Search Results (12)

Experimental animal evidence and a growing body of observational studies suggest that prenatal exposure to phthalates may be a risk factor for childhood obesity. Using data from the Mount Sinai Children’s Environmental Health Study (MSCEHS), which measured urinary phthalate metabolites (including MEP, MnBP, MiBP, MCPP, MBzP, MEHP, MEHHP, MEOHP, and MECPP) during the third trimester of pregnancy (between 25 and 40 weeks) of 382 mothers, we examined adiposity outcomes—body mass index (BMI), fat mass percentage, waist-to-hip ratio, and waist circumference—of 180 children between ages 4 and 9. Our aim was to assess the effects of prenatal exposure to phthalates on these adiposity outcomes, with potential time-varying and sex-specific effects. We applied a novel Bayesian multivariate factor regression (BMFR) that (1) represents phthalate mixtures as latent factors—a DEHP and a non-DEHP factor, (2) borrows information across highly correlated adiposity outcomes to improve estimation precision, (3) models potentially non-linear time-varying effects of the latent factors on adiposity outcomes, and (4) fully quantifies uncertainty using state-of-the-art prior specifications. The results show that in boys, at younger ages (4–6), all phthalate components are associated with lower adiposity outcomes; however, after age 7, they are associated with higher outcomes. In girls, there is no evidence of associations between phthalate factors and adiposity outcomes. Full article

In this study, we aimed to assess the effectiveness of monetary policy in influencing housing prices in Morocco. Bayesian estimation over the period 2007Q2–2017Q2 of a dynamic stochastic general equilibrium model allowed us to reveal a significant impact of the increase in policy interest rates on the prices of residential goods. Indeed, the implementation of a restrictive monetary policy in Morocco will drive the prices of this type of asset downward. Despite this empirical finding, the historical decomposition of shocks impacting the inflation of residential property prices shows that interest rates explain only a small portion of the variations in housing prices in this country. Our results also indicate that an increase in the share of borrowers extends the time required for economic and financial variables to return to their equilibrium state. This is a sign of the potential dangers of fueling housing bubbles through credit booms. Full article

Determining process variances in biopharmaceutical manufacturing is challenging due to limited data availability. To address this, we introduce a Bayesian hierarchical model designed for meta-analysis of process variance. This approach can improve process variance estimation by integrating data from multiple products, providing more reliable estimates of critical quality attributes in cases of data scarcity. Additionally, our model aids in evaluating process models, ensuring quality in process development. The paper demonstrates the new method using a simulation study, showcasing its potential to leverage historical data for both upstream and downstream phases of future CMC drug development. The new statistical model has great potential to expedite the market introduction of therapies while ensuring patient safety, allowing new treatments to reach patients more quickly without compromising quality or efficacy. Full article

This study used the panel data from 15 emerging markets to examine the impact of restrictive macroprudential policies on income inequality from 2000–2019 using Bayesian panel vector autoregression and Bayesian panel dynamics generalised method of moments models. The chosen models are suitable for addressing multiple entity dynamics, accommodating a wide range of variables, handling dense parameterisation, and optimising formativeness and heterogeneous individual-specific factors. The empirical analysis utilised various macroprudential policy proxies and income inequality measures. The results show that when the central banks tighten systems using macroprudential policy instruments to sticker debt-to-income and financial instruments for lower-income borrowers (the bottom 40% of the income distribution), they promote income inequality in these countries while reducing income inequality for high-income borrowers (the high 1 percent of the income distribution). The impact of loan-to-value ratios was found to be insignificant in these countries. Fiscal policy through government expenditure and economic development reduces income inequality, while money supply and oil-price shocks exacerbate it. The study suggests implementing a progressive debt-to-income (DTI) ratio system in emerging markets to address income inequality among lower-income borrowers. This would adjust DTI thresholds based on income brackets, allowing lenient credit access for lower-income borrowers while maintaining stricter limits for higher-income borrowers. This would improve financial stability and reduce income disparities. Additionally, targeted financial literacy programs and a petroleum-linked basic income program could be implemented to distribute oil revenue to lower-income households. A monetary supply stabilisation fund could also be established to maintain financial stability and prevent excessive inflation. Full article

