Uncertainties Associated with Climate Change and Human Health

Current research is revealing the import effects of climate change on human health, especially in vulnerable areas, but there are still many uncertainties associated with health predictions. For example, Kolstad et al. (2011) used global diarrhea as an indicator of the impacts of climate change on human health, while highlighting the uncertainties. The authors examined a range of linear regression coefficients relating diarrheal incidence to climate change using the results from 5 empirical studies and 19 climate models. They found that even under the most conservative climate scenarios, there would be a significant increase in diarrhea, but they also found that further empirical evidence is needed in order to reduce uncertainties.—Simone Berkovitz
Kolstad, E., Johansson, K. 2011. Uncertainties Associated with Quantifying Climate Change Impacts on Human Health: A Case Study for Diarrhea. Environmental Health Perspectives. 119, 299–305.

According to current research, climate change is expected to be a crucial health determinant, especially for people in vulnerable areas. It is predicted that climate change will cause a significant increase in deaths due to disease and malnutrition, however there are many uncertainties associated with the current predictions. Most importantly, there is uncertainty regarding future climate change and how the current climate health relations will be modified by socioeconomic adaption in the future. Therefore Kolstad et al. used a general approach that incorporated the uncertainties in empirical health data and climate projections. The authors analyzed temperature changes in the regions that are currently most affected by diarrhea in order to emphasize the uncertainties associated with trying to quantify the impact of climate change on human health.
            The study utilized temperature data from 19 coupled atmosphere-ocean climate models, based upon the IPCC A1B scenario. Five empirical studies, which analyzed the effects of temperature on diarrhea, were also used. Linear regression coefficients were used to compute the percentage increase in relative risk of diarrhea with each 1°C temperature increase, which was defined as a. In order to quantify the range of uncertainties and temperature projections, the authors created a two dimensional matrix with the relative risk (RR) projections which included 95 elements for each year and location.
            With the 19 climate ensembles, the authors found that temperatures would increase up to 4°C in tropical land over the next century, but it was noted that as temperatures rose the levels of discrepancy among the various models also increased. Once modeled, these temperatures indicated a 8–11% increase in the RR risk of diarrhea worldwide for 2010–2039, 15–20% increase for the 2040–2069, and 22–29% increase for 2070–2099. The results implied that the estimates used to quantify the effect of temperature on diarrhea (a) have a larger impact on the uncertainties in RR projections than the variance between climate models. This showed that picking only one a-value and one climate model could be misleading.

            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.   

Increased Malaria Risk Due to Greenhouse Forcing and Land-Use Changes in Tropical Africa

Increased temperatures and precipitation due to climate change will impact malaria spread and transmission throughout Africa. Ermert at al. (2012) assessed potential changes in the malaria transmission using an integrated weather-disease model. The model incorporated high-resolution regional data along with the impacts of land use changes. Projections were based upon moderate and optimistic climate change scenarios. The results showed both an increase in transmission in areas with increased temperature and rainfall, but extreme increases resulted in a decrease in transmission. In addition, projections showed that epidemic risk will increase in high altitude regions, whereas in lower-altitude regions epidemic risk will decline. —Simone Berkovitz
Ermert, V., Fink, A., Morse, A., Paeth, H., 2012. The Impact of Regional Climate Change on Malaria Risk due to Greenhouse Forcing and Land-Use Changes in Tropical Africa. Environmental Health Perspectives 120, 77-84.

