Bayesian Regression Research Articles

Intelligent Helicopter Turbine Engine Fault Diagnosis Using Multi-Head Attention

A turbine engine provides power to the helicopter, enabling the helicopter to travel and hover in the air. Since the rotorcraft operates at high altitudes, ensuring safety and maintaining a healthy operational status are crucial at all times. Therefore, a prognostics and health management (PHM) system for the turbine engine must be implemented to predict any anomalies or faults to prevent catastrophic accidents. This research proposes a novel fault diagnosis method for helicopter turbine engines based on operational data acquired from actual aircraft. First, the proposed method predicts engine torque using other operational data while accounting for uncertainty. A Bayesian regression approach is employed to predict the engine torque. The torque margin, defined as the difference between the actual torque and the estimated torque, is then used to diagnose engine faults. Specifically, a multi-head attention mechanism is incorporated to capture interactions between various engine parameters. Additionally, domain adaptation techniques are applied to enhance the model's generalization performance, ensuring robustness across diverse operating conditions. The proposed method is validated using seven different datasets, each acquired from a helicopter engine. Four datasets were used for training, while the remaining three were allocated for testing and validation. The results indicated that the proposed method accurately predicted torque. Furthermore, the fault diagnosis showed promising results, leading to a 3rd-place finish in the 2024 PHM Society Data Challenge in terms of validation score.

Population-based cross-sectional survey of cervical cancer screening prevalence and socio-demographic correlates in Bangladeshi women.

Cervical cancer, albeit preventable, is the second-most deadly gynecological cancer in developing nations. Little is known about cervical cancer among Bangladeshi women. This study aims to estimate the prevalence of cervical cancer screening and demographic correlates to identify potential variabilities in screening rates among different demographic groups and regions. This study used secondary data from the WHO STEPS 2018 Survey. We used Bayesian regression to perform the bivariate analyses between the outcome and each explanatory factor, as it generates more acceptable results and improves parameter estimates. The top-ranked socio-demographic factors were identified using a two-step cluster analysis. This method determines the relevance of predictor variables and automatically establishes the number of clusters. The prevalence of Bangladeshi women who had ever been screened for cervical cancer was 6.2%. In the adjusted model, women with the following socio-demographic factors had a higher likelihood of developing cervical cancer: being 18-29 years old (AOR = 3.3, 95% CI: 0.24, 15.27) or 45-59 years old (AOR = 2.8, 95% CI: 1.22, 6.0), currently married (AOR = 2.3, 95% CI: 1.36, 3.70), and employed (AOR = 2.4, 95% CI: 1.40, 4.06). Women in the Barisal division were found to have higher odds of being screened for cervical cancer (AOR = 21, 95% CI: 0.66, 121.97). Cluster analysis found residence status predisposes women to cervical cancer screening. There is a significant potential for substantial reductions in the burden of cervical cancer in Bangladesh by strengthening the application of cervical cancer screening. Future studies should examine how socioeconomic status, culture, and healthcare access affect cervical cancer screening trends for different divisions in Bangladesh. An independent national cancer registry is urgently needed to evaluate screening trends and outcomes.

Impact of active case finding for tuberculosis with mass chest X-ray screening in Glasgow, Scotland, 1950-1963: An epidemiological analysis of historical data.

