Progress Report as of March 2002
Climate Variability and Human Health Impacts in Brazil
The team collected most of the required epidemiological, climatological and
socioeconomic / demographic data.
Monthly cases of dengue fever through 2001 for five Brazilian cities -- Rio de Janeiro, Recife, Fortaleza, Belém and Salvador. These city governments also provided data on environment and infrastructure.
Monthly cases of malaria for Brazilian states in the Amazonian region -- new data for Pará 1971 - 2001 and updates through 2001 for Roraima, Amazonas and Maranhão. The Pará data are for the state as a whole and for 48 municipalities that have precipitation records.
Socioeconomic data at the state/city level for Brazilian states with both epidemiological and meteorological data. These include characteristics of the population under the influence of the BR-174 highway in
Data available in 2002 on (mostly) demography, economy (income, etc.) and infrastructure (housing, sanitation) from the latest Brazilian national census.
The team decided to have a local planning workshop in 2002 in Boa Vista, the capital of Roraima, in order to improve the local networking for the development of entomological fieldwork. The following local institutions made a commitment: Federal University of Roraima; State Department of the Environment; State Department of Health; and the Department of Health of the City of Boa Vista.
A preliminary analysis of the relationships between rainfall and the incidence of malaria in the state of Roraima by month for 1985-1996 reveals heterogeneous findings in different municipalities. In
Roraima, the rainy season runs from May to September and the dry season from October to April. The municipalities
Mucajaí, Boa Vista and Normandia had both positive and negative correlations between rainfall and malaria. Of the other municipalities, Bonfim and Caracaraí demonstrated positive correlations while Alto
Alegre, São Luis and São João da Baliza demonstrated negative correlations. These variations are thought to be due to local differences in non-climatic factors.