Multiple Trigger Points for Quantifying Heat-health Impacts: New Evidence From a Hot Climate

Diana B. Petitti; David M. Hondula; Shuo Yang; Sharon L. Harlan; Gerardo Chowell

Disclosures

Environ Health Perspect. 2016;124(2):176-183. 

In This Article

Discussion

Most prior analyses of temperature/event associations that aim to identify a threshold temperature for heat-related events, including our own work set in Maricopa County (Harlan et al. 2014), define the threshold for action as the temperature at which the frequency of health events begins to rise rapidly (most similar to the ERT in this analysis for all-cause mortality, CVD mortality, and heat-related events) although other definitions have been used (e.g., Armstrong et al. 2011; Hajat and Kosatsky 2010; Loughnan et al. 2010; Zaninović and Matzarakis 2014). A statistically solid and reliable health outcomes–based estimate of temperature trigger points has the potential to guide the implementation of interventions when they are most appropriate. Issuing extreme heat warning products to the general public by weather forecasting offices is one such intervention (e.g., Pascal et al. 2006; Williams et al. 2012a), but triggering criteria for warning systems are often based on threshold conditions for a singular conceptualization of increases in all-cause mortality (e.g., Hondula et al. 2014). An understanding of the broader effects of heat on illness has the potential to suggest enhancements to public messaging efforts as well as interventions other than warnings that might mitigate the adverse effects of heat.

Here, we have interrogated temperature threshold estimates based on three different criteria (MRT, IRT, and ERT). We found large differences across these measures and across different health events and diagnoses. The strongest and most consistent associations for high environmental temperature in our setting were with directly heat-related health events. Trigger points for these events were consistently lower than those derived from all-cause mortality. In a hot location like Maricopa County, using a single high threshold temperature (e.g., ERT for all-cause mortality) vastly discounts the number of days on which heat is associated with an increased risk of heat-related mortality and morbidity. This progression of increasing thresholds for more severe outcomes and the overall finding that heat-related mortality is merely the top of the heat severity pyramid was also reported in Adelaide, South Australia (Williams et al. 2012a). The highest trigger points (ERTs) that we calculated for several health events were near climatological averages for summer daily temperatures (Figure 6). This finding demonstrates a need to reconsider the heat-risk communication paradigm in hot climates. We suggest that one improvement would be for researchers to offer intended end-users an array of trigger points that could be applied for their specific purposes instead of a single, all-purpose threshold temperature. In Maricopa County, we are using the results of this study to begin conversations with a range of end-users about actions they could take when dangerous heat occurs. The ultimate utility of the trigger points will be determined after engaging in dialogue with service providers. Potential applications for these trigger points include identifying days and times to increase enforcement of workplace safety guidelines, running seasonal public awareness campaigns, suspending utility shutoffs, rescheduling or cancelling outdoor school events including athletic practices and competitions, and opening or expanding access to homeless shelters and cooling centers. The trigger point framework may also offer additional opportunities to consider multiple health outcomes, risk levels, and exposure variables in studies that project future heat impacts associated with climate change.

The HI, which is widely used by the NWS and heat-health researchers in the United States (e.g., Anderson et al. 2013), provided information about sensitivity to heat that was not substantively different from information derived from air temperature in Maricopa County. In our study setting, and perhaps in others characterized by low relative humidity, actions to mitigate the effects of heat on health events may not need to use metrics that are more complex than air temperature and are, therefore, more difficult to communicate to the public. Identification of the optimal variable(s) to use when triggering protective actions related to extreme heat depends on rigorous statistical analysis of predictive capacity (e.g., Barnett et al. 2010; Zhang et al. 2012), local context, and public understanding of and receptivity to such information. Exploration of these important dimensions of heat intervention design falls outside the scope of this analysis but is the subject of ongoing efforts by the authors and local public agencies.

Notably, our study did not find an association between high temperatures and CVD hospitalization and/or ED visits (see Supplemental Material, Figures S2 and S3 http://ehp.niehs.nih.gov/wp-content/uploads/124/2/ehp.1409119.s001.acco.pdf). In a recent systematic review of studies of heat and cardiovascular morbidity, Turner et al. (2012) concluded that the effects of temperature on cardiorespiratory morbidity were smaller and more variable than those on mortality. Administrative data have a limited ability to shed light on the effects of temperature on CVD morbidity. As others have noted (Basu et al. 2012), more studies that assess specific symptoms in relation to individual heat exposure are needed.

Our study has several important limitations. We used administrative data to assess hospitalization and ED visits, as has been done in previous studies (e.g., Williams et al. 2012b), although the data sets were created to support insurance billing and not for use in this type of research. Our methodology of using ICD-10 codes to identify heat-related mortality from ADHS records underestimates the number of heat deaths. In particular, Maricopa County's procedures to identify heat-related deaths have been improving over time, and their heat mortality surveillance program detected 312 heat-related deaths during the period 2008–2011 [Maricopa County Department of Public Health (MCDPH) 2014] compared with the 153 heat-related deaths that we identified using procedures more consistent with those employed by ADHS.

It is also worth noting that our study focused on a single setting; thus, our findings may not be generalizable to other settings. There are many human adaptations to high temperatures, and Maricopa County may be particularly heat-adapted (Hartz et al. 2013). Because the presence of dangerously hot weather in the summer is predictable in this setting, some residents travel to cooler places and may be able to avoid activities that involve heat exposure. During the study period, heat warnings, networks for water distribution, and cooling facilities were available to the public. These efforts may have mitigated the effects of heat on illness and death. There are potential modifiers of the temperature-health relationship that we did not examine, including air pollution, time of season, cumulative days of high temperatures, and displacement. The applications of this framework should be updated continually. Trigger points should be monitored and evaluated for changes because of temporal variability in weather and climate [indicated by the reevaluation of climate "normals," Arguez et al. (2012)] and because the behavior of people, the physical environment (e.g., building materials), the availability of technology (e.g., air conditioning), and public health systems adapt to higher temperatures in ways that may affect the human health response to heat (Guo et al. 2012). Finally, the meteorological data were obtained from a single station, whereas the health events were experienced across a larger geographic area.

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