Community active case finding (ACF) for tuberculosis was widely implemented in Europe and North America between 1940 and 1970, when incidence was comparable to many present-day high-burden countries. Using an interrupted time series analysis, we analysed the effect of the 1957 Glasgow mass chest X-ray campaign to inform contemporary approaches to screening. Case notifications for 1950 to 1963 were extracted from public health records and linked to demographic data. We fitted Bayesian multilevel regression models to estimate annual relative case notification rates (CNRs) during and after a mass screening intervention implemented over 5 weeks in 1957 compared to the counterfactual scenario where the intervention had not occurred. We additionally estimated case detection ratios and incidence. From 11 March 1957 to 12 April 1957, 714,915 people (622,349 of 819,301 [76.0%] resident adults ≥15 years) were screened with miniature chest X-ray; 2,369 (0.4%) were diagnosed with tuberculosis. Pre-intervention (1950 to 1956), pulmonary CNRs were declining at 2.3% per year from a CNR of 222/100,000 in 1950. With the intervention in 1957, there was a doubling in the pulmonary CNR (RR: 1.95, 95% uncertainty interval [UI] [1.81, 2.11]) and 35% decline in the year after (RR: 0.65, 95% UI [0.59, 0.71]). Post-intervention (1958 to 1963) annual rates of decline (5.4% per year) were greater (RR: 0.77, 95% UI [0.69, 0.85]), and there were an estimated 4,599 (95% UI [3,641, 5,683]) pulmonary case notifications averted due to the intervention. Effects were consistent across all city wards and notifications declined in young children (0 to 5 years) with the intervention. Limitations include the lack of data in historical reports on microbiological testing for tuberculosis, and uncertainty in contributory effects of other contemporaneous interventions including slum clearances, introduction of BCG vaccination programmes, and the ending of postwar food rationing. A single, rapid round of mass screening with chest X-ray (probably the largest ever conducted) likely resulted in a major and sustained reduction in tuberculosis case notifications. Synthesis of evidence from other historical tuberculosis screening programmes is needed to confirm findings from Glasgow and to provide insights into ongoing efforts to successfully implement ACF interventions in today's high tuberculosis burden countries and with new screening tools and technologies.

Evaluation of Bayesian Linear Regression models for gene set prioritization in complex diseases.

Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the model's performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Comparative analyses with MAGMA (Multi-marker Analysis of GenoMic Annotation) demonstrate BLR's superior performance, especially in highly overlapped gene sets. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR model's ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits.

Mapping out the current and ideal weekly distribution of remote work in France after COVID-19

The recent rise of telework brings new debates on the impacts on travel behavior and transportation systems. One key issue concerns the weekly number and distribution of remote workdays, which affects transportation networks and their congestion levels. Using data from an online survey conducted in spring 2022 in France, Bayesian regressions and a multilevel model are used to analyze employee’s current and ideal weekly number and distribution of full-day home-based remote workdays. Firstly, the findings highlight a latent demand for more remote workdays, especially among people working remotely 1 day a week. Secondly, data show a concentration of current and ideal remote workdays on Fridays, followed by Mondays and Wednesdays. Thirdly, and finally, our results show that employer’s strategies and the nature of the sector (public or private) both contribute to explain the current and ideal weekly distribution of remote workdays. These results indicate that remote work should continue to develop and be distributed unevenly over the days of the week. This uneven distribution should impact travel behavior and traffic congestion.

Bayesian additive regression trees for predicting childhood asthma in the CHILD cohort study

BackgroundAsthma is a heterogeneous disease that affects millions of children and adults. There is a lack of objective gold standard diagnosis that spans the ages; instead, diagnoses are made by clinician assessment based on a cluster of signs, symptoms and objective tests dependent on age. Yet, there is a clear morbidity associated with chronic asthma symptoms. Machine learning has become a popular tool to improve asthma diagnosis and classification. There is a paucity of literature on the use of Bayesian machine learning algorithms to predict asthma diagnosis in children. This paper develops a prediction model using the Bayesian additive regression trees (BART) and compares its performance to various machine learning algorithms in predicting the diagnosis of childhood asthma.MethodsClinically relevant variables collected at or before 3 years of age from 2794 participants in the CHILD Cohort Study were used to predict physician-diagnosed asthma at age 5. BART and six other commonly used machine learning algorithms, namely adaptive boosting, logistic regression, decision tree, neural network, random forest, and support vector machine were trained. Measures of performance including sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were calculated. The confidence intervals were calculated using Bootstrapping samples. Important predictors and interaction effects associated with asthma were also identified using BART.ResultsBART, logistic regression and random forest showed the highest area under the ROC curve compared to other machine learning algorithms. Based on BART, recurrent wheeze, respiratory infection and food sensitization at 3 years of age were the most important predictors. The three most important interaction effects were found to be interaction terms of respiratory infection at 3 years and recurrent wheezing at 3 years, maternal asthma and paternal asthma, and maternal wheezing and inhalant sensitization of child at 3 years.ConclusionsBART demonstrated promising prediction performance when compared to other machine learning algorithms. Future research could validate the BART in an external cohort to evaluate its reliability and generalizability.

