and a decision threshold are reached. The trial can be implemented more efficiently by sharing the master protocol and organizational management mechanisms of the sub-trial we have implemented. The primary patient-reported outcome is a change in the Leicester Cough Questionnaire score, reflecting an improvement in cough-specific quality of life. The adaptive Bayesian trial design may be a promising method to facilitate the rapid clinical evaluation of TCM effectiveness for PPF, and will provide an example for how to evaluate TCM effectiveness in rare and refractory diseases. However, due to the complexity of the trial implementation, sufficient simulation analysis by professional statistical analysts is required to construct a Bayesian response-adaptive randomization procedure for timely response. Moreover, detailed standard operating procedures need to be developed to ensure the feasibility of the trial implementation. Please cite this article as: Zhang C, Nie YS, Zhang CT, Yang HJ, Zhang HR, Xiao W, Cui GF, Li J, Li SJ, Huang QS, Yan SY. An adaptive Bayesian randomized controlled trial of traditional Chinese medicine in progressive pulmonary fibrosis: Rationale and study design. J Integr Med. 2025; 23(2): 138-145.
Female ; Humans ; Male ; Bayes Theorem ; Disease Progression ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional/methods* ; Pulmonary Fibrosis/therapy* ; Quality of Life ; Randomized Controlled Trials as Topic ; Research Design ; Adaptive Clinical Trials as Topic

OBJECTIVE: This study aimed to identify high-risk areas for type 2 diabetes mellitus (T2DM) mortality to provide relevant evidence for interventions in emerging economies. METHODS: Empirical Bayesian Kriging and a discrete Poisson space-time scan statistic were applied to identify the spatiotemporal clusters of T2DM mortality. The relationships between economic factors, air pollutants, and the mortality risk of T2DM were assessed using regression analysis and the Poisson Log-linear Model. RESULTS: A coastal district in East Guangdong, China, had the highest risk (Relative Risk [RR] = 4.58, P < 0.01), followed by the 10 coastal districts/counties in West Guangdong, China (RR = 2.88, P < 0.01). The coastal county in the Pearl River Delta, China (RR = 2.24, P < 0.01), had the third-highest risk. The remaining risk areas were two coastal counties in East Guangdong, 16 districts/counties in the Pearl River Delta, and two counties in North Guangdong, China. Mortality due to T2DM was associated with gross domestic product per capita (GDP per capita). In pilot assessments, T2DM mortality was significantly associated with carbon monoxide. CONCLUSION High mortality from T2DM occurred in the coastal areas of East and West Guangdong, especially where the economy was progressing towards the upper middle-income level.
Diabetes Mellitus, Type 2/epidemiology* ; China/epidemiology* ; Humans ; Risk Factors ; Spatio-Temporal Analysis ; Air Pollutants/analysis* ; Socioeconomic Factors ; Bayes Theorem ; Female ; Male ; Middle Aged

OBJECTIVE: To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis (TB) in the Guangdong Province between 2010 and 2019. METHOD: Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering. Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive (ST-CAR) model. RESULTS: Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000 in 2019. Spatial hotspots were found in northeastern Guangdong, particularly in Heyuan, Shanwei, and Shantou, while Shenzhen, Dongguan, and Foshan had the lowest rates in the Pearl River Delta. The ST-CAR model showed that the TB risk was lower with higher per capita Gross Domestic Product (GDP) [Relative Risk ( RR), 0.91; 95% Confidence Interval ( CI): 0.86-0.98], more the ratio of licensed physicians and physician ( RR, 0.94; 95% CI: 0.90-0.98), and higher per capita public expenditure ( RR, 0.94; 95% CI: 0.90-0.97), with a marginal effect of population density ( RR, 0.86; 95% CI: 0.86-1.00). CONCLUSION The incidence of TB in Guangdong varies spatially and temporally. Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection. Strategies focusing on equitable health resource distribution and economic development are the key to TB control.
Humans ; China/epidemiology* ; Incidence ; Bayes Theorem ; Spatio-Temporal Analysis ; Tuberculosis/epidemiology* ; Socioeconomic Factors

