Campylobacter QMRA: A Bayesian Estimation of Prevalence and Concentration in Retail Foods Under Clustering and Heavy Censoring

A Bayesian statistical temporal‐prevalence‐concentration model (TPCM) was built to assess the prevalence and concentration of pathogenic campylobacter species in batches of fresh chicken and turkey meat at retail. The data set was collected from Finnish grocery stores in all the seasons of the year. Observations at low concentration levels are often censored due to the limit of determination of the microbiological methods. This model utilized the potential of Bayesian methods to borrow strength from related samples in order to perform under heavy censoring. In this extreme case the majority of the observed batch‐specific concentrations was below the limit of determination. The hierarchical structure was included in the model in order to take into account the within‐batch and between‐batch variability, which may have a significant impact on the sample outcome depending on the sampling plan. Temporal changes in the prevalence of campylobacter were modeled using a Markovian time series. The proposed model is adaptable for other pathogens if the same type of data set is available. The computation of the model was performed using OpenBUGS software.     

Shared service center research: A review of the past,present, and future

More than 75% of Fortune 500 companies have established models of shared services with the aim of gaining superior performance by cost savings and service enhancements. Despite scholars' complaints about scant shared service center (SSC) research, this study shows that the actual shortcoming in this stream concerns a high fragmentation of the academic literature (e.g., we found 137 works in the initial search and 83 works in a refined screen). In this first comprehensive literature review, we synthesize peer-reviewed articles and classify them into 4 perspectives according to their research questions (i.e., determinant, process, control, and outcome). We identify 17 major research areas across these perspectives. Additionally, we provide information on methodologies and theories. On the basis of the literature synthesis, we discuss opportunities and gaps and propose an agenda for future research. Specifically, we suggest 3 potential research directions (i.e., direct relationships, mediating, and outcome effects) regarding SSCs during their operational maturity phase.     

Viewing the child as a participant within context

This paper explores tensions between the social nature of learning and the current emphasis upon individual support. The paper contrasts the focus upon context in discussions with parents and practitioners, with the focus upon the individual within formal and informal written documents. The analysis is partly situated within the first author’s experiences as a parent researcher, drawing additionally upon ethnographic research carried out with families involved in early intervention, including an evaluation of over 150 pages of documentation provided by a parent. It identifies the need for assessment and evaluation to focus upon and record the broad range of context that influences learning.     

Pollutant Ground Concentrations of Nonneutrally Buoyant Particles

A methodology is suggested for the estimation of the mass density and the cumulative ground deposition of a nonvolatile, nonneutrally buoyant, air pollutant (liquid or solid) released from a polluted column (following an explosion caused during routine operation in, e.g., the chemical industry or due to any kind of hostile act) and deposited on the ground via gravitational settling. In many cases, the deposited mass due to gravitational settling constitutes a significant fraction of the original inventory released from the source. Implementation of the methodology in preliminary risk assessments can serve as an efficient tool for emergency planning for both immediate and long‐term measures such as evacuation and decontamination. The methodology considers, inter alia, an estimation of the critical particle diameter, particle size, and mass distributions along the polluted column. This methodology was developed to apply in rural regions since proper application of relevant meteorological input data can be accomplished mainly for such areas.     

An Ecological Model for Quantitative Risk Assessment for Schistosomiasis: The Case of a Patchy Environment in the Coastal Tropical Area of Northeastern Brazil

We developed a stochastic model for quantitative risk assessment for the Schistosoma mansoni (SM) parasite, which causes an endemic disease of public concern. The model provides answers in a useful format for public health decisions, uses data and expert opinion, and can be applied to any landscape where the snail Biomphalaria glabrata is the main intermediate host (South and Central America, the Caribbean, and Africa). It incorporates several realistic and case‐specific features: stage‐structured parasite populations, periodic praziquantel (PZQ) drug treatment for humans, density dependence, extreme events (prolonged rainfall), site‐specific sanitation quality, environmental stochasticity, monthly rainfall variation, uncertainty in parameters, and spatial dynamics. We parameterize the model through a real‐world application in the district of Porto de Galinhas (PG), one of the main touristic destinations in Brazil, where previous studies identified four parasite populations within the metapopulation. The results provide a good approximation of the dynamics of the system and are in agreement with our field observations, i.e., the lack of basic infrastructure (sanitation level and health programs) makes PG a suitable habitat for the persistence and growth of a parasite metapopulation. We quantify the risk of SM metapopulation explosion and quasi‐extinction and the time to metapopulation explosion and quasi‐extinction. We evaluate the sensitivity of the results under varying scenarios of future periodic PZQ treatment (based on the Brazilian Ministry of Health's plan) and sanitation quality. We conclude that the plan might be useful to slow SM metapopulation growth but not to control it. Additional investments in better sanitation are necessary.     

