Comparison of regmed and BayesNetty for exploring causal models with many variables.

Here we compare a recently proposed method and software package, regmed, with our own previously developed package, BayesNetty, designed to allow exploratory analysis of complex causal relationships between biological variables. We find that regmed generally has poorer recall but much better precision than BayesNetty. This is perhaps not too surprising as regmed is specifically designed for use with high-dimensional data. BayesNetty is found to be more sensitive to the resulting multiple testing problem encountered in these circumstances. However, as regmed is not designed to handle missing data, its performance is severely affected when missing data is present, whereas the performance of BayesNetty is only slightly affected. The performance of regmed can be rescued in this situation by first using BayesNetty to impute the missing data, and then applying regmed to the resulting "filled-in" data set.

Institutional complexity emerges from socioecological complexity in small-scale human societies.

Human lifestyles vary enormously over time and space and so understanding the origins of this diversity has always been a central focus of anthropology. A major source of this cultural variation is the variation in institutional complexity: the cultural packages of rules, norms, ontologies and expectations passed down through societies across generations. In this article, we study the emergence of institutions in small-scale societies. There are two primary schools of thought. The first is that institutions emerge top-down as rules are imposed by elites on their societies in order to gain asymmetrical access to power, resources and influence over others through coercion. The second is that institutions emerge bottom-up to facilitate interactions within populations as they seek collective solutions to adaptive problems. Here, we use Bayesian networks to infer the causal structure of institutional complexity in 172 small-scale societies across ethnohistoric western North America reflecting the wide diversity of indigenous lifestyles across this vast region immediately prior to European colonization. Our results suggest that institutional complexity emerges from underlying socioecological complexity because institutions are solutions to coordination problems in more complex environments where human-environment interactions require increased management.

Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks.

Dynamic Bayesian networks (DBNs) can be used for the discovery of gene regulatory networks (GRNs) from time series gene expression data. Here, we suggest a strategy for learning DBNs from gene expression data by employing a Bayesian approach that is scalable to large networks and is targeted at learning models with high predictive accuracy. Our framework can be used to learn DBNs for multiple groups of samples and highlight differences and similarities in their GRNs. We learn these DBN models based on different structural and parametric assumptions and select the optimal model based on the cross-validated predictive accuracy. We show in simulation studies that our approach is better equipped to prevent overfitting than techniques used in previous studies. We applied the proposed DBN-based approach to two time series transcriptomic datasets from the Gene Expression Omnibus database, each comprising data from distinct phenotypic groups of the same tissue type. In the first case, we used DBNs to characterize responders and non-responders to anti-cancer therapy. In the second case, we compared normal to tumor cells of colorectal tissue. The classification accuracy reached by the DBN-based classifier for both datasets was higher than reported previously. For the colorectal cancer dataset, our analysis suggested that GRNs for cancer and normal tissues have a lot of differences, which are most pronounced in the neighborhoods of oncogenes and known cancer tissue markers. The identified differences in gene networks of cancer and normal cells may be used for the discovery of targeted therapies.

Broadcasters, receivers, functional groups of metabolites, and the link to heart failure by revealing metabolomic network connectivity.

BACKGROUND AND OBJECTIVE: Blood-based small molecule metabolites offer easy accessibility and hold significant potential for insights into health processes, the impact of lifestyle, and genetic variation on disease, enabling precise risk prevention. In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline. METHODS: We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites. RESULTS: We identified metabolites associated with higher and lower risk of HF incidence, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. These associations were not confounded by the other metabolites due to uncovering the connectivity among metabolites and adjusting each association for the confounding metabolites. Examples of our findings include the direct influence of asparagine on glycine, both of which were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids, which are not synthesized in the human body and are obtained directly from the diet. CONCLUSION: Metabolites may play a critical role in linking genetic background and lifestyle factors to HF incidence. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates studying complex conditions like HF.

Drug-drug interactions and the risk of adverse drug reaction-related hospital admissions in the older population.

