Study on the association between ambient temperature and mortality using spatially resolved exposure data.
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 PM2.5data at a resolution of 1km2were 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.
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA. Electronic address: mhlee@mail.harvard.edu.
Mihye
M
Lee
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA.
Liuhua
L
Shi
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA.
Antonella
A
Zanobetti
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA.
Joel D
JD
Schwartz
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000393:Air Pollutants / Q000009:adverse effects / Q000032:analysis*; D003080:Cold Temperature / Q000009:adverse effects*; D004305:Dose-Response Relationship, Drug; D004781:Environmental Exposure / Q000009:adverse effects / Q000032:analysis* / Q000706:statistics & numerical data; D005260:Female; D005845:Georgia / Q000453:epidemiology; D006358:Hot Temperature / Q000009:adverse effects*; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009026:Mortality / Q000639:trends*; D009657:North Carolina / Q000453:epidemiology; D052638:Particulate Matter / Q000009:adverse effects / Q000032:analysis*; D012423:Rural Health; D012621:Seasons; D013022:South Carolina / Q000453:epidemiology; D014504:Urban Health; D055815:Young Adult
D000393:Air Pollutants; D052638:Particulate Matter
Acute effect; Exposure error; Mortality; Temperature