Human Salmonellosis: Estimation of Dose-Illness from Outbreak Data

The quantification of the relationship between the amount of microbial organisms ingested and a specific outcome such as infection, illness, or mortality is a key aspect of quantitative risk assessment. A main problem in determining such dose-response models is the availability of appropriate data. Human feeding trials have been criticized because only young healthy volunteers are selected to participate and low doses, as often occurring in real life, are typically not considered. Epidemiological outbreak data are considered to be more valuable, but are more subject to data uncertainty. In this article, we model the dose-illness relationship based on data of 20 Salmonella outbreaks, as discussed by the World Health Organization. In particular, we model the dose-illness relationship using generalized linear mixed models and fractional polynomials of dose. The fractional polynomial models are modified to satisfy the properties of different types of dose-illness models as proposed by Teunis et al . Within these models, differences in host susceptibility (susceptible versus normal population) are modeled as fixed effects whereas differences in serovar type and food matrix are modeled as random effects. In addition, two bootstrap procedures are presented. A first procedure accounts for stochastic variability whereas a second procedure accounts for both stochastic variability and data uncertainty. The analyses indicate that the susceptible population has a higher probability of illness at low dose levels when the combination pathogen-food matrix is extremely virulent and at high dose levels when the combination is less virulent. Furthermore, the analyses suggest that immunity exists in the normal population but not in the susceptible population.     

Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Of River Linkage and Issue Linkage: Transboundary Conflict and Cooperation on the River Meuse

Abstract

It is a truism in mainstream International Relations that issue linkage promotes regime formation and integration. The present article applies this idea to the transboundary lower river Meuse and finds its history of integration to be a tortuous one. Contextual political factors have at times promoted integration, at times fragmentation. The path towards regional integration, then, has not been not linear, but has consisted of conflict and cooperation, of (Meuse–Scheldt) river linkage and issue linkage, but also counterlinkage and non-linkage. Clearly linkage is not necessarily positive. I will argue that this does not need to be problematic, but suggest accepting more complexity in the analysis of river integration. I propose a way to create some order in the many available concepts of linkage to map out the role of linkage in integration.     

The ‘Dark Side’ of Social Capital: A Cross-National Examination of the Relationship Between Social Capital and Violence in Africa

Social Indicators Research - Research and policy circles often emphasize the importance of social capital in achieving social transformation and economic development. There is also, however,...     

Undeclared Work in Non-Profit Sports Clubs: A Mixed Method Approach for Assessing the Size and Motives

Non-market economy including volunteer work is often ignored in estimations of the shadow economy because no money changes hands. Whilst volunteers stricto sensu are not paid for their activities, a tendency is noted that legal frameworks do allow for reimbursement for actual expenses incurred. However, if the latter receive reimbursements beyond the scope of regulations and labour law this can also be regarded as undeclared work. In this article, the size and motives of this type of undeclared work in non-profit sports clubs in Flanders (Belgium) are explored. As this kind of informal work, by its nature, is difficult to measure, a mixed method approach was used. The results show that at least 10% of volunteers can be considered as undeclared workers. The tax and social security contribution burdens are considered as the main causes for this undeclared work. It is argued that a new employment status for paid volunteers in sports is necessary to guarantee the provision of qualitative sports services.     

Twinning Rates in Developed Countries: Trends and Explanations

The twinning rate has increased dramatically over the last four decades in developed countries. Two main factors account for this increase: delayed childbearing, as older women tend to have twins more frequently than younger ones, and the expansion of medically assisted reproduction (MAR), which carries an increased probability of multiple births. Using civil registration data, we estimate the share of the increase in twinning rates attributable to the rise in the age at childbearing and to MAR. The effect of MAR is estimated to be about three times as important as the effect of delayed childbearing. Negative health outcomes associated with multiple births and the cost of MAR have raised concerns. We find that in one‐quarter of developed countries with the relevant data, the twinning rate reached a plateau around the early 2000s and decreased thereafter. We examine the reasons for this reversal, in particular changes in MAR policies and practices.     

Bridging the Gap: Overcoming Data Difficulties During 40 Years of Measuring Well-Being in The Netherlands

For more than 40 years the Netherlands Institute for Social Research|SCP has studied the quality of life of the Dutch population. A characteristic element of that research is the use of time series. However, maintaining a time series over such a long period is not easy. All manner of problems arise, such as indicators no longer being relevant (who still has a black and white television?) or the changing importance that is attached to particular topics (sport is more important today than it was in 1974). The method of data collection can also change. In this article we take the perspective of a specific measurement to look at the changes that have taken place over the last 40 years and how those changes have been accommodated. We do this using the SCP Life Situation Index, which measures the objective quality of life of Dutch citizens. The conclusion is that it is possible to construct a long-term time series, but that it is not always possible to interpret changes from year to year: the perspective has to focus primarily on long-term developments.     

Development of a Quantitative Microbial Risk Assessment for Human Salmonellosis Through Household Consumption of Fresh Minced Pork Meat in Belgium

A quantitative microbial risk assessment (QMRA) according to the Codex Alimentarius Principles is conducted to evaluate the risk of human salmonellosis through household consumption of fresh minced pork meat in Belgium. The quantitative exposure assessment is carried out by building a modular risk model, called the METZOON-model, which covers the pork production from farm to fork. In the METZOON-model, the food production pathway is split up in six consecutive modules: (1) primary production, (2) transport and lairage, (3) slaughterhouse, (4) postprocessing, (5) distribution and storage, and (6) preparation and consumption. All the modules are developed to resemble as closely as possible the Belgian situation, making use of the available national data. Several statistical refinements and improved modeling techniques are proposed. The model produces highly realistic results. The baseline predicted number of annual salmonellosis cases is 20,513 ( SD 9061.45). The risk is estimated higher for the susceptible population (estimate  4.713 × 10⁻⁵; SD 1.466 × 10⁻⁵  ) compared to the normal population  (estimate 7.704 × 10⁻⁶; SD 5.414 × 10⁻⁶)  and is mainly due to undercooking and to a smaller extent to cross-contamination in the kitchen via cook's hands.