Balancing the benefits and risks of China's national salt iodization policy over 30 years using disability-adjusted life years (DALYs): a systematic review and meta-analysis.

Both insufficient and excessive iodine intake can lead to thyroid-related disorders. Although China has made progress in eliminating iodine deficiency over the past few decades, the incidence of thyroid cancer is increasing. Currently, there is a lack of relevant research on the tradeoff between the benefits and risks of salt iodization in China. In this study, we developed a method that combines the total probability algorithm and disease burden to evaluate the appropriate amount of salt iodization. Following the principle of minimizing the comprehensive disease burden and using the metabolic model of human iodine nutrition. Based on the average national iodine level in water, the optimal iodine content in Chinese salt is determined to be 17 mg/kg. However, iodine content in water is not evenly distributed in China. Approximately 3.23% of administrative villages have water iodine concentrations exceeding 80 ug/L, eliminating the need for iodine fortification in salt. Approximately 83.51% of administrative villages need to continue implementing the salt iodization policy, with the optimal iodine content in salt ranging from 15 to 18 mg/kg. In 13.16% of administrative villages, the iodine content in salt is determined based on the local water iodine concentration, ranging from 0 to 15 mg/kg. Our study cracks open a window of insight suggesting that the optimal iodine content for salt is lower than the existing benchmark dictated by the prevailing policy in China. Hence, there is an urgent need to refine and advance the iodine supplementation strategy in salt to pave the way for precision medicine and health-centric iodine supplementation strategies.

Metabolic signatures of population exposure to metal mixtures: A metabolome-wide association study.

Numerous studies have explored the health impacts of individual metal exposures, yet the effects of metal mixtures on human endogenous metabolism remain largely unexplored. We aimed to assess the serum metabolic signatures of people exposed to metal mixtures. Serum and urine samples were collected from 186 workers at a steel factory in Anhui, China, in September 2019. Inductively coupled plasma mass spectrometry was used to analyze the concentrations of 23 metal elements. The serum metabolome was determined by liquid chromatography-mass spectrometry (LC-MS). A metabolome-wide association study (MWAS) was performed across the metal exposures and metabolism using quantile g-computation modeling. Pathway enrichment analysis was performed using MetaboAnalyst. We identified 226 metabolites associated with metal mixtures, primarily involving lipid metabolism (glycerophospholipids, sphingolipids), amino acid metabolism (arginine and proline, alanine, aspartate and glutamate metabolism) and caffeine metabolic pathways. Exposure to metal mixtures is mainly associated with alterations in lipid metabolism and amino acid metabolism, particularly in the glycerophospholipid and arginine and proline metabolism pathways.

Educational inequalities in blood pressure across the adult life course: Evidence from a 20-year follow-up study.

AIM: High blood pressure (BP) is a key contributor to the burden of disease. This study aimed to assess: a) educational differences across the entire BP distribution, and b) educational differences in the trajectories of BP across the adult life course. METHOD: Longitudinal data from the Stockholm Diabetes Prevention Program was analysed using quantile regression and linear mixed effects models. Models were adjusted for age, sex, lifestyle, and BP medication. RESULTS: Lower educational level was associated with higher systolic BP (SBP) at all follow-up periods. Age and sex adjusted mean SBP was 2.49 (95% confidence interval (CI) 1.10, 3.87), 3.95 (95% CI 2.45, 5.45) and 2.61 (95% CI 1.09, 4.13) mmHg higher for people with pre-secondary education compared with post-secondary at baseline, 10 years and 20 years follow-up, respectively. Quantile regressions revealed that the inequalities could be observed across the entire BP continuum. Longitudinally analysed, people with pre-secondary education had 3.01 (95% CI 1.91-4.11) mmHg higher SBP than those with post-secondary education, age and sex adjusted. No significant convergence or divergence of the educational gaps in SBP was observed. Educational differences remained even after adjusting for lifestyle and BP medication. CONCLUSIONS: These results imply that public health interventions should aim to bring about distributional shifts in blood pressure, rather than exclusively focusing on hypertensive people, if they are to effectively minimize the educational disparities in blood pressure and its consequences.