An Updated Framework for Modeling White-Tailed Deer (Odocoileus virginianus) Habitat Quality in Illinois, USA.

White-tailed deer (Odocoileus virginianus) are a cervid species found mostly in the Americas. Managing white-tailed deer requires understanding their relationship with the environment, which was characterized by Roseberry and Woolf (Wildlife Society Bulletin 1, 1998, 252) for all counties in Illinois, USA, who incorporated habitat quantity and quality in a deer habitat suitability index. However, this index was based on satellite imagery from 1996 and did not explore the smaller spatial scales used by deer. Our study addressed these gaps by developing a deer land cover utility (LCU) score for each TRS (township, range, and section), township, and county in Illinois based on the methodology outlined in Roseberry and Woolf (Wildlife Society Bulletin 1, 1998, 252) but using data from the National Land Cover Database (2001-2021). These deer LCU scores were validated against minimum deer population data using Bayesian regression with additional covariates relevant to hunting and deer density. These models performed well with Bayesian R 2 values of 0.501 (TRS), 0.5 (township), and 0.969 (county). The regression coefficients for the deer LCU scores were statistically significant (95% credibility interval not containing 0) and positive at the TRS, township, and county levels, reflecting the expected relationship between minimum deer density and deer LCU. Predictions made by these regression models on new data were accurate, with the median absolute difference between the true and predicted values being 0.398 deer/km2 for TRS', 0.085 deer/km2 for townships, and 0.066 deer/km2 for counties. This deer LCU could be used in other studies about deer in Illinois or studies in which deer are a relevant factor such as investigations about deer disease or tick distribution. This modeling approach could also be adapted to different wild species, locations, and/or time periods for which land cover data is available.

Active Inference Models of AV Takeovers: Relating Model Parameters to Trust, Situation Awareness, and Fatigue.

Our objectives were to assess the efficacy of active inference models for capturing driver takeovers from automated vehicles and to evaluate the links between model parameters and self-reported cognitive fatigue, trust, and situation awareness. Control transitions between human drivers and automation pose a substantial safety and performance risk. Models of driver behavior that predict these transitions from data are a critical tool for designing safer, human-centered, systems but current models do not sufficiently account for human factors. Active inference theory is a promising approach to integrate human factors because of its grounding in cognition and translation to a quantitative modeling framework. We used data from a driving simulation to develop an active inference model of takeover performance. After validating the model's predictions, we used Bayesian regression with a spike and slab prior to assess substantial correlations between model parameters and self-reported trust, situation awareness, fatigue, and demographic factors. The model accurately captured driving takeover times. The regression results showed that increases in cognitive fatigue were associated with increased uncertainty about the need to takeover, attributable to mapping observations to environmental states. Higher situation awareness was correlated with a more precise understanding of the environment and state transitions. Higher trust was associated with increased variance in environmental conditions associated with environmental states. The results align with prior theory on trust and active inference and provide a critical connection between complex driver states and interpretable model parameters. The active inference framework can be used in the testing and validation of automated vehicle technology to calibrate design parameters to ensure safety.

Triglyceride-glucose Index and Its combination with obesity indicators mediating the association between 2-hydroxyfluorene and the prevalence of cardiovascular disease: Evidence from the NHANES (2005–2018)

