The authors found that even the most conservative climate change predictions would have a significant impact on the incidence of the diarrhea. However, there are many uncertainties that exist in each of the predictions, the authors argue that nonlinear regression models should be used to predict the temperature impacts on diarrhea. In order to develop this type of modeling accurate empirical data are needed. The authors highlight several adaptive health policies, but in order to substantially decrease future deaths, climate and medical scientists must work together to accurately identify the relationship between climate and health.
Extreme temperature variation has been linked to an increase in human mortality; moreover there is evidence that the effect of temperature is influenced by socioeconomic and socio-demographic factors. Burkart et al. (2011) studied the effect of temperature and thermal atmospheric conditions on all-cause and cardiovascular mortality in Bangladesh. In particular, the authors investigated the differences in mortality rates between rural and urban areas. Generalized additive models (GAMs) were fitted separately for rural and urban areas, and breakpoint models were used to determine mortality at certain temperature thresholds. In most cases a V-shaped temperature-mortality relationship was observed, with a greater increase in mortality in urban areas.—Simone Berkovitz
Burkart, K., Schneider, A., Breitner, S., Khan, M., Kramer, A., Endlicher, W., 2011. The effect of atmospheric thermal conditions and urban thermal pollution on all-cause and cardiovascular mortality in Bangladesh. Environmental Pollution 159, 2035–2043.
Most studies have identified increasing mortality levels at relative high and low temperatures, shown by U and V shaped curves. Moreover, other research indicates that socioeconomic and socio-demographic factors influence the effects of temperature. It has been observed that the effects of heat are stronger in cities with milder climates, high population density, or high levels of urbanization. Previous research on the relationship between temperature and mortality has been conducted in industrialized nations, however the effects on less developed tropical nations have been largely ignored. Therefore, Burkart et al. studied the impact of temperature on mortality in Bangladesh, taking into consideration all relevant meteorological and physiological variables. In addition, the authors compared the effects of temperature on urban and rural populations.
The study used meteorological data comprising daily mean and extreme temperature, humidity, wind speed, and cloud coverage values, collected from 26 sites across Bangladesh. Three thermo-physiological indices (TPIs) were calculated in order to look at human thermoregulation, which is determined by metabolic heat production and energy transfer with the outside environment. Heat Index (HI) combines air temperature and humidity and was used to asses temperatures above 26ºC and humidity above 40%. The physiological equivalent temperature (PET) is the temperature of a typical indoor setting when the human body is balanced with the same core and skin temperatures. The third index used was the universal thermal climate index (UTCI), which measures heat transfer occurring inside the human body. With the exclusion of accidental, infant, and maternity related deaths, crude death rates and age adjusted mortality rates were determined by daily deaths in rural and urban areas. The authors used Poisson generalized additive models to explore the relationship between daily death counts and ambient temperature, HI, PET, and UTCI. In order to emphasize urban and rural differences, separate models were constructed.
The authors found a clear relationship between thermal conditions and mortality; an increase in mortality was observed for both high and low temperatures in both rural and urban areas. For all-cause mortality, a V-shaped temperature mortality relationship was observed. The effect of heat on cardiovascular mortality, however was only observed in urban areas, while in rural areas a decrease in mortality with heat was seen. The heat effects, above a certain temperature threshold, were observed to be stronger than the cold effects. In addition, the effects were more pronounced in urban areas.
Climate variability and extreme hot or cold temperatures as a result of global warming will influence mortality rates and overall human health. The authors found that the effects of heat were stronger, which suggests that more deaths will occur with global warming. The effects were also greater in urban areas compared to rural areas, which is either due to more heat exposure or modified health or age patterns. Socioeconomic factors could also explain this difference. These findings suggest that underdeveloped urban areas will experience greater mortality from global climate change.
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.