Multiple Trigger Points for Quantifying Heat-Health Impacts: New Evidence from a Hot Climate

Abstract

Background: Extreme heat is a public health challenge. The scarcity of directly comparable studies on the association of heat with morbidity and mortality and the inconsistent identification of threshold temperatures for severe impacts hampers the development of comprehensive strategies aimed at reducing adverse heat-health events.

Objectives: This quantitative study was designed to link temperature with mortality and morbidity events in Maricopa County, Arizona, USA, with a focus on the summer season.

Methods: Using Poisson regression models that controlled for temporal confounders, we assessed daily temperature-health associations for a suite of mortality and morbidity events, diagnoses, and temperature metrics. Minimum risk temperatures, increasing risk temperatures, and excess risk temperatures were statistically identified to represent different "trigger points" at which heat-health intervention measures might be activated.

Results: We found significant and consistent associations of high environmental temperature with all-cause mortality, cardiovascular mortality, heat-related mortality, and mortality resulting from conditions that are consequences of heat and dehydration. Hospitalizations and emergency department visits due to heat-related conditions and conditions associated with consequences of heat and dehydration were also strongly associated with high temperatures, and there were several times more of those events than there were deaths. For each temperature metric, we observed large contrasts in trigger points (up to 22 °C) across multiple health events and diagnoses.

Conclusion: Consideration of multiple health events and diagnoses together with a comprehensive approach to identifying threshold temperatures revealed large differences in trigger points for possible interventions related to heat. Providing an array of heat trigger points applicable for different end-users may improve the public health response to a problem that is projected to worsen in the coming decades.

Figure 1

The modeled relationship between the relative risk of all-cause mortality and six different same-day temperature metrics during the warm season for Maricopa County, Arizona, 2000–2011. The solid blue line shows the relative risk of mortality, and the shaded blue region shows the 95% confidence interval. Specific points labeled on the curve identify the minimum risk temperature (MRT, black), the increasing risk temperature (IRT, blue), and the excess risk temperature (ERT, red), representing different conceptualizations of trigger points for intervention activities as discussed in “Methods.”

Figure 2

The modeled relationship between the relative risk of cardiovascular mortality and six different temperature metrics with a 1-day lag, as in Figure 1. Fewer than three points are indicated on the curve if some of the trigger points could not be identified.

Figure 3

The modeled relationship between the relative risk of mortality from consequences of heat and dehydration and six different temperature metrics with a 1-day lag, as in Figure 1. Fewer than three points are indicated on the curve if some of the trigger points could not be identified.

Figure 4

The modeled relationship between the relative risk of heat-related mortality (top panels), heat-related hospitalization (middle panels), and heat-related emergency department visits (lower panels), and three same-day temperature metrics (T_max, T_mean, T_min) during the warm season for Maricopa County, Arizona, 2000–2011 (2008–2012 for morbidity), as in Figure 1. For heat-related events, MRT is the temperature at which the fewest events were observed. Note that the vertical axis scale varies between panels.

Figure 5

The modeled relationship between the relative risk of heat-related mortality (top panels), heat-related hospitalization (middle panels), and heat-related emergency department visits (lower panels), and three same-day heat index metrics (HI_max, HI_mean, HI_min), as in Figure 1. MRT is the temperature at which the fewest events were observed. Note that the vertical axis scale varies between panels.

Figure 6

Minimum, increasing, and excess risk temperatures (MRT, IRT, ERT) based on daily maximum temperature (T_max) for four health events examined in this study. Values on the right-hand side of the figure denote climatological averages at regularly spaced intervals during the warm season in Maricopa County.