18 Accounting for Health Impacts of Climate Change (iv) (v) (vi) (using regional climate models) and statistical downscaling (using empirical relationships). These two approaches will yield different estimates of projected changes in climate variables.10 As a result of the above (selection of an emissions scenario, a GCM, and a downscaling approach), for each grid cell of the world (of say 50 kilometers [km] by 50 km), there can be up to 264 estimates of future climate variables (including temperature and rainfall among other variables). A possible approach to this multiplicity of estimates is to calculate the mean (average) of the projected values. This, in effect, would assume that projected values of future climate variables across all of the Special Report on Emissions Scenarios and GCMs are equally likely. A possibly undesirable outcome of such an approach is that it could result in projecting zero change in the values of climate variables relative to the baseline. An alternative approach is to attempt capturing the full range of results by selecting extreme values on either side of the projected change (e.g., if rainfall or humidity is of interest, by selecting the “wettest” and “driest” projection).11 Assess and estimate the nature of the relationship between climate variables and health (for the selected diseases). Projecting future health impacts of climate change requires some level of understanding of the relationship that may exist between climate variables (such as temperature, rainfall, drought, and heat waves) and the health issues of concern. This may be addressed using a combination of local historical data (time series or panel dataset) and knowledge, as well as national and global data. At the global level, a popular model, the Modeling Framework for the Health Impact Assessment of Man-Induced Atmospheric Changes includes modules for vectorborne diseases, including malaria, dengue fever and schistosomiasis, thermal heat mortality, and ultraviolet-related skin cancer due to stratospheric ozone depletion. Possible outputs are (a) for vector-borne disease modules, cases, and fatalities from malaria, and incident cases for dengue fever and schistosomiasis; (b) for the thermal stress module, cardiovascular, respiratory and total mortality; and (c) for skin cancer module, malignant melanoma and non-melanoma skin cancer. Selecting population projections. The number of cases of different types of climate-dependent diseases will inherently depend on projections related to the number of people that may be exposed to these diseases. While this may be of relative ease at national levels (using national or global datasets), the level of uncertainty pertaining to these population projections will increase as one’s interest moves to subnational levels. Focusing on a specific population, such as those living in poverty, creates challenges. For example, answering the question “What could potentially be the number of poor households in the Central Highlands of Viet Nam in 2050?” would require the use of numerous assumptions. Selecting projections of future socioeconomic characteristics. The incidence (e.g., number of cases of a specific disease per 1,000 people) of climate-dependent diseases depends not only on climate variables, but also on socioeconomic characteristics, among which income and education are known determinants. This implies that estimates of the health impacts of climate change will also depend on projections for these socioeconomic characteristics. Key to the nature of these projections is the recognition that future socioeconomic characteristics will themselves be a function of climate change. Hence, projecting future values of socioeconomic characteristics (in particular, income) must be consistent with the selection of emissions scenarios (see Box 4 for an example). 10 For more details on the issue of downscaling, see Wilby and Wigley (1997), Wilby et al. (1998), and Wood et al. (2004). 11 Institutions such as the Canadian Centre for Climate Research, the United Kingdom Meteorological Office, the UK Hadley Centre, and the Goddard Institute for Space Studies are continuously working to refine the predictions and making adjustments based on new data.