Weather-Related Mortality

By Brooke G. Anderson, Michelle L. Bell. Epidemiology, 2009

1,162 citations recorded by the source corpus.

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.

Subfields: Critical Care and Intensive Care Medicine, Health, Toxicology and Mutagenesis, Physiology

View Paper: https://www.semanticscholar.org/paper/fae7bc1f2b0d5c8b9135495e1e1227703ab4c014

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Citations in the corpus

Distributed lag non-linear models (2010, methodology): “A simple model with the moving average of the exposures in the previous L days as a predictor can be considered as a special case of a DLM: such a model has been extensively used in the field of air pollution epidemiology [20] and sometimes used as well to quantify the effects of temperatures [21].”
Population health effects and health-related costs of extreme temperatures: Comprehensive evidence from Germany (2018, background): “black smoke, O3 cubic splines Anderson and Bell (2002), Epidemiology 107 U.”
Mortality Associated with High Ambient Temperatures, Heatwaves, and the Urban Heat Island in Athens, Greece (2017, background): “Extreme temperatures are a cause of mortality [17] and the leading cause of weather-related mortality [18].”
Heat Waves in the United States: Mortality Risk during Heat Waves and Effect Modification by Heat Wave Characteristics in 43 U.S. Communities (2010, methodology): “Environmental Health Perspectives • volume 119 | number 2 | February 2011 217 We did not control for air pollution in our model, because earlier research showed that temperature effects in the United States were robust to air pollution (Anderson and Bell 2009).”
Reducing and meta-analysing estimates from distributed lag non-linear models (2013, methodology): “Specifically, for region i we derive the vector θ̂ [c]i with 4 reduced parameters of the quadratic B-spline of temperature Z[p] for the overall cumulative summary association, and two vectors θ̂ [0]i and θ̂ [22]i with sets of 5 reduced parameters of the natural cubic B-spline of lags C[p] for predic”