The Regional Impacts of Climate Change

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2.3.7. Human Health

The links between climate and many environmental and vector-borne diseases (VBDs) are felt through the impacts of various climatic components (e.g., temperature, rainfall) on the physiology of pathogens and their vectors. Although there are crude atlases of disease distribution within Africa (Knoch and Schulze, 1956), accurate and verified models that translate these physiological climate-related processes into more detailed maps of disease distribution are scarce. Such maps and models are necessary to set the baseline of current levels and limits of transmission against which projected impacts of climate change can be measured. These changes may include shifts in the distribution of diseases into areas that previously were disease-free or a change in severity at a given location. Although such models now exist (Martens et al., 1995a,b; le Sueur et al., in preparation), most remain hypothetical and largely unverified. However, they may provide good starting points to illustrate the effects of projected climate change. In the case of malaria, a continental effort-Mapping Malaria Risk in Africa (MARA/ARMA)-is now underway; this effort will provide a data base of disease distribution and severity that can be used to verify climate-induced processes. No such parallel efforts, however, currently are underway for other diseases of the African continent that may be affected by climate change (e.g., arboviruses, trypansomiasis, and schistosomiasis).

In Africa, VBDs are major causes of illness and death. Table 2-12 provides global estimates of the number of people at risk from and the number of people who currently are infected by major VBDs. Currently, the distribution of most VBDs remains well within the climatic limits of their vectors. The extent to which disease transmission potential shifts geographically in response to shifts in vector distribution following climate change will depend partly on how human activities modify local ecosystems (McMichael et al., 1996). Rodent-borne diseases that could be affected by climate change include plague and hantavirus pulmonary syndrome. In a warmer and more urbanized world, rodent populations-which act as pathogen reservoirs and as hosts for the relevant arthropod vectors-will tend to increase. Thus, incidences of these diseases can be expected to rise (Shope, 1991).

Table 2-12: Major tropical vector-borne diseases and the likelihood of change in their distribution as a result of climate change.

Distribution Disease Change Vector Number at Risk millions) (1) Number Infected or New Cases/Year Present Distribution Likelihood of Altered Distribution with Climate Change

Malaria Mosquito 2,400 300-500 million Tropics/subtropics +++
Schistosomiasis Water snail 600 200 million Tropics/subtropics ++
Lymphatic filariasis Mosquito 1,094 117 million Tropics/subtropics +
African trypanosomiasis Tsetse fly 55 250,000-300,000 cases/yr Tropical Africa +
Dracunculiasis Crustacean (copepod) 100 100,000/yr South Asia/ Middle East/ Central-West Africa ?
Leishmaniasis Phebotomine sand fly 350 12 million infected, 500,000 new cases/yr (2) Asia/South Europe/Africa/America +
Onchocerciasis Blackfly 123 17.5 million Africa/Latin America ++
American trypanosomiasis Triatomine bug 100 18-20 million Central-South America +
Dengue Mosquito 2,500 50 million/yr Tropics/subtropics ++

Yellow fever Mosquito 450 <5,000 cases/yr Tropical South America and Africa ++

+ = likely; ++ = very likely; +++ = highly likely; ? = unknown.

(1) Top three entries are population prorated projections, based on 1989 estimates.

(2) Annual incidence of visceral leishmaniasis; annual incidence of cutaneous leishmaniasis is 1-1.5 million cases/yr.

Sources: PAHO, 1994; WHO, 1994, 1995; Michael and Bundy, 1996; WHO statistics.

Projected increases in the interannual variability of climate would have marked implications for the impact of seasonal epidemic diseases such as malaria. In general, control and mitigation activities for such diseases are planned around mean expected levels in any one year. Significant interannual variation impedes intervention and mitigation because of the impact on national budgets (which plan for mean circumstances) and lags that occur in relation to responses to climatically induced epidemic situations. In addition, such variation results in intermittent exposure of nonimmune populations-resulting in high levels of morbidity and mortality. The recent degree of variability is clearly illustrated in Table 2-13, which shows data for four southern African countries. This variability highlights the need for more climate-based forecasting systems capable of predicting such interannual variations with a lead time that allows health authorities to respond in a timely manner with preparatory/preventative measures (Jury, 1996; le Sueur and Sharp, 1996).

Table 2-13: Interannual variability of malaria (number of cases) within the southern Africa region.


(unconfirmed) (1)
Namibia (1)
353,593 (2)
South Africa
Zimbabwe (1) 420,137 877,734 797,659 721,376 1,585,850

(1) Clinically diagnosed.

(2) Incomplete

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