Impaired sensitivity to thyroid hormone correlates to all-cause mortality in euthyroid individuals with chronic kidney disease.

BACKGROUND: This study aimed to investigate the association between central sensitivity to thyroid hormones and all-cause mortality in euthyroid patients with chronic kidney disease (CKD). METHODS: ​Data on thyroid function indicators and all-cause mortality for CKD patients were extracted from the NHANES database (2007-2012). Central sensitivities to thyroid hormones were mainly evaluated by Thyroid Feedback Quantile-based Index (TFQI). The Kaplan-Meier method, Cox proportional hazards regression model and subgroup analysis were performed to explore the potential associations between thyroid hormone sensitivity and all-cause mortality. RESULTS: A total of 1303 euthyroid CKD patients were enrolled in this study. After a median follow-up of 115 months, 503 participants died. The Kaplan-Meier analysis demonstrated significant variations in survival rates among different levels of TFQI (P = 0.0015). Cox regression analysis showed that increased levels of TFQI were independent risk factors for all-cause mortality after adjusting for multiple confounding factors (HR = 1.40, 95% CI 1.10-1.79, P = 0.007). Subgroup analysis did not reveal any significant variation in the association between TFQI and all-cause mortality between the subgroups assessed (P for interaction > 0.05). CONCLUSION: Our study suggests that impaired thyroid hormone sensitivity might be linked to increased mortality in euthyroid CKD patients. Further research is needed to confirm and explore this association.

Quantile regressions as a tool to evaluate how an exposure shifts and reshapes the outcome distribution: A primer for epidemiologists.

Quantifying how an exposure affects the entire outcome distribution is often important, e.g., for outcomes such as blood pressure which have non-linear effects on long-term morbidity and mortality. Quantile regressions offer a powerful way of estimating an exposure's relationship with the outcome distribution but remain underused in epidemiology. We introduce quantile regressions with a focus on distinguishing estimators for quantiles of the conditional and unconditional outcome distributions. We also present an empirical example in which we fit mean and quantile regressions to investigate educational attainment's association with later-life systolic blood pressure (SBP). We use data on 8,875 US-born respondents aged 50+ years from the US Health and Retirement Study. More education was negatively associated with mean SBP. Conditional and unconditional quantile regressions both suggested a negative association between education and SBP at all levels of SBP, but the absolute magnitudes of these associations were higher at higher SBP quantiles relative to lower quantiles. In addition to showing that educational attainment shifted the SBP distribution left-wards, quantile regression results revealed that education may have reshaped the SBP distribution through larger protective associations in the right tail, thus benefiting those at highest risk of cardiovascular diseases.

Does an environmental stringent policy really matter to achieve environmental sustainability in BRICS-T region? Evidence from novel method of moments quantile regression approach.

Amidst pressing global environmental challenges, exacerbated by climate change and the imminent threat of global warming, there is a critical need to assess the efficacy of environmental policies. This study centers its attention on the pivotal role of these policies in addressing environmental concerns. Specifically, our research aims to scrutinize the impact of stringent environmental policies on environmental quality under the theoretical underpinnings of environmental Kuznets curve. To achieve this objective, the study collected data from BRICS-T economies over the period of 1990-2020. This study employed the method of moments quantile regression technique for empirical analysis. Our study validates the presence of the Environmental Kuznets curve (EKC hypothesis). Empirical findings reveal the sustained significance of environmental stringency across all quantiles, demonstrating a positive correlation in lower quantiles and a negative correlation in higher quantiles. At lower quantiles, the impact is insignificant initially, but pronounced due to efficiency improvements induced by stringent policies. The effects became negative at middle quantiles, indicating stringent policies might encounter diminishing returns where policy measures start stabilizing ecological impacts. At higher quantiles, the influence of ESI remains significant, reflecting ongoing adaptations in larger economies with higher ecological footprints. This suggests the potential effectiveness of stringent regulatory measures in mitigating environmental impacts and reducing ecological footprints. The identified inverted U-shaped curve signifies that while stringent policies may not inherently enhance environmental health, beyond a certain threshold, they can indeed contribute to its improvement. Our policy recommendation advocates for the widespread adoption and promotion of such stringent measures to safeguard environmental health.

