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Background: Ventilator-associated pneumonia (VAP) is a significant nosocomial infection in critically ill patients, leading to high morbidity, mortality, and increased healthcare costs. The diversity of local microbiology and resistance patterns complicates the empirical treatment selection. The Weighted-Incidence Syndromic Combination Antibiogram (WISCA) offers an innovative tool to optimize empirical antibiotic therapy by integrating local microbiological data and resistance profiles. Objective: To develop a WISCA tailored for VAP in a Mexican tertiary care university hospital, aiming to enhance empirical antibiotic coverage by addressing the unique pathogen distribution and resistance patterns within the institution. Methods: This retrospective study included 197 VAP episodes from 129 patients admitted to a critical care unit between June 2021 and June 2024. Clinical and microbiological data, including pathogen susceptibility profiles, were analyzed using a Bayesian hierarchical model to evaluate the coverage of multiple antibiotic regimens. We also assessed the current impact of inappropriate empiric or directed treatment on in-hospital mortality using Cox regression models to support the development of a WISCA model. Results: The median age of the patients was 44 years (IQR 35–56), with Acinetobacter baumannii (n = 71), Enterobacterales (n = 53) and Pseudomonas aeruginosa (n = 36) identified as the most frequently isolated pathogens. The developed WISCA models showed variable coverage based on antibiotic regimens and the duration of invasive mechanical ventilation (IMV). Inappropriate directed therapy during the VAP episode was associated with increased mortality, as were the diagnosis of Acute Respiratory Distress Syndrome (ARDS) and a high Sequential Organ Failure Assessment (SOFA) score (p < 0.01). Conclusions: The tailored WISCA with Bayesian hierarchical modeling provided more adaptive, subgroup-specific estimates and managed uncertainty better compared to fixed models. The implementation of this WISCA model demonstrated potential to optimize antibiotic strategies and improve clinical outcomes in critically ill patients in our hospital. Topic: Optimizing Empirical Antibiotic Therapy for Ventilator-Associated Pneumonia Using a Weighted-Incidence Syndromic Combination Antibiogram in a Mexican Tertiary Care Hospital. [ABSTRACT FROM AUTHOR]

The accurate estimation of forest carbon stocks with remote sensing technologies helps reveal the spatial patterns of forest carbon stocks within national parks, but the limited number of sample plots in one site often results in difficulty in developing robust estimation models. This study employed a Bayesian hierarchical model to estimate forest carbon stock based on data from 193 sample plots collected across 37 UAV (unmanned aerial vehicle) Lidar sites. The developed model was employed to predict the carbon stock distribution in 17 Lidar sites within Wuyishan National Park (WNP). Then, the carbon stock characteristics along vertical zones of vegetation distribution (VZsVD) were examined. The results showed an overall coefficient of determination (R2) of 0.84 for forest carbon stock estimation across four regions, with a root mean square error (RMSE) of 12.09 t/ha. Within WNP, the overall R2 was 0.73, with specific values of 0.83 for broadleaf forests, 0.61 for mixed forests, 0.53 for Masson pine forests, and 0.46 for Chinese fir forests. Despite variations in R2, the relative RMSE (rRMSE) averaged 20.15%, ranging from 10.83% to 23.57%. The average carbon stock was 52.15 t/ha. Forest diversity and structural complexity emerged as key factors influencing the vertical distribution of carbon stocks. Regions with complex and diverse forest types exhibited higher and more evenly distributed carbon stocks. Chinese fir and Masson pine showed higher carbon stocks in low-altitude regions (350–850 m) than other vegetation types. In medium- to high-elevation regions (1350–1600 m), the carbon stocks of mixed forest and broadleaf forests remained relatively stable. Conversely, coniferous forests at high altitudes (above 1600 m) had lower carbon stocks due to extreme climatic and terrain conditions. This study provided a comprehensive analysis of carbon stock distribution across different VZsVD in WNP, offering valuable insights for enhancing the management of national parks. [ABSTRACT FROM AUTHOR]

Storm surges present a severe risk to coastal communities and infrastructure, underscoring the critical importance of accurately estimating extreme events such as the 100-year return surge. These estimates are essential not only for effective hazard assessment but also for informing resilient coastal design. Inspired by principles of robust statistical modeling, this paper introduces a Bayesian hierarchical model integrated with Gaussian processes to account for spatial random effects. This approach enhances the precision of long return period storm surge estimates and enables the seamless generalization of predictions to nearby unmonitored coastal regions, much like the way advanced Bayesian frameworks are applied to high-dimensional neuroimaging or spatiotemporal data, bridging gaps between observations and uncharted territories. [ABSTRACT FROM AUTHOR]

AbstractWe developed a novel Bayesian hierarchical model (BHM) that incorporates a similarity measure calculated using the standardized chi-square test statistic to evaluate the heterogeneity of response rates between two cancer types of interest for basket trials in oncology. Our proposed design involves the use of the response rates of not only the two cancer types of interest, but also all cancer types in the trials when the similarity between two cancer types is estimated. Simulation studies revealed that the proposed method had comparative type 1 error rate and power, improved accuracy for the posterior estimation of response rate, and reduced number of patients in the trials with interim analysis in many cases compared to the existing BHM using a similarity measure of response rate among the cancer types. We applied the proposed method to real data from two basket trials and determined that its operating characteristics, in terms of the posterior probability of the response rate among cancer types, differed from those of existing designs. Overall, our proposed method is an alternative approach to the existing BHM that provides a more effective and efficient means of evaluating the heterogeneity of response rates between different cancer types and estimating response rates. [ABSTRACT FROM AUTHOR]

