Environmental research · 2017
Does a lag-structure of temperature confound air pollution-lag-response relation? Simulation and application in 7 major cities, Korea (1998-2013).
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Kim Honghyok, Bell Michelle L, Lee Jong-Tae
Аннотация
BACKGROUND: Temperature must be controlled when estimating the associations of short-term exposure to air pollution and mortality. Given that multi-country studies have implied temperature has lagged effects, we aim to explore confounding by temperature-lag-response and investigate PM10-lag-mortality relation in 7 cities, Korea.
METHODS: In a simulation study, we compared the performance of different methods to control for: the same day temperature, a lagged temperature and distributed lags of temperature. In a real data study, we explored PM10-lag-mortality relation in 7 cities using these different methods.
RESULTS: We confirmed that a model with insufficient control of temperature offers a biased estimate of PM10 risk. The degree of bias was from -82% to 95% in simulation settings. A real data study shows estimates among different models by temperature adjustments and PM10 lag variables ranging from -0.3% to 0.4% increase in the risk of all-cause mortality, with a 10μg/m3 increase in PM10. Controlling for temperature as distributed lags for 21 days provided 0.25% (95% CI: 0.1, 0.4) increase in the risk of all-cause mortality.
CONCLUSIONS: A lag structure of temperature can confound the air pollution-lag-response relation. Temperature-lag-response relation should be evaluated when estimating air pollution-lag-response relation. As a corollary, air pollution and temperature risk in mortality can be estimated using the same regression model.
METHODS: In a simulation study, we compared the performance of different methods to control for: the same day temperature, a lagged temperature and distributed lags of temperature. In a real data study, we explored PM10-lag-mortality relation in 7 cities using these different methods.
RESULTS: We confirmed that a model with insufficient control of temperature offers a biased estimate of PM10 risk. The degree of bias was from -82% to 95% in simulation settings. A real data study shows estimates among different models by temperature adjustments and PM10 lag variables ranging from -0.3% to 0.4% increase in the risk of all-cause mortality, with a 10μg/m3 increase in PM10. Controlling for temperature as distributed lags for 21 days provided 0.25% (95% CI: 0.1, 0.4) increase in the risk of all-cause mortality.
CONCLUSIONS: A lag structure of temperature can confound the air pollution-lag-response relation. Temperature-lag-response relation should be evaluated when estimating air pollution-lag-response relation. As a corollary, air pollution and temperature risk in mortality can be estimated using the same regression model.