BackgroundPolycyclic aromatic hydrocarbons (PAHs) were increasingly recognized as a group of environmental pollutants associated with various health issues. The rise in their prevalence in the environment was concerning. Yet, the effects of PAH exposure on cardiovascular diseases (CVDs) were still not well understood. PurposeThis study investigated the impact of exposure to 2-hydroxyfluorene on CVDs prevalence, with a special focus on the mediating role of triglyceride-glucose (TyG) index and its combination with obesity indicators. MethodUsing National Health and Nutrition Examination Survey (NHANES) data from 2005 to 2018, this study assessed how 2-hydroxyfluorene affects CVDs prevalence through various statistical techniques. The investigation began with restricted cubic spline (RCS) analysis to explore the relationship between 2-hydroxyfluorene levels and CVDs prevalence. Logistic regression was then used to examine associations within PAH mixtures, alongside the Bayesian Kernel Machine Regression (BKMR) model. Furthermore, the Quantile G-Computation (QG-comp) model was used to evaluate the influence of weights and directions. A mediation analysis was also performed to assess the mediating role of TyG-related indicators on the relationship between 2-hydroxyfluorene and CVDs prevalence. Besides, the association between 2-hydroxyfluorene and the prevalence of each specific CVDs, congestive heart failure, myocardium infarction, angina pectoris and coronary heart disease, was also assessed and so was the mediated effect. Finally, the subgroup analysis was conducted to assess the association in each specific subgroup. ResultThe study, involving 3645 participants, found a significant positive association between 2-hydroxyfluorene exposure and CVD (OR (95 %CI) = 115.8013 (5.0521 ∼ 2654.3248), P=0.0029), with 2-hydroxyfluorene showing the positive contribution to CVDs prevalence within the phthalate mixture. The positive association also existed between 2-hydroxyfluorene and the prevalence of each specific CVDs. TyG and TyGWC (Triglyceride Glucose-Waist Circumference) were identified as mediators in the link between 2-hydroxyfluorene exposure and the prevalence of total CVD and each specific CVD, while TyGBMI (Triglyceride Glucose-Body Mass Index) can only mediate the association between 2-hydroxyfluorene and the prevalence of total CVD, congestive heart failure and angina pectoris ConclusionThe findings highlighted a significant association between 2-hydroxyfluorene exposure and CVDs prevalence, with TyG-related indicators acting as mediators.

Exposure to alkylphenols during early pregnancy and the risk of gestational diabetes mellitus: Fetal sex-specific effects

Alkylphenols (APs) may cause gestational diabetes mellitus (GDM) in pregnant women by impairing glucose metabolism through endocrine disruption. However, the current literature has limited epidemiological evidence on the association between APs exposure and the risk of GDM, especially the lack of evidence on joint exposure. Thus, we evaluated the effect of exposure to APs during early pregnancy on the risk of GDM. The study involved 2035 pregnant women from Guangxi Zhuang Birth Cohort (GZBC) in China. Poisson regression model, restricted cubic spline (RCS), Bayesian kernel machine regression (BKMR), and quantile g-computation (Qgcomp) were conducted to evaluate the effects of serum APs levels on the risk of GDM in pregnant women. For each Ln-unit increase in the serum nonylphenol (NP) of pregnant women, the adjusted relative risk (RR) for GDM risk was 1.12 (95 % CI: 1.00, 1.24). After sex stratification, the effect was more pronounced among pregnant women carrying female fetuses（RR=1.22; 95 % CI: 1.09, 1.38). The serum 4-t-octylphenol (4-T-OP) of the medium-exposure (adjusted RR = 3.25: 95 % CI: 1.30, 8.12) and high-exposure groups (adjusted RR = 2.90: 95 % CI: 1.15, 7.31) were related to a significantly increased risk of GDM in pregnant women carrying female fetuses only when compared to the low-exposure group. A reverse U-shaped nonlinear association was found between 4-n-octylphenol (4-N-OP) and 4-n-nonylphenol (4-N-NP) concentrations and GDM risk, and it was more susceptible in pregnant women carrying female fetuses. The Qgcomp and BKMR models showed that exposure to APs mixtures was correlated with an elevated risk of GDM in pregnant women carrying female fetuses (adjusted OR = 1.90: 95 % CI: 1.07, 3.38). Exposure to APs during early pregnancy may have potential fetal sex-specific effects on the risk of GDM, with pregnant women carrying female fetuses being more susceptible.

