NIRG Constrained estimation for the association between health and environmental exposures

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Environment-health associations of critical public health importance, in particular relating to impacts of weather and climate, are often complex. This complexity is addressed by advanced statistical methods, used by researchers to estimate various shapes of associations, and then derive various measures of health impacts. However, in contexts with lower mortality baseline, these statistical methods may lack power and lead to unrealistic estimates of environment-health associations, which in turn leads to unaccurate health impacts estimates. This project proposes to add constraints to the statistical methods used by researchers, to include information about assumptions on the shape of environment-health associations and obtain more accurate estimates. Such constraints can impose that the risk of pollution on health increases with the level, or that the risk increase fo both heat and cold compared to the average comfortable temperatures. The project will develop statistical method to include these constraints in two steps. The first step is to develop a general algorithm to fit models estimating association with constraints, along with uncertainty assessment. The second step is to focus on models more specifically suited to environmental epidemiology, including nonlinear associations and delayed response after exposition. Open source software code will be provided to allow other researcher easy application and replication of the developed methods. The final part of the project is to use the newly developed methods in two case studies using an international dataset of mortality and environmental exposures: i) assessment of global variations in the minimum mortality temperature, i.e. the comfortable temperature to understand differences in adaptation, and ii) assessment of the shape of the association between mortality and fine particulate matter.