Estimating associations between high temperature and emergency department visits in six US cities with the use of 1-kilometer temperature products Pubblico
Thomas, Nikita (Spring 2020)
Abstract
Background: High temperatures have significant impacts on society – an effect that is increasing due to climate change and increasingly frequent heat wave events. Exposure to high temperatures has been shown to result in higher rates of emergency department visits. Previous studies typically utilize temperature data collected at airports to define exposures. However, this may not be representative of the true temperature felt by the population, due to the location of airports being situated far from urban areas.
Methods: We use the gridded climate dataset, Daymet, to create three temperature metrics, including two that account for county and ZIP code level populations, for both minimum and maximum temperatures during the warm season (May-September) in six US cities. We use a Poisson log-linear model to estimate the association of temperatures and emergency department visits during the warm season for six health outcomes. We then plot to compare estimated relative risk as determined by the Daymet metrics and the airport monitor metric.
Results: We observed that the Daymet metrics were highly correlated (0.90) with the airport monitor metrics for all cities except San Francisco and Los Angeles. We also observed that acute renal failure, fluid and electrolyte imbalance, and heat related illnesses most consistently had higher relative risk predictions associated with the finer scale temperature metrics.
Conclusions: We found evidence that using finer scale temperature metrics is useful in estimating relative risks of various health outcomes, particularly for cities that have high exposure variability.
Table of Contents
I. Introduction
II. Methods
a. Data Sources and Processing
b. Statistical Analysis
III. Results
IV. Discussion
V. Tables and Figures
a. Table 1. Descriptive statistics for emergency department (ED) visits from May to September in each city.
b. Table 2. Descriptive statistics for four temperature metrics during May to September in each city
c. Figure 1. Relative risk of selected health outcomes vs maximum temperature during May to September in Atlanta, 1993-2012.
d. Figure 2. Relative risk of selected health outcomes vs minimum temperature during May to September in Atlanta, 1993-2012.
e. Figure 3. Relative risk of selected health outcomes vs maximum temperatures during May to September in San Francisco, 2005-2016
f. Figure 4. Relative risk of selected health outcomes vs minimum temperatures during May to September in San Francisco, 2005-2016
g. Figure 5. Relative risk of selected health outcomes vs maximum temperature during May to September in Los Angeles, 2005-2016
h. Figure 6. Relative risk of selected health outcomes vs minimum temperature during May to September in Los Angeles, 2005-2016
i. Figure 7. Relative risk of selected health outcomes vs maximum temperature during May to September in Salt Lake City, 2005-2016
j. Figure 8. Relative risk of selected health outcomes vs minimum temperature during May to September in Salt Lake City, 2005-2016
k. Figure 9. Relative risk of selected health outcomes vs maximum temperature during May to September in Newark, 2005-2016
l. Figure 10. Relative risk of selected health outcomes vs minimum temperature during May to September in Newark, 2005-2016
m. Figure 11. Relative risk of selected health outcomes vs maximum temperature during May to September in Phoenix, 2008-2016
n. Figure 12. Relative risk of selected health outcomes vs minimum temperature during May to September in Phoenix, 2008-2016
VI. References
About this Master's Thesis
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