Markov chain Monte Carlo (MCMC) allows one to generate dependent replicates from a posterior distribution for effectively any Bayesian hierarchical model. However, MCMC can produce a significant computational burden. This motivates us to consider finding expressions of the posterior distribution that are computationally straightforward to obtain independent replicates from directly. We focus on a broad class of Bayesian hierarchical models for spatially dependent data, which are often modeled via a latent Gaussian process (LGP). First, we derive a new class of distributions referred to as the generalized conjugate multivariate (GCM) distribution. The GCM distribution's theoretical development follows that of the conjugate multivariate (CM) distribution with two main differences: the GCM allows for latent Gaussian process assumptions, and the GCM explicitly accounts for hyperparameters through marginalization. The development of GCM is needed to obtain independent replicates directly from the exact posterior distribution, which has an efficient regression form. Hence, we refer to our method as Exact Posterior Regression (EPR). Simulation studies with weakly stationary spatial processes and spatial basis function expansions are provided. We provide an analysis of poverty incidence from the U.S. Census Bureau, and an analysis of high-dimensional remote sensing data. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Amikacin is one of the aminoglycosides with a narrow therapeutic window, significant dose–response relationship, and substantial interindividual pharmacokinetics (PK) variability, thus requiring an individualized dosing regimen. In this paper, a three-stage Bayesian hierarchical model was developed based on the known the PK parameters of amikacin obtained from a nonlinear mixed-effects model established for the Japanese population, and the weights were assigned to the priori and posteriori parts before two-dimensional Gibbs sampling, and simulations were performed using the data of 24 elderly patients with respiratory tract infections in Japan, after analyzing the predicted values and the range between effective trough concentrations (${C_{trough}}$Ctrough) and peak concentrations (${C_{peak}}$Cpeak), and residual plots, the dose for patients 3, 7, 9, and 16 was increased to 600 mg/day, and the dose for patient 20 was decreased to 400 mg/day, while keeping the remaining patients’ doses unchanged, and the serum concentration at the time of the last administration was predicted, which showed that the Bayesian hierarchical model and the Markov chain Monte Carlo algorithm in this study performed well. [ABSTRACT FROM AUTHOR]

For general insurance pricing, aligning losses with accurate premiums is crucial for insurance companies' competitiveness. Traditional actuarial models often face challenges like data heterogeneity and mismeasured covariates, leading to misspecification bias. This paper addresses these issues from a Bayesian perspective, exploring connections between Bayesian hierarchical modeling, partial pooling techniques, and the Gustafson correction method for mismeasured covariates. We focus on Non-Differential Berkson (NDB) mismeasurement and propose an approach that corrects such errors without relying on gold standard data. We discover the unique prior knowledge regarding the variance of the NDB errors, and utilize it to adjust the biased parameter estimates built upon the NDB covariate. Using simulated datasets developed with varying error rate scenarios, we demonstrate the superiority of Bayesian methods in correcting parameter estimates. However, our modeling process highlights the challenge in accurately identifying the variance of NDB errors. This emphasizes the need for a thorough sensitivity analysis of the relationship between our prior knowledge of NDB error variance and varying error rate scenarios. [ABSTRACT FROM AUTHOR]

Estimating relative abundance is critical for informing conservation and management efforts and for making inferences about the effects of environmental change on populations. Freshwater fisheries span large geographic regions, occupy diverse habitats and consist of varying species assemblages. Monitoring schemes used to sample these diverse populations often result in populations being sampled at different times and under different environmental conditions. Varying sampling conditions can bias estimates of abundance when compared across time, location and species, and properly accounting for these biases is critical for making inferences. We develop a joint species distribution model (JSDM) that accounts for varying sampling conditions due to the environment and time of sampling when estimating relative abundance. The novelty of our JSDM is that we explicitly model sampling effort as the product of known quantities based on time and gear type and an unknown functional relationship to capture seasonal variation in species life history. We use the model to study relative abundance of six freshwater fish species across the state of Minnesota, USA. Our model enables estimates of relative abundance to be compared both within and across species and lakes, and captures the inconsistent sampling present in the data. We discuss how gear type, water temperature and day of the year impact catchability for each species at the lake level and throughout a year. We compare our estimates of relative abundance to those obtained from a model that assumes constant catchability to highlight important differences within and across lakes and species. Synthesis and applications: Our method illustrates that assumptions relating indices of abundance to observed catch data can greatly impact model inferences derived from JSDMs. Specifically, not accounting for varying sampling conditions can bias inference of relative abundance, restricting our ability to detect responses to management interventions and environmental change. While our focus is on freshwater fisheries, this model architecture can be adopted to other systems where catchability may vary as a function of space, time and species.

Active learning provides a framework to adaptively query the most informative experiments towards learning an unknown black-box function. Various approaches of active learning have been proposed in the literature, however, they either focus on exploration or exploitation in the design space. Methods that do consider exploration–exploitation simultaneously employ fixed or ad-hoc measures to control the trade-off that may not be optimal. In this article, we develop a Bayesian hierarchical approach, referred to as BHEEM, to dynamically balance the exploration-exploitation trade-off as more data points are queried. To sample from the posterior distribution of the trade-off parameter, we subsequently formulate an approximate Bayesian computation approach based on the linear dependence of queried data in the feature space. Simulated and real-world examples show the proposed approach achieves at least 21% and 11% average improvement when compared to pure exploration and exploitation strategies, respectively. More importantly, we note that by optimally balancing the trade-off between exploration and exploitation, BHEEM performs better or at least as well as either pure exploration or pure exploitation. [ABSTRACT FROM AUTHOR]

Abstract Background One 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. Methods We 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. Results The 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. Conclusions The 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.

AbstractA fundamental task in the automated analysis of images is the development of effective image pair comparison techniques. For two high-dimensional images, a statistical method must automatically label them as “similar” or “different” depending on whether random error and spatial dependencies could account for the pixel-wise differences. We develop a Bayesian strategy by constructing a novel extension of Dirichlet processes called the spatial random partition model (sRPM). The process groups spatially proximal image pixels with similar intensities into clusters, thereby achieving dimension reduction in the large number of pixels. Next, we apply the sRPM-based analytical procedure to compare two images. The image comparison problem is formulated as a hypothesis test involving a univariate metric adaptive to spatial correlations and robust to random variability in the pixel intensities. To handle the computational burden, we foster a two-stage technique for MCMC analysis and hypothesis testing of image pairs. A simulation study analyzes artificial datasets and finds compelling evidence for the high accuracy of sRPM in image comparison. We demonstrate the effectiveness of the technique by statistically analyzing satellite image data. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

In this paper, I present and discuss a novel approach to parameterize a gravity model of migration using Bayesian hierarchical models with random intercepts that are free to vary by country of origin, destination, and directed origin-destination country pairs. I then utilize this model to project transboundary migration flows between African countries to the horizon 2050. To do so, I use data on projected future crop yields and water availability from the ISIMIP2b scenarios in combination with projections on future economic and demographic trends from the Shared Socio-Economic Pathways (SSPs). The results indicate that over the period 2010–2050 between 8 to 17 millions people are projected to migrate internationally on the African continent. Yet, only a small portion of these migrants will be induced to move because of climate change. To the contrary, comparisons between SSPs scenarios suggests that economic development will have a far larger impact on projected level of international migration on the continent than climate change. [ABSTRACT FROM AUTHOR]

