loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Erin Teeple 1 ; Caitlin Kuhlman 2 ; Brandon Werner 1 ; Randy Paffenroth 1 ; 2 ; 3 and Elke Rundensteiner 1 ; 2

Affiliations: 1 Data Science Program, Worcester Polytechnic Institute, Worcester, MA, U.S.A. ; 2 Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, U.S.A. ; 3 Department of Mathematics, Worcester Polytechnic Institute, Worcester, MA, U.S.A.

Keyword(s): Air Quality, Canonical Correlation Analysis, CCA, Epidemiology, Environmental Health.

Abstract: Quantifying health effects resulting from environmental exposures is a complex task. Underestimation of exposure-outcome associations may occur due to factors such as data quality, jointly distributed spectra of possible effects, and uncertainty about exposure levels. Parametric methods are commonly used in population health research because parameter estimates, rather than predictive accuracy, are useful for informing regulatory policies. This project considers complementary approaches for capturing population-level exposure-outcome associations: multiple linear regression and canonical correlation analysis (CCA). We apply these methods for the task of characterizing relationships between air quality and cause-specific mortality. We first create a national air pollution exposures-mortality outcomes data set by integrating United States Environmental Protection Agency (EPA) annual summary county-level air quality measurements for the period 1980-2014 with age-adjusted gender- and cau se-specific county mortality rates from the same time period published by the Institute for Health Metrics and Evaluation (IHME). Code for data integration is made publicly available. We examine our model parameter estimates together with air quality-mortality rate associations, revealing statistically significant correlations between air quality variations and variations in cause-specific mortality which are particularly apparent when CCA is applied to our population health data set. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.147.123.159

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Teeple, E.; Kuhlman, C.; Werner, B.; Paffenroth, R. and Rundensteiner, E. (2020). Air Quality and Cause-specific Mortality in the United States: Association Analysis by Regression and CCA for 1980-2014. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - HEALTHINF; ISBN 978-989-758-398-8; ISSN 2184-4305, SciTePress, pages 228-236. DOI: 10.5220/0009156702280236

@conference{healthinf20,
author={Erin Teeple. and Caitlin Kuhlman. and Brandon Werner. and Randy Paffenroth. and Elke Rundensteiner.},
title={Air Quality and Cause-specific Mortality in the United States: Association Analysis by Regression and CCA for 1980-2014},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - HEALTHINF},
year={2020},
pages={228-236},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009156702280236},
isbn={978-989-758-398-8},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - HEALTHINF
TI - Air Quality and Cause-specific Mortality in the United States: Association Analysis by Regression and CCA for 1980-2014
SN - 978-989-758-398-8
IS - 2184-4305
AU - Teeple, E.
AU - Kuhlman, C.
AU - Werner, B.
AU - Paffenroth, R.
AU - Rundensteiner, E.
PY - 2020
SP - 228
EP - 236
DO - 10.5220/0009156702280236
PB - SciTePress