The association between N-nitrosamines exposure and lipid metabolism in the high incidence area of esophageal cancer: A case-control analysis

Environmental carcinogens such as N-nitrosamines are high-risk factors for the development of esophageal cancer (EC). However, the association between nitrosamines exposure and lipid metabolism disorders in human EC remained largely obscure. Therefore, we conducted a population-based case-control study established with esophageal inflammation (BCH), esophageal heterotrophic hyperplasia (DYS), patients with primary EC and matched controls in high prevalence area of EC in China. Our prospective work investigated the joint and independent effects of N-nitrosamines co-exposure on the risk of EC development. Lipidomics analysis was employed to screen differential lipid biomarkers in serum, and the mediating effects of key lipid metabolites in the association between nitrosamines exposure and EC were evaluated. After adjustment for confounders, N-nitrosodimethylamine (NDMA, 1.79 (95% CI: 1.35, 2.39)), N-nitrosodi-n-propylamine (NDPA, 1.55 (95%CI: 1.15, 2.09)), N-nitrosodiethylamine (NDEA, 1.82 (95%CI: 1.36, 2.45)), N-nitrosodibutylamine (NDBA, 1.60 (95%CI: 1.20, 2.13)), N-nitrosomethylethylamine (NMEA, 1.81 (95%CI: 1.36, 2.41)) and N-nitrosomorpholine (NMOR, 1.84 (95%CI: 1.38, 2.45)) exposure all elevated the risks of EC development. The Bayesian kernel machine regression (BKMR) with hierarchical variable selection showed significant positive joint associations of urinary NDMA, NDPA, NMOR, NDEA and EC development, when all nitrosamines were at the 55th percentiles or above, compared with the median. Lipidomic screening of serum samples from the stages of BCH, DYS and EC suggested the perturbation in the biosynthesis of unsaturated fatty acid metabolism pathway, of which myristic acid (FFA 14:0), palmitoleic acid (FFA 16:1), docosahexaenoic acid (FFA 22:6) exerted remarkable mediation effects in the association between N-nitrosamines exposure and EC development. These findings provided new sights for screening early lipid biomarkers and intervention targets in human EC.

Climatic drought and trophic disruption in an endemic subalpine Hawaiian forest bird

Overexploitation, habitat conversion, and introduced species have caused unprecedented extinctions and heavily degraded native bird populations in island ecosystems. In the Hawaiian Islands, stemming these losses has proven difficult as the highly specialized avifauna are often impacted – among other things – by poorly understood trophic disruptions as well as persistent climatic shifts. Here we investigate these dynamics by examining the trophic positions of 7 different taxa of producers and consumers across the last century in the subalpine māmane (Sophora chrysophylla) forest ecosystem on the island of Hawaiʻi. From museum collections and contemporary sampling, we analyzed the stable nitrogen (δ15N) values of producers and consumers to identify trophic and source amino acids and derive trophic enrichment constants specific to this food web. This enables us to reconstruct the diet of the palila (Loxioides bailleui), a critically endangered finch whose population recently declined 90 %. Our results show that from 1890 to 2006, the palila trophic position declined from 2.6 to 2.2, with cascading implications for its diet. Bayesian mixing model reconstructions indicate that palila trophic position changes likely arose from a 76 % decline (69.3 % to 16.6 % of diet) in the consumption of native moth caterpillars, and a 172 % increase (30.7 % to 83.4 %) in native plants. From the available ecosystem variables, exploratory Bayesian multiple regressions selected surface temperature changes, and the interactions of surface temperatures with drought and caterpillar parasitism as the primary drivers of these trophic changes. Despite the predicted increases of warming and drought, management interventions may build resiliency in this unique island ecosystem.

Associations of mixed metals exposure with cognitive impairment risk: a cross-sectional study in Chinese adults.

