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Reliable prediction for crop yield is crucial for economic planning, food security monitoring, and agricultural risk management. This study aims to develop a crop yield forecasting model at large spatial scales using meteorological variables closely related to crop growth. The influence of climate patterns on agricultural productivity can be spatially inhomogeneous due to local soil and environmental conditions. We propose a Bayesian spatially varying functional model (BSVFM) to predict county-level corn yield for five Midwestern states, based on annual precipitation and daily maximum and minimum temperature trajectories modeled as multivariate functional predictors. The proposed model accommodates spatial correlation and measurement errors of functional predictors, and respects the spatially heterogeneous relationship between the response and associated predictors by allowing the functional coefficients to vary over space. The model also incorporates a Bayesian variable selection device to further expand its capacity to accommodate spatial heterogeneity. The proposed method is demonstrated to outperform other highly competitive methods in corn yield prediction, owing to the flexibility of allowing spatial heterogeneity with spatially varying coefficients in our model. Our study provides further insights into understanding the impact of climate change on crop yield. for this article are available online. [ABSTRACT FROM AUTHOR]

The goal of this article is to improve the predictive performance of a Bayesian hierarchical statistical model by incorporating a criterion typically used for model selection. In this article, we view the problem of prediction of a latent real-valued mean as a model selection problem, where the candidate models are from an uncountable infinite set (i.e., the parameter space of the mean represents the candidate set of models). Specifically, we select a subset of our Bayesian hierarchical statistical model's parameter space with high predictive performance (as measured by a criterion). Explicitly, we truncate the joint support of the data and the parameter space of a given Bayesian hierarchical model to only include small values of the covariance penalized error (CPE) criterion. The CPE is a general expression that contains several information criteria as special cases. Simulation results show that as long as the truncated set does not have near-zero probability, we tend to obtain a lower squared error than Bayesian model averaging. Additional theoretical results are provided asthe foundation for these observations. We apply our approach to a dataset consisting of American Community Survey period estimates to illustrate that this perspective can lead to improvements in a single model. [ABSTRACT FROM AUTHOR]

We developed spatial Bayesian hierarchical models to assess potential climate change impacts on suitable habitat for five important tree species in the Pacific northwestern United States (California, Oregon, and Washington). Individual‐species models were fit with presence–absence data from forest inventory field plots and spatial relationships were specified through a conditional autoregressive model. This modeling approach allowed us to visualize uncertainty in response curves, map current and future prediction uncertainty, and provide interval estimates for change. Upward elevational or northward latitudinal shifts in climatically suitable habitat were projected for all species. Climate change impacts were the most damaging for noble fir (Abies procera), for which 79%–100% of the current range was projected to become climatically unsuitable by the 2080s. Although coastal Douglas‐fir (Pseudotsuga menziesii var. menziesii) has been projected by others to gain habitat in Canada, within our study area we projected a net loss of climatically suitable habitat (ca. 8000–31,400 km2) under three of four future climate scenarios. A net loss in habitat was also projected for Oregon white oak (Quercus garryana) under three of four scenarios, with 40%–60% of the current range becoming unsuitable. Although there was no net loss of habitat for forest land blue oak under any scenario, other factors like competition may inhibit blue oak (Quercus douglasii) and white oak from occupying areas projected to increase in climatic suitability. Additionally, between 13% and 32% of blue oak's current range was projected to become unsuitable; some of these areas aligned with dieback following the 2012–2015 California drought, which our data set predates. Unlike the other four species, we projected a 17%–25% increase in climatically suitable habitat for California black oak (Quercus kelloggii), although 1%–20% of the current range was still projected to become unsuitable. Our findings indicate that, although some species will face more pressure in tracking climatically suitable habitat than others, climate change will impact the location of suitable habitat for many species. [ABSTRACT FROM AUTHOR]

Understanding the relationship between mixed land use and urban vibrancy is vital in advanced urban planning applications. This study presents a Bayesian spatially varying coefficient (SVC) model to explore the spatially nonstationary relationship between mixed land use and urban vibrancy after controlling for other factors. We first use the convolutional conditional autoregressive prior to accommodate the ecological bias resulting from unobserved confounders. Then we develop our approach in the case of a single predictor to allow the spatially varying coefficient process. We further introduce a type of the Bayesian SVC model that considers the stratified heterogeneity of the outcome, allowing the coefficients to simultaneously vary at the local and subregion level. We illustrate the proposed model by conducting a case study in Shenzhen using mobile phone data, an officially registered point-of-interest (POI) dataset, and several supplementary datasets. The model evaluation results show that including spatially unstructured and structured component combinations can improve the model's fitness and predictive ability; additionally, considering spatial stratified heterogeneity can further enhance the model's performance. Our findings provide an alternative for measuring the variable local-scale association between mixed-use and urban vibrancy and offer new insights that broaden the fields of environmental science and spatial statistics. [ABSTRACT FROM AUTHOR]

Cabbage stem flea beetle (CSFB) is an economically important pest of oilseed rape crops responsible for substantial yield losses in recent years, particularly since the restrictions on neonicotinoid seed treatment use came into force in 2013. To effectively time sowing dates and target control measures, it is crucial that accurate estimates of when migratory adult CSFB will arrive at the crop can be made. A Bayesian hierarchical model was fitted to data from 19 sites containing adult CSFB traps over a period of three years to characterise the relationship between the day of year, temperature, rainfall, wind speed and solar radiation on beetle counts and to understand their relative importance. Day of the year was identified as the main driver of migration and temperature was the predominant environmental driver of CSFB migration. A hot day (based on the range of observed temperatures over the trapping window) resulted in approximately 300% of the expected CSFB migration relative to an average day during peak migration. The second most important environmental driver of migration was wind speed, but this resulted in a relatively negligible increase of approximately 15% from an average day to a still day. These findings suggest that efforts to predict timing of adult CSFB migration should focus on understanding how the phenology of CSFB and temperature interact to drive the timing of migration. [Display omitted] • Temperature is the main environmental driver of cabbage stem flea beetle migration. • Wind speed, rainfall and solar radiation had relatively minor effects on migration. • Understanding interactions between temperature and phenology is key to predict risk. [ABSTRACT FROM AUTHOR]

Food availability shapes demographic parameters and population dynamics. Certain species have adapted to predictable anthropogenic food resources like landfills. However, abrupt shifts in food availability can negatively impact such populations. While changes in survival are expected, the age-related effects remain poorly understood, particularly in long-lived scavenger species. We investigated the age-specific demographic response of a Griffon vulture (Gyps fulvus) population to a reduction in organic matter in a landfill and analyzed apparent survival and the probability of transience after initial capture using a Bayesian Cormack-Jolly-Seber model on data from 2012–2022. The proportion of transients among newly captured immatures and adults increased after the reduction in food. Juvenile apparent survival declined, increased in immature residents, and decreased in adult residents. These results suggest that there was a greater likelihood of permanent emigration due to intensified intraspecific competition following the reduction in food. Interestingly, resident immatures showed the opposite trend, suggesting the persistence of high-quality individuals despite the food scarcity. Although the reasons behind the reduced apparent survival of resident adults in the final four years of the study remain unclear, non-natural mortality potentially plays a part. In Europe landfill closure regulations are being implemented and pose a threat to avian scavenger populations, which underlines the need for research on food scarcity scenarios and proper conservation measures.

