Please use this identifier to cite or link to this item:
https://www.arca.fiocruz.br/handle/icict/60863
CONSERVATION EFFORTS AND MALARIA IN THE BRAZILIAN AMAZON
Affilliation
University of Wisconsin. School of Medicine and Public Health. Department of Population Health Sciences. Center for Sustainability and the Global Environment. Nelson Institute for Environmental Studies. Madison, Wisconsin, EUA.
Nanaimo British Columbia. Wildlife Conservation Society-Canada. Canada.
University of Pennsylvania. Department of Medicine. Division of Infectious Diseases. Philadelphia, Pennsylvania, EUA.
University of Texas. Department of Pathology. Galveston, Texas, EUA.
Fundação Oswaldo Cruz. Instituto de Comunicação e Informação Científica e Tecnológica em Saúde. Rio de Janeiro, RJ, Brasil.
Duke University. Nicholas School of the Environment. Durham, North Carolina, EUA.
Nanaimo British Columbia. Wildlife Conservation Society-Canada. Canada.
University of Pennsylvania. Department of Medicine. Division of Infectious Diseases. Philadelphia, Pennsylvania, EUA.
University of Texas. Department of Pathology. Galveston, Texas, EUA.
Fundação Oswaldo Cruz. Instituto de Comunicação e Informação Científica e Tecnológica em Saúde. Rio de Janeiro, RJ, Brasil.
Duke University. Nicholas School of the Environment. Durham, North Carolina, EUA.
Abstract in Portuguese
We respond to Valle and Clark, who assert that "conservation efforts may increase malaria burden in the Brazilian Amazon," because the relationship between forest cover and malaria incidence was stronger than the effect of the deforestation rate. We contend that their conclusion is flawed because of limitations in their methodology that we discuss in detail. Most important are the exclusion of one-half the original data without a discussion of selection bias, the lack of model adjustment for either population growth or migration, and the crude classifications of land cover and protected areas that lead to aggregation bias. Of greater significance, we stress the need for caution in the interpretation of data that could have profound effects on regional land use decisions.
Share