Optimizing doses for multiple indications is challenging. The pooled approach of finding a single optimal biological dose (OBD) for all indications ignores that dose-response or dose-toxicity curves may differ between indications, resulting in varying OBDs. Conversely, indication-specific dose optimization often requires a large sample size. To address this challenge, we propose a Randomized two-stage basket trial design that Optimizes doses in Multiple Indications (ROMI). In stage 1, for each indication, response and toxicity are evaluated for a high dose, which may be a previously obtained maximum tolerated dose, with a rule that stops accrual to indications where the high dose is unsafe or ineffective. Indications not terminated proceed to stage 2, where patients are randomized between the high dose and a specified lower dose. A latent-cluster Bayesian hierarchical model is employed to borrow information between indications, while considering the potential heterogeneity of OBD across indications. Indication-specific utilities are used to quantify response-toxicity trade-offs. At the end of stage 2, for each indication with at least one acceptable dose, the dose with highest posterior mean utility is selected as optimal. Two versions of ROMI are presented, one using only stage 2 data for dose optimization and the other optimizing doses using data from both stages. Simulations show that both versions have desirable operating characteristics compared to designs that either ignore indications or optimize dose independently for each indication. [ABSTRACT FROM AUTHOR]

Extreme value theory (EVT) has been extensively used to assess road safety with traffic conflicts. However, most studies used pooled models that do not account for vehicle heterogeneity which is characterised by different static and dynamic vehicle parameters such as size, speed, acceleration, and braking capacity. This study proposes a risk assessment technique for rear-end crashes while incorporating vehicular heterogeneity. Video-based trajectory data were collected at four uncontrolled intersections, and conflicts were estimated using modified time-to-collision (MTTC). The crash risk was derived from the observed conflicts using pooled as well as Bayesian hierarchical EVT models. Unlike the pooled model, the hierarchical model revealed that crash risk varies across leader-follower pairs. Interactions that involve cars or light commercial vehicles with slow-moving vehicles are riskier. This study highlights the importance of incorporating vehicular heterogeneity in crash risk assessment. The proposed methodology can be utilised for more accurate risk assessment in heterogeneous traffic. [ABSTRACT FROM AUTHOR]

We present a novel space‐time Bayesian hierarchical model (BHM) to reconstruct annual Sea Surface Temperature (SST) over a large domain based on SST at limited proxy (i.e., sediment core) locations. The model is tested in the equatorial Pacific. The BHM leverages Principal Component Analysis to identify dominant space‐time modes of contemporary variability of the SST field at the proxy locations and employs these modes in a Gaussian process framework to estimate SSTs across the entire domain. The BHM allows us to model the mean field and covariance, varying in space and time in the process layers of the hierarchy. Using the Markov Chain Monte Carlo (MCMC) method and suitable priors on the model parameters, posterior distributions of the model parameters and, consequently, posterior distributions of the SST fields and the attendant uncertainties are obtained for any desired year. The BHM is calibrated and validated in the contemporary period (1854–2014) and subsequently applied to reconstruct SST fields during the Holocene (0–10 ka). Results are consistent with prior inferences of La Niña‐like conditions during the Holocene. This modeling framework opens exciting prospects for modeling and reconstruction of other fields, such as precipitation, drought indices, and vegetation. Key Points: We developed a novel Bayesian Hierarchical Space‐time Model for fields over a large domainWe applied modeling and reconstruction of equatorial Pacific SST at contemporary and Holocene time scalesThe framework offers bright prospects for modeling and reconstruction of other climate fields [ABSTRACT FROM AUTHOR]

A new method for measuring scientific productivity is proposed. Each researcher is initially associated with a cumulative score over time, reflecting the quality of the papers based on the journals in which they have published throughout their career. The second measure, an average speed over time from varying production speeds, is derived through the estimation of a two-level hierarchical Bayesian model for piecewise linear regression. These productivity indicators are validated and compared to other commonly used bibliometric indexes. The proposed method is applied to compare the productivity of females and males at different career levels in Italian academia, with a focus on statisticians. The study also contributes to the literature on the gender gap, showing that among those who remain at the lower levels of the university career hierarchy, women tend to have higher and more consistent scientific production over time compared to their male colleagues. [ABSTRACT FROM AUTHOR]

Understanding the causes of mortality for a declining species is essential for developing effective conservation and management strategies, particularly when anthropogenic activities are the primary threat. Using a competing hazards framework allows for robust estimation of the cause‐specific variation in risk that may exist across multiple dimensions, such as time and individual. Here, we estimated cause‐specific rates of severe injury and mortality for North Atlantic right whales (Eubalaena glacialis), a critically endangered species that is currently in peril due to human‐caused interactions. We developed a multistate capture–recapture model that leveraged 30 years of intensive survey effort yielding sightings of individuals with injury assessments and necropsies of carcass recoveries. We examined variation in the hazard rates of severe injury and mortality due to entanglements in fishing gear and vessel strikes as explained by temporal patterns and the age and reproductive status of the individual. We found strong evidence for increased rates of severe entanglement injuries after 2013 and for females with calves, with consequently higher marginal mortality. The model results also suggested that despite vessel strikes causing a lower average rate of severe injuries, the higher mortality rate conditional on injury results in significant total mortality risk, particularly for females resting from a recent calving event. Large uncertainty in the estimation of carcass recovery rate for vessel strike deaths permeated into the apportionment of mortality causes. The increased rates of North Atlantic right whale mortality in the last decade, particularly for reproducing females, has been responsible for the severe decline in the species. By apportioning the human‐caused threats using a quantitative approach with estimation of relevant uncertainty, this work can guide development of conservation and management strategies to facilitate species recovery. Our approach is relevant to other monitored populations where cause‐specific injuries from multiple threats can be observed in live and dead individuals. [ABSTRACT FROM AUTHOR]

Diverse drivers such as climate, soil fertility, neighborhood competition, and functional traits all contribute to variation in tree stem demographic rates. However, these demographic drivers operate at different scales, making it difficult to compare the relative importance of each driver on tree demography. Using c. 20,000 stem records from New Zealand's temperate rain forests, we analyzed the growth, recruitment, and mortality rates of 48 tree species and determined the relative importance of demographic drivers in a multilevel modeling approach. Tree species' maximum height emerged as the one most strongly associated with all demographic rates, with a positive association with growth rate and negative associations with recruitment and mortality rates. Climate, soil properties, neighborhood competition, stem size, and other functional traits also played significant roles in shaping demographic rates. Forest structure and functional composition were linked to climate and soil, with warm, dry climates and fertile soil associated with higher growth and recruitment rates. Neighborhood competition affected demographic rates depending on stem size, with smaller stems experiencing stronger negative effects, suggesting asymmetric competition where larger trees exert greater competitive effects on smaller trees. Our study emphasizes the importance of considering multiple drivers of demographic rates to better understand forest tree dynamics. [ABSTRACT FROM AUTHOR]

