BACKGROUND: The bidirectional relationships between metabolic syndrome (MetS) and major depressive disorder (MDD) were discovered, but the influencing factors of the comorbidity were barely investigated. We aimed to fully explore the factors and their associations with MetS in MDD patients.
METHODS: The data were retrieved from the electronic medical records of a tertiary psychiatric hospital in Beijing from 2016 to 2021. The influencing factors were firstly explored by univariate analysis and multivariate logistic regressions. The propensity score matching was used to reduce the selection bias of participants. Then, the Bayesian networks (BNs) with hill-climbing algorithm and maximum likelihood estimation were preformed to explore the relationships between influencing factors with MetS in MDD patients.
RESULTS: Totally, 4126 eligible subjects were included in the data analysis. The proportion rate of MetS was 32.6 % (95 % CI: 31.2 %-34.1 %). The multivariate logistic regression suggested that recurrent depression, uric acid, duration of depression, marriage, education, number of hospitalizations were significantly associated with MetS. In the BNs, number of hospitalizations and uric acid were directly connected with MetS. Recurrent depression and family history psychiatric diseases were indirectly connected with MetS. The conditional probability of MetS in MDD patients with family history of psychiatric diseases, recurrent depression and two or more times of hospitalizations was 37.6 %.
CONCLUSIONS: Using the BNs, we found that number of hospitalizations, recurrent depression and family history of psychiatric diseases contributed to the probability of MetS, which could help to make health strategies for specific MDD patients.

BACKGROUND: Predicting vaccination behaviors accurately could provide insights for health care professionals to develop targeted interventions.
OBJECTIVE: The aim of this study was to develop predictive models for influenza vaccination behavior among children in China.
METHODS: We obtained data from a prospective observational study in Wuxi, eastern China. The predicted outcome was individual-level vaccine uptake and covariates included sociodemographics of the child and parent, parental vaccine hesitancy, perceptions of convenience to the clinic, satisfaction with clinic services, and willingness to vaccinate. Bayesian networks, logistic regression, least absolute shrinkage and selection operator (LASSO) regression, support vector machine (SVM), naive Bayes (NB), random forest (RF), and decision tree classifiers were used to construct prediction models. Various performance metrics, including area under the receiver operating characteristic curve (AUC), were used to evaluate the predictive performance of the different models. Receiver operating characteristic curves and calibration plots were used to assess model performance.
RESULTS: A total of 2383 participants were included in the study; 83.2% of these children (n=1982) were <5 years old and 6.6% (n=158) had previously received an influenza vaccine. More than half (1356/2383, 56.9%) the parents indicated a willingness to vaccinate their child against influenza. Among the 2383 children, 26.3% (n=627) received influenza vaccination during the 2020-2021 season. Within the training set, the RF model showed the best performance across all metrics. In the validation set, the logistic regression model and NB model had the highest AUC values; the SVM model had the highest precision; the NB model had the highest recall; and the logistic regression model had the highest accuracy, F1 score, and Cohen κ value. The LASSO and logistic regression models were well-calibrated.
CONCLUSIONS: The developed prediction model can be used to quantify the uptake of seasonal influenza vaccination for children in China. The stepwise logistic regression model may be better suited for prediction purposes.

Understanding the strength of trade-off and synergistic relationships among ecosystem services (ESs) is crucial for ecological management and restoration in the Fenhe River Basin. However, there is still a lack of sufficient research on the driving variables and spatial pattern optimization of the strength of ESs relationships in this area. Based on the quantitative assessment of six ESs in the Fenhe River Basin in 2000 and 2020, the ecosystem services trade-off synergy index (TSI) was introduced to quantitatively measure the strength of trade-off and synergistic relationships between each pair of ESs. A Bayesian network was constructed to identify the driving variables of trade-off and synergistic relationships, and sensitivity analysis was conducted to determine the degree of influence of key variables on the strength of these relationships. The optimization area of the strength of ESs trade-off and synergistic relationships was characterized in spatial patterns. The results showed that:① There were significant spatiotemporal differences in the six ESs in the Fenhe River Basin in 2000 and 2020. In terms of time scale, water yield, net primary productivity, crop productivity, soil conservation, and carbon storage all showed a trend of fluctuating increase. In terms of spatial scale, the spatial distribution changes in the six ESs were relatively small over the 20 years. ② The TSI of carbon storage was high in the surrounding area and low in the middle, showing a four-high and four-low pattern. The areas with the highest TSI between grain supply and other services were distributed from north to south. ③ Sensitivity analysis found that the strength of water yield, soil conservation, and habitat quality were significantly affected by precipitation, plant root depth restriction, and rainfall erosion. According to the conditional probability of different states of key variables, Wenshui County, Qingxu County, and Qi County in the central part of the Fenhe River Basin were identified as high-value areas for trade-off and synergistic relationships, which could be used as key areas for ecological restoration. These findings have important theoretical and practical significance for understanding the complex relationship between multiple ESs trade-off and synergistic relationships and their driving variables and for proposing sustainable ecological environment management policies.