AIMS: The aims of this study were to estimate potentially clinically important drug-drug interaction (DDI) prevalence, and the average causal effect of DDI exposure on adverse drug reaction (ADR)-related hospital admission, and to examine differences in health-related quality of life (HRQoL) and length of stay (LOS) per DDI exposure in an older (≥65 years) population acutely hospitalized. METHODS: This was a cross-sectional study conducted among 798 older individuals acutely admitted to hospital in Ireland between 2016 and 2017. Medication (current/recently discontinued/over-the-counter) and clinical data (e.g., creatinine clearance) were available. DDIs were identified using the British National Formulary (BNF) and Stockley's Drug Interactions. Causal inference models for DDI exposure on ADR-related hospital admission were developed using directed acyclic graphs. Multivariable logistic regression was used to estimate the average causal effect. Differences in HRQoL (EQ-5D) and LOS per DDI exposure were examined non-parametrically. DDI prevalence, adjusted odds ratios (aOR), and 95% confidence intervals (CIs) are reported. RESULTS: A total of 782 (98.0%) individuals using two or more drugs were included. Mean age was 80.9 (SD ± 7.5) years (range: 66-105); 52.2% were female; and 45.1% (n = 353) had an ADR-related admission. At admission, 316 (40.4% [95% CI: 37.0-43.9]) patients had at least one DDI. The average causal effect of DDI exposure on ADR-related hospital admission was aOR = 1.21 [95% CI: 0.89-1.64]. This was significantly increased by exposure to: DDIs which increase bleeding risk (aOR = 2.00 [1.26-3.12]); aspirin-warfarin (aOR = 2.78 [1.37-5.65]); and esomeprazole-escitalopram (aOR = 3.22 [1.13-10.25]. DDI-exposed patients had lower HRQoL (mean EQ-5D = 0.49 [±0.39]) compared those non-DDI-exposed (mean EQ-5D = 0.57 [±0.41]), (P = .03); and greater median LOS in hospital (8 [IQR5-16]days) compared those non-DDI-exposed (7 [IQR 4-14] days),(P = .04). CONCLUSIONS: Potentially clinically important DDIs carry an increased average causal effect on ADR-related admission, significantly (two-fold) by exposure to DDIs that increase bleeding risk, which should be targeted for medicine optimization.

Using Causal Bayesian Networks to Assess the Role of Patient-Centered Care and Psychosocial Factors on Durable HIV Viral Suppression.

We assessed the role of patient-centered care on durable viral suppression (i.e., all viral load test results < 200 copies per ml during 2019) by conducting a retrospective cohort study of clients medically case managed by the Miami-Dade County Ryan White Program (RWP). Summary measures of patient-centered care practices of RWP-affiliated providers were obtained from a survey of 1352 clients. Bayesian network models analyzed the complex relationship between psychosocial and patient-centered care factors. Of 5037 clients, 4135 (82.1%) had durable viral suppression. Household income was the factor most strongly associated with durable viral suppression. Further, mean healthcare relationship score and mean "provider knows patient as a person" score were both associated with durable viral suppression. Healthcare relationship score moderated the association between low household income and lack of durable viral suppression. Although patient-centered care supports patient HIV care success, wrap around support is also needed for people with unmet psychosocial needs.

RESUMEN: Evaluamos el rol de la atención centrada en el paciente en la supresión viral duradera (es decir, todos los resultados de las pruebas de carga viral < 200 copias por ml durante 2019) mediante la realización de un estudio de cohorte retrospectivo de clientes manejados médicamente por el Programa Ryan White del condado de Miami-Dade (RWP). Se obtuvieron medidas resumidas de las prácticas de atención centradas en el paciente de los proveedores afiliados a RWP usando una encuesta de 1352 clientes. Los modelos de redes bayesianos analizaron la relación compleja entre los factores psicosociales y de atención centrada en el paciente. De 5037 clientes, 4135 (82,1%) tenían una supresión viral duradera. Los ingresos del hogar fueron el factor asociado con la supresión viral duradera más fuerte. Además, la puntuación promedia de la relación con proveedores de atención médica y la puntuación promedia de "el proveedor conoce al paciente como persona" fueron asociados con una supresión viral duradera. La puntuación de la relación con proveedores de atención médica moderó la asociación entre los ingresos bajos del hogar y la falta de supresión viral duradera. Aunque la atención centrada en el paciente apoya el éxito de la atención médica del VIH, también se necesita un apoyo integral para las personas con necesidades psicosociales insatisfechas.

Updating and recalibrating causal probabilistic models on a new target population.

