Inconclusiveness of chytridiomycosis as the agent in widespread frog declines

Although there is considerable evidence to support the hypothesis that the chytrid fungus Batrachochytrium dendrobatidis is the primary agent responsible for widespread declines in amphibian populations, particularly rainforest frog populations in Australia and Central America, I argue the case has not yet been made conclusively. Few specimens were collected at the time of population declines, so it may never be possible to conclusively determine their cause. It remains unclear whether the pathogen is novel where declines have occurred. Although it is not necessary that the infection be novel for it to be implicated in declines, if a preexisting pathogen has only recently caused extinctions, cofactors must be important. Whether the pattern of outbreaks represents a wave of extinctions is unclear, but if it does, the rate of spread in Australia is implausibly high for a waterborne pathogen, given the most likely estimates of epidemiological parameters. Although B. dendrobatidis is an amphibian pathogen according to Koch's postulates, the postulates are neither necessary nor sufficient criteria to identify a pathogen. The following key pieces of information are necessary to better understand the impact of this fungus on frog communities: better knowledge of the means and rate of transmission under field conditions, prevalence of infection among frog populations, as distinct from morbid individuals, and the effect of the fungus on frogs in the wild. It is crucial to determine whether there are strains of the fungus with differing pathogenicity to particular frog species and whether host-pathogen coevolution has occurred or is occurring. Recently developed diagnostic tools bring into reach the possibility of addressing these questions and thus developing appropriate strategies to manage frog communities that may be affected by this fungus.

The etiology of social phobia: Empirical evidence and an initial model

Research into the etiology of social phobia has lagged far behind that of descriptive and maintaining factors. The current paper reviews data from a variety of sources that have some bearing on questions of the origins of social fears. Areas examined include genetic factors, temperament, childrearing, negative life events, and adverse social experiences. Epidemiological data are examined in detail and factors associated with social phobia such as cognitive distortions and social skills are also covered. The paper concludes with an initial model that draws together some of the current findings and aims to provide a platform for future research directions. (C) 2004 Elsevier Ltd. All rights reserved.

Using epidemiological models to estimate the health effects of diet behaviour change: the example of tailored fruit and vegetable promotion

Objective: To explore the use of epidemiological modelling for the estimation of health effects of behaviour change interventions, using the example of computer-tailored nutrition education aimed at fruit and vegetable consumption in The Netherlands. Design: The effects of the intervention on changes in consumption were obtained from an earlier evaluation study. The effect on health outcomes was estimated using an epidemiological multi-state life table model. input data for the model consisted of relative risk estimates for cardiovascular disease and cancers, data on disease occurrence and mortality, and survey data on the consumption of fruits and vegetables. Results: if the computer-tailored nutrition education reached the entire adult population and the effects were sustained, it could result in a mortality decrease of 0.4 to 0.7% and save 72 to 115 life-years per 100000 persons aged 25 years or older. Healthy life expectancy is estimated to increase by 32.7 days for men and 25.3 days for women. The true effect is likely to lie between this theoretical maximum and zero effect, depending mostly on durability of behaviour change and reach of the intervention. Conclusion: Epidemiological models can be used to estimate the health impact of health promotion interventions.