Assessing the Environmental Burden of Disease: Method Overview

The purpose of environmental burden of disease (EBD) studies is to assess what fraction of the global, national, or regional burden of disease is attributable to selected environmental risks, using an explicit, widely recognized methodology. The method used to estimate the EBD in the United Arab Emirates is based on a method developed in the 1990s by the World Health Organization in the first global burden of disease study. The approach is based on determining the attributable fraction—the proportion of death or disability attributable to a specific risk (e.g., air pollution) or health condition (e.g., high blood pressure). To estimate the environmental burden of disease in the UAE, the research team that conducted this study constructed an innovative computer model, the UAE Environmental Burden of Disease Model, coded in Analytica software. The model, the first of its kind, is designed to facilitate comparing the importance of different risks and testing the effects of various environmental interventions on the UAE’s overall disease burden. The model is divided into subcomponents, each corresponding to one of the eight environmental risk areas retained for analysis as a result of the priority-setting exercise described in Chap. 2. This chapter describes the principles underlying the model, based on steps including exposure assessment, determination of the exposure-­response relationship, estimation of mortality and morbidity, calculation of the attributable fraction, determination of the disease burden attributable to the risk, and uncertainty and sensitivity analysis. Estimation of the burden of disease in the UAE with an easy-to-understand computer model is a state-of-the-art method for analyzing the fragmentary data that were available on the disease distribution in the UAE and for communicating the results effectively. This innovative model allows comparison of the relative importance of various sources of ill health and examination of the effects of alternative interventions on the disease burden. The model also makes it very easy to update future burden of disease estimates when new data become available, and it allows UAE officials to test the effect of various intervention options. Because all assumptions, decisions about input variables, and specific methods are clearly stated in each step, changes to the model structure can be made easily should future research and new data prove it necessary. Because resources are always limited, the model can facilitate identification of the most important risks and prioritize competing actions to recognize the ones with the greatest potential to reduce the burden of disease.