As an alternative to animal use, computer simulations are useful for predicting pharmacokinetics and cardiovascular activities. For this purpose, we constructed a statistical model to simulate the effects of local anesthetic agents. To train the model, animal experiments were performed on 6-week-old male Hartley guinea pigs. Firstly, the guinea pigs’ backs were shaved, then local anesthetic agents were subcutaneously injected, with subsequent stimulation of the anesthetized site with a needle six times at regular intervals. The number of reactions (score value) was counted. In this statistical model, the probability of reacting to needle stimulation was calculated using the elapsed time, type of local anesthetic agent, and presence or absence of adrenaline. Score values were assumed to follow a binomial distribution at the calculated probability. Parameters were estimated using the Bayesian hierarchical model and Hamiltonian Monte Carlo method. The predicted curves using the estimated parameters fitted well the observed animal values. When score values were predicted using randomly generated parameters, the median of duration was similar between animal experiments and simulations (Procaine: 55 min vs. 50 min, Lidocaine: both 60 min, and Mepivacaine: both 85 min). This approach effectively modeled the effects of local anesthetic agents. It is possible to create the simulator using the parameter values estimated in this study.

A Bayesian hierarchical model that integrated information about state and observation processes was used to estimate the number of adult Delta Smelt entrained into the southern Sacramento−San Joaquin Delta during water export operations by the California State Water Project and the Central Valley Project. The model hierarchy accounted for dynamic processes of transport, survival, sampling efficiency, and observation. Water export, mark−recapture, and fish facility count data informed each process. Model diagnostics and simulation testing indicated a good fit of the model, and that parameters were jointly estimable in the Bayesian hierarchical model framework. The model was limited, however, by sparse data to estimate survival and State Water Project sampling efficiency. Total December to March entrainment of adult Delta Smelt ranged from an estimated 142,488 fish in 2000 to 53 fish in 2014, and the efficiency of louvers used to divert entrained fish to fish facilities appeared to decline at high and low primary intake channel velocities. Though applied to Delta Smelt, the hierarchical modeling framework was sufficiently flexible to estimate the entrainment of other pelagic species.

Abstract Background Suicide is a serious worldwide public health concern, and South Korea has shown the highest suicide rate among Organisation for Economic Co-operation and Development (OECD) countries since 2003. Nevertheless, most previous Korean studies on suicide had limitations in investigating various social environment factors using long-term nationwide data. Thus, this study examined how various social environment characteristics are related to the suicide rate at the district-level, using nationwide longitudinal data over 11 years. Methods We used the district-level age-standardized suicide rate and a total of 12 annual social environment characteristics that represented socioeconomic, demographic, urbanicity, general health behaviors, and other environmental characteristics from 229 administrative districts in South Korea. A Bayesian hierarchical model with integrated Laplace approximations (INLA) was used to examine the spatiotemporal association between the rate of suicide and the social environment indicators selected for the study. Results In the total population, the indicators “% of population aged 65 and older eligible for the basic pension”, “% vacant houses in the area”, “% divorce”, “% single elderly households”, “% detached houses”, “% current smokers”, and “% of population with obesity” showed positive associations with the suicide rate. In contrast, “% of people who regularly participated in religious activities” showed negative associations with suicide rate. The associations between these social environment characteristics and suicide rate were generally more statistically significant in males and more urbanized areas, than in females and less urbanized areas; however, associations differed amongst age groups, depending on the social environment characteristic variable under study. Conclusions This study investigated the complex role of social environments on suicide rate in South Korea and revealed that higher suicide rates were associated with lower values of socioeconomic status, physical exercise, and religious activities, and with higher social isolation and smoking practice. Our results can be used in the development of targeted suicide prevention policies.

Abstract Background The sex ratio at birth (SRB; ratio of male to female births) in Nepal has been reported around the normal level on the national level. However, the national SRB could mask the disparity within the country. Given the demographic and cultural heterogeneities in Nepal, it is crucial to model Nepal SRB on the subnational level. Prior studies on subnational SRB in Nepal are mostly based on reporting observed values from surveys and census, and no study has provided probabilistic projections. We aim to estimate and project SRB for the seven provinces of Nepal from 1980 to 2050 using a Bayesian modeling approach. Methods We compiled an extensive database on provincial SRB of Nepal, consisting 2001, 2006, 2011, and 2016 Nepal Demographic and Health Surveys and 2011 Census. We adopted a Bayesian hierarchical time series model to estimate and project the provincial SRB, with a focus on modelling the potential SRB imbalance. Results In 2016, the highest SRB is estimated in Province 5 (Lumbini Pradesh) at 1.102, corresponding to 110.2 male births per 100 female births, with a 95% credible interval (1.044, 1.127) and the lowest SRB is in Province 2 at 1.053 (1.035, 1.109). The SRB imbalance probabilities in all provinces are generally low and vary from 16% in Province 2 to 81% in Province 5 (Lumbini Pradesh). SRB imbalances are estimated to have begun at the earliest in 2001 in Province 5 (Lumbini Pradesh) with a 95% credible interval (1992, 2022) and the latest in 2017 (1998, 2040) in Province 2. We project SRB in all provinces to begin converging back to the national baseline in the mid-2030s. By 2050, the SRBs in all provinces are projected to be around the SRB baseline level. Conclusions Our findings imply that the majority of provinces in Nepal have low risks of SRB imbalance for the period 1980–2016. However, we identify a few provinces with higher probabilities of having SRB inflation. The projected SRB is an important illustration of potential future prenatal sex discrimination and shows the need to monitor SRB in provinces with higher possibilities of SRB imbalance.

Study region: The Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China. Study focus: Using hourly rain gauge data and CMORPH data, we use the duration-dependent generalized extreme value (d-GEV) model and the scaling invariant GEV model inferred by the Bayesian hierarchical model to derive the intensity-duration-frequency IDF characteristics of extreme precipitation in GBA and adjust their uncertainties. New hydrological insights for the region: The GEV location and scale parameters of IDF curves in GBA show similar spatial distribution and the higher-resolution CMORPH can capture more local details than rain gauge data. Meanwhile, compared with the rain gauge data, CMORPH produces significantly lower rainfall intensity of storms with short durations, which leads to large uncertainties of IDF curves derived from CMORPH for the short-duration rainfall. Additionally, the uncertainties of IDF curves can be substantially reduced by using the scaling invariant model that was inferred by the Bayesian hierarchical model, compared with the ordinary d-GEV method. Therefore, the Bayesian inference is suggested to be adopted for regional estimation of IDF curves, especially for regions of limited sub-daily gauge data.

