Uncertainties associated with quantifying climate change impacts on human health: a case study for diarrhea

Environ Health Perspect. 2011 Mar;119(3):299-305. doi: 10.1289/ehp.1002060. Epub 2010 Oct 6.

Abstract

Background: Climate change is expected to have large impacts on health at low latitudes where droughts and malnutrition, diarrhea, and malaria are projected to increase.

Objectives: The main objective of this study was to indicate a method to assess a range of plausible health impacts of climate change while handling uncertainties in a unambiguous manner. We illustrate this method by quantifying the impacts of projected regional warming on diarrhea in this century.

Methods: We combined a range of linear regression coefficients to compute projections of future climate change-induced increases in diarrhea using the results from five empirical studies and a 19-member climate model ensemble for which future greenhouse gas emissions were prescribed. Six geographical regions were analyzed.

Results: The model ensemble projected temperature increases of up to 4°C over land in the tropics and subtropics by the end of this century. The associated mean projected increases of relative risk of diarrhea in the six study regions were 8-11% (with SDs of 3-5%) by 2010-2039 and 22-29% (SDs of 9-12%) by 2070-2099.

Conclusions: Even our most conservative estimates indicate substantial impacts from climate change on the incidence of diarrhea. Nevertheless, our main conclusion is that large uncertainties are associated with future projections of diarrhea and climate change. We believe that these uncertainties can be attributed primarily to the sparsity of empirical climate-health data. Our results therefore highlight the need for empirical data in the cross section between climate and human health.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Air Pollution / statistics & numerical data
  • Diarrhea / epidemiology*
  • Environmental Monitoring / methods*
  • Epidemiological Monitoring
  • Global Warming / statistics & numerical data*
  • Greenhouse Effect / statistics & numerical data
  • Humans
  • Models, Theoretical
  • Risk Assessment
  • Uncertainty*