Background/Objectives: Problematic online gaming and gambling tend to co-occur. The exact mechanisms underlying this phenomenon and the potential effects of gender differences remain unknown. This study aimed to identify the early clustering patterns of problematic online gaming and gambling in a community sample of young adults without a lifetime history of psychiatric treatment. Methods: Data were collected through an online survey and analyzed using partial correlations and Bayesian networks. Results: Altogether, 1441 individuals (aged 18–40 years, 51.4% females) participated in the survey. Both problematic online behaviors were weakly interrelated, suggesting that they serve as distinct constructs. Men’s networks appeared to be more complex and had significantly higher global connectivity. Moreover, men and women differed with respect to the specific nodes that bridged both constructs. In men, the bridge nodes were “being criticized because of betting or being told about gambling problems”, “loss of previous interests due to gaming”, “deceiving other people because of gaming”, and “health consequences of gambling”. Among women, the bridge nodes were “feeling guilty because of gambling”, “loss of previous interests because of gaming”, “social consequences of gaming”, and “continued gaming problems with other people”. In men, the strongest edge was found between “borrowing money/selling anything to gamble” and “financial problems because of gambling”, while in women, the strongest edge appeared between “betting more than afforded to be lost” and “tolerance symptoms of gambling”. Conclusions: The findings indicate that problematic online gaming and gambling tend to emerge in different ways among men and women. Therapeutic interventions should be planned considering gender differences. Full article

Basket trials allow simultaneous evaluation of a single therapy across multiple cancer types or subtypes of the same cancer. Since the same treatment is tested across all baskets, it may be desirable to borrow information across them to improve the statistical precision and power in estimating and detecting the treatment effects in different baskets. We review recent developments in Bayesian methods for the design and analysis of basket trials, focusing on the mechanism of information borrowing. We explain the common components of these methods, such as a prior model for the treatment effects that embodies an assumption of exchangeability. We also discuss the distinct features of these methods that lead to different degrees of borrowing. Through simulation studies, we demonstrate the impact of information borrowing on the operating characteristics of these methods and discuss its broader implications for drug development. Examples of basket trials are presented in both phase I and phase II settings. Full article

With the advancement of spatial analysis approaches, methodological research addressing the technical and statistical issues related to joint spatial and spatiotemporal models has increased. Despite the benefits of spatial modelling of several interrelated outcomes simultaneously, there has been no published systematic review on this topic, specifically when such models would be useful. This systematic review therefore aimed at reviewing health research published using joint spatial and spatiotemporal models. A systematic search of published studies that applied joint spatial and spatiotemporal models was performed using six electronic databases without geographic restriction. A search with the developed search terms yielded 4077 studies, from which 43 studies were included for the systematic review, including 15 studies focused on infectious diseases and 11 on cancer. Most of the studies (81.40%) were performed based on the Bayesian framework. Different joint spatial and spatiotemporal models were applied based on the nature of the data, population size, the incidence of outcomes, and assumptions. This review found that when the outcome is rare or the population is small, joint spatial and spatiotemporal models provide better performance by borrowing strength from related health outcomes which have a higher prevalence. A framework for the design, analysis, and reporting of such studies is also needed. Full article

Many population-based surveys have binary responses from a large number of individuals in each household within small areas. One example is the Nepal Living Standards Survey (NLSS II), in which health status binary data (good versus poor) for each individual from sampled households (sub-areas) are available in the sampled wards (small areas). To make an inference for the finite population proportion of individuals in each household, we use the sub-area logistic regression model with reliable auxiliary information. The contribution of this model is twofold. First, we extend an area-level model to a sub-area level model. Second, because there are numerous sub-areas, standard Markov chain Monte Carlo (MCMC) methods to find the joint posterior density are very time-consuming. Therefore, we provide a sampling-based method, the integrated nested normal approximation (INNA), which permits fast computation. Our main goal is to describe this hierarchical Bayesian logistic regression model and to show that the computation is much faster than the exact MCMC method and also reasonably accurate. The performance of our method is studied by using NLSS II data. Our model can borrow strength from both areas and sub-areas to obtain more efficient and precise estimates. The hierarchical structure of our model captures the variation in the binary data reasonably well. Full article

