Potential risk factors for multiple sclerosis in Rio de Janeiro: a case-control study.

PURPOSE. To evaluate potential risk factors for the development of multiple sclerosis in Brazilian patients. METHOD. A case control study was carried out in 81 patients enrolled at the Department of Neurology of the Hospital da Lagoa in Rio de Janeiro, and 81 paired controls. A standardized questionnaire on demographic, social and cultural variables, and medical and family history was used. Statistical analysis was performed using descriptive statistics and conditional logistic regression models with the SPSS for Windows software program. RESULTS. Having standard vaccinations (vaccinations specified by the Brazilian government) (OR=16.2; 95% CI=2.3-115.2), smoking (OR=7.6; 95% CI=2.1-28.2), being single (OR=4.7; 95% CI=1.4-15.6) and eating animal brain (OR=3.4; 95% CI=1.2-9.8) increased the risk of developing MS. CONCLUSIONS. RESULTS of this study may contribute towards better awareness of the epidemiological characteristics of Brazilian patients with multiple sclerosis.

A framework for assessing the scale of influence of environmental factors on ecological patterns

The distribution of living organisms, habitats and ecosystems is primarily driven by abiotic environmental factors that are spatially structured. Assessing the spatial structure of environmental factors, e.g., through spatial autocorrelation analyses (SAC), can thus help us understand their scale of influence on the distribution of organisms, habitats, and ecosystems. Yet SAC analyses of environmental factors are still rarely performed in biogeographic studies. Here, we describe a novel framework that combines SAC and statistical clustering to identify scales of spatial patterning of environmental factors, which can then be interpreted as the scales at which those factors influence the geographic distribution of biological and ecological features. We illustrate this new framework with datasets at different spatial or thematic resolutions. This framework is conceptually and statistically robust, providing a valuable approach to tackle a wide range of issues in ecological and environmental research and particularly when building predictors for ecological models. The new framework can significantly promote fundamental research on all spatially-structured ecological patterns. It can also foster research and application in such fields as global change ecology, conservation planning, and landscape management.