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Dialysis patients experience frequent hospitalizations and a higher mortality rate compared to other Medicare populations, in whom hospitalizations are a major contributor to morbidity, mortality, and healthcare costs. Patients also typically remain on dialysis for the duration of their lives or until kidney transplantation. Hence, there is growing interest in studying the spatiotemporal trends in the correlated outcomes of hospitalization and mortality among dialysis patients as a function of time starting from transition to dialysis across the United States Utilizing national data from the United States Renal Data System (USRDS), we propose a novel multivariate spatiotemporal functional principal component analysis model to study the joint spatiotemporal patterns of hospitalization and mortality rates among dialysis patients. The proposal is based on a multivariate Karhunen–Loéve expansion that describes leading directions of variation across time and induces spatial correlations among region-specific scores. An efficient estimation procedure is proposed using only univariate principal components decompositions and a Markov Chain Monte Carlo framework for targeting the spatial correlations. The finite sample performance of the proposed method is studied through simulations. Novel applications to the USRDS data highlight hot spots across the United States with higher hospitalization and/or mortality rates and time periods of elevated risk. [ABSTRACT FROM AUTHOR]

China's export benefits from the significant fiscal stimulus in the United States. This paper analyzes the global spillover effect of the American economy on China's macro-economy using the Markov Chain Monte Carlo (MCMC)-Gibbs sampling approach, with the goal of improving the ability of China's financial system to protect against foreign threats. This paper examines the theories of the consequences of uncertainty on macroeconomics first. Then, using medium-sized economic and financial data, the uncertainty index of the American and Chinese economies is built. In order to complete the test and analysis of the dynamic relationship between American economic uncertainty and China's macro-economy, a Time Varying Parameter-Stochastic Volatility-Vector Autoregression (TVP- VAR) model with random volatility is constructed. The model is estimated using the Gibbs sampling method based on MCMC. For the empirical analysis, samples of China's and the United States' economic data from January 2001 to January 2022 were taken from the WIND database and the FRED database, respectively. The data reveal that there are typically fewer than 5 erroneous components in the most estimated parameters of the MCMC model, which suggests that the model's sampling results are good. China's pricing level reacted to the consequences of the unpredictability of the American economy by steadily declining, reaching its lowest point during the financial crisis in 2009, and then gradually diminishing. After 2012, the greatest probability density range of 68% is extremely wide and contains 0, indicating that the impact of economic uncertainty in the United States on China's pricing level is no longer significant. China should therefore focus on creating a community of destiny by working with nations that have economic cooperation to lower systemic financial risks and guarantee the stability of the capital market. [ABSTRACT FROM AUTHOR]

To the best of our knowledge, no previous study has quantitatively estimated the dynamics and cumulative susceptibility to influenza infections after the widespread lifting of COVID‐19 public health measures. We constructed an imitated stochastic susceptible‐infected‐removed model using particle‐filtered Markov Chain Monte Carlo sampling to estimate the time‐dependent reproduction number of influenza based on influenza surveillance data in southern China, northern China, and the United States during the 2022–2023 season. We compared these estimates to those from 2011 to 2019 seasons without strong social distancing interventions to determine cumulative susceptibility during COVID‐19 restrictions. Compared to the 2011–2019 seasons without a strong intervention with social measures, the 2022–2023 influenza season length was 45.0%, 47.1%, and 57.1% shorter in southern China, northern China, and the United States, respectively, corresponding to an 140.1%, 74.8%, and 50.9% increase in scale of influenza infections, and a 60.3%, 72.9%, and 45.1% increase in population susceptibility to influenza. Large and high‐intensity influenza epidemics occurred in China and the United States in 2022–2023. Population susceptibility increased in 2019–2022, especially in China. We recommend promoting influenza vaccination, taking personal prevention actions on at‐risk populations, and monitoring changes in the dynamic levels of influenza and other respiratory infections to prevent potential outbreaks in the coming influenza season. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Susceptible–Infected–Removal (SIR) model with time fused coefficients. In particular, our proposed model discovers the underlying time homogeneity pattern for the SIR model's transmission rate and removal rate via Bayesian shrinkage priors. MCMC sampling for the proposed method is facilitated by the nimble package in R. Extensive simulation studies are carried out to examine the empirical performance of the proposed methods. We further apply the proposed methodology to analyze different levels of COVID-19 data in the United States. [ABSTRACT FROM AUTHOR]

Frequency analysis of tornadoes is very important for risk analysis and disaster control. In this paper, the annual frequency of tornadoes that occurred in the United States from 1967 to 2016 is analyzed. The simple analysis shows that frequencies of tornadoes of different sites are spatially correlated and over-dispersed. To explain the two characteristics of the data, the Bayesian hierarchical model is proposed. For comparison purposes, the Bayesian model with negative binomial distribution, Poisson distribution, Polya distribution, and first-order, non-negative, integer-valued autoregressive model with Bell innovations(BL-INAR(1)) are considered to fit the frequency of tornado. The distribution parameters of all sites are assumed to be spatially correlated, and the corresponding Bayesian hierarchical models were established. MCMC (Markov Chain Monte Carlo) method is applied to parameter estimations and relative statistical inference. By comparison of the analysis results, the negative binomial distribution is recommended to analyze the overdispersion and spatial correlation among the sites of the data. The comparison between the simulated frequencies based on the proposed model and the actual frequencies also verifies that the proposed method is a better model for the data. [ABSTRACT FROM AUTHOR]

The two foremost aims of classical regression are to assess the structure and magnitude of the relationship between variables. Despite the aforementioned benefits, unlike classical regression, which only offers a point estimate and a confidence interval, Bayesian regression offers the whole spectrum of inferential solutions. The results of this study demonstrate the Bayesian approach's suitability for regression tasks and its advantage in accounting for additional a priori data, which often strengthens studies. Using data from Boston Housing from UCI ML Repository, this study proves that the prior distributions have the benefit of producing analytical, closed-form conclusions, which eliminates the need to use numerical techniques like Markov Chain Monte Carlo (MCMC). Second, software implementations are offered together with formulas for the posterior outcomes that are supplied, clarified, and shown. The assumptions supporting the suggested approach are evaluated in the third step using Bayesian tools. Prior elicitation, posterior calculation, and robustness to prior uncertainty and model sufficiency are the three processes that are essential to Bayesian inference. The findings confirm that Bayesian models provide a full posterior distribution over model parameters, which can be used to quantify uncertainty and make more informed predictions and can also incorporate regularization techniques to reduce overfitting and improve generalization performance. [ABSTRACT FROM AUTHOR]

Over 782 000 individuals in the United States have end‐stage kidney disease with about 72% of patients on dialysis, a life‐sustaining treatment. Dialysis patients experience high mortality and frequent hospitalizations, at about twice per year. These poor outcomes are exacerbated at key time periods, such as the fragile period after transition to dialysis. In order to study the time‐varying effects of modifiable patient and dialysis facility risk factors on hospitalization and mortality, we propose a novel Bayesian multilevel time‐varying joint model. Efficient estimation and inference is achieved within the Bayesian framework using Markov chain Monte Carlo, where multilevel (patient‐ and dialysis facility‐level) varying coefficient functions are targeted via Bayesian P‐splines. Applications to the United States Renal Data System, a national database which contains data on nearly all patients on dialysis in the United States, highlight significant time‐varying effects of patient‐ and facility‐level risk factors on hospitalization risk and mortality. Finite sample performance of the proposed methodology is studied through simulations. [ABSTRACT FROM AUTHOR]