OBJECTIVE: Very often the performance of a Bayesian Network (BN) is affected when applied to a new target population. This is mainly because of differences in population characteristics. External validation of the model performance on different populations is a standard approach to test model's generalisability. However, a good predictive performance is not enough to show that the model represents the unique population characteristics and can be adopted in the new environment. METHODS: In this paper, we present a methodology for updating and recalibrating developed BN models - both their structure and parameters - to better account for the characteristics of the target population. Attention has been given on incorporating expert knowledge and recalibrating latent variables, which are usually omitted from data-driven models. RESULTS: The method is successfully applied to a clinical case study about the prediction of trauma-induced coagulopathy, where a BN has already been developed for civilian trauma patients and now it is recalibrated on combat casualties. CONCLUSION: The methodology proposed in this study is important for developing credible models that can demonstrate a good predictive performance when applied to a target population. Another advantage of the proposed methodology is that it is not limited to data-driven techniques and shows how expert knowledge can also be used when updating and recalibrating the model.

Predicting the evolution of clinical skin aging in a multi-ethnic population: Developing causal Bayesian networks using dermatological expertise.

INTRODUCTION: Software to predict the impact of aging on physical appearance is increasingly popular. But it does not consider the complex interplay of factors that contribute to skin aging. OBJECTIVES: To predict the +15-year progression of clinical signs of skin aging by developing Causal Bayesian Belief Networks (CBBNs) using expert knowledge from dermatologists. MATERIAL AND METHODS: Structures and conditional probability distributions were elicited worldwide from dermatologists with experience of at least 15 years in aesthetics. CBBN models were built for all phototypes and for ages ranging from 18 to 65 years, focusing on wrinkles, pigmentary heterogeneity and facial ptosis. Models were also evaluated by a group of independent dermatologists ensuring the quality of prediction of the cumulative effects of extrinsic and intrinsic skin aging factors, especially the distribution of scores for clinical signs 15 years after the initial assessment. RESULTS: For easiness, only models on African skins are presented in this paper. The forehead wrinkle evolution model has been detailed. Specific atlas and extrinsic factors of facial aging were used for this skin type. But the prediction method has been validated for all phototypes, and for all clinical signs of facial aging. CONCLUSION: This method proposes a skin aging model that predicts the aging process for each clinical sign, considering endogenous and exogenous factors. It simulates aging curves according to lifestyle. It can be used as a preventive tool and could be coupled with a generative AI algorithm to visualize aging and, potentially, other skin conditions, using appropriate images.

Risk-Based Fault Detection Using Bayesian Networks Based on Failure Mode and Effect Analysis.

In this article, the authors focus on the introduction of a hybrid method for risk-based fault detection (FD) using dynamic principal component analysis (DPCA) and failure method and effect analysis (FMEA) based Bayesian networks (BNs). The FD problem has garnered great interest in industrial application, yet methods for integrating process risk into the detection procedure are still scarce. It is, however, critical to assess the risk each possible process fault holds to differentiate between non-safety-critical and safety-critical abnormalities and thus minimize alarm rates. The proposed method utilizes a BN established through FMEA analysis of the supervised process and the results of dynamical principal component analysis to estimate a modified risk priority number (RPN) of different process states. The RPN is used parallel to the FD procedure, incorporating the results of both to differentiate between process abnormalities and highlight critical issues. The method is showcased using an industrial benchmark problem as well as the model of a reactor utilized in the emerging liquid organic hydrogen carrier (LOHC) technology.

Bayesian networks for Risk Assessment and postoperative deficit prediction in intraoperative neurophysiology for brain surgery.