Single and mixed associations of composite antioxidant diet on triglyceride-glucose index.

BACKGROUND: Although the relationship between oxidative stress and insulin resistance (IR) has been established, the associations of the composite dietary antioxidant index (CDAI) and its components with the surrogate index of insulin resistance (IR), triglyceride-glucose index (TyG), is still not clear. METHODS: This study analyzed the cross-sectional data of the National Health and Nutrition Examination Survey (NHANES) from 2003 to 2018. Multivariate linear regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) were used to analyze the associations of the CDAI and its components with the TyG. In addition, subgroup analysis and several sensitivity analyses were conducted. RESULTS: A total of 14,673 participants with complete data were included, with a median age of 50 years and 7,257 women (49%). Multivariate linear regression showed that after full adjustment, the CDAI was significantly negatively associated with the TyG [ß: -0.005, 95% CI: (-0.008, -0.002), p = 0.002]. The model in which six nutrients were mutually corrected showed that vitamin E (per-SD increase) was most strongly associated with the TyG [ß: -0.062, 95% CI: (-0.074, -0.050), p < 0.0001]. In the WQS model, the WQS index of the antioxidant diet was negatively associated with the TyG (ß: -0.060; P < 0.0001). Similar effects were observed in the BKMR analysis. Notably, in the WQS and BKMR models, vitamin E became the most influential component. In addition, in the subgroup analysis, the association between the CDAI and the TyG in overweight or obese and diabetic populations was significantly weaker. CONCLUSION: Antioxidant diets, especially vitamin E, are significantly negatively correlated with TyG. This study emphasizes the important value of supplementing vitamin E to improve IR. However, patients with poor weight management and diabetes seem to benefit less from antioxidant diets.

Effect of combined exposure to phthalates and polycyclic aromatic hydrocarbons during early pregnancy on gestational age and neonatal size: A prospective cohort study.

Many studies have indicated that individual exposure to phthalates (PAEs) or polycyclic aromatic hydrocarbons (PAHs) affects pregnancy outcomes. However, combined exposure to PAEs and PAHs presents a more realistic situation, and research on the combined effects of PAEs and PAHs on gestational age and newborn size is still limited. This study aimed to assess the effects of combined exposure to PAEs and PAHs on neonatal gestational age and birth size. Levels of 9 PAE and 10 PAH metabolites were measured from the urine samples of 1030 women during early pregnancy from the Zunyi Birth Cohort in China. Various statistical models, including linear regression, restricted cubic spline, Bayesian kernel machine regression, and quantile g-computation, were used to study the individual effects, dose-response relationships, and combined effects, respectively. The results of this prospective study revealed that each ten-fold increase in the concentration of monoethyl phthalate (MEP), 2-hydroxynaphthalene (2-OHNap), 2-hydroxyphenanthrene (2-OHPhe), and 1-hydroxypyrene (1-OHPyr) decreased gestational age by 1.033 days (95 % CI: -1.748, -0.319), 0.647 days (95 % CI: -1.076, -0.219), 0.845 days (95 % CI: -1.430, -0.260), and 0.888 days (95 % CI: -1.398, -0.378), respectively. Moreover, when the concentrations of MEP, 2-OHNap, 2-OHPhe, and 1-OHPyr exceeded 0.528, 0.039, 0.012, and 0.002 µg/g Cr, respectively, gestational age decreased in a dose-response manner. Upon analyzing the selected PAE and PAH metabolites as a mixture, we found that they were significantly negatively associated with gestational age, birth weight, and the ponderal index, with 1-OHPyr being the most important contributor. These findings highlight the adverse effects of single and combined exposure to PAEs and PAHs on gestational age. Therefore, future longitudinal cohort studies with larger sample sizes should be conducted across different geographic regions and ethnic groups to confirm the impact of combined exposure to PAEs and PAHs on birth outcomes.