            According to previous studies, it has been established that climate change will alter the distribution and transmission of malaria worldwide. In some areas of the world, the disease will contract, while in other areas it will expand, as malaria seasonality is altered. Previous research has also shown that in addition to weather and climate variability, there are many socioeconomic factors, which alter transmission and prevalence levels. In the East African Highlands it has been debated whether increased temperatures or other factors have caused the recent increase in malaria. Therefore, Ermert at al. used a disease-weather integrated model in order to assess the potential changes in malaria transmission due to climate change and land-use changes in tropical Africa. Most studies use general circulation models (GCMs) to model and predict transmission, but according to the authors these models have several limitations. For example, the grid resolution of a GCM is too coarse and does not adequately capture the effects of local terrain on temperature and rainfall. In addition, model biases are not corrected and changes in land surface characteristics are never taken into account. In order to improve upon modeling techniques, Ermert at al. used high-resolution regional climate model (RCM) and land-cover changes (LUC), which were incorporated into a regional model (REMO).
            The study simulated mosquito-biting rates using the Liver Pool Malaria Model (LMM). The LMM used a REMO, which is a limited area climate model with a horizontal grid resolution of 0.5°. Three REMO integrations were created using A1B and B1 IPCC greenhouse gas emissions scenarios. The A1B scenario predicts moderate climate change due to economic growth combined with the development of energy efficient technologies, while the B1 scenario is more ecologically optimistic. LUC changes, surface temperature changes and rainfall changes were also incorporated into the model. Malaria projections for 2001 to 2050 were modeled using the integrated-disease model in order to predict epidemics under the two climate change scenarios.
            The authors found that the model accurately predicted malaria distribution in Africa under the current climate conditions for 1960–2000. From the models it was found that areas with high, but not excessive precipitation, resulted in year-round transmission. According to model predictions, there will be a decreased spread of malaria over many parts of tropical Africa, due to an increase in surface temperatures and a decrease in rainfall. However, in the Southern part of Sahel, an increase in epidemics is predicted. In East Africa, malaria transmission is predicted to increase due to higher temperatures, and slightly higher rainfall. Most notably, it was found that formerly unsuitable areas in the highland could become epidemic areas, while epidemics will decrease in the lower-altitude regions. As predicted, Ermert at al.found the changes to be stronger under the A1B scenario than in the B1 scenario.
            By using models based upon high-resolution data that takes both greenhouse gas and LUC changes into account, Ermert at al. were able to model specific, terrain based malaria projections. With high-resolution data, the authors were able to analyze the effect of altitude on epidemic risk, which has not been analyzed before. In addition, the inclusion of land use changes accelerates precipitation decline, which significantly altered projections. This new type of modeling is going to be crucial in future studies for malaria and other vector born disease predictions. 

Increased Dengue Fever Incidence as a Result of El Niño Events and Higher Temperatures in Mexico

Increased Dengue fever (DF) transmission has been associated with higher temperatures and precipitation in tropical and subtropical regions. Due to climatic factors, in Mexico, DF is endemic year round. Colón-González et al. (2011) studied the effects of El Niño events, temperature, and precipitation on DF incidence in 12 Mexican provinces over a 23-year period. Multiple linear regression models were used to explore the relationship.  The incidence rate of infection was found to be significantly higher during El Niño events in the warm and wet season. Higher temperatures also had a significant effect, while precipitation was found to be not significant. This study complements previous findings on DF dynamics in the region and may be useful for the development of early warning systems.—Simone Berkovitz
Colón-González, F.,Lake, Bentham, G., 2011. Climate Variability and Dengue Fever in Warm and Humid Mexico. The American Journal of Tropical Medicine and Hygiene 5, 757–763.