BACKGROUND: Research has indicated that the disease burden of alopecia areata (AA) in China exceeds the global average. Therefore, accurate and updated epidemiological information is crucial for policymakers. In this study, we aimed to comprehensively assess the disease burden of AA in China. METHODS: The following four key indicators were utilized: the prevalence of cases; disability-adjusted life-years (DALYs); the age-standardized prevalence rate (ASPR); and the age-standardized DALY rate (ASDR) of AA according to the Global Burden of Disease (GBD) study 2021. We analyzed the epidemiological burden of AA in China during 2021, examined changes between 1990 and 2021, and performed a Bayesian age-period-cohort analysis to predict trends over the course of the next decade (2022-2030). Additionally, a Gaussian process regression model was applied to estimate the relationship between the gross domestic product (GDP) and the ASPR and ASDR of AA at the provincial level between 1992 and 2021. RESULTS: In 2021, the estimated number of patients with AA in China was approximately 3.49 million (95% uncertainty interval [UI], 3.37-3.62 million); of these patients, 1.20 million (95% UI, 1.16-1.25 million) were male and 2.29 million (95% UI, 2.20-2.37 million) were female. This large number of patients with AA resulted in a total of 114,431.25 DALYs (95% UI, 74,780.27-160,318.96 DALYs). Additionally, the ASPR and ASDR were 224.61 per 100,000 population (95% UI, 216.73-232.65 per 100,000 population) and 7.41 per 100,000 population (95% UI, 4.85-10.44 per 100,000 population), respectively; both of these rates were higher than the global averages. The most affected demographic groups were young and female individuals 25-39 years of age. Slight regional disparities were observed, with the northern and central regions of China bearing comparatively higher burdens. Between 1990 and 2021, the health loss and disease burden caused by AA in China remained relatively stable. The ASPR and ASDR of AA increased with the GDP when the annual GDP was less than 2 trillion Chinese yuan; however, a downward trend was observed as the GDP surpassed 2 trillion Chinese yuan. A slight upward trend in the disease burden of AA in China is predicted to occur over the next decade. CONCLUSIONS AA continues to be a public health concern in China that shows no signs of declining. Targeted efforts for young individuals and females are necessary because they experience a disproportionately high burden of AA.
Humans ; China/epidemiology* ; Alopecia Areata/epidemiology* ; Global Burden of Disease ; Female ; Male ; Adult ; Disability-Adjusted Life Years ; Middle Aged ; Prevalence ; Adolescent ; Young Adult ; Bayes Theorem ; Child ; Quality-Adjusted Life Years ; Child, Preschool

BACKGROUND: Asthma, one of the most widespread chronic respiratory diseases, has placed a considerable economic and social stress on China. This study examines the burden of asthma in China from 1990 to 2021 and forecasts future trends, providing guidance for establishing focused preventive and regulatory strategies. METHODS: Utilizing data from the Global Burden of Disease Database 2021, the analysis of trends in asthma burden was conducted for China from 1990 to 2021. Key indicators such as incidence, prevalence, mortality, and disability-adjusted life years (DALYs) were analysed. The investigation applied the estimated annual percentage change (EAPC), average annual percentage change (AAPC), and age-period-cohort model (APCM) to evaluate these trends. Furthermore, predictions for incidence and mortality in 2035 were generated using the Bayesian APCM and the Nordpred model. RESULTS: In 2021, there were 25,015,668 prevalent asthma cases in China, alongside 3,934,875 new cases and 26,233 deaths. The age-standardized incidence rate and age-standardized death rate for 2021 were 364.17 (95% uncertainty interval [95% UI]: 283.22-494.1) per 100,000 population and 1.47 (95% UI: 1.15-1.79) per 100,000 population, respectively. The age-standardized rates (ASRs) for incidence were detected to be elevated in the 0-4 years age group, and the prevalence was significantly higher in the 5-9 years age group compared to other cohorts. ASR for incidence and prevalence of asthma in China were lower than that in the global average. Between 1990 and 2021, the ASR of incidence, prevalence, mortality, and DALYs demonstrated a downward trajectory, with EAPC values of -1.17, -1.57, -4.69, and -2.98, respectively. People aged 0-9 years and over 60 years experienced a disproportionately higher disease burden. Projections indicate that the ASRs for incidence will continue to rise, whereas the death will continue to decline by 2035. CONCLUSIONS Between 1990 and 2021, a general reduction in the asthma burden in China was observed. However, the burden remains particularly high among people aged 0-9 years and over 60 years, underscoring the need for targeted interventions and policies to address the ongoing challenges of asthma.
Humans ; Asthma/mortality* ; China/epidemiology* ; Global Burden of Disease ; Incidence ; Disability-Adjusted Life Years ; Male ; Adult ; Middle Aged ; Child ; Adolescent ; Female ; Prevalence ; Child, Preschool ; Infant ; Aged ; Young Adult ; Infant, Newborn ; Bayes Theorem

Basket trial, as an innovative clinical trial design concept, marks the transformation of medical research from the traditional large-scale and single-disease treatment to the precise and individualized treatment. By gradually incorporating the Bayesian method during development, the trial design becomes more scientific and reasonable and increases its efficiency. The fundamental principle of the Bayesian method is the utilization of prior knowledge in conjunction with new observational data to dynamically update the posterior probability. This flexibility enhances the basket trial's capacity to effectively adapt to variations during the research process. Consequently, it enables researchers to dynamically adjust research strategies based on accumulated data and improve the predictive accuracy regarding treatment responses. In addition, the design concept of the basket trial aligns with the traditional Chinese medicine(TCM) principle of "homotherapy for heteropathy". The principle of "homotherapy for heteropathy" emphasizes that under certain conditions, different diseases may have the same treatment. Similarly, basket trials allow using a uniform trial design across multiple diseases, offering enhanced operational and significant practical value in the realm of TCM, particularly within the context of syndrome-based disease research. By introducing basket trials, the design of TCM clinical studies will be more scientific and yield higher-quality evidence. This study systematically categorized various Bayesian methods and models utilized in basket trials, evaluated their strengths and weaknesses, and identified their appropriate application contexts, so as to offer a practical guide for designing basket trials in the realm of TCM.
Bayes Theorem ; Humans ; Medicine, Chinese Traditional/methods* ; Research Design ; Clinical Trials as Topic/methods* ; Drugs, Chinese Herbal/therapeutic use*