Quantile regression for longitudinal data with values below the limit of detection and time-dependent covariates-application to modeling carbon nanotube and nanofiber exposures.

BACKGROUND: In studies of occupational health, longitudinal environmental exposure, and biomonitoring data are often subject to right skewing and left censoring, in which measurements fall below the limit of detection (LOD). To address right-skewed data, it is common practice to log-transform the data and model the geometric mean, assuming a log-normal distribution. However, if the transformed data do not follow a known distribution, modeling the mean of exposure may result in bias and reduce efficiency. In addition, when examining longitudinal data, it is possible that certain covariates may vary over time. OBJECTIVE: To develop predictive quantile regression models to resolve the issues of left censoring and time-dependent covariates and to quantitatively evaluate if previous and current covariates can predict current and/or future exposure levels. METHODS: To address these gaps, we suggested incorporating different substitution approaches into quantile regression and utilizing a method for selecting a working type of time dependency for covariates. RESULTS: In a simulation study, we demonstrated that, under different types of time-dependent covariates, the approach of multiple random value imputation outperformed the other approaches. We also applied our methods to a carbon nanotube and nanofiber exposure study. The dependent variables are the left-censored mass of elemental carbon at both the respirable and inhalable aerosol size fractions. In this study, we identified some potential time-dependent covariates with respect to worker-level determinants and job tasks. CONCLUSION: Time dependency for covariates is rarely accounted for when analyzing longitudinal environmental exposure and biomonitoring data with values less than the LOD through predictive modeling. Mistreating the time-dependency as time-independency will lead to an efficiency loss of regression parameter estimation. Therefore, we addressed time-varying covariates in longitudinal exposure and biomonitoring data with left-censored measurements and illustrated an entire conditional distribution through different quantiles.

Associations of exposure to volatile organic compounds with sleep health and potential mediators: analysis of NHANES data.

Objective: The effect of environmental pollution on sleep has been widely studied, yet the relationship between exposure to volatile organic compounds (VOCs) and sleep health requires further exploration. We aimed to investigate the single and mixed effect of urinary VOC metabolites on sleep health and identify potential mediators. Methods: Data for this cross-sectional study was collected from the National Health and Nutrition Examination Surveys (NHANES) (2005-2006, 2011-2014). A weighted multivariate logistic regression was established to explore the associations of 16 VOCs with four sleep outcomes. Following the selection of important VOCs through the least absolute shrinkage and selection operator (LASSO) regression, principal component analyses (PCA), weight quantile sum (WQS), and Bayesian kernel machine regression (BKMR) analyses were conducted to explore the associations between exposure to single and mixed VOCs and sleep outcomes, as well as identify the most contributing components. A mediation analysis was performed to explore the potential effect of depression scores. Results: Of the 3,473 participants included in the study, a total of 618 were diagnosed with poor sleep patterns. In logistic regression analyses, 7, 10, 1, and 5 VOCs were significantly positively correlated with poor sleep patterns, abnormal sleep duration, trouble sleeping, and sleep disorders, respectively. The PCA analysis showed that PC1 was substantially linked to a higher risk of poor sleep patterns and its components. The WQS model revealed a positive association between VOC mixture of increased concentrations and poor sleep patterns [OR (95% CI): 1.285 (1.107, 1.493)], abnormal sleep duration [OR (95% CI): 1.154 (1.030, 1.295)], trouble sleeping [OR (95% CI): 1.236 (1.090, 1.403)] and sleep disorders [OR (95% CI): 1.378 (1.118, 1.705)]. The BKMR model found positive associations of the overall VOC exposure with poor sleep patterns, trouble sleeping, and sleep disorders. PCA, WQS, and BKMR models all confirmed the significant role of N-acetyl-S-(N-methylcarbamoyl)-l-cysteine (AMCC) in poor sleep patterns and its components. The depression score was a mediator between the positive VOC mixture index and the four sleep outcomes. Conclusion: Exposure to single and mixed VOCs negatively affected the sleep health of American population, with AMCC playing a significant role. The depression score was shown to mediate the associations of VOC mixtures with poor sleep patterns and its components.