In conventional subgroup analyses, subgroup treatment effects are estimated using data from each subgroup separately without considering data from other subgroups in the same study. The subgroup treatment effects estimated this way may be heterogenous with high variability due to small sample sizes in some subgroups and much different from the treatment effect in the overall population. A Bayesian hierarchical model (BHM) can be used to derive more precise, and less heterogenous estimates of subgroup treatment effects that are closer to the treatment effect in the overall population. BHM assumes exchangeability in treatment effect across subgroups after adjusting for effect modifiers and other relevant covariates. In this article, we will discuss the technical details for applying one‐way and multi‐way BHM using summary‐level statistics, and patient‐level data for subgroup analysis. Four case studies based on four new drug applications are used to illustrate the application of these models in subgroup analyses for continuous, dichotomous, time‐to‐event, and count endpoints. [ABSTRACT FROM AUTHOR]

Species richness is an important metric used for undertaking conservation management decisions. However, species richness estimates are influenced by species detection probabilities, with potential to entirely overlook species during surveys. Occupancy models which account for imperfect detection provide unbiased estimates, ensuring accurate estimates of richness. We carried out a camera trap survey in the mountains of north‐central Nepal during 2017 and documented a total of 21 mammal species. Here, we used multi‐species occupancy models within a Bayesian hierarchical framework to reassess our initial species richness estimate and to understand the influence of environmental covariates on occupancy and species richness of mammals in the area. Our model estimated the mean species richness was ~26 species (95% CRI: 21–36 species), suggesting we might have missed ~5 species during the survey. The mean probability of occupancy and detection of mammal species were estimated to be 0.2895%CRI:0.08–0.46$$ 0.28\ \left(95\%\mathrm{CRI}:0.08\hbox{--} 0.46\right) $$ and 0.02 (95% CRI:0.01–0.03) respectively. Mammalian species richness of the area had an anticipated positive relationship with tree canopy cover β=1.908,95%CI=0.989–2.827,p=1.95e−04$$ \left(\beta =1.908,95\%\mathrm{CI}=0.989\hbox{--} 2.827,p=1.95\mathrm{e}-04\right) $$ though its positive relationship with anthropogenic disturbance was surprising β=1.339,95%CI=0.334–2.344,p=0.012$$ \left(\beta =1.339,95\%\mathrm{CI}=0.334\hbox{--} 2.344,p=0.012\right) $$. Mammalian species richness had a quadratic relationship with elevation as is expected. This research contributes to baseline information of the mammal community ecology in north‐central Nepal and supports the need for future multi‐season surveys to understand the influence of temporal factors on mammalian community and species richness in the area. [ABSTRACT FROM AUTHOR]

Background: One 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. Methods: We 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. Results: The 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. Conclusions: The 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. [ABSTRACT FROM AUTHOR]

Although transcriptomics data is typically used to analyze mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g. healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, ie reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. Here, we present DifferentialRegulation , a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, vs. state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. DifferentialRegulation is distributed as a Bioconductor R package. [ABSTRACT FROM AUTHOR]

Rapid morphological and socioeconomic changes have accelerated the urbanization process and urban land use transformation in China. Megacities comprise clusters of urban cities and exhibit both newly formed and well-developed urban land use development beyond administrative boundaries. It is necessary to distinguish the changing effects of spatial-varying driving factors on newly formed urban land uses from well-developed built-up areas in megacities. This study proposed a multi-spatial urbanization framework to quantify region-level socioeconomics, cluster-level ecological morphologies, and grid-level urban functional morphologies. A three-level Bayesian hierarchical model was developed to investigate the impacts of multi-spatial driving factors on urban land use transformation in megacities. The study period focused on the urbanization process between 2000 and 2018 in Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Results revealed that compared with well-developed urban built-up land, changing impacts of three-level driving factors in urban land use transformation could be captured based on the proposed Bayesian hierarchical model. The region-level total population was associated with increasing possibilities in forming new residential land than the well-developed ones in 35 districts/counties/cities in GBA. Cluster-level ecological attributes with higher proportion, lower edge density of urban built areas, and lower-degree ecological complexity showed increasing probability on newly formed industrial and public land. Grid-level urban functional factors including public transportation density and shopping/dining distribution exhibited significantly decreasing probability (coefficients: −2.12 to −0.51) on contributing newly formed land uses compared with the well-developed areas, whereas business/industry distribution represented higher (coefficients: 0.99 and 0.15) and lower probabilities (coefficient: −0.22) of forming industrial/public land and residential land separately. This research shows a new attempt to distinguish multi-spatial morphological and socioeconomic effects in urban land use transformation in megacities.

The management of big game harvest is important for maintaining viable populations and providing recreational opportunities to hunters. There are numerous strategies used by management agencies to achieve these goals, but they are complicated by variation in factors that are difficult to control, such as harvest success rates. For harvest management decisions to have the desired effect on big game populations, the mechanisms affecting factors like harvest success rates need to be properly understood. We used Bayesian hierarchical survival models to explore the factors influencing spatial and temporal variation in moose (Alces alces) harvest success rates in Ontario, Canada. We estimated harvest success rates from hunter reports from 59 Wildlife Management Units from 2000–2019. Overall, harvest success rates were primarily influenced by variables under the control of management agencies, such as season length and tag allocations, but they were also affected by external factors like moose density and weather. Season length, while positively related to harvest success for shorter seasons (e.g., <25 days), exhibited limited influence for longer seasons (>25 days). Our results were largely consistent across spatial and temporal scales, with a similarly strong positive effect of moose density and negative effect of tag allocation between management units and across years. This study emphasizes the need for managers to recognize the inherent uncertainty in harvest outcomes beyond their control and the importance of open communication with hunters in achieving effective harvest management, while offering concrete pathways for influencing harvest success. [ABSTRACT FROM AUTHOR]

A statistical study was made of the summer extreme temperatures over peninsular Spain in the last forty years. Records from 158 observatories regularly distributed over Iberia with no missing data were available for the common period from 1981 to 2020. For this purpose, a hierarchical spatio-temporal model with a Gaussian copula and a generalized extreme value parametrization of the extreme events was used. The temporal trend in maximum extreme temperatures was studied making use of both a stationary model and a nonstationary one that takes into account the influence of anthropogenic climate change on extreme temperatures using the global mean temperature as a function of time for the study period. The results led to a better fit of the nonstationary model, with there being a 3.5-fold greater increase in the 20-year return level of the extreme temperatures than in that corresponding to the global mean temperature. [ABSTRACT FROM AUTHOR]