Abstract We developed spatial Bayesian hierarchical models to assess potential climate change impacts on suitable habitat for five important tree species in the Pacific northwestern United States (California, Oregon, and Washington). Individual‐species models were fit with presence–absence data from forest inventory field plots and spatial relationships were specified through a conditional autoregressive model. This modeling approach allowed us to visualize uncertainty in response curves, map current and future prediction uncertainty, and provide interval estimates for change. Upward elevational or northward latitudinal shifts in climatically suitable habitat were projected for all species. Climate change impacts were the most damaging for noble fir (Abies procera), for which 79%–100% of the current range was projected to become climatically unsuitable by the 2080s. Although coastal Douglas‐fir (Pseudotsuga menziesii var. menziesii) has been projected by others to gain habitat in Canada, within our study area we projected a net loss of climatically suitable habitat (ca. 8000–31,400 km2) under three of four future climate scenarios. A net loss in habitat was also projected for Oregon white oak (Quercus garryana) under three of four scenarios, with 40%–60% of the current range becoming unsuitable. Although there was no net loss of habitat for forest land blue oak under any scenario, other factors like competition may inhibit blue oak (Quercus douglasii) and white oak from occupying areas projected to increase in climatic suitability. Additionally, between 13% and 32% of blue oak's current range was projected to become unsuitable; some of these areas aligned with dieback following the 2012–2015 California drought, which our data set predates. Unlike the other four species, we projected a 17%–25% increase in climatically suitable habitat for California black oak (Quercus kelloggii), although 1%–20% of the current range was still projected to become unsuitable. Our findings indicate that, although some species will face more pressure in tracking climatically suitable habitat than others, climate change will impact the location of suitable habitat for many species.

BACKGROUND:It is widely recognized that there are multiple risk factors for early-life mortality. In practice most interventions to curb early-life mortality target births based on a single risk factor, such as poverty. However, most premature deaths are not from the targeted group. Thus interventions target many births that are at not at high risk and miss many births at high risk. METHODS:Using data from the second wave of Demographic and Health Surveys from India and a hierarchical Bayesian model, we estimate infant mortality risk for 73.320 infants in India as a function of 4 risk factors. We show how this information can be used to improve program targeting. We compare our novel approach against common programs that target groups based on a single risk factor. RESULTS:A conventional approach that targets mothers in the lowest quintile of income correctly identifies only 30% of infant deaths. By contrast, using four risk factors simultaneously we identify a group of births of the same size that includes 57% of all deaths. Using the 2012 census to translate these percentages into numbers, there were 25.642.200 births in 2012 and 4.4% died before the age of one. Our approach correctly identifies 643.106 of 1.128.257 infant deaths while poverty only identifies 338.477 infant deaths. CONCLUSION:Our approach considerably improves program targeting by identifying more infant deaths than the usual approach that targets births based on a single risk factor. This leads to more efficient program targeting. This is particularly useful in developing countries, where resources are lacking and needs are high.

Female sex workers (FSW) are affected by individual, network, and structural risks, making them vulnerable to poor health and well-being. HIV prevention strategies and local community-based programs can rely on estimates of the number of FSW to plan and implement differentiated HIV prevention and treatment services. However, there are limited systematic assessments of the number of FSW in countries across sub-Saharan Africa to facilitate the identification of prevention and treatment gaps. Here we provide estimated population sizes of FSW and the corresponding uncertainties for almost all sub-national areas in sub-Saharan Africa. We first performed a literature review of FSW size estimates and then developed a Bayesian hierarchical model to synthesize these size estimates, resolving competing size estimates in the same area and producing estimates in areas without any data. We estimated that there are 2.5 million (95% uncertainty interval 1.9 to 3.1) FSW aged 15 to 49 in sub-Saharan Africa. This represents a proportion as percent of all women of childbearing age of 1.1% (95% uncertainty interval 0.8 to 1.3%). The analyses further revealed substantial differences between the proportions of FSW among adult females at the sub-national level and studied the relationship between these heterogeneities and many predictors. Ultimately, achieving the vision of no new HIV infections by 2030 necessitates dramatic improvements in our delivery of evidence-based services for sex workers across sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

Single-cell RNA-sequencing (scRNAseq) data contain a high level of noise, especially in the form of zero-inflation, that is, the presence of an excessively large number of zeros. This is largely due to dropout events and amplification biases that occur in the preparation stage of single-cell experiments. Recent scRNAseq experiments have been augmented with unique molecular identifiers (UMI) and External RNA Control Consortium (ERCC) molecules which can be used to account for zero-inflation. However, most of the current methods on graphical models are developed under the assumption of the multivariate Gaussian distribution or its variants, and thus they are not able to adequately account for an excessively large number of zeros in scRNAseq data. In this article, we propose a single-cell latent graphical model (scLGM)—a Bayesian hierarchical model for estimating the conditional dependency network among genes using scRNAseq data. Taking advantage of UMI and ERCC data, scLGM explicitly models the two sources of zero-inflation. Our simulation study and real data analysis demonstrate that the proposed approach outperforms several existing methods. [ABSTRACT FROM AUTHOR]

Bringing historical control information into a new trial appropriately holds the promise of more efficient trial design with more accurate estimates, increased power, and fewer patients allocated to inefficacious control group, provided the historical control data are sufficiently similar to the concurrent control. Interest has been growing over the past few decades in leveraging historical clinical trial on the control arm. However, most of the current historical borrowing methods focus on incorporating patient-level historical control information at only one time point. In this work, we propose a Bayesian hierarchical Mixed effect Models for Repeated Measures to incorporate aggregated study-level longitudinal historical control estimates into the concurrent trial that collected repeated longitudinal data. The simulation study demonstrates that, as compared to one time point data analysis approach, leveraging longitudinal historical control data produces greater power enhancement and mitigates the power loss when the missing data under missing at random mechanism is present. Our work also helps fill the gap of lack of methods borrowing historical longitudinal control data from the published summarized estimates when patient-level control data are not available. [ABSTRACT FROM AUTHOR]

The abundance of Culex restuans and Culex pipiens in relation to ecological predictors is poorly understood in regions of the United States where their ranges overlap. It is suspected that these species play different roles in spreading West Nile virus (WNV) in these regions, but few studies have modeled these species separately or accounted for spatial correlation using Bayesian methods. We used mosquito surveillance data collected by the Pennsylvania Department of Environmental Protection from 2002 to 2016 and integrated nested Laplace approximations with the stochastic partial differential equation approach to predict C. restuans and C. pipiens abundance in relation to several ecological predictors. We then made a predictive risk surface of abundance for each species at locations that were not sampled. Explanatory variables in the models included ecological variables previously described to be important predictors of the abundance of these mosquito species. Developed habitat, temperature, and precipitation were important predictor variables for the abundance of C. restuans, whereas developed habitat, snow water equivalent, and normalized difference water index were important predictor variables for the abundance of C. pipiens. The abundance of C. restuans had a negative relationship with developed habitat in contrast to C. pipiens abundance, which had a positive relationship with developed habitat. Julian date was modeled as a temporal trend for both species and showed C. restuans to be more abundant from late April through late June and C. pipiens to be more abundant from July through September. A seasonal crossover was observed between these two species on Julian day 185, 4 July. We observed different spatial patterns of abundance in the predictive risk maps of each of the species. Our results indicate that modeling the abundance of these species spatially and separately in regions where these two mosquito vectors coexist can help gain further insight into understanding the epidemiology of WNV in human and susceptible animal populations. [ABSTRACT FROM AUTHOR]