BACKGROUND: Data digitization expands data collection opportunities, representing both a chance to understand interrelationships between variables and a challenge to identify the most appropriate clinical factors. Applications of causal inference techniques to clinical trial data is becoming very attractive, especially with the intent to provide insights into the relationships between baseline characteristics and outcomes. Graphical representations of model structures and conditional probabilities can be powerful tools to illustrate relationships in a high-dimensional data setting.
METHODS: We review and apply Bayesian network theory to a clinical case study, presenting an analytical approach to investigating and visualizing causal relationships. We propose the use of the adherence score to compare data networks\' patterns based on different variables\' discretization. Data from adult patients with spasticity related to multiple sclerosis (MSS) from two randomized placebo-controlled clinical trials of nabiximols were used as analysis sets. The training and validation sets included 106 (53 treated, 53 placebo) and 155 (76 treated, 79 placebo) participants, respectively. The primary objective was to create a network and estimate the causal dependencies between participants\' characteristics, changes in MSS severity as reflected by shifts in the patient-reported numeric rating scale (NRS), and changes in symptoms, functional abilities, and quality of life factors.
RESULTS: A causal network was identified between the key factors of assigned treatment, end of study spasticity NRS, and mental health/vitality subscales of the 36-Item Short Form Health Survey questionnaire (4 nodes and 3 edges; adherence score = 93%). In patients with mild spasticity, the impact of nabiximols on mental health or vitality subscales resulted in a probability ratio of 1.63. The decomposed mediation effect of spasticity NRS was observed through a mediation analysis between treatment and mental health (99.4%) or vitality (93.7%) subscales.
CONCLUSIONS: The use of innovative methods such as causal networks is highly encouraged to identify dependent relationships among key factors in clinical trial data and drive insights for additional research.

OBJECTIVE: To investigate the association of baseline and long-term mean hemoglobin A1c (HbA1c) with the risk of stroke.
METHODS: A total of 11,220 participants aged over 45 years and without stroke at baseline were enrolled from the China Health and Retirement Longitudinal Study. Mean HbA1c was calculated as the mean of HbA1c at all previous visits before stroke occurred or the end of follow-up. Multivariable-adjusted Cox regressions and Bayesian network were used for the analysis.
RESULTS: During a median follow-up of 7.50 years, a total of 626 cases of stroke occurred. The risk of stroke increased with quintiles of baseline and mean HbA1c, the hazard ratio (HR) in Q5 versus Q1 was 1.30 (95 % confidence interval [CI],1.03-1.64) and 1.79 (95 % CI, 1.38-2.34), respectively. Per 1 unit increase in baseline and mean HbA1c was associated with 10 % (HR, 1.10; 95 % CI, 1.02-1.18) an 12 % (HR, 1.12; 95 % CI, 1.05-1.19) higher risk of stroke. Bayesian network analysis showed that the pathway from HbA1c to stroke was through hypertension, dyslipidemia, obesity, and inflammation.
CONCLUSIONS: Elevated levels of both baseline and long-term HbA1c were associated with increased risk of stroke, and hypertension and obesity played an important role in the pathway.