Parent-child interaction, appetite self-regulation, and BMIz in Chinese preschoolers: a mediation analysis.

OBJECTIVES: Research on parent-child interaction (PCI) and its impact on children's weight status is a thriving study area. However, their potential pathways have not been established. This study investigated the association between PCI and children's body-mass index z score (BMIz) examining the role of appetite self-regulation (ASR) as a mediator. STUDY DESIGN: Mediation analysis. METHODS: We included children from 33 kindergartens in Wuhan with parents' consent, measuring children's height and weight, and calculating BMIz. To assess the PCI quality, we utilized the Brigance Parent-Child Interactions Scale. Additionally, children's ASR was tested by satiety responsiveness (SR) and food responsiveness (FR) using the Children's Eating Behavior Questionnaire. Quantile regression was employed to examine the PCI-BMIz association, while mediation analysis was conducted to explore ASR's mediating effect on the relationship between PCI and BMIz. RESULTS: Of 3973 children (53.88% boys) included in the analysis, the mean BMIz was 0.24 ± 1.13. The results revealed that children with poorer PCI quality have higher BMIz across all selected BMIz percentiles, except for the 5th percentile. Furthermore, these associations were significant across most percentiles, whether for boys or girls. Mediation analysis suggested that these associations were partially mediated by children's ASR (indFR = -0.026, PFR < 0.001; indSR = -0.058, PSR < 0.001), with stronger effects observed among boys. CONCLUSION: The variation in how strongly BMIz was linked to PCI across different percentiles suggests that children with poorer PCI have higher BMIz. The link is partially mediated through children's ASR. It's important to pay attention to the PCI quality in children with higher BMIz levels, especially in boys.

Association of volatile organic compound levels with chronic obstructive pulmonary diseases in NHANES 2013-2016.

Volatile organic compounds (VOCs) represent a significant component of air pollution. However, studies evaluating the impact of VOC exposure on chronic obstructive pulmonary disease (COPD) have predominantly focused on single pollutant models. This study aims to comprehensively assess the relationship between multiple VOC exposures and COPD. A large cross-sectional study was conducted on 4983 participants from the National Health and Nutrition Examination Survey. Four models, including weighted logistic regression, restricted cubic splines (RCS), weighted quantile sum regression (WQS), and the dual-pollution model, were used to explore the association between blood VOC levels and the prevalence of COPD in the U.S. general population. Additionally, six machine learning algorithms were employed to develop a predictive model for COPD risk, with the model's predictive capacity assessed using the area under the curve (AUC) indices. Elevated blood concentrations of benzene, toluene, ortho-xylene, and para-xylene were significantly associated with the incidence of COPD. RCS analysis further revealed a non-linear and non-monotonic relationship between blood levels of toluene and m-p-xylene with COPD prevalence. WQS regression indicated that different VOCs had varying effects on COPD, with benzene and ortho-xylene having the greatest weights. Among the six models, the Extreme Gradient Boosting (XGBoost) model demonstrated the strongest predictive power, with an AUC value of 0.781. Increased blood concentrations of benzene and toluene are significantly correlated with a higher prevalence of COPD in the U.S. population, demonstrating a non-linear relationship. Exposure to environmental VOCs may represent a new risk factor in the etiology of COPD.