Over 785,000 individuals in the United States have end-stage renal disease (ESRD), with about 70% of patients on dialysis, a life-sustaining treatment. Dialysis patients experience frequent hospitalizations. In order to identify risk factors of hospitalizations, we utilize data from the large national database, United States Renal Data System (USRDS). To account for the hierarchical structure of the data, with longitudinal hospitalization rates nested in dialysis facilities and dialysis facilities nested in geographic regions across the United States, we propose a multilevel varying coefficient spatiotemporal model (M-VCSM) where region- and facility-specific random deviations are modelled through a multilevel Karhunen–Loéve (KL) expansion. The proposed M-VCSM includes time-varying effects of multilevel risk factors at the region- (e.g., urbanicity and area deprivation index) and facility-levels (e.g., patient demographic makeup) and incorporates spatial correlations across regions via a conditional autoregressive (CAR) structure. Efficient estimation and inference are achieved through the fusion of functional principal component analysis (FPCA) and Markov chain Monte Carlo (MCMC). Applications to the USRDS data highlight significant region- and facility-level risk factors of hospitalizations and characterize time periods and spatial locations with elevated hospitalization risk. Finite sample performance of the proposed methodology is studied through simulations. [ABSTRACT FROM AUTHOR]

This paper presents estimates of the prevalence of dementia in the United States from 2000 to 2016 by age, sex, race and ethnicity, education, and a measure of lifetime earnings, using data on 21,442 individuals aged 65 y and older and 97,629 person-year observations from a nationally representative survey, the Health and Retirement Study (HRS). The survey includes a range of cognitive tests, and a subsample underwent clinical assessment for dementia. We developed a longitudinal, latent-variable model of cognitive status, which we estimated using the Markov Chain Monte Carlo method. This model provides more accurate estimates of dementia prevalence in population subgroups than do previously used methods on the HRS. The age-adjusted prevalence of dementia decreased from 12.2% in 2000 (95% CI, 11.7 to 12.7%) to 8.5% in 2016 (7.9 to 9.1%) in the 65+ population, a statistically significant decline of 3.7 percentage points or 30.1%. Females are more likely to live with dementia, but the sex difference has narrowed. In the male subsample, we found a reduction in inequalities across education, earnings, and racial and ethnic groups; among females, those inequalities also declined, but less strongly. We observed a substantial increase in the level of education between 2000 and 2016 in the sample. This compositional change can explain, in a statistical sense, about 40% of the reduction in dementia prevalence among men and 20% among women, whereas compositional changes in the older population by age, race and ethnicity, and cardiovascular risk factors mattered less. [ABSTRACT FROM AUTHOR]

We present the AdaptSPEC-X method for the joint analysis of a panel of possibly nonstationary time series. The approach is Bayesian and uses a covariate-dependent infinite mixture model to incorporate multiple time series, with mixture components parameterized by a time-varying mean and log spectrum. The mixture components are based on AdaptSPEC, a nonparametric model which adaptively divides the time series into an unknown number of segments and estimates the local log spectra by smoothing splines. AdaptSPEC-X extends AdaptSPEC in three ways. First, through the infinite mixture, it applies to multiple time series linked by covariates. Second, it can handle missing values, a common feature of time series which can cause difficulties for nonparametric spectral methods. Third, it allows for a time-varying mean. Through these extensions, AdaptSPEC-X can estimate time-varying means and spectra at observed and unobserved covariate values, allowing for predictive inference. Estimation is performed by Markov chain Monte Carlo (MCMC) methods, combining data augmentation, reversible jump, and Riemann manifold Hamiltonian Monte Carlo techniques. We evaluate the methodology using simulated data, and describe applications to Australian rainfall data and measles incidence in the United States. Software implementing the method proposed in this article is available in the R package BayesSpec. Supplementary files for this article are available online. [ABSTRACT FROM AUTHOR]

Laplace method is a practical tool for obtaining maximum likelihood estimators for a wide class of latent variable models. The main idea is to approximate the integrand using a Gaussian distribution. However, with increasing observations, the Laplace approximation becomes infeasible because the dimension of the correlated latent variables grows, which results in the high‐dimensional optimization problem. One important example is spatial latent variable models, which are widely used in many fields, such as ecology, epidemiology, and sociology. Spatial latent variable models are useful for investigating the relationship between spatial covariates or predicting the unobserved area. Here, we propose a fast Laplace approximation based on the dimension reduction of the latent variables. Our methods are faster and have fewer components to be tuned than simulation‐based methods such as Markov chain Monte Carlo maximum likelihood and Monte Carlo expectation‐maximization. Our approach can be applied to the large non‐Gaussian spatial data sets, commonly used in modern environmental sciences. Especially, we show how we may understand spatial patterns of non‐Gaussian responses for two case studies: confirmed COVID‐19 cases in the United States and thickness of the Antarctic ice sheet. Through simulation studies under different scenarios, we investigate that our method can provide accurate maximum likelihood estimations and predictions quickly. Our study can be widely applicable for practical maximum likelihood inference for high‐dimensional random effect models. We provide a freely available R‐package that can implement the proposed method. [ABSTRACT FROM AUTHOR]

Background: Veterans increasingly utilize both the Veteran's Health Administration (VA) and non-VA hospitals (dual-users). Dual-users are at increased risk of fragmented care and adverse outcomes and often do not receive necessary follow-up care addressing social determinants of health (SDOH). We developed a Veteran-informed social worker-led Advanced Care Coordination (ACC) program to decrease fragmented care and provide longitudinal care coordination addressing SDOH for dual-users accessing non-VA emergency departments (EDs) in two communities.Methods: ACC had four core components: 1. Notification from non-VA ED providers of Veterans' ED visit; 2. ACC social worker completed a comprehensive assessment with the Veteran to identify SDOH needs; 3. Clinical intervention addressing SDOH up to 90 days post-ED discharge; and 4. Warm hand-off to Veteran's VA primary care team. Data was documented in our program database. We performed propensity matching between a control group and ACC participants between 4/10/2018 - 4/1/2020 (N- = 161). A joint survival model using Markov Chain Monte Carlo technique was employed for 30-day outcomes. We performed Difference-In-Difference analyses on number of ED visits, admissions, and primary care physician (PCP) visits 120-day pre/post discharge.Results: When compared to a matched control group ACC had significantly lower risk of 30-day ED visits (Hazard Ratio (HR) = 0.61, 95% Confidence Interval (CI) = (0.42, 0.92)) and a higher probability of PCP visits at 13-30 days post-ED visit (HR = 1.5, 95% CI = (1.01, 2.22)). Veterans enrolled in ACC were connected to VA PCP visits (50%), VA benefits (19%), home health care (10%), mental health and substance use treatment (7%), transportation (7%), financial assistance (5%), and homeless resources (2%).Conclusion: We developed and implemented a program addressing dual-users' SDOH needs post non-VA ED discharge. Social workers connected dual-users to needed follow-up care and resources which reduced fragmentation and adverse outcomes. [ABSTRACT FROM AUTHOR]

