remain more or less constant at the level of about 5 percent between 2000 and 2020, reflecting the stability of the ratio of the population aged under five to the total population and moderate increase in the incidence of diarrheal disease of children under age five associated with the lower and upper bounds of extreme weather scenarios during the study period.

# 7.2 The health impact of climate change projection

The projected climate impact on diarrhea in 2020 is the excess health burden attributable to climate change, net of the population growth effect. This is estimated by comparing the projected number of diarrheal cases with and without climatic changes in 2020. Without climate change, the diarrheal incidence in 2020 is assumed to be the same as that in 2000 based on our sample estimate, i.e. 22.8 percent. The projections presented in Table 5 show that if the probability of extreme rainfall events were to increase to 60 percent or 80 percent, in combination with an increase in minimum and maximum temperature of 1 degree Celsius, there would be an increase in diarrheal cases among children aged under five, of between 1 million or 2.7 million cases in 2020 respectively.

The additional health cost of diarrheal disease attributable to this projected climate change in Sub-Saharan Africa is between 0.2 and 0.5 percent of GDP by 2020. This projection is confined to one disease and to the most vulnerable age group. However a range of other diseases have been found to be affected by climate change, including malaria<sup>9</sup> , and dengue fever (Hales et al, 2002) in Africa. The full health cost of climate change could be much larger, possibly reaching at least 1% GDP, if taking account of all these health indicators

# **8. Conclusion**

This study provides new evidence that extreme precipitation and temperatures have significant impacts on the incidence of diarrhea and malnutrition among children under the age of three in Africa. The effect of extreme weather events remains strong even after controlling for socioeconomic factors, climate zone and country fixed effects. The employment of panel data improves the measurement of the impact of socioeconomic factors by controlling for unobserved heterogeneity. From a policy perspective, the incorporation of weather and socioeconomic factors in one model addresses the issue that the many factors that influence human health cannot be considered in isolation (Scheraga et al., 2003). While adaptive capacity, in terms of income, female education, access to

> 9 There are several studies on the impact of climate change on malaria. These include the study by Martens and et al (1999) that provides global estimates of the additional number of  people at risk based on future climate scenarios and MIASMA malaria model; Bouma and Kaay (1996)  who studied the association between the EI Nino Southern Oscillation and the malaria epidemics on the Indian subcontinent and Sri Lanka;  the study by Zhou and et al (2004) on the association between climate variability and the number of monthly malaria outpatients in seven highland sites in East Africa; and Tanser  et al (2003) who used the climate models  to project the potential effect of climate change on transmission patterns of malaria and projected  a potential increase of 16-28% in person-months of exposure across all three IPCC scenarios (low greenhouse gas emissions, medium-high and high emissions) in Africa.

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