Association between brominated flame retardants (PBDEs and PBB153) exposure and hypertension in U.S. adults: results from NHANES 2005-2016.

BACKGROUND: Brominated Flame Retardants (BFRs) have attracted widespread concern due to their environmental persistence and potential toxicity. This study aims to examine the association between BFRs exposure and hypertension. METHODS: We used data from the National Health and Nutrition Examination Survey (NHANES) spanning 2005 to 2016 for the cross-sectional analysis. To evaluate the individual and combined impacts of BFRs exposure on hypertension, we utilized multivariate models, including generalized additive models, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) models. RESULTS: 9882 individuals (48% male) aged ≥ 20 were included in the final analysis, of whom 4114 had hypertension. After controlling for potential covariates, higher serum concentrations of PBDE100 (OR: 1.26; 95% CI: 1.01, 1.57) and PBDE153 (OR: 1.50; 95% CI: 1.18, 1.88) were significantly associated with hypertension. A nonlinear relationship between PBDE28 and hypertension was observed (P = 0.03). Moreover, BFRs mixture were positively associated with the prevalence of hypertension in both the WQS (ß:1.09; 95% CI: 1.02, 1.17; P = 0.02) and BKMR models. CONCLUSION: Our study suggested that BFRs exposure is positively associated with hypertension in the general population. To confirm this association and elucidate the mechanisms, further research is required.

Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure.

This paper considers estimating functional-coefficient models in panel quantile regression with individual effects, allowing the cross-sectional and temporal dependence for large panel observations. A latent group structure is imposed on the heterogenous quantile regression models so that the number of nonparametric functional coefficients to be estimated can be reduced considerably. With the preliminary local linear quantile estimates of the subject-specific functional coefficients, a classic agglomerative clustering algorithm is used to estimate the unknown group structure and an easy-to-implement ratio criterion is proposed to determine the group number. The estimated group number and structure are shown to be consistent. Furthermore, a post-grouping local linear smoothing method is introduced to estimate the group-specific functional coefficients, and the relevant asymptotic normal distribution theory is derived with a normalisation rate comparable to that in the literature. The developed methodologies and theory are verified through a simulation study and showcased with an application to house price data from UK local authority districts, which reveals different homogeneity structures at different quantile levels.

Associations of sugary beverage consumption with chronic obstructive pulmonary disease, asthma, and asthma-chronic obstructive pulmonary disease overlap syndrome: a prospective cohort study.

BACKGROUND: The associations between specific types of sugary beverages and major chronic respiratory diseases remain relatively unexplored. OBJECTIVES: This study aimed to investigate the associations of sugar-sweetened beverages (SSBs), artificially sweetened beverages (ASBs), and natural juices (NJs) with chronic obstructive pulmonary disease (COPD), asthma, and asthma-chronic obstructive pulmonary disease overlap syndrome (ACOS). METHODS: This prospective cohort study included 210,339 participants from the UK Biobank. Sugary beverage intake was measured in units (glasses/cans/cartons/250 mL) through 24-h dietary questionnaires. Logistic regression and Cox proportional hazards models were used to analyze the prevalence and incidence, respectively. Quantile G-computation was used to estimate the joint associations and relative contributions of the 3 types of sugary beverages. RESULTS: Over a median follow-up of 11.6 y, 3491 participants developed COPD, 4645 asthma, and 523 ACOS. In prevalence analysis, certain categories of SSB and NJ consumption were associated with increased asthma prevalence, while high ASB consumption (>2 units/d) was linked to higher risks of all 3 outcomes. In incidence analysis, high SSB consumption (>2 units/d) was associated with incident COPD (hazard ratio [HR]: 1.53; 95% confidence interval [CI]: 1.19, 1.98) and asthma (HR: 1.22; 95% CI: 0.98, 1.52). Dose‒response relationships were observed for ASB consumption with all 3 outcomes (continuous HR: 1.98; 95% CI: 1.36, 2.87, for COPD; continuous HR: 1.65; 95% CI: 1.24, 2.20, for asthma; and continuous HR: 2.84; 95% CI: 1.20, 6.72, for ACOS). Moderate NJ consumption (>0-1 unit/d) was inversely associated with COPD (HR: 0.89; 95% CI: 0.82, 0.97), particularly grapefruit and orange juice. Joint exposure to these beverages (per unit increase) was associated with COPD (HR: 1.15; 95% CI: 1.02, 1.29) and asthma (HR: 1.16; 95% CI: 1.06, 1.27), with ASBs having greater positive weights than SSBs. CONCLUSIONS: Consumption of SSBs and ASBs was associated with increased risks of COPD, asthma, and potentially ACOS, whereas moderate NJ consumption was associated with reduced risk of COPD, depending on the juice type.

