Temperature Observation Time and Type Influence Estimates of Heat-Related Mortality in Seven U.S. Cities

Abstract

Background:Extreme heat is a leading weather-related cause of mortality in the United States, but little guidance is available regarding how temperature variable selection impacts heat–mortality relationships.Objectives:We examined how the strength of the relationship between daily heat-related mortality and temperature varies as a function of temperature observation time, lag, and calculation method.Methods:Long time series of daily mortality counts and hourly temperature for seven U.S. cities with different climates were examined using a generalized additive model. The temperature effect was modeled separately for each hour of the day (with up to 3-day lags) along with different methods of calculating daily maximum, minimum, and mean temperature. We estimated the temperature effect on mortality for each variable by comparing the 99th versus 85th temperature percentiles, as determined from the annual time series.Results:In three northern cities (Boston, MA; Philadelphia, PA; and Seattle, WA) that appeared to have the greatest sensitivity to heat, hourly estimates were consistent with a diurnal pattern in the heat-mortality response, with strongest associations for afternoon or maximum temperature at lag 0 (day of death) or afternoon and evening of lag 1 (day before death). In warmer, southern cities, stronger associations were found with morning temperatures, but overall the relationships were weaker. The strongest temperature–mortality relationships were associated with maximum temperature, although mean temperature results were comparable.Conclusions:There were systematic and substantial differences in the association between temperature and mortality based on the time and type of temperature observation. Because the strongest hourly temperature–mortality relationships were not always found at times typically associated with daily maximum temperatures, temperature variables should be selected independently for each study location. In general, heat-mortality was more closely coupled to afternoon and maximum temperatures in most cities we examined, particularly those typically prone to heat-related mortality.Citation:Davis RE, Hondula DM, Patel AP. 2016. Temperature observation time and type influence estimates of heat-related mortality in seven U.S. cities. Environ Health Perspect 124:795–804; http://dx.doi.org/10.1289/ehp.1509946

Susceptibility to Heat-Related Fluid and Electrolyte Imbalance Emergency Department Visits in Atlanta, Georgia, USA

“…Our results suggest that temperature, apparent temperature, and heat index may be regarded as similar risk factors for dehydration-related health effects; this finding is relevant to public health practice because the National Weather Service uses heat index for surveillance and for issuing heat warnings. This finding is also relevant given little previous work in comparing temperature metrics for ED visits for a specific outcome [85,86,87,88,89,90,91,92]. …”

Study on the association between ambient temperature and mortality using spatially resolved exposure data

“…The minimum or the maximum temperature on a given day might be more directly related to mortality 47 . Since those numbers were not available, we were not able to assess the hypothesis.…”

Evaluation of individual and area-level factors as modifiers of the association between warm-season temperature and pediatric asthma morbidity in Atlanta, GA

“…Hourly observations were used to create daily ambient meteorological metrics including daily maximum temperature and daily maximum dew-point temperature. We selected daily maximum temperature (in degrees Celsius, °C) as our primary exposure of interest based on our previous work (Winquist et al, 2016) and use of this metric by others in related health studies (Davis et al, 2016; Ebi et al, 2004; Hondula et al, 2014; Linares et al, 2008; Saha et al, 2015; Ye et al, 2001). Additionally, maximum temperature values may coincide with a time of day when children may be most active and exposed to outdoor temperatures (Barnett et al, 2010), and may represent an exposure that could cause the greatest amount of physiological stress due to temperature on a given day.…”

“…high utilization of air conditioning, cooling centers, early warning systems), differences in population-level acclimation to climate, and the use of disparate temperature metrics to capture exposure to high ambient temperature (e.g. daily minimum temperature, daily mean temperature, daily maximum temperature, diurnal temperature ranges, heat waves, and heat stress indices) (Davis et al, 2016; Turner et al, 2012). Certain populations may also be more or less responsive to high temperature and studies that have examined age as a modifying factor have reported stronger associations among children and the elderly compared to other age groups (Bunker et al, 2016; Cheng et al, 2014; Kovats et al, 2004; Michelozzi et al, 2009; Xu et al, 2013).…”