Individual and environmental factors associated with the seroprevalence of Borrelia burgdorferi

Background: Lyme disease (LD) is a common tick-borne disease in Europe. Diverse factors at various scales determine the spatial distribution of Borrelia burgdorferi infection risk and a better understanding of those factors in a spatially explicit framework is needed for disease management and prevention. While the ecology of ticks and the landscape favoring their abundance have been extensively studied, the environmental conditions favoring an intense contact with susceptible humans, including groups at risk, are sparse. The aim of this study is to assess which individual and environmental factors can favor B. burgdorferi infection in a Belgian group professionally at risk. Methods: Serological results of 127 veterinarians and farmers enrolled in this study were analyzed, taking into account their municipality of residence. Using binary logistic regression and considering interaction terms, the joint effects of landscape composition and configuration, and forest and wildlife management were examined. Results: Seven of the 127 workers were seropositive for LD, leading to a seroprevalence of 5.51%. Seropositivity was higher in older persons. The proportion of forest and semi-natural habitats and wetland had a positive impact on LD seroprevalence while arable land–grassland ecotones had a negative one. Our results confirmed the need to consider complex interactions between landscape variables in order to model risk. Conclusions: Our data show that LD has to be considered as a risk for farmers and veterinarians. Rather than focusing either on ecological aspects of tick and pathogen distribution or on purely epidemiological aspects such as individual risk factors, our model highlights the role of human–environment interactions in LD risk assessment

The Benefits of Cooperation Under Uncertainty: the Case of Climate Change

This article presents an analysis of the behaviour of countries defining their climate policies in an uncertain context. The analysis is made using the S-CWS model, a stochastic version of an integrated assessment growth model. The model includes a stochastic definition of the climate sensitivity parameter. We show that the impact of uncertainty on policy design critically depends on the shape of the damage function. We also examine the benefits of cooperation in the context of uncertainty:We highlight the existence of an additional benefit of cooperation, namely risk reduction