Associations between exposure to single metals and cognitive impairment or related outcomes have been reported in many previous studies. However, co-exposure to more than one metal is common situation. In recent years, studies on the effects of exposure to multiple metals on cognitive impairment or related outcomes have increased, but remain very limited, with a focus on populations with occupational exposure to metals, children, and adolescents. The potential relationships between exposure to metal mixtures and risk of cognitive impairment in adults remain to be clarified. To determine the associations between blood metal mixtures and cognitive impairment risk. Inductively coupled plasma mass spectrometry (ICP-MS) was utilized to detect the blood levels of lead (Pb), iron (Fe), copper (Cu), calcium (Ca), magnesium (Mg), and zinc (Zn). Multivariable logistic regression and Bayesian kernel machine regression (BKMR) models were employed to assess the relationships of exposure to these blood metal mixtures with the risk of cognitive impairment. It was found that four metals (Pb, Fe, Cu, and Mg) were positively correlated with cognitive impairment in each single metal model. The association of Pb and Cu remained significant after adjusting for these six metals, with the odds ratios (95% confidence intervals) in the highest quartiles of 9.51 (4.41-20.54, p-trend <0.01) and 4.87 (2.17-10.95, p-trend <0.01), respectively. The BKMR models indicated that co-exposure levels of Ca, Cu, Fe, Mg, Pb, and Zn were related to increased risk of cognitive impairment at ≥25th percentile compared with median, and Pb and Cu mainly contributed to the joint effect. In addition, the interaction effects of Mg and Pb/Pb and Cu on the risk of cognitive impairment were observed. Co-exposure of six metals (Pb, Fe, Cu, Ca, Mg, and Zn) increased the risk of cognitive impairment in Chinese adults, with Pb and Cu likely to have greater impact. Potential interaction effects of Mg and Pb, Pb and Cu on the risk of cognitive impairment may exist.

Intersecting social and environmental determinants of multidrug-resistant urinary tract infections in East Africa beyond antibiotic use

The global health crisis of antibacterial resistance (ABR) poses a particular threat in low-resource settings like East Africa. Interventions for ABR typically target antibiotic use, overlooking the wider set of factors which drive vulnerability and behaviours. In this cross-sectional study, we investigated the joint contribution of behavioural, environmental, socioeconomic, and demographic factors associated with higher risk of multi-drug resistant urinary tract infections (MDR UTIs) in Kenya, Tanzania, and Uganda. We sampled outpatients with UTI symptoms in healthcare facilities and linked their microbiology data with patient, household and community level data. Using bivariate statistics and Bayesian profile regression on a sample of 1610 individuals, we show that individuals with higher risk of MDR UTIs were more likely to have compound and interrelated social and environmental disadvantages: they were on average older, with lower education, had more chronic illness, lived in resource-deprived households, more likely to have contact with animals, and human or animal waste. This suggests that interventions to tackle ABR need to take account of intersectional socio-environmental disadvantage as a priority.

Mortality from type 2 diabetes mellitus across municipalities in Mexico

BackgroundOne in six Mexican adults’ lives with type 2 diabetes mellitus (T2DM), which is the third leading cause of death in the country. Analyzing the geographic distribution of T2DM mortality helps identify regions with higher mortality rates. This study aimed to examine the spatial patterns of mortality from type 2 diabetes mellitus (T2DM) across municipalities in Mexico and to analyze the main contextual factors linked to this cause of death in 2020.MethodsWe employed a spatial Bayesian hierarchical regression model to estimate the risk and probability of death from type 2 diabetes mellitus (T2DM) across Mexico’s municipalities.ResultsThe SMR results revealed geographic and age-specific patterns. Central Mexico and the Yucatán Peninsula exhibited the highest excess mortality rates. For the population under 50 years of age, municipalities in Oaxaca had the highest T2DM mortality rates, whereas those aged 50 years old and older had the highest rates in Tlaxcala and Puebla. Socioeconomic factors such as low levels of educational attainment, lack of health services, dietary deficiency, and marginalization were positively associated with increased T2DM mortality risk. By contrast, GDP per capita showed a negative association. High-risk areas for T2DM mortality were prominent along the south of the Pacific Coast, the Bajío, Central Mexico, and southern Yucatán for those under 50, and along a central strip extending to the Yucatán Peninsula for the older population. Significant uncertainties in mortality risk were identified, with Central Mexico, Oaxaca, Chiapas, and Tabasco showing high probabilities of excess risk for those under 50 years of age and extended risk areas along the Gulf of Mexico for those 50 years old and older.ConclusionsThe assessment and identification of spatial distribution patterns associated with T2DM mortality, and its main contextual factors, are crucial for informing effective public health policies aimed at reducing the impact of this chronic disease in Mexico.

