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.     

Gender‐specific Effects at Work: An Empirical Study of Four Countries

Using a questionnaire based upon the work of Geert Hofstede, we examined the effects of gender on the following three work‐related dimensions: the importance of work‐related goals, the preference for performance rewards and the preference for management styles. The study was conducted for a multinational corporation headquartered in Germany with branches in China, Japan and the USA. Even though some inter‐country differences in the importance of work‐related matters are identified and intra‐country gender differences exist in work goal evaluation, overall we find that men and women exhibit similar preferences concerning performance rewards and managerial styles. Finally, for the three dimensions studied, our data do not confirm the stereotypical work‐related gender differences often reported in the literature and popular press.     

Aligning Chemical Assessment Tools Across the Hazard-Risk Continuum

With the growing number and diversity of hazard and risk assessment algorithms, models, databases, and frameworks for chemicals and their applications, risk assessors and managers are challenged to select the appropriate tool for a given need or decision. Some decisions require relatively simple tools to evaluate chemical hazards (e.g., toxicity), such as labeling for safe occupational handling and transport of chemicals. Others require assessment tools that provide relative comparisons among chemical properties, such as selecting the optimum chemical for a particular use among a group of candidates. Still other needs warrant full risk characterization, coupling both hazard and exposure considerations. Examples of these include new chemical evaluations for commercialization, evaluations of existing chemicals for novel uses, and assessments of the adequacy of risk management provisions. Even well-validated tools can be inappropriately applied, with consequences as severe as misguided chemical management, compromised credibility of the tool and its developers and users, and squandered resources. This article describes seven discrete categories of tools based on their information content, function, and the type of outputs produced. It proposes a systematic framework to assist users in selecting hazard and risk assessment tools for given applications. This analysis illustrates the importance of careful selection of assessment tools to achieve responsible chemical assessment communication and sound risk management.     

Bayesian Temporal Source Attribution of Foodborne Zoonoses: Campylobacter in Finland and Norway

Statistical source attribution approaches of food‐related zoonoses can generally be based on reported diagnosed human cases and surveillance results from different food sources or reservoirs of bacteria. The attribution model, or probabilistic classifier, can thus be based on the (sub)typing information enabling comparison between human infections and samples derived from source surveillance. Having time series of both data allows analyzing temporal patterns over time providing a repeated natural experiment. A Bayesian approach combining both sources of information over a long time series is presented in the case of Campylobacter in Finland and Norway. The full model is transparently presented and derived from the Bayes theorem. Previous statistical source attribution approaches are here advanced (1) by explicit modeling of the cases not associated with any of the sources under surveillance over time, (2) by modeling uncertain prevalence in a food source by bacteria type over time, and (3) by implementing formal model fit assessment using posterior predictive discrepancy functions. Large proportion of all campylobacteriosis can be attributed to broiler, but considerable uncertainty remains over time. The source attribution is inherently incomplete if only the sources under surveillance are included in the model. All statistical source attribution approaches should include a model fit assessment for judgment of model performance with respect to relevant quantities of interest. It is especially relevant when the model aims at a synthesis of several incomplete information sources under significant uncertainty of explanatory variables.     

Representing the ‘Voice’ of Patients: How Third Sector Organisations Conceptualise and Communicate Experiential Knowledge in Health Service Development

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - The growing prominence of patient and public involvement in health services has led to the increased use of experiential...     

From where do legislators draw scientific knowledge? Organizations as scientific authorities in four countries' parliamentary debates

Organizations far beyond traditional academic institutions have become prolific science producers, with many now providing evidence-based advice for national governments and policy-makers. Neo-institutional sociology explains organizations' growing investment in research activities and research-based policy advice by the all-embracing scientization and the expansion of the educated population, phenomena observable throughout the world. There is, however, considerably less knowledge about how the organizations' increased knowledge production and the supply of science-based policy advice are reflected in national policy-making, including the legislative work of parliaments, and to what extent distinct organizations are deemed authoritative in different countries. In this paper, we examine how different organizations are used as scientific authorities in parliamentary debates over new legislation. Drawing on analyses of 576 parliamentary debates from Australia, Finland, Kenya, and the United Kingdom, we study what organizations are acknowledged as scientific authorities and the relative weight of different organization types in the context of political debates over new legislation. The results reveal that while organizations in general are frequently evoked as scientific authorities in all four countries, there is remarkable variation in the types of organizations considered authoritative in different national contexts. We elaborate these findings by analysing ways in which politicians evaluate organizations as sources of scientific authority. While the same set of evaluative schemas are used in all four countries, each is typically applied to certain types of organization. The results suggest that both the supply of scientific policy advice and political culture shape legislators' rhetorical practices when drawing on organizations' scientific authority.