Risk Assessment of Human Listeriosis from Semisoft Cheeses Made from Raw Sheep's Milk in Lazio and Tuscany (Italy)

Semisoft cheese made from raw sheep's milk is traditionally and economically important in southern Europe. However, raw milk cheese is also a known vehicle of human listeriosis and contamination of sheep cheese with Listeria monocytogenes has been reported. In the present study, we have developed and applied a quantitative risk assessment model, based on available evidence and challenge testing, to estimate risk of invasive listeriosis due to consumption of an artisanal sheep cheese made with raw milk collected from a single flock in central Italy. In the model, contamination of milk may originate from the farm environment or from mastitic animals, with potential growth of the pathogen in bulk milk and during cheese ripening. Based on the 48‐day challenge test of a local semisoft raw sheep's milk cheese we found limited growth only during the initial phase of ripening (24 hours) and no growth or limited decline during the following ripening period. In our simulation, in the baseline scenario, 2.2% of cheese servings are estimated to have at least 1 colony forming unit (CFU) per gram. Of these, 15.1% would be above the current E.U. limit of 100 CFU/g (5.2% would exceed 1,000 CFU/g). Risk of invasive listeriosis per random serving is estimated in the 10^?12 range (mean) for healthy adults, and in the 10^?10 range (mean) for vulnerable populations. When small flocks (10–36 animals) are combined with the presence of a sheep with undetected subclinical mastitis, risk of listeriosis increases and such flocks may represent a public health risk.     

Open innovation and quality management: the moderating role of interorganisational IT infrastructure and complementary learning styles

This study seeks to explain the influence of quality management on the relationship between open innovation and performance. We propose that, to contribute to open innovation performance, an organisation’s commitment to quality improvement must rely on the combination of its own resources and capabilities and its open innovation partners’ resources and capabilities. Specifically, we examine the moderating role of an interorganisational IT infrastructure and the complementarity of learning styles among an organisation committed to quality improvement and its supply network. Using data from 270 managers of European firms, our results show that the negative effects of quality management on open innovation performance can be overcome by complementing the organisation’s learning style with that of its open innovation partner, particularly, its supply network, and, most importantly, obtaining information technologies compatible with those of its supply network members.     

Dual class shares,board of directors’ effectiveness and firm’s market value: an empirical study

This study aims to identify whether a relationship exists between the controlling shareholders’ voting power and outside directors’ effectiveness in maximizing firms’ financial performance. We analyze a panel data with 3057 observations for the 2000–2012 period using a random effects model, logit and probit regressions, and the two-stage model of Heckman in the Brazilian stock market. Our findings show that firms whose controlling shareholders use dual class shares to leverage their voting power have less independence from the board and worse financial performance and market value. Further, the percentage of outside directors tends to be ineffective in increasing the firm’s value, and in changing the firm’s chief executive officer (CEO) when (1) the controlling shareholder’s voting power is leveraged, or (2) when the CEO assumes a position on the board of directors simultaneously. We interpreted that these results are in line with the arguments in favor of the existence of a new agency cost, which is related to the undue obedience of board members to authority, such as the largest controlling shareholder or the CEO in Brazilian listed firms.     

<Emphasis Type="BoldItalic">MAD-STEC</Emphasis>: a method for multiple automatic detection of space-time emerging clusters

Crime or disease surveillance commonly rely in space-time clustering methods to identify emerging patterns. The goal is to detect spatial-temporal clusters as soon as possible after its occurrence and to control the rate of false alarms. With this in mind, a spatio-temporal multiple cluster detection method was developed as an extension of a previous proposal based on a spatial version of the Shiryaev–Roberts statistic. Besides the capability of multiple cluster detection, the method have less input parameter than the previous proposal making its use more intuitive to practitioners. To evaluate the new methodology a simulation study is performed in several scenarios and enlighten many advantages of the proposed method. Finally, we present a case study to a crime data-set in Belo Horizonte, Brazil.     

Can Quasi‐market and Multi‐level Governance Co‐exist? Insights from the Case of Lombardy's Employment Services System

This article explores the functioning of Lombardy's networked employment services system, inspired by quasi‐market and horizontal subsidiarity principles, and specifically addresses a gap in the quasi‐market literature, where little attention is devoted to the role played by institutions at lower levels of government. A qualitative study of the Lombardy system, with a focus on the municipality of Milan, is relied upon in order to explore the extent to which the principles of quasi‐market and multi‐level governance pursued by the regional government are allowed to co‐exist in practice. Here, sub‐regional levels of government are directly involved in services provision, but enjoy a more privileged condition relative to the private providers, thereby jeopardizing the implementation of an effective quasi‐market. The article contributes to existing theories by suggesting that horizontal subsidiarity and marketization cannot neglect multi‐level governance in those sectors where public bodies at various levels of government are directly involved in implementation.     

A copula-based Markov chain model for the analysis of binary longitudinal data

A fully parametric first-order autoregressive (AR(1)) model is proposed to analyse binary longitudinal data. By using a discretized version of a copula, the modelling approach allows one to construct separate models for the marginal response and for the dependence between adjacent responses. In particular, the transition model that is focused on discretizes the Gaussian copula in such a way that the marginal is a Bernoulli distribution. A probit link is used to take into account concomitant information in the behaviour of the underlying marginal distribution. Fixed and time-varying covariates can be included in the model. The method is simple and is a natural extension of the AR(1) model for Gaussian series. Since the approach put forward is likelihood-based, it allows interpretations and inferences to be made that are not possible with semi-parametric approaches such as those based on generalized estimating equations. Data from a study designed to reduce the exposure of children to the sun are used to illustrate the methods.     

Bayesian robustness of the compound Poisson distribution under bidimensional prior: an application to the collective risk model

The distribution of the aggregate claims in one year plays an important role in Actuarial Statistics for computing, for example, insurance premiums when both the number and size of the claims must be implemented into the model. When the number of claims follows a Poisson distribution the aggregated distribution is called the compound Poisson distribution. In this article we assume that the claim size follows an exponential distribution and later we make an extensive study of this model by assuming a bidimensional prior distribution for the parameters of the Poisson and exponential distribution with marginal gamma. This study carries us to obtain expressions for net premiums, marginal and posterior distributions in terms of some well-known special functions used in statistics. Later, a Bayesian robustness study of this model is made. Bayesian robustness on bidimensional models was deeply treated in the 1990s, producing numerous results, but few applications dealing with this problem can be found in the literature.     

A simple two-stage design for quantitative responses with application to a study in diabetic neuropathic pain

Two-stage designs offer substantial advantages for early phase II studies. The interim analysis following the first stage allows the study to be stopped for futility, or more positively, it might lead to early progression to the trials needed for late phase II and phase III. If the study is to continue to its second stage, then there is an opportunity for a revision of the total sample size. Two-stage designs have been implemented widely in oncology studies in which there is a single treatment arm and patient responses are binary. In this paper the case of two-arm comparative studies in which responses are quantitative is considered. This setting is common in therapeutic areas other than oncology. It will be assumed that observations are normally distributed, but that there is some doubt concerning their standard deviation, motivating the need for sample size review. The work reported has been motivated by a study in diabetic neuropathic pain, and the development of the design for that trial is described in detail.     

The generalized log-gamma mixture model with covariates: local influence and residual analysis

In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death, failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data. The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction. Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate model. The authors would like to thank the editor and referees for their helpful comments. This work was supported by CNPq, Brazil.