Background: Renal cell carcinoma (RCC) remains a global health concern due to its poor survival rate. This study aimed to investigate the influence of medical determinants and socioeconomic status on survival outcomes of RCC patients. We analyzed the survival data of 41,563 RCC patients recorded under the Surveillance, Epidemiology, and End Results (SEER) program from 2012 to 2020., Methods: We employed a competing risk model, assuming lifetime of RCC patients under various risks follows Chen distribution. This model accounts for uncertainty related to survival time as well as causes of death, including missing cause of death. For model analysis, we utilized Bayesian inference and obtained the estimate of various key parameters such as cumulative incidence function (CIF) and cause-specific hazard. Additionally, we performed Bayesian hypothesis testing to assess the impact of multiple factors on the survival time of RCC patients., Results: Our findings revealed that the survival time of RCC patients is significantly influenced by gender, income, marital status, chemotherapy, tumor size, and laterality. However, we observed no significant effect of race and origin on patient's survival time. The CIF plots indicated a number of important distinctions in incidence of causes of death corresponding to factors income, marital status, race, chemotherapy, and tumor size., Conclusions: The study highlights the impact of various medical and socioeconomic factors on survival time of RCC patients. Moreover, it also demonstrates the utility of competing risk model for survival analysis of RCC patients under Bayesian paradigm. This model provides a robust and flexible framework to deal with missing data, which can be particularly useful in real-life situations where patients information might be incomplete., Competing Interests: Declaration of Competing Interest There is no conflict of interest in the present work., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Public health officials discouraged travel and non-household gatherings for Thanksgiving, but data suggests that travel increased over the holidays. The objective of this analysis was to assess associations between holiday gatherings and SARS-CoV-2 positivity in the weeks following Thanksgiving. Using an online survey, we sampled 7770 individuals across 10 US states from December 4–18, 2020, about 8–22 days post-Thanksgiving. Participants were asked about Thanksgiving, COVID-19 symptoms, and SARS-CoV-2 testing and positivity in the prior 2 weeks. Logistic regression was used to identify factors associated with SARS-CoV-2 positivity and COVID-19 symptoms in the weeks following Thanksgiving. An activity score measured the total number of non-essential activities an individual participated in the prior 2 weeks. The probability of community transmission was estimated using Markov Chain Monte Carlo (MCMC) methods. While 47.2% had Thanksgiving at home with household members, 26.9% had guests and 25.9% traveled. There was a statistically significant interaction between how people spent Thanksgiving, the frequency of activities, and SARS-CoV-2 test positivity in the prior 2 weeks (p < 0.05). Those who had guests for Thanksgiving or traveled were only more likely to test positive for SARS-CoV-2 if they also had high activity (e.g., participated in > one non-essential activity/day in the prior 2 weeks). Had individuals limited the number and frequency of activities post-Thanksgiving, cases in surveyed individuals would be reduced by > 50%. As travel continues to increase and the more contagious Delta variant starts to dominate transmission, it is critical to promote how to gather in a "low-risk" manner (e.g., minimize other non-essential activities) to mitigate the need for nationwide shelter-at-home orders. [ABSTRACT FROM AUTHOR]

Context: Maternal oxidative stress in pregnancy can arise through a multitude of sources and may have lifelong consequences for the child. Animal studies suggest that prenatal oxidative stress may contribute to metabolic dysfunction and excessive weight gain in the offspring. However, this relationship has been studied minimally in humans.Objective: Determine the association between prenatal oxidative stress biomarkers and child weight and body mass index (BMI) z-scores from birth to age 6.Methods: Within The Infant Development and the Environment Study (TIDES) prospective pregnancy cohort, we calculated age- and sex-specific Z-scores for child weight and BMI, measured between birth and age 6 (N = 736). Three oxidative stress biomarkers were quantified in third-trimester urine, including 8-iso-prostaglandin F2α (8-iso-PGF2α), its primary metabolite, and prostaglandin F2α (PGF2α). We examined associations between each biomarker and Z-scores using linear regression as well as group-based trajectory modeling.Results: Prenatal 8-iso-PGF2α and its metabolite were associated with lower birth weight and higher weight at age 4. For example, an ln-unit increase in 8-iso-PGF2α was associated with 0.17 SD higher weight at age 4 (95% CI 0.01, 0.33). These biomarkers were also associated with higher BMI at age 4. Finally, within 4 unique weight trajectories (low, normal, high, and low-high), children of mothers with higher 8-iso-PGF2α were 2.56 times more likely (95% CI 1.22, 5.41) to be in the low-high trajectory than children in the normal group.Conclusion: We observed associations between third-trimester oxidative stress and lower birth weight as well as higher early childhood weight and BMI. These findings have important implications for understanding the developmental origins of childhood weight gain and metabolic disease. [ABSTRACT FROM AUTHOR]

Background: Cardioversion is common among patients with atrial fibrillation (AF). We hypothesised that novel oral anticoagulants (NOAC) used in clinical practice resulted in similar rates of stroke compared with vitamin K antagonists (VKA) for cardioversion.Methods: Using the Outcomes Registry for Better Informed Treatment of Atrial Fibrillation II, patients with AF who had a cardioversion, follow-up data and an AF diagnosis within 6 months of enrolment were identified retrospectively. Clinical outcomes were compared for patients receiving a NOAC or VKA for 1 year following cardioversion.Results: Among 13 004 patients with AF, 2260 (17%) underwent cardioversion. 1613 met the inclusion criteria for this analysis. At the time of cardioversion, 283 (17.5%) were receiving a VKA and 1330 (82.5%) a NOAC. A transoesophageal echocardiogram (TOE) was performed in 403 (25%) cardioversions. The incidence of stroke/transient ischaemic attack (TIA) at 30 days was the same for patients having (3.04 per 100 patient-years) or not having (3.04 per 100 patient-years) a TOE (p=0.99). There were no differences in the incidence of death (HR 1.19, 95% CI 0.62 to 2.28, p=0.61), cardiovascular hospitalisation (HR 1.02, 95% CI 0.76 to 1.35, p=0.91), stroke/TIA (HR 1.18, 95% CI 0.30 to 4.74, p=0.81) or bleeding-related hospitalisation (HR 1.29, 95% CI 0.66 to 2.52, p=0.45) at 1 year for patients treated with either a NOAC or VKA.Conclusions: Cardioversion was a low-risk procedure for patients treated with NOAC, and there were statistically similar rates of stroke/TIA 30 days after cardioversion as for patients treated with VKA. There were no statically significant differences in death, stroke/TIA or major bleeding at 1 year among patients treated with NOAC compared with VKA after cardioversion. [ABSTRACT FROM AUTHOR]

Spatially explicit information on crop yields is essential for farmers and agricultural agencies to make well-informed decisions. One approach to estimate crop yield with remote sensing is data assimilation, which integrates sequential observations of canopy development from remote sensing into model simulations of crop growth processes. However, it remains challenging to efficiently scale this approach to large areas while maintaining reliable prediction at field scales. In this paper, we explored the factors limiting the generalization of the data assimilation approach and found that the accuracy of crop model prediction and the systematic model errors can significantly affect the performance of data assimilation and the yield estimation. To address these issues, we propose a hierarchical data assimilation framework, which enables maize yield estimation at field levels across large areas for the Midwestern US with no a priori knowledge about the management of individual fields. This approach applies data assimilation algorithms at two spatial scales. At the county scale, we adopted a Markov Chain Monte Carlo algorithm to recalibrate uncertain and sensitive model parameters based on aggregated Leaf Area Index (LAI) time series derived from Landsat images and county-level yield statistics. Using the county-specific models, we assimilated LAI time series into crop model simulations using Ensemble Kalman Filter for individual fields or pixels. This method was validated by multiple field-level maize yield datasets across major production states in the US Midwest. The Root Mean Squared Error ranges from 1.4 to 2.3 ton/ha, and the percentage error is between 9% and 21%. The hierarchical data assimilation framework provides a novel solution that downscales county-level yield statistics to 30-meter resolution yield maps, which can inform between and within field maize yield variability. This study contributes valuable insights towards practical large-scale crop yield mapping at high resolutions. • A methodology for field-level crop yield estimation is presented. • Satellite images downscale crop model results to individual fields over large areas. • Estimation error of maize yields at field level is 17% in the midwestern U.S. • Yield maps derived from Landsat images resolve between- and within-field variability. [ABSTRACT FROM AUTHOR]