This study's focus is the association of end-of-therapy (EOT) PET results with progression-free (PFS) and overall survival (OS) in patients with diffuse large B-cell lymphoma receiving first-line chemoimmunotherapy. We develop a Bayesian hierarchical model for predicting PFS and OS from EOT PET-complete response (PET-CR) using a literature-based meta-analysis of 20 treatment arms and a substudy of 4 treatment arms in 3 clinical trials for which we have patient-level data. The PET-CR rate in our substudy was 72%. The modeled estimates for hazard ratio (PET-CR/non-PET-CR) were 0.13 for PFS (95% CI 0.10, 0.16) and 0.10 for OS (CI 0.07, 0.12). Hazard ratios varied little by patient subtype and were confirmed by the overall meta-analysis. We link these findings to designing future clinical trials and show how our model can be used in adapting the sample size of a trial to accumulating results regarding treatment benefits on PET-CR and a survival endpoint. [ABSTRACT FROM AUTHOR]

Assessments of objective and subjective resilience are the main methods for understanding disaster preparedness and developing policy responses. However, the two assessment outcomes are not directly comparable. A model combining objective and subjective resilience assessments provides a better approximation of the underlying resilience. Building on previous comparisons, we have developed a robust Bayesian hierarchical model for comparing and linking these assessments. Three modeling scenarios provide the opportunity to explore the complex relationship between resilience data and explanatory variables under different conditions and prior knowledge about variables. Our strategy reasonably shares the uncertainty with the posterior distribution of all model parameters, instead of leaving all uncertainties to the variance parameter estimation, thus reducing methodological bias and providing a robust uncertainty estimation. The approach allows connecting and comparing objective and subjective resilience assessments, laying a strong foundation for developing a fully integrated resilience assessment. [ABSTRACT FROM AUTHOR]

Background: The 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., Methods: We 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., Results: The 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 < 20 years (AOR = 0.82; 95% CrI: 0.79, 0.84), had at least one ANC visit (AOR = 1.93; 95% CrI: 1.8, 2.08), delivered at health facilities (AOR = 2.57; 95% CrI: 2.46, 2.68), had average birth size (AOR = 0.94; 95% CrI: 0.91, 0.98) and small birth size child (AOR = 0.88; 95% CrI: 0.84, 0.92), had twin newborns (AOR = 1.15; 95% CrI: 1.02, 1.3), and fourth and above birth order (AOR = 0.88; 95% CrI: 0.82, 0.95) were individual-driven women who have been independently associated with PNC, respectively. Regarding community-level variables, rural women (AOR = 0.76; 95% CrI: 0.72, 0.79), high media exposure communities (AOR = 1.1; 95% CrI: 1.04, 1.18), communities with high wealth levels (AOR = 0.88 95% CrI: 0.83, 0.94), communities with high antenatal care (ANC) utilization (AOR = 1.13, 95% CrI: 1.07, 1.19), and long distance to health facilities (AOR = 1.5; 95% CrI: 1.38, 1.63) were among the community factors associated with PNC, respectively., Conclusion: One of the significant public health priorities in East Africa continues to be the underutilization of immediate PNC. The government ought to prioritize improving maternity and child health services, collaborating with interested parties in the area, reducing health disparities, educating mothers about child health, and other connected issues that are very beneficial., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Terefe, Asgedom, Arage, Aychiluhm and Ayele.)

Abstract Background Invasive reptiles pose a serious threat to global biodiversity, but early detection of individuals in an incipient population is often hindered by their cryptic nature, sporadic movements, and variation among individuals. Little is known about the mechanisms that affect the movement of these species, which limits our understanding of their dispersal. Our aim was to determine whether translocation or small-scale landscape features affect movement patterns of brown treesnakes (Boiga irregularis), a destructive invasive predator on the island of Guam. Methods We conducted a field experiment to compare the movements of resident (control) snakes to those of snakes translocated from forests and urban areas into new urban habitats. We developed a Bayesian hierarchical model to analyze snake movement mechanisms and account for attributes unique to invasive reptiles by incorporating multiple behavioral states and individual heterogeneity in movement parameters. Results We did not observe strong differences in mechanistic movement parameters (turning angle or step length) among experimental treatment groups. We found some evidence that translocated snakes from both forests and urban areas made longer movements than resident snakes, but variation among individuals within treatment groups weakened this effect. Snakes translocated from forests moved more frequently from pavement than those translocated from urban areas. Snakes translocated from urban areas moved less frequently from buildings than resident snakes. Resident snakes had high individual heterogeneity in movement probability. Conclusions Our approach to modeling movement improved our understanding of invasive reptile dispersal by allowing us to examine the mechanisms that influence their movement. We also demonstrated the importance of accounting for individual heterogeneity in population-level analyses, especially when management goals involve eradication of an invasive species.

Abstract Environmental temperature is a crucial abiotic factor that influences the success of ectothermic organisms, including hosts and pathogens in disease systems. One example is the amphibian chytrid fungus, Batrachochytrium dendrobatidis (Bd), which has led to widespread amphibian population declines. Understanding its thermal ecology is essential to effectively predict outbreaks. Studies that examine the impact of temperature on hosts and pathogens often do so in controlled constant temperatures. Although varying temperature experiments are becoming increasingly common, it is unrealistic to test every temperature scenario. Thus, reliable methods that use constant temperature data to predict performance in varying temperatures are needed. In this study, we tested whether we could accurately predict Bd growth in three varying temperature regimes, using a Bayesian hierarchical model fit with constant temperature Bd growth data. We fit the Bayesian hierarchical model five times, each time changing the thermal performance curve (TPC) used to constrain the logistic growth rate to determine how TPCs influence the predictions. We then validated the model predictions using Bd growth data collected from the three tested varying temperature regimes. Although all TPCs overpredicted Bd growth in the varying temperature regimes, some functional forms performed better than others. Varying temperature impacts on disease systems are still not well understood and improving our understanding and methodologies to predict these effects could provide insights into disease systems and help conservation efforts.

Abstract The abundance of Culex restuans and Culex pipiens in relation to ecological predictors is poorly understood in regions of the United States where their ranges overlap. It is suspected that these species play different roles in spreading West Nile virus (WNV) in these regions, but few studies have modeled these species separately or accounted for spatial correlation using Bayesian methods. We used mosquito surveillance data collected by the Pennsylvania Department of Environmental Protection from 2002 to 2016 and integrated nested Laplace approximations with the stochastic partial differential equation approach to predict C. restuans and C. pipiens abundance in relation to several ecological predictors. We then made a predictive risk surface of abundance for each species at locations that were not sampled. Explanatory variables in the models included ecological variables previously described to be important predictors of the abundance of these mosquito species. Developed habitat, temperature, and precipitation were important predictor variables for the abundance of C. restuans, whereas developed habitat, snow water equivalent, and normalized difference water index were important predictor variables for the abundance of C. pipiens. The abundance of C. restuans had a negative relationship with developed habitat in contrast to C. pipiens abundance, which had a positive relationship with developed habitat. Julian date was modeled as a temporal trend for both species and showed C. restuans to be more abundant from late April through late June and C. pipiens to be more abundant from July through September. A seasonal crossover was observed between these two species on Julian day 185, 4 July. We observed different spatial patterns of abundance in the predictive risk maps of each of the species. Our results indicate that modeling the abundance of these species spatially and separately in regions where these two mosquito vectors coexist can help gain further insight into understanding the epidemiology of WNV in human and susceptible animal populations.