            Dengue fever is a mosquito-borne infectious disease caused by the dengue virus. It is present in over 100 tropical and subtropical countries and approximately 50–100 mill­ion cases are reported each year. As a vector-borne disease, the influence of climate on transmission has been the subject of numerous studies. Due to Mexico’s climate, DF is endemic across the country and transmission occurs year round. Previous research in Mexico has shown that increases in temperature, sea surface temperature, precipitation, and the presence of El Niño events have been associated with an increase in DF. However, past studies have only looked at DF incidence in shorter time series and smaller geographical areas. Therefore, Colón-González et al. found it desirable to analyze 23 years ( 1985-2007) of reported DF cases across 12 Mexican provinces in order to examine the associations between temperature, precipitation, and El Niño events on DF incidence.
            The study utilized monthly DF notifications from 12 warm and humid provinces of Mexico, which encompassed 60% of all total cases in Mexico. In order to measure the strength of El Niño, monthly sea surface temperatures  (SST) were obtained. Monthly minimum and maximum temperatures and precipitation were obtained for each province. From the data it was found that year-round climate has two distinct seasons: cool and dry from November to May and warm and wet from June to October. The DF data were converted to a Cumulative Incidence Rate (CIR) based on the region population, which was than linearized by taking the natural logarithm (Ln-CIR). The authors used linear regression models, using the Ln-CIR as the dependent variable, which were separated by El Niño, non-El-Niño, warm and wet season, and cool and dry season.
            The authors found that in the presence of an El Niño event, the risk of DF infection is 2.77 times higher and a significant association between DF incidence and the strength of El Niño was found. The authors also found a significant relationship between increased temperature and DF incidence, which was still significant when the El Niño temperatures were removed. However, the effect of precipitation was found to be not statistically significant.
            The authors state that although the effects of El Niño were found to be significant, the reason behind the influence is not completely clear. Lower temperatures are associated with decreased transmission due to increased development time and larval mortality. Rising temperatures increase transmission by shortening the development time. In addition, feeding is increased because mosquitoes digest blood faster at higher temperatures. Human behavior may also play a role, as time spent indoors during the wet and warm season often leads to greater vector-host contact. Precipitation was not found to be significant because there is enough rainfall year round to create breeding sites. Human storage of water creates additional breeding sites independent of rainfall.
Global climate change, along with an increase of severe weather events, will increase the prevalence of Dengue fever, especially in warm and humid regions.  Climate change is likely to increase the warm and wet seasons, which will elevate disease transmission. These results can be used to predict future incidences in the region, which can provide opportunities to improve the control measures of the disease and strengthen population adaptation. 

Modelling Climatic Suitability and Dispersal of European Sandfly

Climate change has been linked to an increase in the spreading of vector-borne diseases. Fischer et al. (2011) used ecological niche modelling to predict sandfly dispersal in Central Europe, in the face of climate change. The model combined climate suitability and specific dispersal pathways for the Phlebotomus perniciosus species. A cost surface analysis was used to show potential dispersal areas based upon geographically suitable habitats. It was found that climate change significantly increased vector dispersal. River valleys would be dispersal pathways, while mountains would inhibit species dispersal.—Simone Berkovitz
Fischer, D., Thomas, S., Beierkuhnlein, C. 2011. Modelling climatic suitability and dispersal for disease vectors: the example of a phlebotomine sandfly in Europe. Procedia Enviro Sciences. 7, 164–169.

            Previous research has indicated that because most disease vectors are ectothermal arthropods, which cannot regulate their body temperatures, climate change influences disease prevalence. The majority of research focuses on mosquito-borne diseases such as Malaria and Dengue-fever, while sandfly-borne diseases are less studied. However, Leishmaniasis the disease associated with sandflies, constitutes a serious animal and health concern in most areas of the world. Recently, phlebotomine sandflies, which were thought to be restricted to the Mediterranen, have been found in typically colder Northern areas of central Europe, which may indicate expansion due to increasing temperatures. Therefore Fischer et al. found it necessary to investigate the relationship between climate suitability and species dispersal ability. The authors proposed an ecological niche model that combined specific dispersal pathways of the P. perniciosus species with changing climatic niches.
            Using bioclimatic variables, maximum entropy algorithms were used in order to model the distribution due to climate suitability of P. perniciosus  in Bavaria (Southeast Germany). In order to predict the future climate suitability, a regional climate model of Europe was applied. Then, a least-cost analysis was used to identify potential dispersal pathways. Least costs refer to the least amount of effort for a species moving through a geographic area, which helps to predict species’ pathways. The analysis consisted of identifying the cost surface, the climatic suitability of the current and future time periods, and cost distance, which quantifies the dispersal pathways based upon accessibility.
            From the first model, Fischer et al. found that currently only a very small region in the outermost Northwest of Bavaria is climatically suitable for P.perniciosus. However, when the future climate model was applied, significantly more regions were found to be suitable.  The cost analysis showed river valleys to be the preferred dispersal pathways, while high mountainous regions were found to be barriers that exclude dispersal. From the model, the authors were able to predict that P. perniciosus may disperse from Western Europe towards Bavaria, but a direct northward spread from Italy may be blocked by the Alps.
            Climate change alters dispersal and movement patterns of insects. Through ecological niche modelling in combination with a least-cost analysis, the authors were able to model the potential dispersal of sandflies in a small region of Europe. However, it is acknowledged that dispersal may not always correlate with model predictions, and dispersal behavior often varies between populations. In addition, human and wind influence are not taken into account into the models. Neverless, this type of modelling provides a powerful tool for detecting vector and disease prevalence. 