Abstract The Northeast United States, particularly New York State has experienced an increase in extreme daily precipitation during the past 50 years. Recent events such as Hurricane Irene and Superstorm Sandy, have revealed vulnerability to the intense precipitation within the transportation sector. In the scale of New York City, where transit system is the most dominant mode of transportation and daily mobility of millions of passengers depends on it, any disruption in the transit service would result in gridlocks and massive delays. To assess the impacts of rainfall on the subway ridership, we merged high resolution radar rainfall and subway ridership data to conduct a detailed analysis for each of the 116 subway stations at the borough of Manhattan. The analysis is carried out on both hourly and daily resolution level, where a spatial-temporal Bayesian multi-level regression model is used to capture the underlying dependency between the parameters. The estimation results are obtained through Markov Chain Monte Carlo sampling method. The results for daily analysis indicate that during weekdays, transit ridership in the stations located in commercial zones are less sensitive to the rainfall compared to the ones in residential zones. Highlights • Impacts of spatial variability of rainfall on subway ridership is assessed using large scale ridership and rainfall data. • A unified multi scale model, combining time and spatial scales for ridership is proposed. • The study framework can be used by agencies to improve system efficiency and resilience at the station level. [ABSTRACT FROM AUTHOR]

A stationary bilinear (SB) model can be used to describe processes with a time-varying degree of persistence that depends on past shocks. This study develops methods for Bayesian inference, model comparison, and forecasting in the SB model. Using monthly U.K. inflation data, we find that the SB model outperforms the random walk, first-order autoregressive AR(1), and autoregressive moving average ARMA(1,1) models in terms of root mean squared forecast errors. In addition, the SB model is superior to these three models in terms of predictive likelihood for the majority of forecast observations. [ABSTRACT FROM AUTHOR]

Recently developed vaccines provide a new way of controlling rotavirus in sub-Saharan Africa. Models for the transmission dynamics of rotavirus are critical both for estimating current burden from imperfect surveillance and for assessing potential effects of vaccine intervention strategies. We examine rotavirus infection in the Maradi area in southern Niger using hospital surveillance data provided by Epicentre collected over two years. Additionally, a cluster survey of households in the region allows us to estimate the proportion of children with diarrhea who consulted at a health structure. Model fit and future projections are necessarily particular to a given model; thus, where there are competing models for the underlying epidemiology an ensemble approach can account for that uncertainty. We compare our results across several variants of Susceptible-Infectious-Recovered (SIR) compartmental models to quantify the impact of modeling assumptions on our estimates. Model-specific parameters are estimated by Bayesian inference using Markov chain Monte Carlo. We then use Bayesian model averaging to generate ensemble estimates of the current dynamics, including estimates of R 0 , the burden of infection in the region, as well as the impact of vaccination on both the short-term dynamics and the long-term reduction of rotavirus incidence under varying levels of coverage. The ensemble of models predicts that the current burden of severe rotavirus disease is 2.6–3.7% of the population each year and that a 2-dose vaccine schedule achieving 70% coverage could reduce burden by 39–42%. [ABSTRACT FROM AUTHOR]

Aims: Haemodynamic-guided heart failure (HF) management effectively reduces decompensation events and need for hospitalizations. The economic benefit of clinical improvement requires further study.Methods and Results: An estimate of the cost-effectiveness of haemodynamic-guided HF management was made based on observations published in the randomized, prospective single-blinded CHAMPION trial. A comprehensive analysis was performed including healthcare utilization event rates, survival, and quality of life demonstrated in the randomized portion of the trial (18 months). Markov modelling with Monte Carlo simulation was used to approximate comprehensive costs and quality-adjusted life years (QALYs) from a payer perspective. Unit costs were estimated using the Truven Health MarketScan database from April 2008 to March 2013. Over a 5-year horizon, patients in the Treatment group had average QALYs of 2.56 with a total cost of US$56 974; patients in the Control group had QALYs of 2.16 with a total cost of US$52 149. The incremental cost-effectiveness ratio (ICER) was US$12 262 per QALY. Using comprehensive cost modelling, including all anticipated costs of HF and non-HF hospitalizations, physician visits, prescription drugs, long-term care, and outpatient hospital visits over 5 years, the Treatment group had a total cost of US$212 004 and the Control group had a total cost of US$200 360. The ICER was US$29 593 per QALY.Conclusions: Standard economic modelling suggests that pulmonary artery pressure-guided management of HF using the CardioMEMS™ HF System is cost-effective from the US-payer perspective. This analysis provides the background for further modelling in specific country healthcare systems and cost structures. [ABSTRACT FROM AUTHOR]

Previous research on service failures, often measured by customer complaints, has not examined how organizations can measure or monitor their service efficiency. In this article, we introduce a new model that is suitable for measuring both service efficiency and technical efficiency when both bad outputs (i.e. service complaints) and good outputs (i.e. passenger trips and flights) are present. We develop our model with an output distance function, using Bayesian methods of inference organized around Markov chain Monte Carlo (MCMC). We illustrate our model with an application in the U.S. airline industry, an industry sector beset with service failures affecting both revenues and costs. We present the service inefficiency results of various US airlines and discuss the determinants of bad outputs in this industry. We also test whether our results are in line with market expectations by comparing the service efficiency estimates against the “American Customer Satisfaction Index” data. [ABSTRACT FROM AUTHOR]

This paper investigates the gasoline price adjustment to changes in the input cost price for a panel of 48 US states using a monthly data set covering the period 1994–2011. We build, for the first time, a non-linear threshold panel vector-error-correction model (PVECM) and propose efficient Markov chain Monte Carlo (MCMC) Bayesian techniques. Our findings indicate that states with high margin experience a slower adjustment and a more asymmetric response to input price cost shocks. Our results are robust to potential structural breaks in the threshold parameter, which is important as market conditions change over time and are very sensitive to production/consumption constraints. Lastly, we attribute fluctuations in the gasoline prices to input cost shocks, arguing that the peak responses occurring one month after the shock are short-lived. [ABSTRACT FROM AUTHOR]

Transportation system models rely heavily upon value of time (VOT) estimates to predict customer behavior. Accurate VOT estimates are particularly vital for planning new services such as on-demand ride hailing or microtransit because customers' sensitivity to wait time, walk time, and route detour time affects their likelihood of selecting these modes. If an incorrect VOT is assumed during service planning, then ridership will be depressed because of a mismatch between their preferences and how the system is designed. In this paper, we report on the measurement of VOT for microtransit, a shared first-mile/last-mile mobility service, obtained using stated preference microdata from four U.S. cities. We found a median in-vehicle VOT for microtransit of $18.63 (95% CI: $13.39–$24.46) and an access VOT of $75.38 (95% CI: $59.22–$94.96). The former is practically equal to the VOT we found for respondents' current modes ($20.24, 95% CI: $13.71–$26.94). We also found that men, younger riders, the highly educated, and transit riders are more likely to be interested in microtransit. Since the disutility of time spent on microtransit is not higher than that of other modes, we believe this new service has the potential to attract riders, and particularly if the system is designed with low waiting and walking times. [ABSTRACT FROM AUTHOR]

