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

Abstract

There are many studies that have posited an association between extreme temperature and increased mortality. However, most studies use temperature at a single station per city as the reference point to analyze deaths. This leads to exposure misclassification and usually the exclusion of exurban, small town, and rural populations. In addition, few studies control for confounding by PM2.5, which is expected to induce upward bias. The high-resolution temperature and PM2.5 data at a resolution of 1 km2 were derived from satellite images and other land use sources. To capture the nonlinear association of temperature with mortality we fit a piecewise linear spline function for temperature, with a change in slope at −1 °C and 28 °C, the temperature threshold at which mortality in Georgia, North Carolina, and South Carolina increases due to cold and heat, respectively. We conducted stratified analyses by age group, sex, race, education, and urban vs nonurban, as well as sensitivity analyses of different temperature threshold and covariate sets. We found a 0.19% (95% CI = −0.98, 1.34%) increase in mortality for each 1 °C decrease in temperature below −1 °C and a 2.05% (95% CI = 0.87, 3.24%) increase in mortality for each 1°C increase in temperature above 28 °C, a 79.8% larger effect size for heat compared to the station-based metric. The effect estimates relying on the monitoring stations were 0.09% (95% CI = −0.79, 0.95%) and 1.14 % (95% CI = 0.08, 1.57%) for the equivalent temperature changes. The estimates were not confounded by PM2.5. Children under 15 years of age had the largest percentage increase per 1 °C increase in temperature (8.19%, 95% CI = −0.38 to 17.49%) followed by Blacks (4.35%, 95% CI = 2.22 to 6.53%). Higher education was a protective factor for the effect of extreme temperature on mortality. There was a suggestion that people in less urban areas were more susceptible to extreme temperature. The relationship between temperature and mortality was stronger when using exposure data with more spatial variability than using exposure data based on existing monitors alone.

Heat Wave and Mortality: A Multicountry, Multicommunity Study

“…Similar to other studies using time-series data, we used the data on temperature from fixed sites to define heat waves, rather than individual exposure, which could introduce exposure error to some extent. However, these exposure errors are likely to be random, which would usually result in an underestimation of the estimated relative risks ( Lee et al 2016 ). Air pollutants were not adjusted for in this study because the data were not available for some countries.…”

Diurnal Temperature Range in Relation to Daily Mortality and Years of Life Lost in Wuhan, China

“…The present study had some limitations. Firstly, our assessment of temperature exposure across a relatively large urban area was based on only one fixed monitoring site, and failed to account for the intra-urban variation (e.g., spatial distribution), which could potentially introduce some inevitable measurement bias and limit model predictability in the actual effects of temperature on mortality [ 48 ]. Future evidence should focus on more tenuous exposure-response patterns using spatiotemporal models.…”

Spatial Variability of Heat-Related Mortality in Barcelona from 1992–2015: A Case Crossover Study Design

“…Here, we attempt to examine the high precision spatially referenced mortality and UrbClim model data. Recent research, for example, shows that static (airport) location temperature data may underestimate the effect of temperature on mortality when compared with a higher resolution, satellite-derived, spatially continuous estimate of the same variables [36]. Future epidemiological research on assessments of climatic exposures could potentially benefit from using spatially explicit weather data, particularly in places with few weather observation stations or in cities where the weather reported at the observatory is not actually representative of the weather experienced by the people [37].…”