![]() The new approach MV DLNM models lag outcomes within 10 days and lag exposures up to 1 month and provide valid results.Įxtensive studies have indicated the association between temperature and human health, which arouses public health concerns as the climate has changed drastically on a worldwide scale due to global warming in recent years (Basu 2009 Gasparrini et al. ![]() The joint modeling of the lag exposure and the delayed outcome enhances the power to discover such a complex association structure. The results suggest that, in public health or environmental research, not only the exposure may post a delayed effect but also the outcome of interest could provide the lag association signals. With a real-life application, the MV DLNM that incorporates both the current and lag mortalities revealed a more significant association than the conventional model that only fits the current mortality. According to simulation results, only one strategy, named MV DLNM, could yield valid type I errors, while the other four strategies demonstrated much more inflated type I errors. The longitudinal climate and daily mortality data in Taipei, Taiwan, from 2012 to 2016 were implemented to generate the null distribution. Five strategies are evaluated by simulation studies based on permutation techniques. Several attempts are derived by various statistical concepts, such as summation, autoregressive, principal component analysis, baseline adjustment, and modeling the offset in the DLNM. ![]() We propose several novel strategies to model mortality with the effects of distributed lag temperature measures and the delayed effect of mortality. Instead of the conventional cross-sectional analysis that focuses on the association between a predictor and the single dependent variable, the distributed lag non-linear model (DLNM) has been widely adopted to examine the effect of multiple lag environmental factors health outcome. The effects of meteorological factors on health outcomes have gained popularity due to climate change, resulting in a general rise in temperature and abnormal climatic extremes. ![]()
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