Livestock movements between agricultural premises is an important pathway for the spread of infectious disease. Data providing details about the origin and destination of shipments, as well as information about the shipment size is an important component of computer models used to formulate mitigation strategies and design surveillance programs. The United States (U.S.) currently lacks a comprehensive database of farm animal shipments, which hinders such efforts. With the U.S. Animal Movement Model (USAMM), earlier work has successfully scaled up from limited data based on interstate certificates of veterinary inspection (CVI) to comprehensive county-level shipment networks at the national scale. In this work, we present three major improvements to earlier versions of USAMM: (1) increased resolution of the model and simulated networks to the level of individual premises; (2) predictions of shipment sizes; (3) taking into account the types and herd sizes of the premises. We fitted parameters in a Bayesian framework to two sets of CVI data consisting of sub-samples of one year's between-state beef and dairy shipments. Through posterior predictive simulation, we then created 1,000 synthetic beef and dairy networks, which we make publicly available to support livestock disease modeling. The simulated networks were validated against summary statistics of the training data as well as out-of-sample CVI data from subsequent years. This new development opens up the possibility of using USAMM in a broader spectrum of applications where information about shipment size and premises identity is necessary and gives novel insights into the U.S. cattle shipment network. • A Bayesian model of the U.S. cattle shipment network is presented. • Scales up incomplete shipment data to networks covering the contiguous U.S. • Predicts spatial network structure and shipment sizes. • Takes detailed premises demography with different premises types into account. [ABSTRACT FROM AUTHOR]

Background: This study contributes to dialogue on quality versus quantity of life by examining years older persons can expect to live in various states of pain.Methods: Data from seven waves of the Health and Retirement Study; N = 26,896; age 55+. Estimations using the Interpolative Markov Chain approach apply probability transitions to multistate life table functions. Two estimates are interpreted: (i) population-based, which provide population averages aggregated across baseline states and (ii) status-based, which provide independent estimates by baseline state. Age- and sex-specific years with no pain, milder nonlimiting, and severe or limiting pain are reported as is percent of life in states of pain.Results: Females have higher life expectancy than males but similar expectations of pain-free life. Total life expectancy varies only slightly by baseline pain states but pain-free life expectancy varies greatly. For example, an 85-year-old female pain-free at baseline expects 7.04 more years, 5.28 being pain-free. An 85-year-old female with severe pain at baseline expects 6.42 years with only 2.66 pain-free. Percent of life with pain decreases by age for those pain-free at baseline and increases for those with pain at baseline.Conclusion: Pain is moderately associated with quantity of or total life but substantially and importantly associated with quality of or pain-free life. [ABSTRACT FROM AUTHOR]

Regional monitoring strategies frequently employ a nested sampling design where a finite set of study areas from throughout a region are selected and intensive sampling occurs within a subset of sites within the individual study areas. This sampling protocol naturally lends itself to a hierarchical analysis to account for dependence among subsamples. Implementing such an analysis using a classic likelihood framework is computationally challenging when accounting for detection errors in species occurrence models. Bayesian methods offer an alternative approach for fitting models that readily allows for spatial structure to be incorporated. We demonstrate a general approach for estimating occupancy when data come from a nested sampling design. We analyzed data from a regional monitoring program of wood frogs ( Lithobates sylvaticus) and spotted salamanders ( Ambystoma maculatum) in vernal pools using static and dynamic occupancy models. We analyzed observations from 2004 to 2013 that were collected within 14 protected areas located throughout the northeast United States. We use the data set to estimate trends in occupancy at both the regional and individual protected area levels. We show that occupancy at the regional level was relatively stable for both species. However, substantial variation occurred among study areas, with some populations declining and some increasing for both species. In addition, When the hierarchical study design is not accounted for, one would conclude stronger support for latitudinal gradient in trends than when using our approach that accounts for the nested design. In contrast to the model that does not account for nesting, the nested model did not include an effect of latitude in the 95% credible interval. These results shed light on the range-level population status of these pond-breeding amphibians, and our approach provides a framework that can be used to examine drivers of local and regional occurrence dynamics. [ABSTRACT FROM AUTHOR]

ABSTRACT Knowledge of factors influencing animal abundance is important to wildlife biologists developing management plans. This is especially true for economically important species such as blackbirds (Icteridae), which cause more than $100 million in crop damages annually in the United States. Using data from the North American Breeding Bird Survey, the National Land Cover Dataset, and the National Climatic Data Center, we modeled effects of regional environmental variables on relative abundance of 3 blackbird species (red-winged blackbird, Agelaius phoeniceus; yellow-headed blackbird, Xanthocephalus xanthocephalus; common grackle, Quiscalus quiscula) in the Prairie Pothole Region of the central United States. We evaluated landscape covariates at 3 logarithmically related spatial scales (1,000 ha, 10,000 ha, and 100,000 ha) and modeled weather variables at the 100,000-ha scale. We constructed models a priori using information from published habitat associations. We fit models with WinBUGS using Markov chain Monte Carlo techniques. Both landscape and weather variables contributed strongly to predicting blackbird relative abundance (95% credibility interval did not overlap 0). Variables with the strongest associations with blackbird relative abundance were the percentage of wetland area and precipitation amount from the year before bird surveys were conducted. The influence of spatial scale appeared small-models with the same variables expressed at different scales were often in the best model subset. This large-scale study elucidated regional effects of weather and landscape variables, suggesting that management strategies aimed at reducing damages caused by these species should consider the broader landscape, including weather effects, because such factors may outweigh the influence of localized conditions or site-specific management actions. The regional species distributional models we developed for blackbirds provide a tool for understanding these broader landscape effects and guiding wildlife management practices to areas that are optimally beneficial. Published 2015. This article is a U.S. Government work and is in the public domain in the USA. [ABSTRACT FROM AUTHOR]

Atrazine is a herbicide frequently detected in both surface and groundwater in the United States (U.S.), but its spatiotemporal distribution and concentration trends have only been analyzed recently at regional or local scales. We employed a Bayesian hierarchical modeling approach to assess spatial and seasonal variation in atrazine concentration trends between 1990 and 2010 for the contiguous U.S. A Markov chain Monte Carlo simulation algorithm was used to address the problem of left-censored data (i.e., atrazine concentration values below method reporting levels). We observed opposing temporal trends in the northern (flat or decreasing) and southern (increasing) regions of the U.S. This spatial variation in temporal trends can be partially explained by the relative amount of cropland in the region. Flat or decreasing trends in the north are more likely in regions with high cropland coverage while positive trends in the south are more likely in regions with low cropland coverage. [ABSTRACT FROM AUTHOR]