BACKGROUND: The psychometric properties of the Satisfaction With Life Scale (SWLS) have been evaluated across numerous languages and population groups, primarily from a factor analysis perspective. In some studies, inconsistencies in structural invariance have been identified.
OBJECTIVE: This study aims to analyze the properties and gender invariance of the SWLS from a network analysis perspective.
METHODS: A total of 857 Spanish university students were obtained through a stratified random cluster sampling method in a cross-sectional survey design study. Descriptive analysis of the items, partial-correlation network, Bayesian network model estimation, and invariance analysis by gender were conducted.
RESULTS: The instrument did not exhibit any floor or ceiling effects. Each item can be considered univariately normally distributed, and all items clustered in a single and stable community. The partial-correlation network model and centrality measures were stable in the full sample and invariant across genders. Item 3 emerged as the most central node in the network with the highest predictability. The Bayesian network indicated that items 2 and 4 initiate the process, while item 5 acts as the sink, and items 1 and 3 act as mediators.
CONCLUSIONS: The SWLS can be used as a unidimensional measure, and the total score and relationships among items are stable and reliable. Any potential differences among genders cannot be associated with the functioning of the instrument. The predictability of every item was high, and the Bayesian network clearly identified different roles among the items.

BACKGROUND: Environmental lead (Pb) exposure have been suggested as a causative factor for amyotrophic lateral sclerosis (ALS). However, the role of Pb content of human body in ALS outcomes has not been quantified clearly. The purpose of this study was to apply Bayesian networks to forecast the risk of Pb exposure on the disease occurrence.
METHODS: We retrospectively collected medical records of ALS inpatients who underwent blood Pb testing, while matched controlled inpatients on age, gender, hospital ward and admission time according to the radio of 1:9. Tree Augmented Naïve Bayes (TAN), a semi-naïve Bayes classifier, was established to predict probability of ALS or controls with risk factors.
RESULTS: A total of 140 inpatients were included in this study. The whole blood Pb levels of ALS patients (57.00 μg/L) were more than twice as high as the controls (27.71 μg/L). Using the blood Pb concentrations to calculate probability of ALS, TAN produced the total coincidence rate of 90.00%. The specificity, sensitivity of Pb for ALS prediction was 0.79, or 0.74, respectively.
CONCLUSIONS: Therefore, these results provided quantitative evidence that Pb exposure may contribute to the development of ALS. Bayesian networks may be used to predict the ALS early onset with blood Pb levels.

An understanding of the combined effects of climate change (CC) and other anthropogenic stressors, such as chemical exposures, is essential for improving ecological risk assessments of vulnerable ecosystems. In the Great Barrier Reef, coral reefs are under increasingly severe duress from increasing ocean temperatures, acidification, and cyclone intensities associated with CC. In addition to these stressors, inshore reef systems, such as the Mackay-Whitsunday coastal zone, are being impacted by other anthropogenic stressors, including chemical, nutrient, and sediment exposures related to more intense rainfall events that increase the catchment runoff of contaminated waters. To illustrate an approach for incorporating CC into ecological risk assessment frameworks, we developed an adverse outcome pathway network to conceptually delineate the effects of climate variables and photosystem II herbicide (diuron) exposures on scleractinian corals. This informed the development of a Bayesian network (BN) to quantitatively compare the effects of historical (1975-2005) and future projected climate on inshore hard coral bleaching, mortality, and cover. This BN demonstrated how risk may be predicted for multiple physical and biological stressors, including temperature, ocean acidification, cyclones, sediments, macroalgae competition, and crown of thorns starfish predation, as well as chemical stressors such as nitrogen and herbicides. Climate scenarios included an ensemble of 16 downscaled models encompassing current and future conditions based on multiple emission scenarios for two 30-year periods. It was found that both climate-related and catchment-related stressors pose a risk to these inshore reef systems, with projected increases in coral bleaching and coral mortality under all future climate scenarios. This modeling exercise can support the identification of risk drivers for the prioritization of management interventions to build future resilient reefs. Integr Environ Assess Manag 2024;20:401-418. © 2023 Norwegian Institute for Water Research and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver condition that affects a quarter of the global adult population. To date, only a few NAFLD risk prediction models have been developed for Chinese older adults aged ≥ 60 years. This study presented the development of a risk prediction model for NAFLD in Chinese individuals aged ≥ 60 years and proposed personalised health interventions based on key risk factors to reduce NAFLD incidence among the population.
METHODS: A cross-sectional survey was carried out among 9,041 community residents in Shanghai. Three NAFLD risk prediction models (I, II, and III) were constructed using multivariate logistic regression analysis based on the least absolute shrinkage and selection operator regression analysis, and random forest model to select individual characteristics, respectively. To determine the optimal model, the three models\' discrimination, calibration, clinical application, and prediction capability were evaluated using the receiver operating characteristic (ROC) curve, calibration plot, decision curve analysis, and net reclassification index (NRI), respectively. To evaluate the optimal model\'s effectiveness, the previously published NAFLD risk prediction models (Hepatic steatosis index [HSI] and ZJU index) were evaluated using the following five indicators: accuracy, precision, recall, F1-score, and balanced accuracy. A dynamic nomogram was constructed for the optimal model, and a Bayesian network model for predicting NAFLD risk in older adults was visually displayed using Netica software.
RESULTS: The area under the ROC curve of Models I, II, and III in the training dataset was 0.810, 0.826, and 0.825, respectively, and that of the testing data was 0.777, 0.797, and 0.790, respectively. No significant difference was found in the accuracy or NRI between the models; therefore, Model III with the fewest variables was determined as the optimal model. Compared with the HSI and ZJU index, Model III had the highest accuracy (0.716), precision (0.808), recall (0.605), F1 score (0.692), and balanced accuracy (0.723). The risk threshold for Model III was 20%-80%. Model III included body mass index, alanine aminotransferase level, triglyceride level, and lymphocyte count.
CONCLUSIONS: A dynamic nomogram and Bayesian network model were developed to identify NAFLD risk in older Chinese adults, providing personalized health management strategies and reducing NAFLD incidence.