Balancing the maintenance strategies to making decisions using Monte Carlo method.

This study aims to develop comprehensive maintenance strategies tailored to enhance the dependability, performance, and lifespan of critical assets within industrial and organizational settings. By integrating proactive, preventive, predictive, and corrective maintenance tactics, our strategy seeks to minimize downtime, reduce costs, and optimize asset performance. Drawing from extensive case studies across various industrial sectors, our research utilizes robust data analysis to inform strategy development. We employ mathematical cost models and simulations using the Monte Carlo Method in MATLAB to evaluate the performance and robustness of different maintenance strategies, including time-based and condition-based approaches. Our findings demonstrate that a holistic maintenance approach significantly improves operational efficiency and asset longevity. Specifically, our analysis reveals that integrated maintenance strategies lead to reduced downtime, lower maintenance costs, and enhanced asset reliability. Policy implications of our research suggest that organizations should adopt integrated maintenance strategies to enhance asset reliability and performance, ultimately achieving sustained operational excellence. By emphasizing the importance of proactive maintenance measures alongside traditional reactive approaches, organizations can effectively manage their critical assets, leading to improved operational outcomes and long-term success.-Integration of proactive, preventive, predictive, and corrective maintenance tactics-Evaluation of performance and robustness through mathematical cost models-Application of the Monte Carlo Method in MATLAB for comparative analysis.

Development of American Association for Thoracic Surgery Quality Gateway Outcome Models, Analytics, and Visualizations for Quality Assurance.

OBJECTIVE: To develop comprehensive quality assurance models for procedural outcomes after adult cardiac surgery. METHODS: Based on 52,792 cardiac operations in adults performed in 19 hospitals of 3 high-performing hospital systems, models were developed for operative mortality (n=1,271), stroke (n=895), deep sternal wound infection (n=122), prolonged intubation (6,182), renal failure (1,265), prolonged postoperative stay (n=5,418), and reoperations (n=1,693). Random forest quantile classification, a method tailored for challenges of rare events, and model-free variable priority screening were used to identify predictors of events. RESULTS: A small set of preoperative variables was sufficient to model procedural outcomes for virtually all cardiac operations, including older age; advanced symptoms; left ventricular, pulmonary, renal, and hepatic dysfunction; lower albumin; higher acuity; and greater complexity of the planned operation. Geometric mean performance ranged from .63 to .76. Calibration covered large areas of probability. Continuous risk factors provided high information content, and their association with outcomes was visualized with partial plots. These risk factors differed in strength and configuration among hospitals, as did their risk-adjusted outcomes according to patient risk as determined by counterfactual causal inference within a framework of virtual (digital) twins. CONCLUSIONS: Using a small set of variables and contemporary machine-learning methods, comprehensive models for procedural operative mortality and major morbidity after adult cardiac surgery were developed based on data from 3 exemplary hospital systems. They provide surgeons, their patients, and hospital and hospital systems with 21st century tools for assessing their risks compared to these advanced hospital systems and improving cardiac surgery quality.