PURPOSE: To this day there is no consensus regarding evidence of usefulness of Intraoperative Neurophysiological Monitoring (IONM). Randomized controlled trials have not been performed in the past mainly because of difficulties in recruitment control subjects. In this study, we propose the use of Bayesian Networks to assess evidence in IONM. METHODS: Single center retrospective study from January 2020 to January 2022. Patients admitted for cranial neurosurgery with intraoperative neuromonitoring were enrolled. We built a Bayesian Network with utility calculation using expert domain knowledge based on logistic regression as potential causal inference between events in surgery that could lead to central nervous system injury and postoperative neurological function. RESULTS: A total of 267 patients were included in the study: 198 (73.9%) underwent neuro-oncology surgery and 69 (26.1%) neurovascular surgery. 50.7% of patients were female while 49.3% were male. Using the Bayesian Network´s original state probabilities, we found that among patients who presented with a reversible signal change that was acted upon, 59% of patients would wake up with no new neurological deficits, 33% with a transitory deficit and 8% with a permanent deficit. If the signal change was permanent, in 16% of the patients the deficit would be transitory and in 51% it would be permanent. 33% of patients would wake up with no new postoperative deficit. Our network also shows that utility increases when corrective actions are taken to revert a signal change. CONCLUSIONS: Bayesian Networks are an effective way to audit clinical practice within IONM. We have found that IONM warnings can serve to prevent neurological deficits in patients, especially when corrective surgical action is taken to attempt to revert signals changes back to baseline properties. We show that Bayesian Networks could be used as a mathematical tool to calculate the utility of conducting IONM, which could save costs in healthcare when performed.

Risk assessment of inter-basin water transfer plans through integration of Fault Tree Analysis and Bayesian Network modelling approaches.

Inter-basin water transfer projects are a common method used to balance water resources and meet regional demand, particularly in the drinking water supply sector. The potential failure risk associated with inter-basin water transfer projects was examined using Fault Tree Analysis (FTA) and Fuzzy Fault Tree Analysis (FFTA) methodologies in this study. Additionally, the conversion of Fault Tree models into Bayesian Network (BN) and Fuzzy Bayesian Network (FBN) models was explored. Ten basic events were identified as factors that could affect the success of inter-basin water transfer plans, including socio-political, environmental, water resource, economic, and technical criteria. Fault Tree and Fuzzy Fault Tree models were utilized to conduct a risk analysis, which was then converted into crisp and fuzzy FTA-BN through an integrated approach. This approach was applied to evaluate inter-basin water transfer scenarios from the Great Karun basin to the Central Iran Plateau. The superior scenario among eight water transfer scenarios was found to be water transfer from the Behesht-Abad Basin to Isfahan province and from the Khersan Basin to Kerman and Yazd provinces, with a failure risk of 0.649 and 0.601 respectively, based on the crisp and fuzzy integrated models. Basic events were ranked based on their contribution to the occurrence of the top event using two FIM and BI indices in the Fault Tree model and two indices of MI and SI in the Bayesian Network. Furthermore, after considering the correlation between basic events and risk factors, the risk obtained by crisp and fuzzy integrated models was found to increase to 0.811 and 0.789 respectively. The results of this study demonstrate that an integrated approach can assist decision-makers and stakeholders in evaluating inter-basin water transfer projects.

Elicitation of Rank Correlations with Probabilities of Concordance: Method and Application to Building Management.

Constructing Bayesian networks (BN) for practical applications presents significant challenges, especially in domains with limited empirical data available. In such situations, field experts are often consulted to estimate the model's parameters, for instance, rank correlations in Gaussian copula-based Bayesian networks (GCBN). Because there is no consensus on a 'best' approach for eliciting these correlations, this paper proposes a framework that uses probabilities of concordance for assessing dependence, and the dependence calibration score to aggregate experts' judgments. To demonstrate the relevance of our approach, the latter is implemented to populate a GCBN intended to estimate the condition of air handling units' components-a key challenge in building asset management. While the elicitation of concordance probabilities was well received by the questionnaire respondents, the analysis of the results reveals notable disparities in the experts' ability to quantify uncertainty. Moreover, the application of the dependence calibration aggregation method was hindered by the absence of relevant seed variables, thus failing to evaluate the participants' field expertise. All in all, while the authors do not recommend to use the current model in practice, this study suggests that concordance probabilities should be further explored as an alternative approach for the elicitation of dependence.

[Bayesian network prediction study on the impact of occupational health psychological factors on insomnia among thermal power generation workers].