Accurately predicting species' distributions is critical for the management and conservation of fish and wildlife populations. Joint species distribution models (JSDMs) account for dependencies between species often ignored by traditional species distribution models. We evaluated how a JSDM approach could improve predictive strength for stream fish communities within large watersheds (the Chesapeake Bay Watershed, USA), using a cross-validation study of JSDMs fit to data from over 50 species. Our results suggest that conditional predictions from JSDMs have the potential to make large improvements in predictive accuracy for many species, particularly for more generalist species where single species models may not perform well. For some species there was no added explanatory effect from conditional information, most of which already exhibited strong marginal predictive ability. For several rare species there were significant improvements in occurrence predictions, while the results for two invasive species considered did not show the same improvements. Overall, the optimal number of species to condition upon, as well as the effects of conditioning upon an increasing number of species, varied widely among species. [ABSTRACT FROM AUTHOR]

In many domains, it is necessary to combine opinions or forecasts from multiple individuals. However, the average or modal judgment is often incorrect, shared information across respondents can result in correlated errors, and weighting judgments by confidence does not guarantee accuracy. We develop a Bayesian hierarchical model of crowd wisdom that incorporates predictions about others to address these aggregation challenges. The proposed model can be applied to single questions, and it can also estimate respondent expertise given multiple questions. Unlike existing Bayesian hierarchical models for aggregation, the model does not link the correct answer to consensus or privilege majority opinion. The model extends the "surprisingly popular algorithm" to enable statistical inference and in doing so, overcomes several of its limitations. We assess performance on empirical data and compare the results with other aggregation methods, including leading Bayesian hierarchical models. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. Funding: This work was supported in part by the National Science Foundation [Grant MMS 2019982] and All Souls College Oxford [Visiting Fellowships in 2020 and 2022 to D. Prelec]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4955. [ABSTRACT FROM AUTHOR]

Rapid morphological and socioeconomic changes have accelerated the urbanization process and urban land use transformation in China. Megacities comprise clusters of urban cities and exhibit both newly formed and well-developed urban land use development beyond administrative boundaries. It is necessary to distinguish the changing effects of spatial-varying driving factors on newly formed urban land uses from well-developed built-up areas in megacities. This study proposed a multi-spatial urbanization framework to quantify region-level socioeconomics, cluster-level ecological morphologies, and grid-level urban functional morphologies. A three-level Bayesian hierarchical model was developed to investigate the impacts of multi-spatial driving factors on urban land use transformation in megacities. The study period focused on the urbanization process between 2000 and 2018 in Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Results revealed that compared with well-developed urban built-up land, changing impacts of three-level driving factors in urban land use transformation could be captured based on the proposed Bayesian hierarchical model. The region-level total population was associated with increasing possibilities in forming new residential land than the well-developed ones in 35 districts/counties/cities in GBA. Cluster-level ecological attributes with higher proportion, lower edge density of urban built areas, and lower-degree ecological complexity showed increasing probability on newly formed industrial and public land. Grid-level urban functional factors including public transportation density and shopping/dining distribution exhibited significantly decreasing probability (coefficients: −2.12 to −0.51) on contributing newly formed land uses compared with the well-developed areas, whereas business/industry distribution represented higher (coefficients: 0.99 and 0.15) and lower probabilities (coefficient: −0.22) of forming industrial/public land and residential land separately. This research shows a new attempt to distinguish multi-spatial morphological and socioeconomic effects in urban land use transformation in megacities. [ABSTRACT FROM AUTHOR]

Flood frequency analysis at large scales, essential for the development of flood risk maps, is hindered by the scarcity of gauge flow data. Suitable methods are thus required to predict flooding in ungauged basins, a notoriously complex problem in hydrology. We develop a Bayesian hierarchical model (BHM) based on the generalized extreme value (GEV) and the generalized Pareto distribution for regional flood frequency analysis at high resolution across a large part of North America. Our model leverages annual maximum flow data from ≈20,000 gauged stations and a dataset of 130 static catchment-specific covariates to predict extreme flows at all catchments over the continent as well as their associated statistical uncertainty. Additionally, a modification is made to the data layer of the BHM to include peaks over threshold flow data when available, which improves the precision of the discharge level estimates. We validated the model using a hold-out approach and found that its predictive power is very good for the GEV distribution location and scale parameters and improvable for the shape parameter, which is notoriously hard to estimate. The resulting discharge return levels yield a satisfying agreement when compared with the available design peak discharge from various government sources. The assessment of the covariates' contributions to the model is also informative with regard to the most relevant underlying factors influencing flood-inducing peak flows. According to the developed aggregate importance score, the key covariates in our model are temperature-related bioindicators, the catchment drainage area and the geographical location. [ABSTRACT FROM AUTHOR]

Examining the relationship between renewable and non-renewable energy sources and economic growth is crucial for designing sustainable growth policies in the context of global sustainability efforts. Previous studies relying on frequentist inference have faced challenges in disentangling the individual effects of these energy sources on economic growth due to their high degree of correlation, often leading to biased results. The Bayesian approach offers an alternative estimation method to address this multicollinearity issue. This study aims to demonstrate one of the advantages of the Bayesian hierarchical framework in handling multicollinearity by using a sample of 72 countries to evaluate the distinct impacts of renewable and non-renewable energy on economic growth. By incorporating specific priors into a Bayesian model to guide the estimation process, the findings confirm that both energy sources play significant roles in driving economic growth, with renewable energy sources exhibiting a comparatively weaker effect. These results align with theoretical expectations, indicating that renewables make a limited contribution to economic growth due to high investment costs, intermittency issues, and supply chain constraints. This study establishes a solid foundation for sustainable growth policy formulation by providing robust evidence. Plain language summary: In studying the energy-growth nexus, the Bayesian approach offers a robust and reliable estimation method to address multicollinearity issues, which makes it impossible for frequentist methods. The study aims to demonstrate one of the advantages of the Bayesian hierarchical framework in handling multicollinearity on a sample of 72 countries to access energy on economic growth. Renewable and non-renewable energy sources are significant drivers of economic growth, but renewable energy sources exert a comparatively weaker effect. These results are consistent with theoretical expectations that renewables make a limited contribution to economic growth owing to high investment costs, intermittency issues, and supply chain constraints. The study provides a robust empirical foundation for sustainable growth policy formulation. The main limitation of the study is the use of empirical rather than informative priors. [ABSTRACT FROM AUTHOR]