Prenatal metal exposures and kidney function in adolescence in Project Viva.

The developing kidney is vulnerable to prenatal environmental factors such as metal exposure, potentially altering the risk of later-life kidney dysfunction. This study examines the relationship between prenatal metal exposures, individually and as mixtures, and adolescent kidney function in Project Viva, a prospective longitudinal birth cohort in Massachusetts, USA. We used data on metals measured in blood during pregnancy including 15 in the first trimester and four in the second trimester. We calculated estimated glomerular filtration rate (eGFR) in adolescents (mean: 17.7 years) using cystatin C- (eGFRcys) and creatinine-based (eGFRcreat) equations for children. We used linear regression for single metal analyses, and Bayesian kernel machine regression and quantile-based g-computation for mixture analyses, adjusting for relevant covariates. To account for multiple comparisons in the single metal analyses, we applied the Holm-Bonferroni procedure to control the false discovery rate. This study included 371 participants with first trimester metals and adolescent eGFR, and 256 with second trimester metals. Each doubling in first trimester cadmium concentration was associated with lower adolescent eGFRcys (β:-1.51; 95% CI:-2.83, -0.18). Each doubling in first trimester chromium (β:-1.45; 95% CI:-2.71, -0.19), nickel (β:-1.91; 95% CI:-3.65, -0.16), and vanadium (β:-1.69; 95% CI:-3.21, -0.17) was associated with lower adolescent eGFRcreat. After adjusting for multiple comparisons, p-values for associations between adolescent eGFR and chromium, nickel, vanadium and cadmium did not meet the criteria for significance. Metal mixture analyses did not identify statistically significant associations with adolescent eGFR. These findings have important implications for future studies investigating the potential mechanisms through which prenatal metal exposures affect long-term kidney health in children.

Curious Choices: Infants' moment-to-moment information sampling is driven by their exploration history

Infants explore the world around them based on their intrinsically motivated curiosity. However, the cognitive mechanisms underlying such curiosity-driven exploratory behaviour remain largely unknown. Here, infants could freely explore two novel categories, triggering a new exemplar from a category by fixating on either of the two associated areas on a computer screen. This gaze-contingent design enabled us to distinguish between exploration – switching from one category to another – and exploitation – consecutively triggering exemplars from the same category. Data from 10 to 12-month-old infants (N = 68) indicated that moment-to-moment sampling choices were non-random but guided by the infants' exploration history. Self-generated sequences grouped into three clusters of brief yet explorative, longer exploitative, and overall more balanced sampling patterns. Bayesian hierarchical binomial regression models indicated that across sequence patterns, infants' longer trigger time, shorter looking time, and more gaze-shifting were associated with trial-by-trial decisions to disengage from exploiting one category and making an exploratory switch, especially after consecutively viewed stimuli of high similarity. These findings offer novel insights into infants' curiosity-driven exploration and pave the way for future investigations, also regarding individual differences.

The influence of drinking consequences on alcohol expectancy likelihoods and valences: an item-level multi-level approach.