Increase in Childhood Asthma Morbidity as a Result of Climate Induced Ground-Level Ozone

An increase in respiratory illnesses has been linked to ground-level ozone pollution, which is projected to increase with climate change. Sheffıeld et al. (2011) predicted future asthma emergency department visits associated with ground level ozone changes in 2020 compared to 1990. The authors used data for emergency department visits, the relationship between ozone concentrations and visits, and projected concentrations from a well-known climate model in order to determine the relationship. A significant increase in pediatric emergency asthma visits during the summer months of 2020 was found. The effect was increased with the inclusion of population growth, but was decreased with the inclusion of ozone precursor changes.—Simone Berkovitz
Sheffield, P., Knowlton, K., Carr, J., Kinney, P,. 2011. Modeling of Regional Climate Change Effects on Ground-Level Ozone and Childhood Asthma. Am J Prev Med. 41, 251–257.

Recent research has highlighted the potential future health impacts of climate change on respiratory illnesses. Exacerbation of asthma has been linked to air pollutants, specifically ground-level ozone (O3), which is predicted to increase with climate change. O3 is not directly emitted into the air, but is formed through a reaction between oxides of nitrogen (NOx) and volatile organic compounds (VOC) in the presence of sunlight. Ground-level ozone is known as a summertime pollutant and significantly increases with temperature. Several studies have modeled global mortality and morbidity of asthma due to climate change, however regional projections for pediatric asthma have not been explored. Therefore, Sheffıeld et al. aimed to predict the effects of climate-driven, ozone related pediatric asthma in the New York Metropolitan area. 
            This study used a health impact assessment to look at O3-related asthma emergency department visits for children aged 0–17 in the 2020s compared to the 1990s. A 36 x 36 km grid of the New York City Metropolitan area, which included 14 counties, was used to conduct the study. By linking models for global climate, regional climate, and regional air quality, the authors were able to develop projections for ground-level O3. Greenhouse gas emission future predictions were based upon the IPCC A2 scenario, which assumes relatively rapid emissions and population growth.  Due to computational constraints, the study only modeled outputs for June-August. A morbidity analysis was performed in order to determine the mean number of daily asthma emergency department visits due to daily 8-hour maximum O3 concentrations. This was calculated using population, the county-level daily asthma emergency department visits rate, and the ERC, which is the exposure-risk for asthma morbidity due to a change in O3. First, the effects of climate change on summer O3 concentrations were looked at in order to project asthma emergency department visits, then two sensitivity analyses were conducted to incorporate the effects of population growth and other pollutants.
            Sheffıeld et al. found that in all of the fourteen counties, an increase in O3 would result in a greater number of asthma emergency department visits in both surrounding metropolitan counties and the central urban areas.  The results suggest that climate change could cause a 7.3% regional summer increase in asthma emergency department visits in children aged 0–17, however the percentage increase varied across individual counties. The sensitivity analysis demonstrated that as population is predicted to increase in urban areas, a higher percent increase in morbidity was found. The second analysis found a decrease in visits when the effects of climate change were isolated, due to the influence of other pollutants, which can mix to decrease O3 levels.
            An increase in pollution and temperature will have serious adverse effects on respiratory illnesses. The authors realized that their study had several limitations such as the assumption of uniform exposure to ozone across all the counties and the application of a single ERC. It is clear this model simplifies the complex relationship between climate change, O3 prevalence, population growth, and asthma prevalence, but it is important to realize the effects of climate change on disease in children. This study provides an important modeling approach, but the quantitative future predictions should be interpreted loosely. 

Temperature Variation and Mortality

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.