Abstract Understanding the causes of mortality for a declining species is essential for developing effective conservation and management strategies, particularly when anthropogenic activities are the primary threat. Using a competing hazards framework allows for robust estimation of the cause‐specific variation in risk that may exist across multiple dimensions, such as time and individual. Here, we estimated cause‐specific rates of severe injury and mortality for North Atlantic right whales (Eubalaena glacialis), a critically endangered species that is currently in peril due to human‐caused interactions. We developed a multistate capture–recapture model that leveraged 30 years of intensive survey effort yielding sightings of individuals with injury assessments and necropsies of carcass recoveries. We examined variation in the hazard rates of severe injury and mortality due to entanglements in fishing gear and vessel strikes as explained by temporal patterns and the age and reproductive status of the individual. We found strong evidence for increased rates of severe entanglement injuries after 2013 and for females with calves, with consequently higher marginal mortality. The model results also suggested that despite vessel strikes causing a lower average rate of severe injuries, the higher mortality rate conditional on injury results in significant total mortality risk, particularly for females resting from a recent calving event. Large uncertainty in the estimation of carcass recovery rate for vessel strike deaths permeated into the apportionment of mortality causes. The increased rates of North Atlantic right whale mortality in the last decade, particularly for reproducing females, has been responsible for the severe decline in the species. By apportioning the human‐caused threats using a quantitative approach with estimation of relevant uncertainty, this work can guide development of conservation and management strategies to facilitate species recovery. Our approach is relevant to other monitored populations where cause‐specific injuries from multiple threats can be observed in live and dead individuals.

In this paper, I present and discuss a novel approach to parameterize a gravity model of migration using Bayesian hierarchical models with random intercepts that are free to vary by country of origin, destination, and directed origin-destination country pairs. I then utilize this model to project transboundary migration flows between African countries to the horizon 2050. To do so, I use data on projected future crop yields and water availability from the ISIMIP2b scenarios in combination with projections on future economic and demographic trends from the Shared Socio-Economic Pathways (SSPs). The results indicate that over the period 2010–2050 between 8 to 17 millions people are projected to migrate internationally on the African continent. Yet, only a small portion of these migrants will be induced to move because of climate change. To the contrary, comparisons between SSPs scenarios suggests that economic development will have a far larger impact on projected level of international migration on the continent than climate change.

BackgroundThe postnatal period is a critical period for both mothers and their newborns for their health. Lack of early postnatal care (PNC) services during a 2-day period is a life-threatening situation for both the mother and the babies. However, no data have been examined for PNCs in East Africa. Hence, using the more flexible Bayesian multilevel modeling approach, this study aims to investigate the pooled prevalence and potential factors for PNC utilization among women after delivery in East African countries.MethodsWe retrieved secondary data from the Kids Record (KR) demographic and health surveys (DHS) data from 2015 to 2022 from 10 East African countries. A total of 77,052 weighted women were included in the study. We used R 4.3.2 software for analysis. We fitted Bayesian multilevel logistic regression models. Techniques such as Rhat, effective sample size, density, time series, autocorrelation plots, widely applicable information criterion (WAIC), deviance information criterion (DIC), and Markov Chain Monte-Carlo (MCMC) simulation were used to estimate the model parameters using Hamiltonian Monte-Carlo (HMC) and its extensions, No-U-Turn Sampler (NUTS) techniques. An adjusted odds ratio (AOR) with a 95% credible interval (CrI) in the multivariable model to select variables that have a significant association with PNC was used.ResultsThe overall pooled prevalence of PNC within 48 hrs. of delivery was about 52% (95% CrI: 39, 66). A higher rate of PNC usage was observed among women aged 25–34 years (AOR = 1.21; 95% CrI: 1.15, 1.27) and 35–49-years (AOR = 1.61; 95% CrI: 1.5, 1.72) as compared to women aged 15–24 years; similarly, women who had achieved primary education (AOR = 1.96; 95% CrI: 1.88, 2.05) and secondary/higher education (AOR = 3.19; 95% CrI: 3.03, 3.36) as compared to uneducated women; divorced or widowed women (AOR = 0.83; 95% CrI: 0.77, 0.89); women who had currently working status (AOR = 0.9; 95% CrI: 0.87, 0.93); poorer women (AOR = 0.88; 95% CrI: 0.84, 0.92), middle-class women (AOR = 0.83; 95% CrI: 0.79, 0.87), richer women (AOR = 0.77; 95% CrI: 0.73, 0.81), and richest women (AOR = 0.59; 95% CrI: 0.55, 0.63) as compared to the poorest women; women who had media exposure (AOR = 1.32; 95% CrI: 1.27, 1.36), were having 3–5 children (AOR = 0.89; 95% CrI: 0.84, 0.94), had >5 children (AOR = 0.69; 95% CrI: 0.64, 0.75), had first birth at age

The state of in situ stress is a crucial parameter in subsurface engineering, especially for critical projects like nuclear waste repository. As one of the two ISRM suggested methods, the overcoring (OC) method is widely used to estimate the full stress tensors in rocks by independent regression analysis of the data from each OC test. However, such customary independent analysis of individual OC tests, known as no pooling, is liable to yield unreliable test-specific stress estimates due to various uncertainty sources involved in the OC method. To address this problem, a practical and no-cost solution is considered by incorporating into OC data analysis additional information implied within adjacent OC tests, which are usually available in OC measurement campaigns. Hence, this paper presents a Bayesian partial pooling (hierarchical) model for combined analysis of adjacent OC tests. We performed five case studies using OC test data made at a nuclear waste repository research site of Sweden. The results demonstrate that partial pooling of adjacent OC tests indeed allows borrowing of information across adjacent tests, and yields improved stress tensor estimates with reduced uncertainties simultaneously for all individual tests than they are independently analysed as no pooling, particularly for those unreliable no pooling stress estimates. A further model comparison shows that the partial pooling model also gives better predictive performance, and thus confirms that the information borrowed across adjacent OC tests is relevant and effective.