Recent disruptions in global and local supply chains (SCs) due to manmade and natural disasters have drawn a lot of attention to academics and practitioners. Such disruptions are often characterized by a lack of insufficient data. To tackle such a data scarce supply chain ecosystem, this article examines the potential disruption risks in SCs under insufficient input data. For this, a decision support system (DSS) based on the Bayesian hierarchical approach and value at risk (VaR) reduction analysis is proposed to assess a supplier's disruption risk events probability as well as the supplier revenue impact on a company of interest. Empirical data are used to examine the DSS. The findings show that the DSS is effective in generating the suppliers’ risk profiles. The proposed DSS can be utilized by supply chain managers and practitioners to manage SC disruption risks in a more efficient manner.

Understanding long-term trends in streamflow is important for water resource management. In this study, we investigate the long-term streamflow trends at 47 gauging sites within the southern Murray-Darling Basin (MDB), Australia. This study aims to estimate regional streamflow trends while understanding the impact of catchment characteristics on the spatial variation in these trends. To achieve this, we applied a Bayesian hierarchical model (BHM) to make the best use of available streamflow records from multiple sites and catchment characteristics such as climate, terrain, geology, land use and vegetation. The results show that streamflow trends from tested sites are consistently negative, with magnitudes of up to 2.7% per year relative to the annual average flow. We also find that spatial variability in trends can be best linked to differences in average climatic and terrain conditions. This finding can be used to inform future water planning for consumptive and environmental uses in the MDB. [ABSTRACT FROM AUTHOR]

Biomarker‐guided phase II trials have become increasingly important for personalized cancer treatment. In this paper, we propose a Bayesian two‐stage sequential enrichment design for such biomarker‐guided trials. We assumed that all patients were dichotomized as marker positive or marker negative based on their biomarker status; the positive patients were considered more likely to respond to the targeted drug. Early stopping rules and adaptive randomization methods were embedded in the design to control the number of patients receiving inferior treatment. At the same time, a Bayesian hierarchical model was used to borrow information between the positive and negative control arms to improve efficiency. Simulation results showed that the proposed design achieved higher empirical power while controlling the type I error and assigned more patients to the superior treatment arms. The operating characteristics suggested that the design has good performance and may be useful for biomarker‐guided phase II trials for evaluating anticancer therapies. [ABSTRACT FROM AUTHOR]

Paddy fields are essential habitats for frogs. We evaluated the impacts of both farmland consolidation including agricultural road improvement and farmland abandonment on the two Rana species using a model incorporating spatial autocorrelation. A sampling unit consists of several paddy fields that share a ditch and are isolated from other blocks by roads or other land covers. We surveyed 619 blocks in an area of about 1000 km2 from the plain to the mountains of Toyota City in central Japan. Among them, 124 blocks included at least a flooded paddy field where frogs could lay eggs. R. ornativentris and R. japonica bred in 50 and 25 blocks, respectively. We constructed models to explain the presence/absence of two species by GLM (non-spatial model) and hierarchical Bayesian model with INLA (spatial model) that includes spatial autocorrelation as a random effect. Explanatory variables of the local scale were the altitude, location of the paddy field (yatsuda (valley bottom paddy fields) or non-yatsuda), farmland consolidated or not consolidated, and under cultivation or abandoned. Those of the landscape scale were areas of forest and paddy fields, and road density in 14 circles with different radius from 50 to 2000 m. Both species' distribution had significant spatial autocorrelation. The spatial model had a higher discriminative ability than the non-spatial model. Farmland consolidation and the forest area in the 400 m radius had a positive effect on R. ornativentris. Altitude and road density in the 50 m radius had negative effects, cultivation had a positive effect, and farmland consolidation and yatsuda had no or negative effects on R. japonica. R. ornativentris was threatened by farmland abandonment, but the urbanization and/or farmland consolidation threatened R. japonica. [ABSTRACT FROM AUTHOR]

Reducing neonatal and child mortality is a global priority. In countries without comprehensive vital registration data to inform policy and planning, statistical modelling is used to estimate the distribution of key causes of death. This modelling presents challenges given that the input data are few, noisy, often not nationally representative of the country from which they are derived, and often do not report separately on all of the key causes. As more nationally representative data come to be available, it becomes possible to produce country estimates that go beyond fixed‐effects models with national‐level covariates by incorporating country‐specific random effects. However, the existing frequentist multinomial model is limited by convergence problems when adding random effects, and had not incorporated a covariate selection procedure simultaneously over all causes. We report here on the translation of a fixed effects, frequentist model into a Bayesian framework to address these problems, incorporating a misclassification matrix with the potential to correct for mis‐reported as well as unreported causes. We apply the new method and compare the model parameters and predicted distributions of eight key causes of death with those based on the previous, frequentist model. [ABSTRACT FROM AUTHOR]

Background: Smoking is one of the leading causes of preventable mortality and morbidity worldwide, with the European Region having the highest prevalence of tobacco smoking among adults compared to other WHO regions. The Belgian Health Interview Survey (BHIS) provides a reliable source of national and regional estimates of smoking prevalence; however, currently there are no estimates at a smaller geographical resolution such as the municipality scale in Belgium. This hinders the estimation of the spatial distribution of smoking attributable mortality at small geographical scale (i.e., number of deaths that can be attributed to tobacco). The objective of this study was to obtain estimates of smoking prevalence in each Belgian municipality using BHIS and calculate smoking attributable mortality at municipality level.Methods: Data of participants aged 15 + on smoking behavior, age, gender, educational level and municipality of residence were obtained from the BHIS 2018. A Bayesian hierarchical Besag-York-Mollie (BYM) model was used to model the logit transformation of the design-based Horvitz-Thompson direct prevalence estimates. Municipality-level variables obtained from Statbel, the Belgian statistical office, were used as auxiliary variables in the model. Model parameters were estimated using Integrated Nested Laplace Approximation (INLA). Deviance Information Criterion (DIC) and Conditional Predictive Ordinate (CPO) were computed to assess model fit. Population attributable fractions (PAF) were computed using the estimated prevalence of smoking in each of the 589 Belgian municipalities and relative risks obtained from published meta-analyses. Smoking attributable mortality was calculated by multiplying PAF with age-gender standardized and stratified number of deaths in each municipality.Results: BHIS 2018 data included 7,829 respondents from 154 municipalities. Smoothed estimates for current smoking ranged between 11% [Credible Interval 3;23] and 27% [21;34] per municipality, and for former smoking between 4% [0;14] and 34% [21;47]. Estimates of smoking attributable mortality constituted between 10% [7;15] and 47% [34;59] of total number of deaths per municipality.Conclusions: Within-country variation in smoking and smoking attributable mortality was observed. Computed estimates should inform local public health prevention campaigns as well as contribute to explaining the regional differences in mortality. [ABSTRACT FROM AUTHOR]

