Weather-related mortality: how heat, cold, and heat waves affect mortality in the United States

Abstract

Background: Many studies have linked weather to mortality; however, role of such critical factors as regional variation, susceptible populations, and acclimatization remain unresolved.

Methods: We applied time-series models to 107 US communities allowing a nonlinear relationship between temperature and mortality by using a 14-year dataset. Second-stage analysis was used to relate cold, heat, and heat wave effect estimates to community-specific variables. We considered exposure timeframe, susceptibility, age, cause of death, and confounding from pollutants. Heat waves were modeled with varying intensity and duration.

Results: Heat-related mortality was most associated with a shorter lag (average of same day and previous day), with an overall increase of 3.0% (95% posterior interval: 2.4%-3.6%) in mortality risk comparing the 99th and 90th percentile temperatures for the community. Cold-related mortality was most associated with a longer lag (average of current day up to 25 days previous), with a 4.2% (3.2%-5.3%) increase in risk comparing the first and 10th percentile temperatures for the community. Mortality risk increased with the intensity or duration of heat waves. Spatial heterogeneity in effects indicates that weather-mortality relationships from 1 community may not be applicable in another. Larger spatial heterogeneity for absolute temperature estimates (comparing risk at specific temperatures) than for relative temperature estimates (comparing risk at community-specific temperature percentiles) provides evidence for acclimatization. We identified susceptibility based on age, socioeconomic conditions, urbanicity, and central air conditioning.

Conclusions: Acclimatization, individual susceptibility, and community characteristics all affect heat-related effects on mortality.

FIGURE 1

Risk of mortality for heat (comparison of 99th vs. 90th percentile of mean temperature) and cold (comparison first vs. 10th percentile of mean temperature) for different lag structures. Points show central estimates and the vertical lines show 95% posterior intervals.

FIGURE 2

Relationship between temperature and risk of mortality, comparing various temperature levels with a reference temperature of 60°F for New York City.

FIGURE 3

Map of the relative heat effect (percentage increase in mortality risk comparing 99th and 90th percentiles of T_lag0–1). The color of each community corresponds to the level of the estimate; the size of the circle corresponds to the inverse of the variance of the estimate (ie, larger circles are more certain). The 2 noncontinental cities included in the dataset, Honolulu HI and Anchorage AK, are not included in this regional analysis.

FIGURE 4

Percentage increase in mortality risk for the relative cold effect (comparison of the 1st to 10th percentile temperature) (A), and heat effect (comparison of the 99th to the 90th percentile temperature) (B), by cause and age. The point represents the central estimates; the vertical lines represent 95% posterior intervals.