we used monthly reports of slide - confirmed malaria and annual census  based population data from 434 counties ( municpios ) in the brazilian amazon region for 19961999 , during which no coordinated national malaria interventions occurred ( 12 ) . to study the relationship of reported malaria cases to climate , we used monthly precipitation and temperature from the cru ts 2.1 gridded climate data set for selected states ( 13 ) ( figure 1 ) . to consider how the precipitation  malaria relationship depends on surface water conditions , including the extent of open water and wetlands , we used 100 m  100 m maps from the jers-1 synthetic aperture radar satellite and calculated the percentage of maximum inundatable open water and wetland coverage for each county ( figure 2 , panel a ) ( 14 ) . in this region , monthly temperatures were between 24.6c and 29.4c ( well within the range for optimal malaria transmission ) for 95% of the observations ( 18,416 of 19,364 ) included in the analysis ( temperature relationships not shown ) . malaria incidence per 1,000 population ( black lines ) and mean monthly precipitation ( blue lines ) during la nia ( orange bars ) and el nio ( red bars ) events for the states of a ) amazonas , b ) mato grosso , and c ) roraima . a ) percentage of wetlands in amazon basin counties ( shades of blue ) , counties without wetlands data ( yellow ) , and counties with < 80 total malaria cases ( gray ) . wetland colors correspond to percentage wetland values in panel d. b ) risk ratios for malaria incidence for 1 sd ( 14 cm ) change in monthly precipitation ( january 1996december 1999 ) , plotted at each county seat of government ; c ) spatially smoothed risk ratios for 14-cm changes in monthly precipitation . in both panels , red shaded squares show reduced risk for 14-cm increase in monthly precipitation ; blue shaded squares show increased risk for malaria with increased precipitation . d ) boxplot of risk ratios for malaria incidence for 14-cm changes in monthly precipitation , by percentage wetland cover . if the lag and the rainfall coefficient vary across regions , meaningful geographic comparisons would be difficult to achieve because neither the lag nor coefficient have consistent meanings across models . to interpret results , either the coefficient must be fixed and the lags varied ( difficult to do ) or the lags must be fixed and the coefficients varied ( relatively easy to do ) . the aim is to describe the variable patterns of malaria incidence and precipitation , not create a highly predictive model . we chose to fix the lag and vary the coefficients . to assess the association between malaria incidence and precipitation data , we estimated the rate ratio of malaria incidence associated with 1 sd - increase in monthly precipitation ( 14 cm ) for each county by using the following poisson regression model , which includes a flexible temporal trend represented as a natural cubic spline with 6 degrees of freedom ( figure 2 , panel b ) : malariait  poisson(it ) log it = log popit + i + iprecipit + fi(t ) i  normal(g(lati , loni ) ,  ) the ( estimated ) regression coefficients from the county - specific models were then modeled as a spatially smooth surface , a thin - plate spline . degrees of freedom for the thin - plate spline were selected using generalized cross - validation ( figure 2 , panel c ) . the relationships between precipitation and malaria incidence in the amazon basin are spatially varied and change signs , depending on the region . positive correlations between monthly precipitation and malaria incidence ( rate ratios > 1 ) occur in the upland regions of the southwest and central amazon basin , whereas negative correlations between precipitation and incidence ( rate ratios < 1 ) occur in the north , largely along the main waterways of the amazon river and the major wetland regions of the basin ( figure 2 ) . for a 14-cm increase in monthly rainfall , the malaria rate can double in the upland area , yet decrease by up to 80% along the main amazon channel . the p values of the precipitation coefficient are 0.00020.0009 along the main waterways and 0.0040.10 in uplands areas . we hypothesize that this reversal of the malaria  precipitation relationship from positive to negative is related to the extent of open water and wetlands in the basin . mosquito habitats in wetlands or along large rivers may wash out or become too deep during months with high precipitation , but in areas with fewer wetlands , mosquito habitats are limited by precipitation . to test this hypothesis , we compared the malaria  precipitation association for 338 counties that reported > 80 cases of malaria over the 48 months against the estimated percentage of open water and wetland cover for each county ( figure 2 , panel d ) . as expected , the precipitation - linked risk for malaria fell as the percentage of wetland in each county increased , but the risk for malaria varied in counties with low percentages of wetlands . the central - east region had the lowest level of malaria incidence , which may explain why this region also lacked a malaria - precipitation relationship . studies have proposed that flooding created new pools of water suitable for mosquito larvae as the water levels slowly receded from alluvial forests along the rio branco river in roraima and the maroni river on the frontiers of suriname and french guiana ( 6,15 ) . our results suggest that monthly precipitation along the amazon basin can have both strong positive and negative associations with malaria incidence . further research is needed to address the limitations of our study , including its short time frame and the crude countywide approximation of percentage wetlands as an exposure . the quality and reliability of the health data were concerns , but we verified that the distribution of null reporting was unbiased temporally and spatially . also , our study did not quantify increasing malaria incidence in response to increasing or decreasing precipitation or the impact of lag factors . instead , we focused on the seasonality of these patterns until longer data series of malaria incidence and climate data are available . our evidence suggests that precipitation drives malaria risk in the amazon basin , but the relationship varies in the uplands ( more precipitation , more / less malaria ) and is negative in areas dominated by wetlands and large rivers ( more precipitation , less malaria ) . our findings show the need to account for local landscape characteristics , especially the extent of wetlands and open water , in regional to global projections of the effects of climate change on malaria . better understanding the impact of climate and landscape on malaria will improve our ability to assess health risks .
climate changes are altering patterns of temperature and precipitation , potentially affecting regions of malaria transmission . 
 we show that areas of the amazon basin with few wetlands show a variable relationship between precipitation and malaria , while areas with extensive wetlands show a negative relationship with malaria incidence .