Abstract
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. 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. We propose several novel strategies to model mortality with the effects of distributed lag temperature measures and the delayed effect of mortality. Several attempts are derived by various statistical concepts, such as summation, autoregressive, principal component analysis, baseline adjustment, and modeling the offset in the DLNM. Five strategies are evaluated by simulation studies based on permutation techniques. The longitudinal climate and daily mortality data in Taipei, Taiwan, from 2012 to 2016 were implemented to generate the null distribution. According to simulation results, only one strategy, named MVDLNM, could yield valid type I errors, while the other four strategies demonstrated much more inflated type I errors. With a real-life application, the MVDLNM that incorporates both the current and lag mortalities revealed a more significant association than the conventional model that only fits the current mortality. 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. The joint modeling of the lag exposure and the delayed outcome enhances the power to discover such a complex association structure. The new approach MVDLNM models lag outcomes within 10 days and lag exposures up to 1 month and provide valid results.
Highlights
Extensive 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 2010)
Several types of research have examined the relationship between PM10, PM2.5, and daily mortality
Since the distributed lag non-linear model (DLNM) is widely adopted in public health and environmental research (Vicedo-Cabrera et al 2016), we aim to extend the DLNM with Poisson link function and natural cubic splines (Bhaskaran et al 2013) to model the cumulative mortality outcomes using lag predictors
Summary
Extensive 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 2010). It has been documented that exposure to air pollutants, which includes particulate matter (PM), ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2) according to the 2005 WHO Air Quality Guidelines, leads to adverse effects on human health, especially the respiratory and cardiovascular diseases. The DLNM fits the non-linear association between the outcome variable and predictors. In 2018, a new approach assessed both the same-day and 1-day lag mortality in DLNM (Chen et al 2018). Associations in both lag outcomes and exposures need more attention to describe such a complex structure
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