Sunday, February 17, 2013
Room 313 (Hynes Convention Center)
Estimating the magnitude of the risks heat waves pose to human health is a critical
part of assessing the future impact of climate change. In this paper we propose a flexible
class of time series models to estimate the relative risk of mortality associated with heat
waves and conduct Bayesian model averaging (BMA) to account for the multiplicity of
potential models. Applying these methods to data from 105 U.S. cities for the period
1987-2005, we examine the heterogeneity of the posterior distributions of mortality risk
across cities, assess sensitivity of the results to the selection of prior distributions, and
compare our BMA results to a model selection approach. Our results show that no
single model best characterizes risk across the majority of cities, and that for some cities
heat wave risk estimation is sensitive to model choice. While model averaging leads
to posterior distributions with increased variance as compared to statistical inference
conditional on a particular model, we find that heat wave mortality risk is robust to
accounting for model uncertainty over a broad class of models.
part of assessing the future impact of climate change. In this paper we propose a flexible
class of time series models to estimate the relative risk of mortality associated with heat
waves and conduct Bayesian model averaging (BMA) to account for the multiplicity of
potential models. Applying these methods to data from 105 U.S. cities for the period
1987-2005, we examine the heterogeneity of the posterior distributions of mortality risk
across cities, assess sensitivity of the results to the selection of prior distributions, and
compare our BMA results to a model selection approach. Our results show that no
single model best characterizes risk across the majority of cities, and that for some cities
heat wave risk estimation is sensitive to model choice. While model averaging leads
to posterior distributions with increased variance as compared to statistical inference
conditional on a particular model, we find that heat wave mortality risk is robust to
accounting for model uncertainty over a broad class of models.
Applying our approach to 105 U.S. cities, we found a heightened risk of mortality during
heat wave days compared to non-heat wave days, especially in northern regions of the U.S.
For example, we estimated a percent increase in mortality (95% HPD interval) of 8.9%
(5.4% to 11.9%) in Washington, D.C., 3.4% (0.7% to 6.5%) in St. Louis, and 5.5% (3.9%
to 7.6%) in Seattle. We generally did not find increased risk in southern regions. Though
temperatures are higher at lower latitudes causing heat waves to be more extreme, it is
possible that individuals and/or communities in hot climates have adopted precautions to
limit the impact of heat, such as air conditioning use.