The United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) conducts the Farm Labor Survey to produce estimates of the number of workers, duration of the workweek, and wage rates for all agricultural workers. Traditionally, expert opinion is used to integrate auxiliary information, such as the previous year’s estimates, with the survey’s direct estimates. Alternatively, implementing small area models for integrating survey estimates with additional sources of information provides more reliable official estimates and valid measures of uncertainty for each type of estimate. In this paper, several hierarchical Bayesian subarea-level models are developed in support of different estimates of interest in the Farm Labor Survey. A 2020 case study illustrates the improvement of the direct survey estimates for areas with small sample sizes by using auxiliary information and borrowing information across areas and subareas. The resulting framework provides a complete set of coherent estimates for all required geographic levels. These methods were incorporated into the official Farm Labor publication for the first time in 2020. Full article

Trust is central to a large variety of social interactions. Different research fields have empirically and theoretically investigated trust, observing trusting behaviors in different situations and pinpointing their different components and constituents. However, a unifying, computational formalization of those diverse components and constituents of trust is still lacking. Previous work has mainly used computational models borrowed from other fields and developed for other purposes to explain trusting behaviors in empirical paradigms. Here, I computationally formalize verbal models of trust in a simple model (i.e., vulnerability model) that combines current and prospective action values with beliefs and expectancies about a partner’s behavior. By using the classic investment game (IG)—an economic game thought to capture some important features of trusting behaviors in social interactions—I show how variations of a single parameter of the vulnerability model generates behaviors that can be interpreted as different “trust attitudes”. I then show how these behavioral patterns change as a function of an individual’s loss aversion and expectations of the partner’s behavior. I finally show how the vulnerability model can be easily extended in a novel IG paradigm to investigate inferences on different traits of a partner. In particular, I will focus on benevolence and competence—two character traits that have previously been described as determinants of trustworthiness impressions central to trust. The vulnerability model can be employed as is or as a utility function within more complex Bayesian frameworks to fit participants’ behavior in different social environments where actions are associated with subjective values and weighted by individual beliefs about others’ behaviors. Hence, the vulnerability model provides an important building block for future theoretical and empirical work across a variety of research fields. Full article

Spatio-temporal Bayesian disease mapping is the branch of spatial epidemiology interested in providing valuable risk estimates in certain geographical regions using administrative areas as statistical units. The aim of the present paper is to describe spatio-temporal distribution of cardiovascular mortality in the Province of Pavia in 2010 through 2015 and assess its association with environmental pollution exposure. To produce reliable risk estimates, eight different models (hierarchical log-linear model) have been assessed: temporal parametric trend components were included together with some random effects that allowed the accounting of spatial structure of the region. The Bayesian approach allowed the borrowing information effect, including simpler model results in the more complex setting. To compare these models, Watanabe–Akaike Information Criteria (WAIC) and Leave One Out Information Criteria (LOOIC) were applied. In the modelling phase, the relationship between the disease risk and pollutants exposure (PM2.5) accounting for the urbanisation level of each geographical unit showed a strong significant effect of the pollutant exposure (OR = 1.075 and posterior probability, or PP, >0.999, equivalent to p < 0.001). A high-risk cluster of Cardiovascular mortality in the Lomellina subareas in the studied window was identified. Full article

Borrowing the approaches of population genetics, evolutionary toxicology was particularly useful in assessing the transgenerational effects of a substance at sublethal concentrations, as well as evaluating genetic variation in populations exposed to pollutants. Starting from assays in controlled conditions, in recent years this approach has also found successful applications multi-stressed natural systems. It is also able to exploit the huge amount of data provided by Next Generation Sequencing (NGS) techniques. Similarly, the focus has shifted from effects on the overall genetic variability, the so-called “genetic erosion”, to selective effects induced by contaminants at more specific pathways. In the aquatic context, effects are usually assessed on non-model species, preferably native fish or macroinvertebrates. Here we provide a review of current trends in this specific discipline, with a focus on population genetics and genomics approaches. In addition, we demonstrate the potential usefulness of predictive simulation and Bayesian techniques. A focused collection of field and laboratory studies is discussed to demonstrate the effectiveness of this approach, covering a range of molecular markers, different endpoints of genetic variation, and different classes of chemical contaminants. Moreover, guidelines for a future implementation of evolutionary perspective into Ecological Risk Assessment are provided. Full article