Objective: To explore the risk factors of insomnia among employees in the thermal power generation industry and the network relationships between their interactions, and to provide scientific basis for personalized interventions for high-risk groups with insomnia. Methods: In November 2022, 860 employees of a typical thermal power generation enterprise were selected as the research subjects by cluster sampling. On-site occupational health field surveys and questionnaire surveys were used to collect basic information, occupational characteristics, anxiety, depression, stress, occupational stress, and insomnia. The interaction between insomnia and occupational health psychological factors was evaluated by using structural equation model analysis and Bayesian network construction. Results: The detection rates of anxiety, depression and stress were 34.0% (292/860), 32.1% (276/860) and 18.0% (155/860), respectively. The total score of occupational stress was (445.3±49.9) points, and 160 workers (18.6%) were suspected of insomnia, and 578 workers (67.2%) had insomnia. Structural equation model analysis showed that occupational stress had a significant effect on the occurrence of insomnia in thermal power generation workers (standardized load coefficient was 0.644), and occupational health psychology had a low effect on insomnia (standardized load coefficient was 0.065). However, the Bayesian network model further analysis found that anxiety and stress were the two parent nodes of insomnia, with direct causal relationships, the arc strength was-8.607 and -15.665, respectively. The model prediction results showed that the probability of insomnia occurring was predicted to be 0 in the cases of no stress and anxiety, low stress without anxiety, and no stress with low anxiety. When high stress with low anxiety and low stress with high anxiety occurred, the predicted probability of insomnia occurring were 0.38 and 0.47, respectively. When both high stress and high anxiety occurred simultaneously, the predicted probability of insomnia occurring was 0.51. Conclusion: Bayesian network risk assessment can intuitively reveal and predict the insomnia risk of thermal power generation workers and the network interaction relationship between the risks. Anxiety and stress are the direct causal risks of insomnia, and stress is the main risk of individual insomnia of thermal power generation workers. The occurrence of insomnia can be reduced based on scientific intervention of stress conditions.

Network modeling of major depressive disorder symptoms in adult women.

BACKGROUND: Major depressive disorder (MDD) is one of the growing human mental health challenges facing the global health care system. In this study, the structural connectivity between symptoms of MDD is explored using two different network modeling approaches. METHODS: Data are from 'the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders (VATSPSUD)'. A cohort of N = 2163 American Caucasian female-female twins was assessed as part of the VATSPSUD study. MDD symptoms were assessed using personal structured clinical interviews. Two network analyses were conducted. First, an undirected network model was estimated to explore the connectivity between the MDD symptoms. Then, using a Bayesian network, we computed a directed acyclic graph (DAG) to investigate possible directional relationships between symptoms. RESULTS: Based on the results of the undirected network, the depressed mood symptom had the highest centrality value, indicating its importance in the overall network of MDD symptoms. Bayesian network analysis indicated that depressed mood emerged as a plausible driving symptom for activating other symptoms. These results are consistent with DSM-5 guidelines for MDD. Also, somatic weight and appetite symptoms appeared as the strongest connections in both networks. CONCLUSIONS: We discuss how the findings of our study might help future research to detect clinically relevant symptoms and possible directional relationships between MDD symptoms defining major depression episodes, which would help identify potential tailored interventions. This is the first study to investigate the network structure of VATSPSUD data using both undirected and directed network models.

A Bayesian network perspective on neonatal pneumonia in pregnant women with diabetes mellitus.

OBJECTIVE: To predict the influencing factors of neonatal pneumonia in pregnant women with diabetes mellitus using a Bayesian network model. By examining the intricate network connections between the numerous variables given by Bayesian networks (BN), this study aims to compare the prediction effect of the Bayesian network model and to analyze the influencing factors directly associated to neonatal pneumonia. METHOD: Through the structure learning algorithms of BN, Naive Bayesian (NB), Tree Augmented Naive Bayes (TAN), and k-Dependence Bayesian Classifier (KDB), complex networks connecting variables were presented and their predictive abilities were tested. The BN model and three machine learning models computed using the R bnlean package were also compared in the data set. RESULTS: In constraint-based algorithms, three algorithms had different presentation DAGs. KDB had a better prediction effect than NB and TAN, and it achieved higher AUC compared with TAN. Among three machine learning modes, Support Vector Machine showed a accuracy rate of 91.04% and 67.88% of precision, which was lower than TAN (92.70%; 72.10%). CONCLUSION: KDB was applicable, and it can detect the dependencies between variables, identify more potential associations and track changes between variables and outcome.

