Novel metrics for relating personal heat exposure to social risk factors and outdoor ambient temperature

David M Hondula; Evan R Kuras; Summer Betzel; Lauren Drake; Jason Eneboe; Miranda Kaml; Mary Munoz; Mara Sevig; Marianna Singh; Benjamin L Ruddell; Sharon L Harlan

doi:10.1016/j.envint.2020.106271

Novel metrics for relating personal heat exposure to social risk factors and outdoor ambient temperature

Environ Int. 2021 Jan:146:106271. doi: 10.1016/j.envint.2020.106271. Epub 2020 Dec 8.

Authors

Affiliations

¹ School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85281, USA. Electronic address: david.hondula@asu.edu.
² School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85281, USA; Department of Environmental Conservation, University of Massachusetts Amherst, Amherst, MA 01003, USA.
³ School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85281, USA.
⁴ School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA.
⁵ Department of Health Sciences and Department of Sociology and Anthropology, Northeastern University, Boston, MA 02115, USA.

PMID: 33395929
DOI: 10.1016/j.envint.2020.106271

Abstract

A more precise understanding of individual-level heat exposure may be helpful to advance knowledge about heat-health impacts and effective intervention strategies, especially in light of projected increases in the severity and frequency of extreme heat events. We developed and interrogated different metrics for quantifying personal heat exposure and explored their association with social risk factors. To do so, we collected simultaneous personal heat exposure data from 64 residents of metropolitan Phoenix, Arizona. From these data, we derived five exposure metrics: Mean Individually Experienced Temperature (IET), Maximum IET, Longest Exposure Period (LEP), Percentage Minutes Above Threshold (PMAT), and Degree Minutes Above Threshold (DMAT), and calculated each for Day Hours, Night Hours, and All Hours of the study period. We then calculated effect sizes for the associations between those metrics and four social risk factors: neighborhood vulnerability, income, home cooling type, and time spent outside. We also investigated exposure misclassification by constructing linear regression models of observations from a regional weather station and hourly IET for each participant. Our analysis revealed that metric choice and timeframe added depth and nuance to our understanding of differences in exposure within and between populations. We found that time spent outside and income were the two risk factors most strongly associated with personal heat exposure. We also found evidence that Mean IET is a good, but perhaps not optimal, measure for assessing group differences in exposure. Most participants' IETs were poorly correlated with regional weather station observations and the slope and correlation coefficient for linear regression models between regional weather station data and IETs varied widely among participants. We recommend continued efforts to investigate personal heat exposure, especially in combination with physiological indicators, to improve our understanding of links between ambient temperatures, social risk factors, and health outcomes.

Keywords: Adaptation; Climate; Health; Individually experienced temperature; Misclassification; Urban.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Benchmarking*
Hot Temperature*
Humans
Risk Factors
Temperature
Weather