DLNMDate of Last Normal Menses
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The results of the distributed lag nonlinear model (dlnm) analysis are presented in Figure 3, which depicts the nonlagged, for example, lag 0, RR of mortality across the daily temperature strata for each year.
The strengths of our work include 1) a relatively large number of diagnosed ED visits (6, 697 cases), 2) a widely accepted statistical method (CC), 3) adjustment in the models for weather factors and influenza, 4) analysis performed for predefined age groups, 5) use of the DLNM technique to realize constrained lag distributions (time-series approach), 6) separate analyses by seasons and 7) analysis performed for both the AQHI and for the three air pollutants used to calculate the AQHI.
The dlnm package was used to create the DLNM (Gasparrini 2011), and the mvmeta and metafor packages were used to conduct the meta-analysis (Gasparrini and Armstrong 2013; Viechtbauer 2010).
We estimated warm- and cold-weather outage relative risks and 95% confidence intervals for each network using Poisson time-series DLNM models consistent with the approach used for the individual outage analyses (Model 1).
The R statistical software (R Development Core Team 2015) was used for all analyses, the DLNM package (Gasparrini 2011, 2014a) for DLNM modeling and the VGAM package for the Tobit regression models.
Second, we controlled for confounding by temperature using alternative lag structures, including parallel lags of temperature on the same day and averaging lags over 1-3 d (abbreviated as "lag 0 and 1-3"), DLNM 0-3, and DLNM 0-6.
The dlnm package was used to create the distributed lag model (Gasparrini et al.
We applied a Poisson regression with an overdispersion parameter and a distributed lag nonlinear model (DLNM) (Gasparrini 2011) for the temperature term for two analytical approaches: the ITS and daily electricity consumption analyses.
All Poisson regression analyses were performed in R using the mgcv (Mixed GAM Computation Vehicle with GCV/ AIC/REML smoothness) (Wood 2011) and dlnm (Distributed Lag Non-linear Models) (Gasparrini 2011) packages.
We used the distributed lag nonlinear model (DLNM) module in R (Gasparrini 2011) to characterize the temperature-mortality relationships for each time period.
In the first stage, we applied a distributed lag nonlinear model (DLNM) with a quasi-Poisson regression to evaluate the heat-mortality relationship in each county.
The "dlnm" package was used to create the distributed lag nonlinear model (Gasparrini et al.