BACKGROUND: Identifying and managing serious spinal pathology (SSP) such as cauda equina syndrome or spinal infection in patients presenting with low back pain is challenging. Traditional red flag questioning is increasingly criticized, and previous studies show that many clinicians lack confidence in managing patients presenting with red flags. Improving decision-making and reducing the variability of care for these patients is a key priority for clinicians and researchers.
OBJECTIVE: We aimed to improve SSP identification by constructing and validating a decision support tool using a Bayesian network (BN), which is an artificial intelligence technique that combines current evidence and expert knowledge.
METHODS: A modified RAND appropriateness procedure was undertaken with 16 experts over 3 rounds, designed to elicit the variables, structure, and conditional probabilities necessary to build a causal BN. The BN predicts the likelihood of a patient with a particular presentation having an SSP. The second part of this study used an established framework to direct a 4-part validation that included comparison of the BN with consensus statements, practice guidelines, and recent research. Clinical cases were entered into the model and the results were compared with clinical judgment from spinal experts who were not involved in the elicitation. Receiver operating characteristic curves were plotted and area under the curve were calculated for accuracy statistics.
RESULTS: The RAND appropriateness procedure elicited a model including 38 variables in 3 domains: risk factors (10 variables), signs and symptoms (17 variables), and judgment factors (11 variables). Clear consensus was found in the risk factors and signs and symptoms for SSP conditions. The 4-part BN validation demonstrated good performance overall and identified areas for further development. Comparison with available clinical literature showed good overall agreement but suggested certain improvements required to, for example, 2 of the 11 judgment factors. Case analysis showed that cauda equina syndrome, space-occupying lesion/cancer, and inflammatory condition identification performed well across the validation domains. Fracture identification performed less well, but the reasons for the erroneous results are well understood. A review of the content by independent spinal experts backed up the issues with the fracture node, but the BN was otherwise deemed acceptable.
CONCLUSIONS: The RAND appropriateness procedure and validation framework were successfully implemented to develop the BN for SSP. In comparison with other expert-elicited BN studies, this work goes a step further in validating the output before attempting implementation. Using a framework for model validation, the BN showed encouraging validity and has provided avenues for further developing the outputs that demonstrated poor accuracy. This study provides the vital first step of improving our ability to predict outcomes in low back pain by first considering the problem of SSP.
UNASSIGNED: RR2-10.2196/21804.