Objective: The outbreak of the novel coronavirus disease 2019 (COVID-19) is still affecting African countries. The pandemic presents challenges on how to measure governmental, and community responses to the crisis. Beyond health risks, the socio-economic implications of the pandemic motivated us to examine the transmission dynamics of COVID-19 and the impact of non-pharmaceutical interventions (NPIs). The main objective of this study was to assess the impact of BCG vaccination and NPIs enforced on COVID-19 case-death-recovery counts weighted by age-structured population in Ethiopia, Kenya, and Rwanda. We applied a semi-mechanistic Bayesian hierarchical model (BHM) combined with Markov Chain Monte Carlo (MCMC) simulation to the age-structured pandemic data obtained from the target countries. Results: The estimated mean effective reproductive number (Rt) for COVID-19 was 2.50 (C1: 1.99–5.95), 3.51 (CI: 2.28–7.28) and 3.53 (CI: 2.97–5.60) in Ethiopia, Kenya and Rwanda respectively. Our results indicate that NPIs such as lockdowns, and curfews had a large effect on reducing Rt. Current interventions have been effective in reducing Rt and thereby achieve control of the epidemic. Beyond age-structure and NPIs, we found no significant association between COVID-19 and BCG vaccine-induced protection. Continued interventions should be strengthened to control transmission of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Understanding how and why animals use the environments where they occur is both foundational to behavioral ecology and essential to identify critical habitats for species conservation. However, some behaviors are more difficult to observe than others, which can bias analyses of raw observational data. To our knowledge, no method currently exists to model how animals use different environments while accounting for imperfect behavior‐specific detection probability. We developed an extension of a binomial N‐mixture model (hereafter the behavior N‐mixture model) to estimate the probability of a given behavior occurring in a particular environment while accounting for imperfect detection. We then conducted a simulation to validate the model's ability to estimate the effects of environmental covariates on the probabilities of individuals performing different behaviors. We compared our model to a naïve model that does not account for imperfect detection, as well as a traditional N‐mixture model. Finally, we applied the model to a bird observation data set in northwest Costa Rica to quantify how three species behave in forests and farms. Simulations and sensitivity analyses demonstrated that the behavior N‐mixture model produced unbiased estimates of behaviors and their relationships with predictor variables (e.g., forest cover, habitat type). Importantly, the behavior N‐mixture model accurately characterized uncertainty, unlike the naïve model, which often suggested erroneous effects of covariates on behaviors. When applied to field data, the behavior N‐mixture model suggested that Hoffmann's woodpecker (Melanerpes hoffmanii) and Inca dove (Columbina inca) behaved differently in forested versus agricultural habitats, while turquoise‐browed motmot (Eumomota superciliosa) did not. Thus, the behavior N‐mixture model can help identify habitats that are essential to a species' life cycle (e.g., where individuals nest, forage) that nonbehavioral models would miss. Our model can greatly improve the appropriate use of behavioral survey data and conclusions drawn from them. In doing so, it provides a valuable path forward for assessing the conservation value of alternative habitat types. [ABSTRACT FROM AUTHOR]

Dedicated long‐term monitoring at appropriate spatial and temporal scales is necessary to understand biodiversity losses and develop effective conservation plans. Wildlife monitoring is often achieved by obtaining data at a combination of spatial scales, ranging from local to broad, to understand the status, trends, and drivers of individual species or whole communities and their dynamics. However, limited resources for monitoring necessitates tradeoffs in the scope and scale of data collection. Careful consideration of the spatial and temporal allocation of finite sampling effort is crucial for monitoring programs that span multiple spatial scales. Here we evaluate the ability of five monitoring designs—stratified random, weighted effort, indicator unit, rotating panel, and split panel—to recover parameter values that describe the status (occupancy), trends (change in occupancy), and drivers (spatially varying covariate and an autologistic term) of wildlife communities at two spatial scales. Using an amphibian monitoring program that spans a network of US national parks as a motivating example, we conducted a simulation study for a regional community occupancy sampling program to compare the monitoring designs across varying levels of sampling effort (ranging from 10% to 50%). We found that the stratified random design outperformed the other designs for most parameters of interest at both scales and was thus generally preferable in balancing the estimation of status, trends, and drivers across scales. However, we found that other designs had improved performance in specific situations. For example, the rotating panel design performed best at estimating spatial drivers at a regional level. Thus, our results highlight the nuanced scenarios in which various design strategies may be preferred and offer guidance as to how managers can balance common tradeoffs in large‐scale and long‐term monitoring programs in terms of the specific knowledge gained. Monitoring designs that improve accuracy in parameter estimates are needed to guide conservation policy and management decisions in the face of broad‐scale environmental challenges, but the preferred design is sensitive to the specific objectives of a monitoring program. [ABSTRACT FROM AUTHOR]

We propose a method for forecasting global human migration flows. A Bayesian hierarchical model is used to make probabilistic projections of the 39,800 bilateral migration flows among the 200 most populous countries. We generate out-of-sample forecasts for all bilateral flows for the 2015 to 2020 period, using models fitted to bilateral migration flows for five 5-y periods from 1990 to 1995 through 2010 to 2015. We find that the model produces well-calibrated out-of-sample forecasts of bilateral flows, as well as total country-level inflows, outflows, and net flows. The mean absolute error decreased by 61% using our method, compared to a leading model of international migration. Out-of-sample analysis indicated that simple methods for forecasting migration flows offered accurate projections of bilateral migration flows in the near term. Our method matched or improved on the out-of-sample performance using these simple deterministic alternatives, while also accurately assessing uncertainty. We integrate the migration flow forecasting model into a fully probabilistic population projection model to generate bilateral migration flow forecasts by age and sex for all flows from 2020 to 2025 through 2040 to 2045. [ABSTRACT FROM AUTHOR]

Abstract The Arctic Ocean is undergoing rapid transformation toward a seasonally ice‐free ecosystem. As ice‐adapted apex predators, polar bears (Ursus maritimus) are challenged to cope with ongoing habitat degradation and changes in their prey base driven by food‐web response to climate warming. Knowledge of polar bear response to environmental change is necessary to understand ecosystem dynamics and inform conservation decisions. In the southern Beaufort Sea (SBS) of Alaska and western Canada, sea ice extent has declined since satellite observations began in 1979 and available evidence suggests that the carrying capacity of the SBS for polar bears has trended lower for nearly two decades. In this study, we investigated the population dynamics of polar bears in Alaska's SBS from 2001 to 2016 using a multistate Cormack–Jolly–Seber mark–recapture model. States were defined as geographic regions, and we used location data from mark–recapture observations and satellite‐telemetered bears to model transitions between states and thereby explain heterogeneity in recapture probabilities. Our results corroborate prior findings that the SBS subpopulation experienced low survival from 2003 to 2006. Survival improved modestly from 2006 to 2008 and afterward rebounded to comparatively high levels for the remainder of the study, except in 2012. Abundance moved in concert with survival throughout the study period, declining substantially from 2003 and 2006 and afterward fluctuating with lower variation around an average of 565 bears (95% Bayesian credible interval [340, 920]) through 2015. Even though abundance was comparatively stable and without sustained trend from 2006 to 2015, polar bears in the Alaska SBS were less abundant over that period than at any time since passage of the U.S. Marine Mammal Protection Act. The potential for recovery is likely limited by the degree of habitat degradation the subpopulation has experienced, and future reductions in carrying capacity are expected given current projections for continued climate warming.

