Novel metrics for relating personal heat exposure to social risk factors and outdoor ambient temperature
| Autor: | Mary Munoz, Mara Sevig, Benjamin L. Ruddell, David M. Hondula, Miranda Kaml, Jason Eneboe, Sharon L. Harlan, Evan R. Kuras, Marianna Singh, Summer Betzel, Lauren Drake |
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| Rok vydání: | 2019 |
| Předmět: |
Misclassification
Hot Temperature 010504 meteorology & atmospheric sciences Correlation coefficient Climate Individually experienced temperature Vulnerability 010501 environmental sciences Health outcomes 01 natural sciences Weather station Risk Factors Statistics Linear regression Urban Humans Adaptation Weather lcsh:Environmental sciences 0105 earth and related environmental sciences General Environmental Science lcsh:GE1-350 Social risk Temperature Benchmarking Health Environmental science Metric (unit) Exposure data |
| Zdroj: | Environment International, Vol 146, Iss, Pp 106271-(2021) |
| ISSN: | 1873-6750 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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