The global distribution of malaria has been linked to climatic and socioeconomic factors, which has caused researchers to study the effects of climate change and socioeconomic development; however, previous studies have failed to incorporate both these factors. Béguin et al. (2011) studied the influence of both climatic and socioeconomic factors on the past, present, and future distribution of malaria. A logistic regression model using temperature, precipitation, and gross domestic product per capita (GDPpc) was found to accurately identify recent distribution. Projections from a widely accepted climate change scenario, and an economic growth model were used. The models revealed that climate change increases malaria presence and distribution, while socioeconomic growth significantly decreases global distribution. However, it was found that the effects of climate change were much weaker than the effects of socioeconomic factors. –Simone Berkovitz
Béguin A, Hales S, Rocklöv J, Åström C, Louis V, Sauerborn R, 2011. The opposing effects of climate change and socio-economic development on the global distribution of malaria. Global Environmental Change 4: 1209—1214
The relationship between climatic factors and the prevalence of malaria vectors and parasite development has been established by numerous studies. Previous studies have assessed the influence of climate change on malaria, but Béguin et al. are the first to incorporate the combined influence of climatic and socio-economic factors. Climate and GDP per capita (GDPpc) both influence the risk of malaria, therefore it is necessary to distinguish and separate the two factors. The authors aimed to map the global distribution of malaria using empirical and logistic regression models, which incorporated both the effects of climate change and socioeconomic development.
In the study, Béguin et al. quantified the independent effects of climate change and socio-economic factors for the past and future malaria distribution. Current global malaria presence was determined using data from the World Health Organisation (WHO) and other researchers who estimated the risk and presence of both Plasmodium vivax and Plasmodium falciparum. In order to quantify socio-economic factors, GDP and population data, and future predictions of them were used in order to determine GDP per capita (GDPpc). The Climate Change scenario A1B developed for the IPCC special report was used as a basis for future predictions. Estimates for the years 1990, 2010, and 2050 were taken from a model developed by the Netherlands Environmental Assessment Agency, but in order to conduct a sensitivity analysis the predications were modified. A worst-case scenario, in which GPDpc declines, a scenario in which GDPpc is slightly reduced, and a scenario in which GDPpc remains constant at 2010 levels were used. To measure the effect of climate change, three different climate model simulations were used, which were statistically altered to fit a common temperature scale. In addition, temperatures recorded for the years 1991–2005 by the Climate Research Unit were used as a “no climate change” dataset. For each location, an average temperature and precipitation were calculated for the three past, present, and future time periods. In order to predict the probability that malaria will be present, Béguin et al. used a logistic regression model, with the presence of malaria as the outcome variable, and temperature, precipitation, and GDPpc as predictors. The most accurate model used the mean temperature of the coldest month and the mean temperature of the warmest month during the 1961–1990 period and the total annual GDP in a certain area divided by the population. These temperatures were used in order to indicate the “typical winter severity” and “intensity of the rainy season”.
Béguin et al. found the logistic regression model accurately identified the global malaria distribution for recent years. The test revealed a sensitivity of 85%, and a specificity of 95%; correctly classifying 85% of the data points in malarious-areas and 95% of the data points in malaria-free areas. In order to show the accuracy of the predictions, maps were created which diagramed correctly and incorrectly predicted absence or presence. In order to show the accuracy of the model, the first map used temperature and rainfall data together with GDPpc to compare the WHO data with the data calculated from the regression model. According to the world map diagram, the WHO data and the 1990 model were in agreement for most regions a few areas in the Middle East, Zambia, and Mexico.
When calculating the projected malaria risk, the fitted model parameters and the future projections of climate and GDP for the years 2030 and 2050 were used. Four scenarios were tested in order to differentiate between the climate and socioeconomic effects. The first scenario showed the effect of climate change without GDPpc growth, in which it was found the projected population at risk, is 5.2 billion. The second scenario predicted the effect of only GDPpc increase according to the A1B scenario and did not take into account climate change, which revealed only 1.74 billion would be at risk. The third scenario showed the combined effects of GDPpc increase and climate change, which revealed 1.95 billion people to be at risk. The fourth scenario showed the differences between the scenarios. In order to conduct sensitivity analysis, a range of economic situations were created, which resulted in a range of values produced.
The authors found that if global climate changed according to climate model predictions, but GDP remained constant, a modest expansion of malaria risk is predicted. If GDP changed as predicted by economic models, but climate change remained constant, much of the world, with the exception of Africa, would be malaria free. The authors note that the model was constructed on the basis of spatial patterns and does not account for year-to-year variability. However, Béguin et al were able to conclude that there is a strong relationship between GDPpc, climate, and malaria risk. Although climate change is a factor, socioeconomic development has the most dominant influence on the geographic contraction of malaria. Therefore, future economic developments could be most beneficial towards malaria risk mitigation.