Abstract Background Reproductive containment provides an opportunity to implement a staged-release strategy for genetic control of malaria vectors, in particular allowing predictions about the spread and persistence of (self-limiting) sterile and male-biased strains to be compared to outcomes before moving to (self-sustaining) gene-drive strains. Methods In this study, we: (i) describe a diffusion–advection–reaction model of the spread and persistence of a single cohort of male mosquitoes; (ii) elicit informative prior distributions for model parameters, for wild-type (WT) and genetically modified dominant sterile strains (DSM); (iii) estimate posterior distributions for WT strains using data from published mark-recapture-release (MRR) experiments, with inference performed through the Delayed-Rejection Adaptive Metropolis algorithm; and (iv) weight prior distributions, in order to make predictions about genetically modified strains using Bayes factors calculated for the WT strains. Results If a single cohort of 5000 genetically modified dominant sterile male mosquitoes are released at the same location as previous MRR experiments with their WT counterparts, there is a 90% probability that the expected number of released mosquitoes will fall to < 1 in 10 days, and that by 12 days there will be a 99% probability that no mosquitoes will be found more than 150 m from the release location. Conclusions Spread and persistence models should form a key component of risk assessments of novel genetic control strategies for malaria vectors. Our predictions, used in an independent risk assessment, suggest that genetically modified sterile male mosquitoes will remain within the locality of the release site, and that they will persist for a very limited amount of time. Data gathered following the release of these mosquitoes will enable us to test the accuracy of these predictions and also provide a means to update parameter distributions for genetic strains in a coherent (Bayesian) framework. We anticipate this will provide additional insights about how to conduct probabilistic risk assessments of stage-released genetically modified mosquitoes. Graphical abstract

BackgroundThe Human Microbiome has been variously associated with the immune-regulatory mechanisms involved in the prevention or development of many non-infectious human diseases such as autoimmunity, allergy and cancer. Integrative approaches which aim at associating the composition of the human microbiome with other available information, such as clinical covariates and environmental predictors, are paramount to develop a more complete understanding of the role of microbiome in disease development.ResultsIn this manuscript, we propose a Bayesian Dirichlet-Multinomial regression model which uses spike-and-slab priors for the selection of significant associations between a set of available covariates and taxa from a microbiome abundance table. The approach allows straightforward incorporation of the covariates through a log-linear regression parametrization of the parameters of the Dirichlet-Multinomial likelihood. Inference is conducted through a Markov Chain Monte Carlo algorithm, and selection of the significant covariates is based upon the assessment of posterior probabilities of inclusions and the thresholding of the Bayesian false discovery rate. We design a simulation study to evaluate the performance of the proposed method, and then apply our model on a publicly available dataset obtained from the Human Microbiome Project which associates taxa abundances with KEGG orthology pathways. The method is implemented in specifically developed R code, which has been made publicly available.ConclusionsOur method compares favorably in simulations to several recently proposed approaches for similarly structured data, in terms of increased accuracy and reduced false positive as well as false negative rates. In the application to the data from the Human Microbiome Project, a close evaluation of the biological significance of our findings confirms existing associations in the literature.

In this article a multi-subject vector autoregressive (VAR) modeling approach was proposed for inference on effective connectivity based on resting-state functional MRI data. Their framework uses a Bayesian variable selection approach to allow for simultaneous inference on effective connectivity at both the subject- and group-level. Furthermore, it accounts for multi-modal data by integrating structural imaging information into the prior model, encouraging effective connectivity between structurally connected regions. They demonstrated through simulation studies that their approach resulted in improved inference on effective connectivity at both the subject- and group-level, compared with currently used methods. It was concluded by illustrating the method on temporal lobe epilepsy data, where resting-state functional MRI and structural MRI were used. Hum Brain Mapp 38:1311-1332, 2017. © 2016 Wiley Periodicals, Inc.

Social networks are often measured as conduits of infection. Our prior cross-sectional analyses found that denser social ties among individuals reduces transmission of acute gastrointestinal illness (AGI) in coastal Ecuador; social networks can describe both risk and protection. We extend findings to examine how social connectedness influences AGI longitudinally in Ecuador from 2007 to 2013, a time of rapid development, using a two-stage Bayesian hierarchical model to estimate multiple network effects. A larger community network of people to discuss important matters with was consistently protective against AGI over time, and a network defined by people passing time together became a stronger measure of risk, due to increasing population density and travel. These networks were interdependent: the joint effect of having a small passing time network and large important matters network reduced the odds of AGI over time (2007: OR 1.16 (95% CI: 0.94, 1.44), 2013: OR 0.56 (95% CI: 0.45, 0.71)); and synergistic: the people an individual passed time with became the people they discussed important matters with. Focus groups emphasized that with greater remoteness came greater community cohesion resulting in safer WASH practices. Social networks can enhance and reduce health differently as social infrastructure evolves, highlighting the importance of community-level factors in a period of rapid development.

Abstract Background Like many developing countries, Thailand has experienced a rapid rise in obesity, accompanied by a rapid change in occupational structure. It is plausible that these two trends are related, with movement into sedentary occupations leading to increases in obesity. National health examination survey data contains information on obesity and socioeconomic conditions that can help untangle the relationship, but analysis is challenging because of small sample sizes. Methods This paper explores the relationship between occupation and obesity using data on 10,127 respondents aged 20–59 from the 2009 National Health Examination Survey. Obesity is measured using waist circumference. Modelling is carried out using an approach known as Multiple Regression with Post-Stratification (MRP). We use Bayesian hierarchical models to construct prevalence estimates disaggregated by age, sex, education, urban-rural residence, region, and occupation, and use census population weights to aggregate up. The Bayesian hierarchical model is designed to protect against overfitting and false discovery, which is particularly important in an exploratory study such as this one. Results There is no clear relationship between the overall sedentary nature of occupations and obesity. Instead, obesity appears to vary occupation by occupation. For instance, women in professional occupations, and men who are agricultural or fishery workers, have relatively low rates of obesity. Conclusion Bayesian hierarchical models plus post-stratification offers new possibilities for using surveys to learn about complex health issues.