The joint impact of PM2.5 constituents on the risk of cerebrovascular diseases hospitalization: A large community-based cohort study.

Air pollution poses significant health risks to urban areas, with limited focus on the chronic association of PM2.5 and its constituents on cerebrovascular diseases (CERs), especially regarding the joint associations. This study explores the individual and joint associations between PM2.5 constituents and CER hospitalization risks through a cohort analysis of 36,271 adults in the Pearl River Delta, South China, from 2015 to 2020. Cox proportional hazards regression and quantile-based g-computation models were used to quantify the individual and joint associations of annual mean concentrations of PM2.5 constituents with hospitalization for CERs. 1151 participants were hospitalized due to CERs during the five-year follow-up period. Joint associations analyses identified that one quartile increase in co-exposure may result in hazard ratios of 1.530 (1.441-1.623), 1.840 (1.710-1.980), and 1.609 (1.491-1.737) for CERs, total, and ischemic stroke hospitalization, respectively. The adverse effect was primarily driven by organic matter and chlorine. Men, those with a history of tobacco or alcohol use or with low residential greenness, were more susceptible to CERs hospitalization following PM2.5 constituents co-exposure. Upcoming strategies should focus on monitoring and regulating PM2.5 constituents, encouraging healthy lifestyles, and enhancing urban greenery.

Association of Life's Essential 8 with metabolic dysfunction-associated steatotic liver disease (MASLD), a cross-sectional study from the NHANES 2003-2018.

There is limited research on the relationship between Life's Essential 8 (LE8) score and metabolic dysfunction-associated steatotic liver disease (MASLD). Our aim is to investigate the relationship between overall lifestyle assessed by LE-8 score and MASLD in a nationally representative sample. We employed the LE8 score to comprehensively evaluate cardiovascular health, the assessment of MASLD primarily utilized the Fatty Liver Index. The weighted logistic regression models, restrictive cubic splines (RCS), subgroup analyses and the weighted quantile sum (WQS) regression were used to evaluate the relationship between the cardiovascular health and MASLD. Logistic regression models revealed that higher LE8 scores were associated with lower odds of having MASLD. The RCS revealed a significant nonlinear dose-response relationship between LE8 scores and MASLD. The WQS regression model indicated that blood glucose contributed the most to the risk of MASLD. The subgroup analysis indicates that there are significant differences in this association across age, educational level, and poverty income ratio. Our study suggests that an inverse correlation between LE8 and the risk of MASLD. Our findings underscore the utility of the LE8 algorithm in MASLD risk assessment and provide support for MASLD prevention through the promotion of healthy lifestyles.

Can forests realize the carbon neutrality dream? Evidence from a global sample.

The enlarge in economic activities and the urban population at the global level has brought about an increase in the demand for energy, food, and natural resources, as well as an exacerbation in global climate change concerns. In this respect, it is important to ensure the balance between global climate change and global economic activities. Therefore, a wide literature has emerged that searches for alternative solutions to improve climate change and carbon dioxide (CO2) emissions. The majority of existing studies emphasize the importance of renewable energy sources in environmental improvement efforts. Few studies highlight the importance of forestation in environmental improvement efforts, highlighting the non-linear effects of forestation. To fill this gap, this study uses panel data from 181 countries between 1990 and 2022 and evaluates the non-linear impact of economic growth, forest extent, energy efficiency, and urban growth on per capita CO2 emissions using a dynamic panel threshold and dynamic panel quantile threshold methods. Furthermore, we extend the model and conduct robustness tests examining the non-linear threshold effects of renewable and non-renewable energy consumption on per capita CO2 emissions. Our findings provide pieces of evidence that forest extents are an alternative solution to renewable energy use and energy efficiency in environmental improvement efforts.