Comparison of Bayesian Networks, G-estimation and linear models to estimate causal treatment effects in aggregated N-of-1 trials with carry-over effects.

BACKGROUND: The aggregation of a series of N-of-1 trials presents an innovative and efficient study design, as an alternative to traditional randomized clinical trials. Challenges for the statistical analysis arise when there is carry-over or complex dependencies of the treatment effect of interest. METHODS: In this study, we evaluate and compare methods for the analysis of aggregated N-of-1 trials in different scenarios with carry-over and complex dependencies of treatment effects on covariates. For this, we simulate data of a series of N-of-1 trials for Chronic Nonspecific Low Back Pain based on assumed causal relationships parameterized by directed acyclic graphs. In addition to existing statistical methods such as regression models, Bayesian Networks, and G-estimation, we introduce a carry-over adjusted parametric model (COAPM). RESULTS: The results show that all evaluated existing models have a good performance when there is no carry-over and no treatment dependence. When there is carry-over, COAPM yields unbiased and more efficient estimates while all other methods show some bias in the estimation. When there is known treatment dependence, all approaches that are capable to model it yield unbiased estimates. Finally, the efficiency of all methods decreases slightly when there are missing values, and the bias in the estimates can also increase. CONCLUSIONS: This study presents a systematic evaluation of existing and novel approaches for the statistical analysis of a series of N-of-1 trials. We derive practical recommendations which methods may be best in which scenarios.

Risk patterns of consecutive adverse events in airway management: a Bayesian network analysis.

BACKGROUND: Minor adverse airway events play a pivotal role in the safety of airway management. Changes in airway management strategies can reduce such events, but the broader impact on airway management remains unclear. METHODS: Minor, frequently occurring adverse airway events were audited before and after implementation of changes to airway management strategies. We used two Bayesian networks to examine conditional probabilities of subsequent airway events and to compute the likelihood of certain events given that certain previous events occurred. RESULTS: Independent of sex, age, and American Society of Anesthesiologists physical status, targeted changes to airway management strategies reduced the risk of a first event. Obese patients were an exception, in whom no risk reduction was achieved. Frequently occurring event sequences were identified, for example the most likely event to follow difficult bag-mask ventilation was a Cormack-Lehane grade ≥3, with a risk of 14.3% (95% credible interval [CI], 11.4-17.2%). An impact of the targeted changes was detected on the likelihood of some event sequences, for example the likelihood of no consecutive event after a tracheal tube-related event increased from 43.3% (95% CI, 39.4-47.6%) to 56.4% (95% CI, 52.0-60.5%). CONCLUSIONS: Identification of risk patterns and typical structures of event sequences provides a clinically relevant perspective on airway incidents. It further provides a means to quantify the impact of targeted airway management changes. These targeted changes can influence some event sequences, but overall, the benefit results from the cumulative effect of improvements in multiple events. Targeted airway management changes with knowledge of risk patterns and event sequences can potentially further improve patient safety in airway management. CLINICAL TRIAL REGISTRATION: NCT02743767.

Decoupling the heterogeneity of sediment microbial communities along the urbanization gradients: A Bayesian-based approach.

Comprehending the response of microbial communities in rivers along urbanization gradients to hydrologic characteristics and pollution sources is critical for effective watershed management. However, the effects of complex factors on riverine microbial communities remain poorly understood. Thus, we established a bacteria-based index of biotic integrity (Ba-IBI) to evaluate the microbial community heterogeneity of rivers along an urbanization gradient. To examine the response of Ba-IBI to multiple stressors, we employed a Bayesian network based on structural equation modeling (SEM-BN) and revealed the key control factors influencing Ba-IBI at different levels of urbanization. Our findings highlight that waterborne nutrients have the most significant direct impact on Ba-IBI (r = -0.563), with a particular emphasis on ammonia nitrogen, which emerged as the primary driver of microbial community heterogeneity in the Liuyang River basin. In addition, our study confirmed the substantial adverse effects of urbanization on river ecology, as urban land use had the greatest indirect effect on Ba-IBI (r = -0.460). Specifically, the discharge load from wastewater treatment plants (WWTP) was found to significantly negatively affect the Ba-IBI of the entire watershed. In the low urbanized watersheds, rice cultivation (RC) and concentrated animal feeding operations (CAFO) are key control factors, and an increase in their emissions can lead to a sharp decrease in Ba-IBI. In moderately urbanized watersheds, the Ba-IBI tended to decrease as the level of RC emissions increased, while in those with moderate RC emissions, an increase in point source emissions mitigated the negative impact of RC on Ba-IBI. In highly urbanized watersheds, Ba-IBI was not sensitive to changes in stressors. Overall, our study presents a novel approach by integrating Ba-IBI with multi-scenario analysis tools to assess the effects of multiple stressors on microbial communities in river sediments, providing valuable insights for more refined environmental decision-making.

