Disease Burden - Implementation and Interpretation

Implementation and Interpretation

The public health impacts of air pollution (PM10 – annual mean of particulate matter – and ozone), noise, radiation (radon and UV) and indoor dampness were quantified using DALYs. In DALY calculations:

DALYs = number of people with a certain disease × duration of the disease (or loss of life expectancy in case of mortality) × severity of the disorder (varying from 0 for perfect health to 1 for death)

According to this equation, morbidity and mortality can be expressed in one similar value, making environmental health problems more or less comparable and providing ways to plan or evaluate environmental health related policies. But DALYs are a simplification of a very complex reality, and therefore only give a very crude indication of environmental health impact. A potential consequence of relying on DALYs to simplify this complex reality is that donors may take a narrow approach to health care programs. Foreign aid is most often directed at diseases with the highest DALYs, ignoring the fact that other diseases, although representing lower DALYs, are still major contributors to this disease burden. This narrow approach leaves less publicized diseases with little or no funding for health efforts. An example can be found in the issue surrounding maternal death, which is one of the top three killers in most poor countries today. Although maternal death and pediatric respiratory and intestinal infections maintain a high disease burden, safe pregnancy and the prevention of coughs in infants do not receive adequate funding. Without funding for diseases that represent lower DALYs, prevention of diseases that are deemed to have a high burden will not be achieved.

The researchers appraised which effects are relevant to investigate each environmental factor, and which data are best to use. Necessary data include prevalence numbers, exposure-response relationships, and weighting factors that give an indication of the severity of a certain disorder. When information is missing or vague, experts will be consulted in order to decide which (alternative) data sources to use. An uncertainty analysis is carried out so as to analyze the effects of different assumptions.