Int. J. Environ. Res. Public Health 2011, 8 920 Table 2. Linear coefficient estimates (b). Variables Coefficients Std. Dev. T-Stats. Other Stats. CAPGDP −0.0008 0.0004 −1.86 ** GINI 0.3721 0.0821 4.53 * POPDENS 0.0001 0.0047 0.03 CAPEXP −0.0266 0.0056 −4.71 * CAPBED 1.3648 0.4467 3.06 * CONSTANT 0.0321 0.0567 0.56 Fisher-Stats (5,271) 12.87 * R2 0.22 Hausman (5)-Stats (a) 13.24 * * significant at 1% critical level; ** significant at 10% critical level. (a) The significance of the Hausman statistic implies the rejection of the hypothesis of independence between the unobserved effects and the socio-economic variables. Therefore, the result presented in this table is obtained from fixed effects model specification. (b) Since the dependent variable is the log of malaria cases per 1,000 people, the linear coefficients could not be directly interpreted as marginal effects but for any regressor the marginal effect should be calculated as by using logarithmic functions derivative rule. The nonparametric component estimation results show that the three climate factors (temperature levels, precipitation levels and precipitation variability) explain 36% of the variation in the log of malaria cases. The 36% explanatory power of malaria cases by climate variables is lower than has been found in previous literature (e.g., Zhou et al.[7] and Ndiaye et al.[24]) likely because these previous models did not control for the relevant socio-economic confounding factors and included fewer countries. A nonlinear relationship is detected between malaria cases and climate variability and temperature but a linear constant effect of precipitation levels at any given climate variability realization. The effect of climatic factors on the number of malaria cases follows patterns shown in Figures 1 to 3. Lower climate variability (less than 0.19 standard deviations) has positive impact on malaria cases but as variability increases above 0.19 standard deviations, we observe a decrease in the number of cases but with high uncertainty, as shown by wider 95% confidence intervals (Figure 1).The effect of temperature on malaria cases at any given climate variability follows an increasing but not linear trend (Figure 2). Below 20 degrees the effect is negative and decreasing as expected since lower temperature levels do not permit survival of adult mosquitoes and therefore reduce their abundance. The effect is increasing for temperature levels between 20 ºC and 25 ºC but becomes positive only from 22 ºC as shown in Figure 2. At temperature levels above 25 ºC, the effect slows down but remains positive with an increasing trend. The pattern of the effect of temperature on malaria cases is consistent with the description given in MARA/ARMA report [12]. Note that since the range of temperatures in our sample is between 16.7 ºC and 29.2 ºC with the mean at 24.1 ºC we expect marginal increases in temperature to increase malaria prevalence in many of the countries of study. Precipitation levels have small but statistically significant effect on the number of cases (Figure 3). Also we conducted a nonparametric Fisher test [26,27,43] which fail to reject the hypothesis that all estimated coefficients are statistically different from zero at 1% significance level.