In the USA, the Centers for Medicare and Medicaid Services use 30-day readmission, following hospitalization, as a proxy outcome to monitor quality of care. These efforts generally focus on treatable health conditions, such as pneumonia and heart failure. Expanding quality-of-care systems to monitor conditions for which treatment options are limited or non-existent, such as pancreatic cancer, is challenging because of the non-trivial force of mortality; 30-day mortality for pancreatic cancer is approximately 30%. In the statistical literature, data that arise when the observation of the time to some non-terminal event is subject to some terminal event are referred to as 'semicompeting risks data'. Given such data, scientific interest may lie in at least one of three areas: estimation or inference for regression parameters, characterization of dependence between the two events and prediction given a covariate profile. Existing statistical methods focus almost exclusively on the first of these; methods are sparse or non-existent, however, when interest lies with understanding dependence and performing prediction. We propose a Bayesian semiparametric regression framework for analysing semicompeting risks data that permits the simultaneous investigation of all three of the aforementioned scientific goals. Characterization of the induced posterior and posterior predictive distributions is achieved via an efficient Metropolis-Hastings-Green algorithm, which has been implemented in an R package. The framework proposed is applied to data on 16051 individuals who were diagnosed with pancreatic cancer between 2005 and 2008, obtained from Medicare part A. We found that increased risk for readmission is associated with a high comorbidity index, a long hospital stay at initial hospitalization, non-white race, being male and discharge to home care. [ABSTRACT FROM AUTHOR]

One of the longest running questions that has been regularly included in US national public opinion polls is 'Are you in favor of the death penalty for persons convicted of murder?'. Because the death penalty is governed by state laws rather than federal laws, it is of special interest to know how public opinion varies by state, and how it has changed over time within each state. We combine dozens of national polls taken over a 50-year span and fit a Bayesian multilevel logistic regression model to estimate support for the death penalty as a function of the year, the state, state level variables and various individual level demographic variables. Among our findings were that support levels in northeastern and southern states have moved in opposite directions over the past 50 years, support among blacks has decreased relative to non-blacks, but at slightly different rates for men and women, and support among some education groups varies widely by region. Throughout the paper, we highlight the use of a variety of analytical and graphical tools for model understanding, including average predictive comparisons, finite population contrasts for overparameterized models and graphical summaries of posterior distributions of group level variance parameters. [ABSTRACT FROM AUTHOR]

Abstract: A biomathematical model was previously developed to describe the long-term clearance and retention of particles in the lungs of coal miners. The model structure was evaluated and parameters were estimated in two data sets, one from the United States and one from the United Kingdom. The three-compartment model structure consists of deposition of inhaled particles in the alveolar region, competing processes of either clearance from the alveolar region or translocation to the lung interstitial region, and very slow, irreversible sequestration of interstitialized material in the lung-associated lymph nodes. Point estimates of model parameter values were estimated separately for the two data sets. In the current effort, Bayesian population analysis using Markov chain Monte Carlo simulation was used to recalibrate the model while improving assessments of parameter variability and uncertainty. When model parameters were calibrated simultaneously to the two data sets, agreement between the derived parameters for the two groups was very good, and the central tendency values were similar to those derived from the deterministic approach. These findings are relevant to the proposed update of the ICRP human respiratory tract model with revisions to the alveolar-interstitial region based on this long-term particle clearance and retention model. [Copyright &y& Elsevier]

This paper investigates the spillover effects from U.S. and regional stock markets on local stock markets in the Pacific Basin region and China. We also analyze if the spillover depends on countries' financial and economic integration. We apply a stochastic volatility model with jumps in order to separate the spillover of extreme shocks from those of normal shocks. We find that the spillovers of both normal and extreme shocks are significant for almost all Asian countries except China. We also find that the time-variation in stock market interdependence can largely be associated with economic integration. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the timing of structural changes in yield curve dynamics in the context of an arbitrage-free, one latent and two macroeconomic factors, affine term structure model. We suppose that all parameters in the model are subject to changes at unknown time points. We fit a number of models to the U.S. term structure data and find support for three change-points. We also find that the term structure and the risk premium are materially different across regimes and that the out-of-sample forecasts of the term structure improve from incorporating regime changes. [ABSTRACT FROM PUBLISHER]

This paper proposes a semiparametric Bayesian framework for the analysis of associations among multivariate longitudinal categorical variables in high-dimensional data settings. This type of data is frequent, especially in the social and behavioral sciences. A semiparametric hierarchical factor analysis model is developed in which the distributions of the factors are modeled nonparametrically through a dynamic hierarchical Dirichlet process prior. A Markov chain Monte Carlo algorithm is developed for fitting the model, and the methodology is exemplified through a study of the dynamics of public attitudes toward science and technology in the United States over the period 1992-2001. [ABSTRACT FROM AUTHOR]

Carbon (C) residence time is one of the key factors that determine the capacity of ecosystem C storage. However, its uncertainties have not been well quantified, especially at regional scales. Assessing uncertainties of C residence time is thus crucial for an improved understanding of terrestrial C sequestration. In this study, the Bayesian inversion and Markov Chain Monte Carlo (MCMC) technique were applied to a regional terrestrial ecosystem (TECO-R) model to quantify C residence times and net primary productivity (NPP)- driven ecosystem C uptake and assess their uncertainties in the conterminous USA. The uncertainty was represented by coefficient of variation (CV). The 13 spatially distributed data sets of C pools and fluxes have been used to constrain TECO-R model for each biome (totally eight biomes). Our results showed that estimated ecosystem C residence times ranged from 16.6±1.8 (cropland) to 85.9±15.3 yr (evergreen needleleaf forest) with an average of 56.8±8.8 yr in the conterminous USA. The ecosystem C residence times and their CV were spatially heterogeneous and varied with vegetation types and climate conditions. Large uncertainties appeared in the southern and eastern USA. Driven by NPP changes from 1982 to 1998, terrestrial ecosystems in the conterminous USA would absorb 0.20±0.06 Pg C yr-1. Their spatial pattern was closely related to the greenness map in the summer with larger uptake in central and southeast regions. The lack of data or timescale mismatching between the available data and the estimated parameters lead to uncertainties in the estimated C residence times, which together with initial NPP resulted in the uncertainties in the estimated NPP-driven C uptake. The Bayesian approach with MCMC inversion provides an effective tool to estimate spatially distributed C residence time and assess their uncertainties in the conterminous USA. [ABSTRACT FROM AUTHOR]

We propose a new approach to model a sequence of spatially distributed time series of extreme values. Unlike common practice, we incorporate spatial dependence directly in the likelihood and allow the temporal component to be captured at the second level of hierarchy. Inferences about the parameters and spatio-temporal predictions are obtained via MCMC technique. The model is fitted to a gridded precipitation data set collected over 99 years across the continental U.S. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Often, in environmental data collection, data arise from two sources: numerical models and monitoring networks. The first source provides predictions at the level of grid cells, while the second source gives measurements at points. The first is characterized by full spatial coverage of the region of interest, high temporal resolution, no missing data but consequential calibration concerns. The second tends to be sparsely collected in space with coarser temporal resolution, often with missing data but, where recorded, provides, essentially, the true value. Accommodating the spatial misalignment between the two types of data is of fundamental importance for both improved predictions of exposure as well as for evaluation and calibration of the numerical model. In this article we propose a simple, fully model-based strategy to downscale the output from numerical models to point level. The static spatial model, specified within a Bayesian framework, regresses the observed data on the numerical model output using spatially-varying coefficients which are specified through a correlated spatial Gaussian process. As an example, we apply our method to ozone concentration data for the eastern U.S. and compare it to Bayesian melding (Fuentes and Raftery ) and ordinary kriging (Cressie ; Chilès and Delfiner ). Our results show that our method outperforms Bayesian melding in terms of computing speed and it is superior to both Bayesian melding and ordinary kriging in terms of predictive performance; predictions obtained with our method are better calibrated and predictive intervals have empirical coverage closer to the nominal values. Moreover, our model can be easily extended to accommodate for the temporal dimension. In this regard, we consider several spatio-temporal versions of the static model. We compare them using out-of-sample predictions of ozone concentration for the eastern U.S. for the period May 1–October 15, 2001. For the best choice, we present a summary of the analysis. Supplemental material, including color versions of Figures 4, 5, 6, 7, and 8, and MCMC diagnostic plots, are available online. [ABSTRACT FROM AUTHOR]

