A Multifactorial Risk Prioritization Framework for Foodborne Pathogens

We develop a prioritization framework for foodborne risks that considers public health impact as well as three other factors (market impact, consumer risk acceptance and perception, and social sensitivity). Canadian case studies are presented for six pathogen‐food combinations: Campylobacter spp. in chicken; Salmonella spp. in chicken and spinach; Escherichia coli O157 in spinach and beef; and Listeria monocytogenes in ready‐to‐eat meats. Public health impact is measured by disability‐adjusted life years and the cost of illness. Market impact is quantified by the economic importance of the domestic market. Likert‐type scales are used to capture consumer perception and acceptance of risk and social sensitivity to impacts on vulnerable consumer groups and industries. Risk ranking is facilitated through the development of a knowledge database presented in the format of info cards and the use of multicriteria decision analysis (MCDA) to aggregate the four factors. Three scenarios representing different stakeholders illustrate the use of MCDA to arrive at rankings of pathogen‐food combinations that reflect different criteria weights. The framework provides a flexible instrument to support policymakers in complex risk prioritization decision making when different stakeholder groups are involved and when multiple pathogen‐food combinations are compared.     

Efficient and effective learning for safety from incidents

Learning from incidents is important for improving safety. Many companies spend a great deal of time and money on such learning procedures. The objectives of this paper are to present some early results from a project aimed at revealing weaknesses in the procedures for learning from incidents and to discuss improvements in these procedures, especially in chemical process industries. The empirical base comes from a project assessing organizational learning and the effectiveness of the different steps of the learning cycle for safety and studying relations between safety-specific transformational leadership, safety climate, trust, safety-related behavior and learning from incidents. The results point at common weaknesses in the organizational learning, both in the horizontal learning (geographical spread) and in vertical learning (double-loop learning). Furthermore, the effectiveness in the different steps of the learning cycle is low due to insufficient information in incident reports, very shallow analyses of reports, decisions that focus at solving the problem only at the place where the incident took place, late implementations and weak solutions. Strong correlations with learning from incidents were found for all safety climate variables as well as for safety-related behaviors and trust. The relationships were very strong for trust, safety knowledge, safety participation and safety compliance.     

Spatial configurations of urban forest in different landscape and socio-political contexts: identifying patterns for green infrastructure planning

Rapid urbanisation and climate change have motivated the development of urban green infrastructure (UGI) as a planning strategy to support the wellbeing of urban people and ecosystems while parallel adapting cities to climate change. Forest (tree-covered areas >0.5 ha) is a key UGI component that afford a wider range of ecosystem services and mitigate urban heat islands more effectively than non-wooded green spaces. However, understanding of spatial configurations (variation in patch size and frequency) of forests across the gradient of urbanisation and between cities is limited to case studies. This represents a considerable knowledge gap for identification of general patterns that can inform integration of forest resources in UGI planning that have value beyond the individual city level. In this study we used Geographic Information Systems to explore the spatial configuration of forests across cities located within landscapes characterised by different levels of anthropogenic modification (degree of forest cover) and socio-political contexts, i.e. all Danish and Swedish cities >10,000 inhabitants (n = 176). We applied general linear modelling to investigate the relationship between forest cover, patch size and frequency with 1) regional landscape type, 2) demographic trends 1960–2010, and 3) the gradient of urbanisation (measured in three zones: urban core (0.2 km from city boundary), urban fringe (0.2–2 km), and urban periphery (2–5 km)). Regardless of demographic trends, forest cover was lowest in cities settled in large-scale agricultural regions, higher in regions with mosaics of forest and farming, and highest in forest-dominated regions. However, in all cities forest cover was lowest in the urban zone and peaked on the urban fringe rather than on the urban periphery. Furthermore, pocket woods (0.5–2 ha) accounted for over 50 % of patches in all three urban zones, irrespective of regional landscape type. We conclude by discussing how these general patterns could inform strategies for integration of urban forests in UGI planning.     

Sharp Threshold Detection Based on Sup-Norm Error Rates in High-Dimensional Models

We propose a new estimator, the thresholded scaled Lasso, in high-dimensional threshold regressions. First, we establish an upper bound on the ?_∞ estimation error of the scaled Lasso estimator of Lee, Seo, and Shin. This is a nontrivial task as the literature on high-dimensional models has focused almost exclusively on ?₁ and ?₂ estimation errors. We show that this sup-norm bound can be used to distinguish between zero and nonzero coefficients at a much finer scale than would have been possible using classical oracle inequalities. Thus, our sup-norm bound is tailored to consistent variable selection via thresholding. Our simulations show that thresholding the scaled Lasso yields substantial improvements in terms of variable selection. Finally, we use our estimator to shed further empirical light on the long-running debate on the relationship between the level of debt (public and private) and GDP growth. Supplementary materials for this article are available online.     

“Better safe than sorry”—Reasons for consulting care due to decreased fetal movements

Background

Experience of reduced fetal movements is a common reason for consulting health care in late pregnancy. There is an association between reduced fetal movements and stillbirth.

On the History of the Correction for Grouping, 1873–1922

The correction for grouping is a sum of two terms, the first depending on the length of the grouping interval, the second being a periodic function of the position. Thiele (1873) studied the second term, but missed the first. Sheppard (1898) studied the first term, but missed the second. Bruns (1906) derived the first term as the aperiodic term of a Fourier series and the second as the sum of the periodic terms. He found the correction to the coefficients of the Gram–Charlier series and proved that the second term is negligible for a grouped normal distribution with at least eight groups. Independently, Fisher (1922) used the same method to derive the correction to the moments. For the normal distribution with a grouping interval less than the standard deviation Fisher proved that the second term is negligible compared with the first and with the standard error of the first four moments. Moreover, he proved that the estimates of the mean and the standard deviation obtained by the method of moments for a grouped sample with Sheppard's corrections have nearly the same variances as the maximum likelihood estimates, thus providing a new and compelling reason for using Sheppard's corrections.