Conditions for addressing environmental determinants of health behavior in intersectoral policy networks: A fuzzy set Qualitative Comparative Analysis

Improving health requires changes in the social, physical, economic and political determinants of health behavior. For the realization of policies that address these environmental determinants, intersectoral policy networks are considered necessary for the pooling of resources to implement different policy instruments. However, such network diversity may increase network complexity and therefore hamper network performance. Network complexity may be reduced by network management and the provision of financial resources. This study examined whether network diversity amidst the other conditions is indeed needed to address environmental determinants of health behavior. We included 25 intersectoral policy networks in Dutch municipalities aimed at reducing overweight, smoking, and alcohol/drugs abuse. For our fuzzy set Qualitative Comparative Analysis we used data from three web-based surveys among (a) project leaders regarding network diversity and size (n = 38); (b) project leaders and project partners regarding management (n = 278); and (c) implementation professionals regarding types of environmental determinants addressed (n = 137). Data on budgets were retrieved from project application forms. Contrary to their intentions, most policy networks typically addressed personal determinants. If the environment was addressed too, it was mostly the social environment. To address environmental determinants of health behavior, network diversity (>50% of the actors are non-public health) was necessary in networks that were either small (<16 actors) or had small budgets (< 183,172), when both were intensively managed. Irrespective of network diversity, environmental determinants also were addressed by small networks with large budgets, and by large networks with small budgets, when both provided network management. We conclude that network diversity is important although not necessary for resource pooling to address environmental determinants of health behavior, but only effective in the presence of network management. Our findings may support intersectoral policy networks in improving health behaviors by addressing a variety of environmental determinants. (C) 2017 Published by Elsevier Ltd

Breastfeeding and food pattern in overweight children in the Caribbean

Background: As in most countries around the globe, overweight and obesity are a major threat to public health on the Caribbean island of Aruba. Increasing evidence confirms that breastfeeding protects against overweight and obesity. However, little is known about the mechanism underlying the association between breastfeeding and obesity. One possibility is that breastfed infants are better able to control their meal size and intervals than formula-fed infants. This might lead to a healthier diet in later life and protect against overweight and obesity.Objective: To determine the relationship between breastfeeding, food pattern and being overweight in the Caribbean. Methods: In a cross-sectional school-based study in 2004-2005, weight and height were measured by two research assistants in 1776 children aged 6-11 years on Aruba, an island in the Caribbean. BMI was defined according to guidelines by the International Obesity Task Force. Parents completed a questionnaire pertaining to breastfeeding and dietary food pattern. Results: 1451/1776 (81.7%) children were breastfed; 851/1766 (47.9%) children were breastfed for <4 months, 227/1776 (12.8%) for 4-6 months and 373/1776 (21.0%) for ≧6 months. Children who were breastfed for ≥4 months had lower odds (OR 0.32, 95%CI 0.25-0.40) of being overweight including obesity than those who either were not breastfed or who were breastfed for <4 months. Children who were breastfed for ≥4 months were more likely to have a structured food pattern of six eating moments a day (OR 7.43, 95% CI 5.87-9.39, P<0.001) and to have breakfast every day (OR 2.86, 95% CI 2.17-3.78, P<0.001) than those who were not or who were breastfed for <4 months. Conclusions: Breastfeeding for ≥4 months is associated with a structured food pattern (six eating moments a day including a daily breakfast) and carries a strikingly lower risk of overweight in children. Promoting prolonged breastfeeding together with a focus on a subsequent structured food pattern could be a cheap method of preventing overweight. © W. S. Maney & Son Ltd 2013

Sensitivity of the Cherenkov Telescope Array to spectral signatures of hadronic PeVatrons with application to Galactic Supernova Remnants

The local Cosmic Ray (CR) energy spectrum exhibits a spectral softening at energies around 3 PeV. Sources which are capable of accelerating hadrons to such energies are called hadronic PeVatrons. However, hadronic PeVatrons have not yet been firmly identified within the Galaxy. Several source classes, including Galactic Supernova Remnants (SNRs), have been proposed as PeVatron candidates. The potential to search for hadronic PeVatrons with the Cherenkov Telescope Array (CTA) is assessed. The focus is on the usage of very high energy γ-ray spectral signatures for the identification of PeVatrons. Assuming that SNRs can accelerate CRs up to knee energies, the number of Galactic SNRs which can be identified as PeVatrons with CTA is estimated within a model for the evolution of SNRs. Additionally, the potential of a follow-up observation strategy under moonlight conditions for PeVatron searches is investigated. Statistical methods for the identification of PeVatrons are introduced, and realistic Monte-Carlo simulations of the response of the CTA observatory to the emission spectra from hadronic PeVatrons are performed. Based on simulations of a simplified model for the evolution for SNRs, the detection of a γ-ray signal from in average 9 Galactic PeVatron SNRs is expected to result from the scan of the Galactic plane with CTA after 10 h of exposure. CTA is also shown to have excellent potential to confirm these sources as PeVatrons in deep observations with O(100) hours of exposure per source