基于学分绩点与综合素质测评的换算模型建立

学生综合素质测评体系的完善对有效实施大学生综合素质测评工作以及指导大学生健康发展有重要意义,其中,学生综合素质测评体系的关键在于学分绩点与综合素质测评的换算模型的转化。通过分析高校现行学分绩点测评公式,发现其存在的主要缺陷是转化公式不满足统计学原理,并指出学分绩点测评的换算公式应建立在对特定学生群体特征参数群数值分析的基础上。以西南石油大学某专业学生某学年的成绩分布数据进行验证,采取统计原理、常数变异法和不动点原理进行建模,利用求导方法对学分绩点测评中的特征参数群进行层次划分,然后对每段划分积分,通过归一化来确定变异项,得出新的换算模型。结果表明新模型能更好地反映学生的学习成绩在综合素质测评中的地位。     

Evaluation of Inhaled Versus Deposited Dose Using the Exponential Dose‐Response Model for Inhalational Anthrax in Nonhuman Primate,Rabbit, and Guinea Pig

The application of the exponential model is extended by the inclusion of new nonhuman primate (NHP), rabbit, and guinea pig dose‐lethality data for inhalation anthrax. Because deposition is a critical step in the initiation of inhalation anthrax, inhaled doses may not provide the most accurate cross‐species comparison. For this reason, species‐specific deposition factors were derived to translate inhaled dose to deposited dose. Four NHP, three rabbit, and two guinea pig data sets were utilized. Results from species‐specific pooling analysis suggested all four NHP data sets could be pooled into a single NHP data set, which was also true for the rabbit and guinea pig data sets. The three species‐specific pooled data sets could not be combined into a single generic mammalian data set. For inhaled dose, NHPs were the most sensitive (relative lowest LD₅₀) species and rabbits the least. Improved inhaled LD₅₀s proposed for use in risk assessment are 50,600, 102,600, and 70,800 inhaled spores for NHP, rabbit, and guinea pig, respectively. Lung deposition factors were estimated for each species using published deposition data from Bacillus spore exposures, particle deposition studies, and computer modeling. Deposition was estimated at 22%, 9%, and 30% of the inhaled dose for NHP, rabbit, and guinea pig, respectively. When the inhaled dose was adjusted to reflect deposited dose, the rabbit animal model appears the most sensitive with the guinea pig the least sensitive species.     

Small area estimation strategies for large population surveys: a comparison of design and model-based methods

Small area estimation (SAE) concerns with how to reliably estimate population quantities of interest when some areas or domains have very limited samples. This is an important issue in large population surveys, because the geographical areas or groups with only small samples or even no samples are often of interest to researchers and policy-makers. For example, large population health surveys, such as Behavioural Risk Factor Surveillance System and Ohio Mecaid Assessment Survey (OMAS), are regularly conducted for monitoring insurance coverage and healthcare utilization. Classic approaches usually provide accurate estimators at the state level or large geographical region level, but they fail to provide reliable estimators for many rural counties where the samples are sparse. Moreover, a systematic evaluation of the performances of the SAE methods in real-world setting is lacking in the literature. In this paper, we propose a Bayesian hierarchical model with constraints on the parameter space and show that it provides superior estimators for county-level adult uninsured rates in Ohio based on the 2012 OMAS data. Furthermore, we perform extensive simulation studies to compare our methods with a collection of common SAE strategies, including direct estimators, synthetic estimators, composite estimators, and Datta GS, Ghosh M, Steorts R, Maples J.'s [Bayesian benchmarking with applications to small area estimation. Test 2011;20(3):574–588] Bayesian hierarchical model-based estimators. To set a fair basis for comparison, we generate our simulation data with characteristics mimicking the real OMAS data, so that neither model-based nor design-based strategies use the true model specification. The estimators based on our proposed model are shown to outperform other estimators for small areas in both simulation study and real data analysis.     

Foodborne Exposure to Pesticides and Methylmercury in the United Arab Emirates

As part of a comprehensive environmental health strategic planning project initiated by the government of Abu Dhabi, we assessed potential dietary exposure in the United Arab Emirates (UAE) to methylmercury (in seafood) and pesticides (in fruits and vegetables) above international guideline levels. We present results for the UAE population by age, gender, and body mass index. Our results show very low daily risks of exposure to pesticides in fruits and vegetables at levels exceeding WHO guidelines even under the conservative assumption that no pesticides are removed during washing and food preparation. Thus, exposure to pesticides on fruits and vegetables does not appear to be a major public health concern in the UAE. The chances of exposure to methylmercury in seafood are much higher; our model estimates a mean 1 in 5 daily risk of exceeding the FAO/WHO provisional tolerable weekly intake. However, great caution should be used in interpreting these results, as we analyzed only the risks and not the substantial benefits of fish consumption. In fact, previous studies have demonstrated that exposure to the n‐3 polyunsaturated fatty acids in fish can increase IQ in developing children, and it can substantially decrease the risk in adults of coronary heart disease and stroke. Further research is warranted to compare the risk of Me‐Hg exposure from fish to the nutritional benefits of fish consumption in the UAE and to determine appropriate methods to communicate risk and benefit information to the UAE population.     

Bayesian Treatment of Model Uncertainty for Partially Applicable Models

This article discusses how analyst's or expert's beliefs on the credibility and quality of models can be assessed and incorporated into the uncertainty assessment of an unknown of interest. The proposed methodology is a specialization of the Bayesian framework for the assessment of model uncertainty presented in an earlier paper. This formalism treats models as sources of information in assessing the uncertainty of an unknown, and it allows the use of predictions from multiple models as well as experimental validation data about the models’ performances. In this article, the methodology is extended to incorporate additional types of information about the model, namely, subjective information in terms of credibility of the model and its applicability when it is used outside its intended domain of application. An example in the context of fire risk modeling is also provided.