The growing complexity of Power Distribution Systems, driven by distributed generation, renewable energy integration, and increasing demand, has led to restricted access to DS data due to security and privacy concerns. This study addresses limited data accessibility by proposing a hybrid approach for crafting synthetic power distribution systems tailored for power system analysis and control. Synthetic power distribution systems refer to artificially generated models that faithfully replicate real-world DS features while upholding security and privacy constraints. This innovative methodology merges a Bayesian Hierarchical Model with Markov Chain Monte Carlo techniques, utilizing georeferenced data to capture intricate system dependencies, feeder configurations, switch statuses, and load node distributions. Leveraging OpenStreetMaps for DS topology, the approach incorporates expert knowledge and real-world data. Results highlight the methodology's ability to evaluate credible intervals for parameters, facilitating a probabilistic assessment of uncertainties and enhancing decision support in power system analysis and control. Findings affirm the hybrid approach's efficacy in generating realistic synthetic DSs, bridging the gap between statistical and georeferenced methodologies for advanced power system analysis and control. The capacity to generate synthetic DSs provides valuable insights into power system dynamics, addressing security, privacy, and data accessibility concerns for a more informed decision-making process. • Synthetic System Creation: Bayesian Hierarchical Model ensures robust generation. • Methodological Strength: Parameters treated as independent random variables. • HDI-Backed Distributions: Invaluable insights for well-informed decisions. • Computational Efficiency: Process completion in 460.4 s. • Rapid Iterations: Swift generation of synthetic power systems in < 15 s. [ABSTRACT FROM AUTHOR]

Factors contributing to social inequalities are associated with negative mental health outcomes and disparities in mental well-being. We propose a Bayesian hierarchical controlled interrupted time series to evaluate the impact of policies on population well-being whilst accounting for spatial and temporal patterns. Using data from the UKs Household Longitudinal Study, we apply this framework to evaluate the impact of the UKs welfare reform implemented in the 2010s on the mental health of the participants, measured using the GHQ-12 index. Our findings indicate that the reform led to a 2.36% (95% CrI: 0.57%–4.37%) increase in the national GHQ-12 index in the exposed group, after adjustment for the control group. Moreover, the geographical areas that experienced the largest increase in the GHQ-12 index are from more disadvantage backgrounds than affluent backgrounds. [ABSTRACT FROM AUTHOR]

In oncology drug development, indication selection and optimal dose identification are the primary objectives for the early phase of clinical trials and could significantly impact the probability of success. Master protocols, e.g., basket trial, umbrella trial, and platform trial, have become popular in practice considering the connection of trial designs with multiple indications and treatment candidates. They also enable the optimization of operational resources and maximize the capability of data-driven decision-making. However, most of the available designs are developed with the efficacy endpoint only for treatment effect estimation and testing, without consideration of the safety end point. Thus, it often lacks a comprehensive quantitative framework to allow optimal treatment selection, which could put future development at risk. We propose an optimal Bayesian platform trial design with multiple end points (PMED) to characterize the overall benefit-risk profile. The design is further extended to allow treatment and indication selection within and across arms, with continuous monitoring on multiple interim analyses for futility. In addition, we propose dynamic borrowing across arms to increase the efficiency and accuracy of estimation given the level of similarity across arms. A hierarchical hypothesis structure is utilized to achieve optimal indication and treatment combination selection by controlling family-wise error. Through simulation studies, we show that PMED is a robust design under the studied scenarios with superb power and controlled family-wise error rate. [ABSTRACT FROM AUTHOR]

A basket trial is a trial that investigates a single experimental therapy simultaneously across multiple different patient population groups. When groups are heterogeneous, the optimal analysis is independence but when groups are homogeneous the optimal analysis strategy is pooling. A Bayesian Hierarchical Model (BHM) analysis strategy offers a compromise between independence and pooling. There are multiple different parameterizations of BHMs in the literature offering different ways to account for possible lack of exchangeability and heterogeneity across the groups in the basket. We selected 4 different BHMs that fit under the same general structure but have increasing levels of complexity to accommodate group heterogeneity for comparison. Because the choice of prior for a BHM affects the behavior of the model, we first optimize each model's prior selection in terms of maximizing the expected number of correct decisions across six efficacy scenarios. After optimization, differences between the performance of the models were small. In the context we evaluated, a simpler model may provide similar operating characteristics to a more complex model even when groups are truly heterogeneous. [ABSTRACT FROM AUTHOR]

• The novel Bayesian Hierarchical model predicts m6A-reg-exp genes with m6A reader binding from limited samples in specific context. • The predicted m6A-reg-exp genes with different binding reader have different regulation pattern. • Reveal the complex mechanism of m6A regulation on gene expression. • Indicate the distinct m6A regulated mode of reader targeted genes in cancer. N6-methyladenosine (m6A) is the most abundant form of mRNA modification and plays an important role in regulating gene expression. However, the mechanisms of m6A regulated gene expression in cell or condition specific, are still poorly understood. Even though, some methods are able to predict m6A regulated expression (m6A-reg-exp) genes in specific context, they don't introduce the m6A reader binding information, while this information can help to predict m6A-reg-exp genes and more clearly to explain the mechanisms of m6A-mediated gene expression process. Thus, by integrating m6A sites and reader binding information, we proposed a novel method (called m6Aexpress-Reader) to predict m6A-reg-exp genes from limited MeRIP-seq data in specific context. m6Aexpress-Reader adopts the reader binding signal strength to weight the posterior distribution of the estimated regulatory coefficients for enhancing the prediction power. By using m6Aexpress-Reader , we found the complex characteristic of m6A on gene expression regulation and the distinct regulated pattern of m6A-reg-exp genes with different reader binding. m6A readers, YTHDF2 or IGF2BP1/3 all play an important role in various cancers and the key cancer pathways. In addition, m6Aexpress-Reader reveals the distinct m6A regulated mode of reader targeted genes in cancer. m6Aexpress-Reader could be a useful tool for studying the m6A regulation on reader target genes in specific context and it can be freely accessible at: https://github.com/NWPU-903PR/m6AexpressReader. [ABSTRACT FROM AUTHOR]

Purpose: To assess the association of neighborhood demographic and socioeconomic characteristics with COVID-19 incidence and mortality in New York City (NYC) over the first two waves of outbreak.Methods: This retrospective study used neighborhood-level data from 177 modified ZIP code tabulation areas in NYC between March 01, 2020 and April 30, 2021.Results: Neighborhoods that were most severely impacted in wave 1 were also more affected in wave 2. Neighborhoods with a higher percentage of seniors (≥75 years), males, Black and Hispanic population, and large-size households had higher incidence rates of COVID-19 in wave 1 but not in wave 2. Neighborhoods with higher percentage of Black and Hispanic population and lower insurance coverage had higher death rate per capita and case fatality ratio in wave 1, and neighborhoods with higher percentage of Black and Asian population had elevated case fatality ratio in wave 2. Median household income was negatively associated with incidence rate and death rate per capita but not associated with case fatality ratio in both waves. Neighborhoods with more seniors had higher death rate and case fatality ratio in both waves.Conclusions: Neighborhood disparities in COVID-19 incidence and mortality across NYC neighborhoods were dynamic during the first two waves of outbreak. [ABSTRACT FROM AUTHOR]

We propose a multistage method for making inference at all levels of a Bayesian hierarchical model (BHM) using natural data partitions to increase efficiency by allowing computations to take place in parallel form using software that is most appropriate for each data partition. The full hierarchical model is then approximated by the product of independent normal distributions for the data component of the model. In the second stage, the Bayesian maximum a posteriori (MAP) estimator is found by maximizing the approximated posterior density with respect to the parameters. If the parameters of the model can be represented as normally distributed random effects, then the second-stage optimization is equivalent to fitting a multivariate normal linear mixed model. We consider a third stage that updates the estimates of distinct parameters for each data partition based on the results of the second stage. The method is demonstrated with two ecological data sets and models, a generalized linear mixed effects model (GLMM) and an integrated population model (IPM). The multistage results were compared to estimates from models fit in single stages to the entire data set. In both cases, multistage results were very similar to a full MCMC analysis. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]