Climate Change and Cholera in Tanzania

Increased rainfall and temperatures are widely recognized to increase the risk of diarrheal diseases.  Trærup et al. (2011) used historical climatic and cholera data along with socioeconomic data in order to predict the future risk of Cholera in Tanzania under a moderate climate change scenario. The results revealed a significant relationship between temperature and the incidence of cholera. It was found that a 1ºC increase would cause a 15–20% risk increase. In addition, Trærup et al. predicted the costs associated with the disease, which were estimated to account for 0.32–1.4 % of GDP in Tanzania in 2030.—Simone Berkovitz
Trærup S., Ortiz R., Markandya A., 2011.The Costs of Climate Change: A Study of Cholera in Tanzania. Int. J. Environ. Res. Public Health 8, 4386–4405.

Recent studies have associated temperature and rainfall variability with the prevalence of diarrhea and cholera transmission. Therefore, climate change is of particular concern when studying the impacts of cholera and other waterborne diseases.  Higher air temperatures lead to higher water temperatures in shallow bodies of water, which become bacterial breeding grounds. In addition, increased flooding due to climate change can contaminate drinking water, which increases the disease prevalence. Trærup et al. utilize Tanzanian local national data in order to assess the impacts of climate change on cholera prevalence, and the economic costs of the disease. The authors used primary data sources in order to estimate the relationship between climate change and cholera, and then made future predictions for 2030.
The study utilized historical data on deaths and cases of cholera, rainfall and temperature recordings, and socioeconomic data. The data for cases were converted into a graph that showed seasonal distribution over the past 6 years. The second graph showed the average monthly rainfall over the same 6-year span. These figures indicated that from June to October, lower total rainfall coincided with fewer cases of cholera.
In order to analyze and predict the impact of climate change on the burden of cholera, two separate economic and climate scenarios were used. Scenario C2030, was a baseline scenario that did not include the effects of climate change. This scenario incorporated WHO estimates, which included predictions for socio-economic growth that would lower the burden of the disease. Using these data, the authors were able to calculate the average case fatality rate (CFR) for cholera in Tanzania. In addition, population health was estimated by combining data on mortality and non-fatal health outcomes in a single figure, which was measured as Disability-Adjusted Life-Years (DALY). DALY measures the disease burden and combines years of life lost (YLL) from premature deaths with years of life lived with disabilities (YLD). Scenario C1, accounts for climate change when estimating the number of cholera cases, deaths, and DALYs. This scenario assumes a 1ºC and 2ºCtemperature increase based on the IPCC, A1B middle scenario temperature predictions for Tanzania on 2030. The burden of the disease attributable to climate change was found by calculating the Impact Fraction, which is the proportion of the population exposed, combined with the burden of climate change.. A negative binomial regression model was used in order to estimate the incidences of cholera cases or deaths in a time period, based upon the rainfall, temperature, and a vector of socioeconomic factors.
In order to assess the total costs of the health impacts attributable to climate change, Trærup et al. looked at reactive adaptation measures in addition to the costs of residual damages. The authors discussed the costs of preventive measures, however these measures were not taken into account because it was assumed that they were included in economic development. Reactive measures included the cost of treatment per case. The loss of short-term productivity was accounted for by looking at lost working hours. In addition, the cost of lives lost was calculated by using a GDP-adjusted Value of Statistical Life (VOSL) method, which broadly measures the individual’s willingness to pay to reduce the risk of death in order to place a value on the impacts of mortality.
According to the statistical tests, Trærup et al. found that cholera cases were positively correlated with temperature, however the correlation between cases and rainfall was found to be not significant. This suggests that cholera cases in Tanzania are better explained by temperature rather than amount of rainfall. In the first model, it was found that an increase equal to 1ºC would increase the relative risk for cholera cases in Tanzania by 29%, while the second model predicted that a 1ºC increase would increase the relative risk by 15%. A table was created, which displayed the estimated number of diseases in the year 2030, in each of the different scenarios. The second table displayed the estimated costs in 2030 for the different scenarios. It is revealed that a 1ºC temperature increase costs 44–47 % less than a 2ºC increase. The authors note that preventative measures are not included and most likely there are other health variables that are going to be affected by climate change which were not accounted for. This study confirms the correlation between environmental risk factors and cholera in Africa and reveals how climate change will exacerbate the situation. Climate change will negatively influence overall human health and cause large additional economic burdens. 

The Effects of Climate Change and Socio-economic Development on the Global Distribution of Malaria

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.