Alcohol expectancy theory proposes that beliefs about drinking motivate or deter drinking. Although expectancies influence drinking, less is known about how the consequences of drinking influence expectancies. We modeled a feedback conceptualization of how the experience of specific consequences influenced people's beliefs about how likely a consequence will occur (i.e., likelihoods) and how positive the consequence will be (i.e., valences). We re-analyzed cross-sectional data from college drinkers (n = 504), using Bayesian cross-classified multilevel ordinal regressions to estimate associations between consequences, likelihoods, and valences. We performed a preregistered replication in new data (n = 362). Participants had higher likelihoods (95% CIStudy 1 = [2.06, 2.43], 95% CIStudy 2 = [1.75, 2.12]) and valences (95% CIStudy 1 = [0.28, 0.52], 95% CIStudy 2 = [0.33, 0.60]) when they had experienced consequences more often, but these associations leveled off at higher consequence frequencies. Participants also believed consequences to be more likely when they viewed them as more positive, and vice versa, and again, these associations leveled off at higher levels of the predictor. Critically, the strength of these associations varied across both people and consequences. Experiencing specific consequences more often was related to people judging them to be more likely and more positive in the future, aligning with alcohol expectancy theory. This may lead to experiencing negative consequences repeatedly because people are not being demotivated from drinking in the same fashion. Given the person and consequence level variability, clinicians should consider an individualized approach when targeting drinking consequences.

Mediating role of systemic inflammation in the association between volatile organic compounds exposure and periodontitis: NHANES 2011–2014

BackgroundVolatile organic compounds (VOCs) are ubiquitous environmental pollutants which have been suggested to have adverse effects on human health. While the influence of environmental pollutant exposures on periodontitis has attracted elevating attention in recent years, the epidemiological evidence on the association between VOCs exposure and periodontitis was scarce. This study aimed to investigate the potential mediating role of systemic inflammation factors in the complex association between VOCs exposure and periodontitis.MethodsUtilizing data from the National Health and Nutrition Examination Survey (NHANES) 2011–2014, we examined the impacts of VOCs exposure on periodontitis. Concentrations of urinary metabolites of VOCs (mVOCs) were measured using electrospray tandem mass spectrometry to evaluate internal VOCs exposure. Multivariable logistic regression, restricted cubic spline regression (RCS), Bayesian kernel machine regression (BKMR) and Quantile g-computation (QGC) models were performed to investigate the impacts of VOCs exposure on periodontitis. Mediation models were applied to assess the mediated effects of systemic inflammation on the association between mixed VOCs exposure and periodontitis. Besides, we analyzed the association between mixed VOCs exposure and periodontitis in stratified age, gender, and smoking status subgroups.Results1,551 participants were ultimately included for further analyses, of whom 45.20% suffering from periodontitis. Multivariable logistic regression and RCS identified positive associations between single urinary mVOCs and periodontitis (P < 0.05). Notably, BKMR and QGC models suggested that mixed VOCs exposure was significantly associated with periodontitis, with 2-Aminothiazoline-4-carboxylic acid (ATCA) contributing the most (conditional posterior inclusion probability = 0.997). Moreover, systemic inflammation markers (leukocyte and lymphocyte counts) were found to partly mediate the association between VOCs exposure and periodontitis (P < 0.05). No interaction effect was identified between mixed VOCs exposure and periodontitis in age, gender and smoking status subgroups (P > 0.05).ConclusionThis study demonstrated a positive association between VOCs exposure and periodontitis, which was potentially mediated by systemic inflammation factors. Further longitudinal researches are demanded to clarify the underlying mechanisms.

Determining the prior mean in Bayesian logistic regression with sparse data: a nonarbitrary approach

Abstract Logistic regression models lead to a bias of the odds ratio (OR) as estimated using the maximum-likelihood (ML) method, when there are few study participants at the binary outcome and factor levels. Although Bayesian methods are frequently applied to reduce this sparse data bias, the specification of the prior distribution for regression coefficients is the most controversial feature. We propose a nonarbitrary and empirical method to determine the prior mean for regression coefficients in Bayesian logistic regression analysis for sparse data. The proposed prior mean is calculated as the difference between the observed log OR and the quasi-expectation of log OR, and is interpreted as a shrinkage statistic of the ML estimate. Further, for easy and fast inference, the proposed method applies to Bayesian logistic regression with the normal prior and the log-F prior via data augmentation. Simulation results indicate that the OR bias based on the proposed method is consistently smaller than that based on the ML method. The OR bias estimated using the proposed method is generally smaller than that based on the mean prior of zero for the regression coefficient. We apply the proposed methods to 2 real data sets.