Small area estimation models are critical for dissemination and understanding of important population characteristics within sub-domains that often have limited sample size. The classic Fay-Herriot model is perhaps the most widely used approach to generate such estimates. However, a limiting assumption of this approach is that the latent true population quantity has a linear relationship with the given covariates. Through the use of random weight neural networks, we develop a Bayesian hierarchical extension of this framework that allows for estimation of nonlinear relationships between the true population quantity and the covariates. We illustrate our approach through an empirical simulation study as well as an analysis of median household income for census tracts in the state of California. [ABSTRACT FROM AUTHOR]

Multi-gene panel testing allows many cancer susceptibility genes to be tested quickly at a lower cost making such testing accessible to a broader population. Thus, more patients carrying pathogenic germline mutations in various cancer-susceptibility genes are being identified. This creates a great opportunity, as well as an urgent need, to counsel these patients about appropriate risk-reducing management strategies. Counseling hinges on accurate estimates of age-specific risks of developing various cancers associated with mutations in a specific gene, ie, penetrance estimation. We propose a meta-analysis approach based on a Bayesian hierarchical random-effects model to obtain penetrance estimates by integrating studies reporting different types of risk measures (eg, penetrance, relative risk, odds ratio) while accounting for the associated uncertainties. After estimating posterior distributions of the parameters via a Markov chain Monte Carlo algorithm, we estimate penetrance and credible intervals. We investigate the proposed method and compare with an existing approach via simulations based on studies reporting risks for two moderate-risk breast cancer susceptibility genes, ATM and PALB2. Our proposed method is far superior in terms of coverage probability of credible intervals and mean square error of estimates. Finally, we apply our method to estimate the penetrance of breast cancer among carriers of pathogenic mutations in the ATM gene. [ABSTRACT FROM AUTHOR]

Long-lived territorial bird populations often consist of a few territorial breeding adults and many nonbreeding individuals. Some populations are threatened by anthropogenic activities, because of human conflicts for high-quality breeding habitat. Therefore, habitat restoration projects have been widely implemented to improve avian population status. In conjunction with habitat restoration, conservation translocations have been increasingly implemented. Adequate nonbreeder survival can be a key factor in the success of these attempts because nonbreeding birds may represent reservoirs for the replacement of breeders. The maintenance of breeding pair numbers is also influenced by the transition rate of nonbreeders to breeders. The reintroduction of Oriental Stork (Ciconia boyciana), a long-lived, territorial, endangered species, was initiated in Japan in 2005 using captive birds in hopes of increasing the population's use of restored habitat. Our objective of this study was to elucidate the factors determining reintroduced stork survival and recruitment to the breeding populations. We estimated the survival rate and breeding participation rate by sex, age, generation, wild-born or not, haplotypes, and breeding status in storks reintroduced during 2005–2022 using Bayesian hierarchical models. There was no significant difference in survival rate between nonbreeders and breeders. However, the survival rate was lower in wild-born birds than released birds, which may be related to the longer-distance natal dispersal of new generations. Accelerated habitat restoration around breeding areas and preventive measures for collision with human-built structures should be implemented for the sustained growth of reintroduced populations. A low survival rate was also detected for a specific mitochondrial DNA (mtDNA) haplotype that accounts for the majority of the reintroduced population. This phenomenon might be explained by mtDNA-encoded mutations. Moreover, captive breeding and release history might contribute to an increase in the proportion of this haplotype in the wild. [ABSTRACT FROM AUTHOR]

Simple Summary: Lung cancer is one of the most frequently diagnosed cancers worldwide. Radon is a radioactive gas whose concentrations can accumulate indoors. The long-term exposure to radon is considered carcinogenic to humans and is an important risk factor for lung cancer. Our epidemiological study investigated the impact of indoor radon exposure on the incidence of lung cancer in Slovenia over a period of 40 years. Around 60 newly diagnosed cases of lung cancer per year (out of a population of around 2 million) can be attributed to radon exposure in residential environments in the period 1978–2017, which corresponds to 5.5% of all lung cancer cases. The most important information that needs to be communicated to the Slovenian public and decision-makers about the health risk and about support for preventive measures is that living in areas with elevated radon levels is associated with a higher risk of lung cancer. Indoor radon is an important risk factor for lung cancer, as 3–14% of lung cancer cases can be attributed to radon. The aim of our study was to estimate the impact of indoor radon exposure on lung cancer incidence over the last 40 years in Slovenia. We analyzed the distribution of lung cancer incidence across 212 municipalities and 6032 settlements in Slovenia. The standardized incidence ratios were smoothed with the Besag–York–Mollie model and fitted with the integrated nested Laplace approximation. A categorical explanatory variable, the risk of indoor radon exposure with low, moderate and high risk values, was added to the models. We also calculated the population attributable fraction. Between 2.8% and 6.5% of the lung cancer cases in Slovenia were attributable to indoor radon exposure, with values varying by time period. The relative risk of developing lung cancer was significantly higher among the residents of areas with a moderate and high risk of radon exposure. Indoor radon exposure is an important risk factor for lung cancer in Slovenia in areas with high natural radon radiation (especially in the southern and south-eastern parts of the country). [ABSTRACT FROM AUTHOR]

Spatial capture–recapture (SCR) using search–encounter methods estimate population abundance from encounters of individually recognisable animals along an a priori-designated search path. We applied search–encounter SCR methods and photographic sampling to estimate the abundance of plains zebras (Equus quagga) at Telperion and Ezemvelo nature reserves, South Africa. We analysed encounter data by comparing four hazard-function models for the detection process. The abundance estimate under three models was just above 1000 animals (95% credible intervals c. 960, 1220) versus 811 (719, 917) for the remaining model. The former estimates were broadly similar to aerial counts conducted around the same time. Standard deviation in locations around individual activity centres ( σ move ) was c. 0.8 km, with little difference between models. In situations where structured surveys are not possible, the approach presented here has the potential to estimate abundance from opportunistic animal encounters (e.g. generated via citizen science schemes) within an SCR framework. [ABSTRACT FROM AUTHOR]

Multiple-type outcomes are often encountered in many statistical applications, one may want to study the association between multiple responses and determine the covariates useful for prediction. However, literature on variable selection methods for multiple-type data is arguably underdeveloped. In this article, we develop a novel global–local shrinkage prior in multiple response-types settings, where the observed dataset consists of multiple response-types (e.g., continuous, count-valued, Bernoulli trials, etc.), by combining the perspectives of global–local shrinkage and the conjugate multivaraite distribution. One benefit of our model is that a transformation or a Gaussian approximation on the data is not needed to perform variable selection for multiple response-type data, and thus one can avoid computational difficulties and restrictions on the joint distribution of the responses. Another benefit is that it allows one to parsimoniously model cross-variable dependence. Specifically, our method uses basis functions with random effects, which can be presented as known covariates or pre-defined basis functions, to model dependence between responses and dependence can be detected by our proposed global–local shrinkage model with a sparsity-inducing model. We provide connections to the original horseshoe model and existing basis function models. An efficient block Gibbs sampler is developed, which is found to be effective in obtaining accurate estimates and variable selection results. We also provide a motivating analysis of public health and financial costs from natural disasters in the U.S. using data provided by the National Centers for Environmental Information. [ABSTRACT FROM AUTHOR]

