Information entropy tradeoffs for efficient uncertainty reduction in estimates of air pollution mortality.

Journal Article (Journal Article)

Implementing effective policy to protect human health from the adverse effects of air pollution, such as premature mortality, requires reducing the uncertainty in health outcomes models. Here we present a novel method to reduce mortality uncertainty by increasing the amount of input data of air pollution and health outcomes, and then quantifying tradeoffs associated with the different data gained. We first present a study of long-term mortality from fine particulate matter (PM2.5 ) based on simulated data, followed by a real-world application of short-term PM2.5 -related mortality in an urban area. We employ information yield curves to identify which variables more effectively reduce mortality uncertainty when increasing information. Our methodology can be used to explore how specific pollution scenarios will impact mortality and thus improve decision-making. The proposed framework is general and can be applied to any real case-scenario where knowledge in pollution, demographics, or health outcomes can be augmented through data acquisition or model improvements to generate more robust risk assessments.

Full Text

Duke Authors

Cited Authors

  • Alifa, M; Castruccio, S; Bolster, D; Bravo, M; Crippa, P

Published Date

  • September 2022

Published In

Volume / Issue

  • 212 / Pt D

Start / End Page

  • 113587 -

PubMed ID

  • 35654155

Electronic International Standard Serial Number (EISSN)

  • 1096-0953

International Standard Serial Number (ISSN)

  • 0013-9351

Digital Object Identifier (DOI)

  • 10.1016/j.envres.2022.113587

Language

  • eng