Do the New Rural Pension Scheme promote the health status of chronic patients in old age? -Evidence from CHARLS 2018 in China.

BACKGROUND: Many researchers have examined the impact of social insurance on health in elderly. However, in most cases, they have only demonstrated correlational results and have not been able to determine causal effects, possibly because confounding biases have not been fully addressed. In this study, we investigated the health effects of the New Rural Pension Scheme (NRPS) on the elderly (age≥60 years old) with chronic diseases in rural areas, and to explore the causal relationship and effects of NRPS and health status. METHODS: This paper used data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) and applied Bayesian networks and fuzzy regression discontinuity design to conduct causal analysis. Bayesian networks were used to explore the causal directed acyclic graphs of factors related to NRPS and health status. Based on the results of Bayesian network, a fuzzy regression discontinuity design was employed to estimate the causal effect of NRPS on health status. RESULTS: Among rural elderly with chronic diseases, Bayesian network mapping of causal relationships among NRPS, health status and covariates showed that age was a common cause of NRPS receipt and satisfaction with health. The results of the fuzzy regression discontinuity analysis showed that the effect of receiving NRPS on the health status was positive, but there was no statistically significant difference concerning the interval estimates. The results of the subgroup analysis with chronic obstructive pulmonary disease (COPD) and asthma indicated that the effect of NRPS receipt on the health status of elderly people with COPD was positive. There was a statistically significant effect of receiving NRPS on self-rated health description ([Formula: see text]) and health satisfaction ([Formula: see text]) in COPD population and a statistically significant effect of receiving NRPS on health satisfaction in asthma population ([Formula: see text]). CONCLUSION: This paper has confirmed the contribution and positive causal effect of NRPS on health status in a subgroup of older adults with COPD and asthma, using the CHARLS database as evidence. Thus, Chinese government should increase the take-up rate of the NRPS to enhance their positive impact on health status of elderly people with chronic diseases in rural areas.

Predictive factors of ergonomic behaviors based on social cognitive theory among women workers on assembly lines: application of Bayesian networks.

BACKGROUND: This study focuses on identifying the key factors associated with ergonomic behaviors (ERBE) among women workers on assembly lines (WwAL) to prevent musculoskeletal disorders (MSDs) caused by repetitive motions and unfavorable body postures. To achieve this objective, this study employed Bayesian networks (BN) analysis based on social cognitive theory (SCT). METHODS: A cross-sectional study was conducted to examine the predictive factors of ERBE among 250 WwAL from six different industries located in Neyshabur, a city in northeastern Iran. The study used a two-stage cluster sampling method for participant selection and self-report questionnaires to collect data on demographic characteristics, variables associated with SCT, ERBE, and the standard Nordic questionnaire. The collected data were analyzed using Netica and SPSS version 21, which involved statistical analyses such as independent t-tests, Pearson correlation, and ANOVA tests at a significance level of p < 0.05. BN analysis was conducted to identify the important factors that impact ERBE. RESULTS: The majority of individuals reported experiencing chronic pain in their back, neck, and shoulder areas. Engaging in physical activity, consuming dairy products, and attaining a higher level of education were found to be significantly associated with the adoption of ERBE p < 0.05. Among the various SCT constructs, observational learning, intention, and social support demonstrated the highest levels of sensitivity towards ERBE, with scores of 4.08, 3.82, and 3.57, respectively. However, it is worth noting that all SCT constructs exhibited a certain degree of sensitivity towards ERBE. CONCLUSIONS: The research findings demonstrate that all constructs within SCT are effective in identifying factors associated with ERBE among WwAL. The study also highlights the importance of considering education levels and variables related to healthy lifestyles when promoting ERBE in this specific population.