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Survival estimates are critical components of avian ecology. In well‐intentioned efforts to maximize the utility of one's research, survival estimates often derive from data that were not originally collected for survival assessments, and such post hoc analyses may include unintentional biases. We estimated the survival of Whimbrels captured and marked at two breeding sites in Alaska using divergent data streams that in isolation were subject to methodological biases. Although both capture sites were chosen to study the migration ecology of Alaska‐breeding Whimbrels, maximizing the conservation value of the data we collected was obviously desirable. We used multi‐year telemetry information to infer survival from one site (Colville River) and mark–resight techniques to estimate survival from a second site (Kanuti River). At Colville River, we could not feasibly include a control group of birds to assess potential survival effects of externally mounted transmitters, and at Kanuti River we were unable to account accurately for potential emigration events because we used resightings alone. We integrated these datasets in a Bayesian hierarchical framework, an approach that permitted insights across sites that moderated methodological biases within sites. Using telemetry enabled us to detect permanent emigration events from breeding sites in two of 10 birds, results that informed estimates for birds without tracking devices. These datasets yielded point estimates of true survival of Whimbrels from Colville River equipped with solar‐powered satellite transmitters that were higher (0.83) than true survival estimates of Whimbrels from Kanuti River marked with leg flags alone (0.74) or equipped with surgically implanted satellite transmitters (0.50), but the 95% credible intervals on these estimates overlapped across groups. For species such as Whimbrels that are difficult and costly to study, combining information from disparate data streams allowed us to derive novel demographic estimates, an approach with clear application to other similar studies. [ABSTRACT FROM AUTHOR]

This pilot randomized controlled trial protocol aims to (1) assess the impact on the wellbeing of Syrian refugee young adults (18–24 years) of being a community mental health worker (CMHW) implementing WHO's evidence-based psychosocial intervention - Problem Management Plus (PM+) - with adults in their community, and (2) identify the mechanisms associated with the outcomes of enhanced wellbeing and coping, and reduced stress among these CMHWs. Over 108 million people have been forcibly displaced as of the end of 2022. Mental health consequences of these displacements are significant, yet human resources for health are not sufficient to meet the needs. A large proportion of refugee populations are youth and young adults (YA). Evidence indicates their engagement in supporting their communities leads to their own enhanced wellbeing and that of their community. This trial trains Syrian refugees to serve their communities as CMHW (n=19) or tutors (n=19) and compare wellbeing, stress and coping outcomes between these two groups and a control group (n = 40). We will also assess 7 mechanisms as potential pathways for the interventions to influence outcomes. Surveys will assess outcomes and mechanisms, hair samples will measure stress cortisol. The primary analysis will use a Bayesian Hierarchical Model approach to model the trajectories of the mechanisms and primary study endpoints over time for individuals in each of the arms. Our results will elucidate critical mechanisms in which engagement of young adults to support their community enhances their own wellbeing. Trial registration: National Institutes of Mental Health, NCT05265611, Registered prospectively in 2021. Lebanon clinical trials registry #: LBCTR2023015206, Registered in 2023.

The present study examined the impact of aging on ethical decision-making in simulated critical driving scenarios. 204 participants from North America, grouped into two age groups (18–30 years and 65 years and above), were asked to decide whether their simulated automated vehicle should stay in or change from the current lane in scenarios mimicking the Trolley Problem. Each participant viewed a video clip rendered by the driving simulator at Old Dominion University and pressed the space-bar if they decided to intervene in the control of the simulated automated vehicle in an online experiment. Bayesian hierarchical models were used to analyze participants’ responses, response time, and acceptability of utilitarian ethical decision-making. The results showed significant pedestrian placement, age, and time-to-collision (TTC) effects on participants’ ethical decisions. When pedestrians were in the right lane, participants were more likely to switch lanes, indicating a utilitarian approach prioritizing pedestrian safety. Younger participants were more likely to switch lanes in general compared to older participants. The results imply that older drivers can maintain their ability to respond to ethically fraught scenarios with their tendency to switch lanes more frequently than younger counterparts, even when the tasks interacting with an automated driving system. The current findings may inform the development of decision algorithms for intelligent and connected vehicles by considering potential ethical dilemmas faced by human drivers across different age groups.

For general insurance pricing, aligning losses with accurate premiums is crucial for insurance companies’ competitiveness. Traditional actuarial models often face challenges like data heterogeneity and mismeasured covariates, leading to misspecification bias. This paper addresses these issues from a Bayesian perspective, exploring connections between Bayesian hierarchical modeling, partial pooling techniques, and the Gustafson correction method for mismeasured covariates. We focus on Non-Differential Berkson (NDB) mismeasurement and propose an approach that corrects such errors without relying on gold standard data. We discover the unique prior knowledge regarding the variance of the NDB errors, and utilize it to adjust the biased parameter estimates built upon the NDB covariate. Using simulated datasets developed with varying error rate scenarios, we demonstrate the superiority of Bayesian methods in correcting parameter estimates. However, our modeling process highlights the challenge in accurately identifying the variance of NDB errors. This emphasizes the need for a thorough sensitivity analysis of the relationship between our prior knowledge of NDB error variance and varying error rate scenarios.

The seabird meta-population viability model (mPVA) uses a generalized approach to project abundance and quasi-extinction risk for 102 seabird species under various conservation scenarios. The mPVA is a stage-structured projection matrix that tracks abundance of multiple populations linked by dispersal, accounting for breeding island characteristics and spatial distribution. Data are derived from published studies, grey literature, and expert review (with over 500 contributions). Invasive species impacts were generalized to stage-specific vital rates by fitting a Bayesian state-space model to trend data from Islands where invasive removals had occurred, while accounting for characteristics of seabird biology, breeding islands and invasive species. Survival rates were estimated using a competing hazards formulation to account for impacts of multiple threats, while also allowing for environmental and demographic stochasticity, density dependence and parameter uncertainty.•The mPVA provides resource managers with a tool to quantitatively assess potential benefits of alternative management actions, for multiple species•The mPVA compares projected abundance and quasi-extinction risk under current conditions (no intervention) and various conservation scenarios, including removal of invasive species from specified breeding islands, translocation or reintroduction of individuals to an island of specified location and size, and at-sea mortality amelioration via reduction in annual at-sea deaths.