261531 Impact of climate and land use influencing the spatial and temporal distribution of malaria risk in the Amazon

Tuesday, October 30, 2012

Beth J. Feingold, PhD, MESc, MPH , Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD
Benjamin Zaitchik, PhD , Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD
Victoria Shelus, BSc , Nicholas School of the Environment, Duke University, Durham, NC
William Kuang-Yao Pan, DrPH MS MPH , Duke Global Health Institute, Duke University, Durham, NC
Malaria continues to be one of the world's most devastating threats to public health. In Peru, 75% of malaria cases occur in the Department of Loreto, in the Northern Amazonian Region, and most cases (80%) are concentrated in just 10 of the department's 51 districts. Loreto is the least densely populated region of Peru, and also the largest, covering 29% of Peru's land mass. There, fevers suspected to be malaria are the #1 cause of morbidity in adolescents and adults. Malaria cases have declined ~4% per year since 2000; however, to continue reducing the burden of disease in these rural populations, better knowledge of where, when and why people are infected is needed. Key factors related to continued malaria endemicity in this region are the expansion of vector habitats from land use change (deforestation for logging and road development) and social and ecological processes that increase human exposure to the Amazon's most efficient malaria vector, Anopheles darlingi. In order to refine and focus prevention strategies across the region, spatially explicit risk estimates are necessary. In this ongoing study, we investigate how malaria risk varies across time and space in this region by modeling the relationship among climate, land use, and malaria risk from 2006 to 2011. We incorporate local climate variables and satellite-derived land use information with data on monthly malaria counts at each of the regional government's health posts in the region. To assess malaria risk, we estimate populations associated with delineated geographic catchment areas for each of the health posts. Our novel approach uses simulated temperature, precipitation, and soil moisture from a Land Data Assimilation System (LDAS) as well as direct information from climate stations. Our spatially and temporally explicit estimations of malaria risk will enable our local partners to implement more efficient prevention and control efforts.

Learning Areas:
Biostatistics, economics
Environmental health sciences
Protection of the public in relation to communicable diseases including prevention or control
Public health or related research

Learning Objectives:
1) Evaluate the relative importance of climatic drivers of malaria in the Peruvian Amazon 2) Assess the geographic variation in malaria risk in the Peruvian Amazon 3) Demonstrate the potential utilization of an early warning system for malaria in the Amazon 4) Compare the value of a range of satellite-derived and modeled climate variables for disease monitoring and early warning systems, and identify additional data needs.

Keywords: Climate, Infectious Diseases

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: this is the subject of my postdoctoral work.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.