Biostatistics Seminar: Howard Chang, PhD

Ann Arbor MI 01-16-2020 01-16-2020

Accurate and reliable exposure estimates are crucial to the success of any environmental health study. However, monitoring measurements are often only available sparsely in space and time. One approach to improve exposure assessment is by supplementing measurements with additional data sources, such as computer model simulations and satellite imagery. These data products are increasingly being used to support spatially-resolved health effect analyses and perform impact assessments in low- and middle-income countries. This presentation will discuss three approaches to combine different data sources for modeling environmental exposures: statistical downscaling, ensemble averaging, and quantile mapping. These methods are applied to estimate daily fine particulate matter concentration at fine spatial resolution and to bias-correct climate model projections. Several studies on health effects of ambient air pollution and extreme temperature will also be presented to highlight advantages and challenges associated with the use of these novel data products.

icon to add this event to your google calendarJanuary 16, 2020
3:30 pm - 5:00 pm
1690 SPH I
1415 Washington Heights
Ann Arbor, MI 48109-2029

Contact Information: Irene Felicetti 764-5452

Accurate and reliable exposure estimates are crucial to the success of any environmental health study. However, monitoring measurements are often only available sparsely in space and time. One approach to improve exposure assessment is by supplementing measurements with additional data sources, such as computer model simulations and satellite imagery. These data products are increasingly being used to support spatially-resolved health effect analyses and perform impact assessments in low- and middle-income countries. This presentation will discuss three approaches to combine different data sources for modeling environmental exposures: statistical downscaling, ensemble averaging, and quantile mapping. These methods are applied to estimate daily fine particulate matter concentration at fine spatial resolution and to bias-correct climate model projections. Several studies on health effects of ambient air pollution and extreme temperature will also be presented to highlight advantages and challenges associated with the use of these novel data products.

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