We used band-recovery data from 2 populations of greater sage-grouse (Centrocercus urophasianus), one in Colorado, USA, and another in Nevada, USA, to examine the relationship between harvest rates and annual survival. We used a Seber parameterization to estimate parameters for both populations. We estimated the process correlation between reporting rate and annual survival using Markov chain Monte Carlo methods implemented in Program MARK. If hunting mortality is additive to other mortality factors, then the process correlation between reporting and survival rates will be negative. Annual survival estimates for adult and juvenile greater sage-grouse in Nevada were 0.42±0.07 (x¯±SE) for both age classes, whereas estimates of reporting rate were 0.15±0.02 and 0.16±0.03 for the 2 age classes, respectively. For Colorado, average reporting rates were 0.14±0.016, 0.14±0.010, 0.19±0.014, and 0.18±0.014 for adult females, adult males, juvenile females, and juvenile males, respectively. Corresponding mean annual survival estimates were 0.59±0.01, 0.37±0.03, 0.78±0.01, and 0.64±0.03. Estimated process correlation between logit-transformed reporting and survival rates for greater sage-grouse in Colorado was ρ = 0.68±0.26, whereas that for Nevada was ρ = 0.04±0.58. We found no support for an additive effect of harvest on survival in either population, although the Nevada study likely had low power. This finding will assist mangers in establishing harvest regulations and otherwise managing greater sage-grouse populations. [ABSTRACT FROM AUTHOR]

In finance and economics much work has been done on the theoretical modeling and statistical estimation of the yield curve, defined as the relationship between -1/τ logpt(τ) and r, where pt(τ) is the time t price of a zero-coupon bond with payoff I at maturity date t + τ. Of considerable current interest are models of the yield curve in which a collection of observed and latent factors determine the market price of factor risks, the stochastic discount factor, and the arbitrage-free bond prices. The model is particularly interesting from a statistical perspective, because the yields are complicated nonlinear functions of the underlying parameters (e.g., those appearing in the evolution dynamics of the factors and those appearing in the model of the factor risks). This nonlinearity tends to produce a likelihood function that is multimodal. In this article we revisit the question of how such models should be fit from the Bayesian viewpoint. Key aspects of the inferential framework include (a) a prior on the parameters of the model that is motivated by economic considerations, in particular, those involving the slope of the implied yield curve; (b) posterior simulation of the parameters in ways to improve the efficiency of the MCMC output, for example, through sampling of the parameters marginalized over the factors and tailoring of the proposal densities in the Metropolis-Hastings steps using information about the mode and curvature of the current target based on the output of a simulating annealing algorithm; and (c) measures to mitigate numerical instabilities in the fitting through reparameterizations and square root filtering recursions. We apply the techniques to explain the monthly yields on nine U.S. Treasury Bills (with maturities ranging from I month to 120 months) over the period January 1986-December 2005. The model contains three factors, one latent and two observed. We also consider the problem of predicting the nine yields for each month of 2006. We show that the (multi-step-ahead) prediction regions properly bracket the actual yields in those months, thus highlighting the practical value of the fitted model. [ABSTRACT FROM AUTHOR]

Health care quality surveys ill the United States are administered to individual respondents (i.e., hospital patients, health plan members) to evaluate the performance of health care organizations (i.e., hospitals, health plans), which thus constitute estimation domains. For better understanding and more parsimonious reporting of dimensions of quality, we analyze relationships among quality measures at the domain level. Rather than specifying a full parametric model for the observed responses and the nonresponse patterns at the lower (patient) level, we first lit generalized variance-covariance functions that take into account nonresponse patterns in the survey responses, then specify a likelihood function for the domain mean responses using these generalized variance-covariance functions. This allows us to model directly the relationships among domain means for different items. Because the response scales are bounded, we assume that these means follow a truncated multivariate normal distribution. We calculate maximum likelihood (ML) estimates using the EM algorithm and sample under Bayesian models using Markov chain Monte Carlo. Finally, we perform factor analysis on the estimated or sampled between-domain covariance matrixes. Using posterior draws, we assess posterior distributions of the number of selected factors and the assignment of items to groups under conventional rules. We compare ML estimates of this factor structure with those from several Bayesian models with different prior distributions for the between-domain covariance. We present analyses of data from the Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey of Medicare Advantage health plans. [ABSTRACT FROM AUTHOR]

BACKGROUND: One problem of interpreting population-based biomonitoring data is the reconstruction of corresponding external exposure in cases where no such data are available. OBJECTIVES: We demonstrate the use of a computational framework that integrates physiologically based pharmacokinetic (PBPK) modeling, Bayesian inference, and Markov chain Monte Carlo simulation to obtain a population estimate of environmental chloroform source concentrations consistent with human biomonitoring data. The biomonitoring data consist of chloroform blood concentrations measured as part of the Third National Health and Nutrition Examination Survey (NHANES III), and for which no corresponding exposure data were collected. METHODS: We used a combined PBPK and shower exposure model to consider several routes and sources of exposure: ingestion of tap water, inhalation of ambient household air, and inhalation and dermal absorption while showering. We determined posterior distributions for chloroform concentration in tap water and ambient household air using U.S. Environmental Protection Agency Total Exposure Assessment Methodology (TEAM) data as prior distributions for the Bayesian analysis. RESULTS: Posterior distributions for exposure indicate that 95% of the population represented by the NHANES III data had likely chloroform exposures ≤ 67 µg/L in tap water and ≤ 0.02 µg/L in ambient household air. CONCLUSIONS: Our results demonstrate the application of computer simulation to aid in the interpretation of human biomonitoring data in the context of the exposure--health evaluation--risk assessment continuum. These results should be considered as a demonstration of the method and can be improved with the addition of more detailed data. [ABSTRACT FROM AUTHOR]

A systematic method for identification and estimation of regional scale stressor-response models in aquatic ecosystems will be useful in monitoring and assessment of aquatic resources, determination of regional nutrient criteria and for increased understanding of the differences between regions. The model response variable is chlorophyll a, a measure of algal density, while the stressors include nutrient concentrations from the USEPA Nutrient Criteria Database (NCD) for lakes/ponds and reservoirs of the continental United States. The NCD has observations for both stressors and biological responses determined using methods that are not consistently available at the continental scale. To link multiple environmental stressors to biological responses and quantify uncertainty in model predictions, we take a multilevel modeling approach to the estimation of a linear model for prediction of log Chlorophyll a using predictors log TP and log TN. The multilevel modeling approach allows us to adjust the impact of covariates at all levels (observation, higher level groups) for the simultaneous operation of contextual and individual variability in the outcome. Here, we wish to allow separate regression coefficients for inference regarding similarities and differences between each of 14 ecoregions, and between the two water-body types, lakes/ponds and reservoirs. We are also interested in the nuisance effects of the categorical variables indicating the type of nitrogen measurements (three levels) and the type of chlorophyll a measurements (four levels) used. Model-based determination of nutrient criteria points to an apparent incompatibility of criteria developed for nutrient stressors and eutrophication responses using current Environmental Protection Agency’s guidance. [ABSTRACT FROM AUTHOR]

