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.   

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