IPCC Fourth Assessment Report: Climate Change 2007
Climate Change 2007: Working Group II: Impacts, Adaptation and Vulnerability Sensitivity/vulnerability of the health sector

Assessments of health in Africa show that many communities are already impacted by health stresses that are coupled to several causes, including poor nutrition. These assessments repeatedly pinpoint the implications of the poor health status of many Africans for future development (Figure 9.1a-d) (e.g., Sachs and Malaney, 2002; Sachs, 2005). An estimated 700,000 to 2.7 million people die of malaria each year and 75% of those are African children (see http://www.cdc.gov/malaria/; Patz and Olson, 2006). Incidences of malaria, including the recent resurgence in the highlands of East Africa, however, involve a range of multiple causal factors, including poor drug-treatment implementation, drug resistance, land-use change, and various socio-demographic factors including poverty (Githeko and Ndegwa, 2001; Patz et al., 2002; Abeku et al., 2004; Zhou et al., 2004; Patz and Olson, 2006). The economic burden of malaria is estimated as an average annual reduction in economic growth of 1.3% for those African countries with the highest burden (Gallup and Sachs, 2001).

The resurgence of malaria and links to climate and/or other causal ‘drivers’ of change in the highlands of East Africa has recently attracted much attention and debate (e.g., Hay et al., 2002a; Pascual et al., 2006). There are indications, for example, that in areas that have two rainy seasons – March to June (MAMJ) and September to November (SON) – more rain is falling in SON than previously experienced in the northern sector of East Africa (Schreck and Semazzi, 2004). The SON period is relatively warm, and higher rainfall is likely to increase malaria transmission because of a reduction in larval development duration. The spread of malaria into new areas (for example, observations of malaria vector Anopheles arabiensis in the central highlands of Kenya, where no malaria vectors have previously been recorded) has also been documented (Chen et al., 2006). Recent work (e.g., Pascual et al., 2006) provides further new insights into the observed warming trends from the end of the 1970s onwards in four high-altitude sites in East Africa. Such trends may have significant biological implications for malaria vector populations.

New evidence regarding micro-climate change due to land-use changes, such as swamp reclamation for agricultural use and deforestation in the highlands of western Kenya, suggests that suitable conditions for the survival of Anopheles gambiae larvae are being created and therefore the risk of malaria is increasing (Munga et al., 2006). The average ambient temperature in the deforested areas of Kakamega in the western Kenyan highlands, for example, was 0.5°C higher than that of the forested area over a 10-month period (Afrane et al., 2005). Mosquito pupation rates and larval-to-pupal development have been observed to be significantly faster in farmland habitats than in swamp and forest habitats (Munga et al., 2006). Floods can also trigger malaria epidemics in arid and semi-arid areas (e.g., Thomson et al., 2006).

Other diseases are also important to consider with respect to climate variability and change, as links between variations in climate and other diseases, such as cholera and meningitis, have also been observed. About 162 million people in Africa live in areas with a risk of meningitis (Molesworth et al., 2003; Figure 9.1d). While factors that predispose populations to meningococcal meningitis are still poorly understood, dryness, very low humidity and dusty conditions are factors that need to be taken into account. A recent study, for example, has demonstrated that wind speeds in the first two weeks of February explained 85% of the variation in the number of meningitis cases (Sultan et al., 2005).

Figure 9.1

Figure 9.1. Examples of current ‘hotspots’ or risk areas for Africa: (a) ‘hunger’; (b) ‘natural hazard-related disaster risks’; (c) regions prone to malaria derived from historical rainfall and temperature data (1950-1996); and (d) modelled distribution of districts where epidemics of meningococcal meningitis are likely to occur, based on epidemic experience, relative humidity (1961-1990) and land cover (adapted from IRI et al., 2006, p. 5; for further details see also Molesworth et al., 2003; Balk et al., 2005; Dilley et al., 2005; Center for International Earth Science Information Network, 2006; Connor et al., 2006).