Since the U.S. Environmental Protection Agency began widespread monitoring of PM2.5 (particulate matter <2.5 p in diameter) concentration levels in the late 1990s, the epidemiological community has performed several observational studies directly relating PM2.5 concentration to various health endpoints including mortality and morbidity. However, recent research suggests that human exposure to the constituents of PM2.5 may differ significantly from ambient (or outdoor) PM2.5 concentration measured by monitors because people spend a great deal of time in environments, such as various indoor environments, where they are partially shielded from ambient sources of PM and are exposed to nonambient sources of PM. Recent research has provided some ways to include exposure information, but little has been done to determine the impact of including such information in a statistical model. To address this concern, we develop a three-stage Bayesian hierarchical model based on the Poisson regression model that is traditionally used to characterize the relationship between PM2.5 concentration and health endpoints. Our approach includes a spatial model relating monitor readings to average county PM2.5 concentration and an exposure simulator that links average ambient PM2.5 concentration to average personal exposure using activity pattern data. We apply our model to a study population in North Carolina and explore the impact of various exposure modeling assumptions on the conclusions that can be drawn about the link between PM2.5 exposure and cardiovascular mortality. [ABSTRACT FROM AUTHOR]

Analysts in the natural and environmental sciences often encounter spatially referenced data sets arising from designs that are spatially replicated, where we have a set of main plots, each subdivided into several subplots. Spatial variation, therefore, possibly exists at two resolutions: macro-level variation between the main plots and micro-level variation between the subplots within each main plot. Scientific interest centers around estimating the underlying spatial associations and effects at multiple resolutions. These objectives introduce fresh challenges in statistical modeling, especially with regard to constructing rich association structures that yield valid probability models. We outline a spatial-process based versatile methodological framework to accomplish such modeling within a hierarchical Bayesian paradigm. We illustrate the proposed method using forest biomass data from the Forest Inventory and Analysis program of the United States Department of Agriculture (USDA) Forest Service. [Copyright &y& Elsevier]

This paper focuses on interest rate models with regime switching and extends previous nonlinear threshold models by relaxing the assumption of a fixed number of regimes. Instead we suggest automatic model determination through Bayesian inference via the reversible jump Markov Chain Monte Carlo (MCMC) algorithm. Moreover, we allow the thresholds in the volatility to be driven not only by the interest rate but also by other economic factors. We illustrate our methodology by applying it to interest rates and other economic factors of the American economy. [ABSTRACT FROM AUTHOR]

A Bayesian approach, implemented using Markov Chain Monte Carlo (MCMC) analysis, was applied with a physiologically-based pharmacokinetic (PBPK) model of methylmercury (MeHg) to evaluate the variability of MeHg exposure in women of childbearing age in the U.S. population. The analysis made use of the newly available National Health and Nutrition Survey (NHANES) blood and hair mercury concentration data for women of age 16–49 years (sample size, 1,582). Bayesian analysis was performed to estimate the population variability in MeHg exposure (daily ingestion rate) implied by the variation in blood and hair concentrations of mercury in the NHANES database. The measured variability in the NHANES blood and hair data represents the result of a process that includes interindividual variation in exposure to MeHg and interindividual variation in the pharmacokinetics (distribution, clearance) of MeHg. The PBPK model includes a number of pharmacokinetic parameters (e.g., tissue volumes, partition coefficients, rate constants for metabolism and elimination) that can vary from individual to individual within the subpopulation of interest. Using MCMC analysis, it was possible to combine prior distributions of the PBPK model parameters with the NHANES blood and hair data, as well as with kinetic data from controlled human exposures to MeHg, to derive posterior distributions that refine the estimates of both the population exposure distribution and the pharmacokinetic parameters. In general, based on the populations surveyed by NHANES, the results of the MCMC analysis indicate that a small fraction, less than 1%, of the U.S. population of women of childbearing age may have mercury exposures greater than the EPA RfD for MeHg of 0.1 μg/kgg/day, and that there are few, if any, exposures greater than the ATSDR MRL of 0.3 μgg/kgg/day. The analysis also indicates that typical exposures may be greater than previously estimated from food consumption surveys, but that the variability in exposure within the population of U.S. women of childbearing age may be less than previously assumed. [ABSTRACT FROM AUTHOR]

We used an over-dispersed Poisson regression with fixed and random effects, fitted by Markov chain Monte Carlo methods, to model population spatial patterns of relative abundance of American woodcock (Scolopax minor) across its breeding range in the United States. We predicted North American woodcock Singing Ground Survey counts with a log-linear function of explanatory variables describing habitat, year effects, and observer effects. The model also included a conditional autoregressive term representing potential correlation between adjacent route counts. Categories of explanatory habitat variables in the model included land-cover composition, climate, terrain heterogeneity, and human influence. Woodcock counts were higher in landscapes with more forest, especially aspen (Populus tremuloides) and birch (Betula spp.) forest, and in locations with a high degree of interspersion among forest, shrubs, and grasslands. Woodcock counts were lower in landscapes with a high degree of human development. The most noteworthy practical application of this spatial modeling approach was the ability to map predicted relative abundance. Based on a map of predicted relative abundance derived from the posterior parameter estimates, we identified major concentrations of woodcock abundance in east-central Minnesota, USA, the intersection of Vermont, USA, New York, USA, and Ontario, Canada, the upper peninsula of Michigan, USA, and St. Lawrence County, New York. The functional relations we elucidated for the American woodcock provide a basis for the development of management programs and the model and map may serve to focus management and monitoring on areas and habitat features important to American woodcock. [ABSTRACT FROM AUTHOR]

We present two models for the short-term prediction of the number of deaths arising from common cancers in the United States. The first is a local linear model, in which the slope of the segment joining the number of deaths for any two consecutive time period is assumed to be random with a nonparametric distribution, which has a Dirichlet process prior. For slightly longer prediction periods, we present a local quadratic model. This extension of the local linear model includes an additional "acceleration" term that allows it to quickly adjust to sudden changes in the time series. The proposed models can be used to obtain the predictive distributions of the future number of deaths. as well their meals and variances through Markov chain Monte Carlo techniques. We illustrate our methods by runs on data from selected cancer sites. [ABSTRACT FROM AUTHOR]

Observed differences in medical utilization between the privately insured and uninsured reflect the combined effects of self-selection and insurance incentives (moral hazard). This article provides a Bayesian framework for decomposing the disparity into incentive and selection components. The effect of self- selection in private insurance on the number of doctor visits is estimated using a multiyear sample of the U.S. adult non-Medicare population obtained from the Medical Expenditure Panel Survey. We use a flexible econometric framework based on the "Roy model" and develop a Markov chain Monte Carlo algorithm. We estimate the distribution of treatment effects and find strong evidence indicating selection, which accounts, on average, for 50% or more of the observed disparity in doctor visits. [ABSTRACT FROM AUTHOR]